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Gopinath K, Greve DN, Magdamo C, Arnold S, Das S, Puonti O, Iglesias JE, Alzheimer’s Disease Neuroimaging Initiative. "Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI. Med Image Anal 2025; 103:103608. [PMID: 40300378 PMCID: PMC12124945 DOI: 10.1016/j.media.2025.103608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 04/11/2025] [Accepted: 04/13/2025] [Indexed: 05/01/2025]
Abstract
Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for tasks like cortical registration, parcellation, and thickness estimation. Traditionally, such analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a T1-weighted scan with 1 mm resolution. This requirement precludes application of these techniques to most MRI scans acquired for clinical purposes, since they are often anisotropic and lack the required T1-weighted contrast. To overcome this limitation and enable large-scale neuroimaging studies using vast amounts of existing clinical data, we introduce recon-all-clinical, a novel methodology for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs), and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We evaluated recon-all-clinical on multiple public datasets like ADNI, HCP, AIBL, OASIS and including a large clinical dataset of over 9,500 scans. The results indicate that our method produces geometrically precise cortical reconstructions across different MRI contrasts and resolutions, consistently achieving high accuracy in parcellation. Cortical thickness estimates are precise enough to capture aging effects, independently of MRI contrast, even though accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce.
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Affiliation(s)
- Karthik Gopinath
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America.
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, United States of America
| | - Steve Arnold
- Department of Neurology, Massachusetts General Hospital, United States of America
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, United States of America
| | - Oula Puonti
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Denmark
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America; Centre for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, United States of America
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Park G, Ha J, Lee JS, Ahn JH, Cho JW, Seo SW, Youn J, Kim H. Data-driven, cross-sectional image-based subtyping and staging of brain volumetric changes in Parkinson's disease. Neurobiol Dis 2025:106970. [PMID: 40418995 DOI: 10.1016/j.nbd.2025.106970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2025] [Revised: 05/11/2025] [Accepted: 05/20/2025] [Indexed: 05/28/2025] Open
Abstract
BACKGROUND Several subtyping methods have been proposed to characterize Parkinson's disease (PD) progression, yet the trajectory of subcortical and cortical neurodegeneration and its clinical implications remain unclear. OBJECTIVES We aimed to conduct a strictly image-based, data-driven classification of PD progression through Subtype and Stage Inference (SuStaIn) algorithm. METHODS Brain volumetric data from 565 patients with PD and 150 propensity-matched healthy controls were analyzed. 16 regions of interest, including 9 cortical and 7 deep grey matter structures, were segmented from T1-weighted magnetic resonance images. Clinical data, including REM sleep behavior disorder (RBD), levodopa equivalent daily dose (LEDD), and motor complications were collected. SuStaIn was trained and tested using a 10-folds cross-validation and identified two distinct PD progression subtypes, which were compared for differences in clinical and radiological characteristics. RESULTS We found two distinct neurodegenerative trajectories: deep grey matter (DG)-first and cortex (CO)-first. The CO-first subtype had a higher prevalence of RBD (p = 0.009) and levodopa-induced dyskinesia (p = 0.024) than the DG-first subtype. Disease progression was faster in the CO-first subtype (0.203 year/stage, LEDD increase 59.3 mg/year), than in the DG-first subtype (0.081 year/stage, LEDD increase 45.1 mg/year, respectively). Regardless of the subtypes, the sensorimotor and auditory cortices were the earliest affected cortical regions, while the amygdala was the first affected subcortically. A subset of participants (n = 186) showed no significant atrophy progression. CONCLUSIONS Our findings support the existence of two distinct subtypes of PD progression based on neuroimaging data. Longitudinal studies are warranted to track their evolution.
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Affiliation(s)
- Gilsoon Park
- Keck School of Medicine of University of Southern California, USC Steven Neuroimaging and Informatics Institute, Los Angeles, CA 90033, USA
| | - Jongmok Ha
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea; Department of Neurology, Emory School of Medicine, Atlanta, GA, USA
| | - Jun Seok Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jong Hyeon Ahn
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jin Whan Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Research Institute for Future Medicine, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Republic of Korea; Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Republic of Korea; Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jinyoung Youn
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.
| | - Hosung Kim
- Keck School of Medicine of University of Southern California, USC Steven Neuroimaging and Informatics Institute, Los Angeles, CA 90033, USA
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Shen Q, Tan C, Wang M, Cai S, Liu Q, Li X, Tang Y, Liao H. Pattern of cortical thickness in depression among early-stage Parkinson's disease: A potential neuroimaging indicator for early recognition. Behav Brain Res 2025; 490:115622. [PMID: 40319944 DOI: 10.1016/j.bbr.2025.115622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2024] [Revised: 04/20/2025] [Accepted: 05/02/2025] [Indexed: 05/07/2025]
Abstract
PURPOSE This study aims to investigate the early change in cortical thickness and surface area in early-stage depressed PD (dPD) patients, and its associations with severity of depression. METHODS 59 patients with dPD, 27 patients with non-depressed PD (ndPD), and 43 healthy controls (HC) were recruited. The dPD patients were subclassified into mild-depressed PD (mi-dPD, n = 24), moderate-depressed PD (mo-dPD, n = 21) and severe-depressed PD (se-dPD, n = 14) subgroups. Structural MRI and surface-based morphometry analysis were applied to compare differences in cortical thickness and surface area among groups, and their correlations with Beck Depression Inventory (BDI) scores were analyzed. RESULTS Compared with ndPD, dPD exhibited cortical thinning in the dorsolateral prefrontal cortex (dlPFC, mainly involving the left superior frontal and bilateral middle frontal gyri), the right pars opercularis and bilateral lateral occipital gyri. The mean cortical thickness values within these regions negatively correlated with BDI scores. Subgroup analysis revealed that patients with mi-dPD had cortical thinning only in the right middle frontal gyrus, while se-dPD showed cortical thinning more extensively involving the right fusiform gyrus, posterior cingulate gyrus, and pars opercularis. There was no significant change in cortical surface area in either the dPD or its subgroups. CONCLUSION Our findings indicated that PD-related depression was associated with decrease of cortical thickness, instead of surface area, of which the patterns correlated with the severity of depression. Cortical thinning in dlPFC, mainly involving the left middle frontal gyrus, may serve as a potential neuroimaging indicator for early recognition of depression in PD patients.
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Affiliation(s)
- Qin Shen
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Changlian Tan
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Min Wang
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Sainan Cai
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Qinru Liu
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xv Li
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Yuqing Tang
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Haiyan Liao
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China.
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Pola I, Ashton NJ, Antônio De Bastiani M, Brum WS, Rahmouni N, Tan K, Machado LS, Servaes S, Stevenson J, Tissot C, Therriault J, Pascoal TA, Blennow K, Zetterberg H, Zimmer ER, Rosa‐Neto P, Benedet AL. Exploring inflammation-related protein expression and its relationship with TSPO PET in Alzheimer's disease. Alzheimers Dement 2025; 21:e70171. [PMID: 40289873 PMCID: PMC12035552 DOI: 10.1002/alz.70171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2024] [Revised: 03/10/2025] [Accepted: 03/13/2025] [Indexed: 04/30/2025]
Abstract
INTRODUCTION To understand the role of neuroinflammation in Alzheimer's disease (AD), we characterized immune-related proteins in central and peripheral biofluids. METHODS Selection of participants from the Translational Biomarker of Aging and Dementia (TRIAD) cohort with available translocator protein (TSPO) positron emission tomography (PET), cerebrospinal fluid (CSF) (n = 97), and plasma (n = 165). Biofluid samples analyzed with Olink technology (368 inflammation proteins). RESULTS Elevated proteins levels in CSF of TSPO-positive individuals were identified. Functional enrichment analysis of CSF proteins revealed processes implicated in AD (MAPK, ERK cascades, cytokine, and leukocyte signaling). Selected candidates (CXCL1 and TNFRSF11B) showed high correlation with each other in CSF and with TSPO PET signal, but weaker associations with amyloid and tau PET. No significantly changed proteins in plasma between TSPO groups were found. DISCUSSION This explorative study identified two potential targets in CSF showing correlations with TSPO, amyloid and tau PET, suggesting a direct link between neuroinflammation, expression of these proteins and their potential implication in AD. HIGHLIGHTS Several proteins are elevated in CSF of TSPO PET-positive individuals, linking them to neuroinflammation. Elevated CSF proteins were enriched in pathways such as MAPK, ERK, and cytokine signaling, linking them to the AD pathophysiology. Candidate proteins (CXCL1 and TNFRSF11B) correlated strongly with TSPO PET, particularly in brain regions known to be affected in AD. Although none of the plasma proteins remained significant after multiple comparisons correction when comparing their expression between TSPO groups, as done for CSF, candidate CSF proteins were found to correlate with plasmatic proteins, highlighting the complexity of the immune system.
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Affiliation(s)
- Ilaria Pola
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
| | - Nicholas J. Ashton
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Banner Alzheimer's Institute and University of ArizonaPhoenixArizonaUSA
- Centre for Age‐Related MedicineStavanger University HospitalStavangerNorway
- King's College LondonInstitute of PsychiatryPsychology & NeuroscienceMaurice Wohl Clinical Neuroscience InstituteLondonUK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS FoundationLondonUK
| | - Marco Antônio De Bastiani
- Graduate Program in Biological Sciences: BiochemistryUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil
| | - Wagner S. Brum
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Graduate Program in Biological Sciences: BiochemistryUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil
| | - Nesrine Rahmouni
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityVerdunQuebecCanada
| | - Kubra Tan
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
| | - Luiza Santos Machado
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
| | - Stijn Servaes
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityVerdunQuebecCanada
| | - Jenna Stevenson
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityVerdunQuebecCanada
| | - Cécile Tissot
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Joseph Therriault
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Tharick A. Pascoal
- Departments of PsychiatryUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
- Department of NeurologySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Kaj Blennow
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Henrik Zetterberg
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative DiseaseUCL Institute of Neurology, Queen SquareLondonUK
- UK Dementia Research Institute at UCLLondonUK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water BayHong KongChina
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Eduardo R. Zimmer
- Graduate Program in Biological Sciences: BiochemistryUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Department of PharmacologyGraduate Program in Biological Sciences: Pharmacology and TherapeuticsUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil
- Brain Institute of Rio Grande do SulPUCRSPorto AlegreBrazil
| | - Pedro Rosa‐Neto
- McGill Centre for Studies in AgingMcGill UniversityVerdunQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityVerdunQuebecCanada
| | - Andréa L. Benedet
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
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Ma Q, Liang K, Li L, Masui S, Guo Y, Nosarti C, Robinson EC, Kainz B, Rueckert D. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. Med Image Anal 2025; 100:103394. [PMID: 39631250 DOI: 10.1016/j.media.2024.103394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 10/07/2024] [Accepted: 11/12/2024] [Indexed: 12/07/2024]
Abstract
The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain magnetic resonance (MR) images for the dHCP neonatal dataset. However, the current implementation of the pipeline requires more than 6.5 h to process a single MRI scan, making it expensive for large-scale neuroimaging studies. In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation. We introduce a multiscale deformation network to learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted brain MRI. A fast unsupervised spherical mapping approach is integrated to minimize metric distortions between cortical surfaces and projected spheres. The entire workflow of our DL-based dHCP pipeline completes within only 24 s on a modern GPU, which is nearly 1000 times faster than the original dHCP pipeline. The qualitative assessment demonstrates that for 82.5% of the test samples, the cortical surfaces reconstructed by our DL-based pipeline achieve superior (54.2%) or equal (28.3%) surface quality compared to the original dHCP pipeline.
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Affiliation(s)
- Qiang Ma
- Department of Computing, Imperial College London, UK.
| | - Kaili Liang
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Liu Li
- Department of Computing, Imperial College London, UK
| | - Saga Masui
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Yourong Guo
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Chiara Nosarti
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Emma C Robinson
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Bernhard Kainz
- Department of Computing, Imperial College London, UK; FAU Erlangen-Nürnberg, Germany
| | - Daniel Rueckert
- Department of Computing, Imperial College London, UK; Chair for AI in Healthcare and Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany
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Park G, Khan MH, Andrushko JW, Banaj N, Borich MR, Boyd LA, Brodtmann A, Brown TR, Buetefisch CM, Conforto AB, Cramer SC, Dimyan M, Domin M, Donnelly MR, Egorova-Brumley N, Ermer ER, Feng W, Geranmayeh F, Hanlon CA, Hordacre B, Jahanshad N, Kautz SA, Salah Khlif M, Liu J, Lotze M, MacIntosh BJ, Mohamed FB, Nordvik JE, Piras F, Revill KP, Robertson AD, Schranz C, Schweighofer N, Seo NJ, Soekadar SR, Srivastava S, Tavenner BP, Thielman GT, Thomopoulos SI, Vecchio D, Werden E, Westlye LT, Winstein CJ, Wittenberg GF, Yu C, Thompson PM, Liew SL, Kim1 H. Severe motor impairment is associated with lower contralesional brain age in chronic stroke. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.10.26.24316190. [PMID: 39574865 PMCID: PMC11581069 DOI: 10.1101/2024.10.26.24316190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2024]
Abstract
Background Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain-predicted age difference (brain-PAD) has emerged as a sensitive biomarker. Our previous study showed higher global brain-PAD associated with poorer motor function post-stroke. However, the relationship between local stroke lesion load, regional brain age, and motor impairment remains unclear. Methods We studied 501 individuals with chronic unilateral stroke (>180 days post-stroke) from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts). Structural T1-weighted MRI scans were used to estimate regional brain-PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated based on lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain-PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain-PADs. Structural equation modeling examined directional relationships among corticospinal tract lesion load (CST-LL), ipsilesional brain-PAD, motor outcomes, and contralesional brain-PAD. Findings Larger total lesion size was positively associated with higher ipsilesional regional brain-PADs (older brain age) across most regions (p < 0.05), and with lower contralesional brain-PAD, notably in the ventral attention-language network (p < 0.05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain-PADs across both hemispheres. Machine learning models identified CST-LL, salience network lesion load, and regional brain-PAD in the contralesional frontoparietal network as the top three predictors of motor outcomes. Structural equation modeling revealed that larger stroke damage was associated with poorer motor outcomes (β = -0.355, p < 0.001), which were further linked to younger contralesional brain age (β = 0.204, p < 0.001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age. Interpretation Our findings reveal that larger stroke lesions are associated with accelerated aging in the ipsilesional hemisphere and paradoxically decelerated brain aging in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age may serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke. Fundings Micheal J Fox Foundation, National Institutes of Health, Canadian Institutes of Health Research, National Health and Medical Research Council, Australian Brain Foundation, Wicking Trust, Collie Trust, and Sidney and Fiona Myer Family Foundation, National Heart Foundation, Hospital Israelita Albert Einstein, Australian Research Council Future Fellowship, Wellcome Trust, National Institute for Health Research Imperial Biomedical Research Centre, European Research Council, Deutsche Forschungsgemeinschaft, REACT Pilot, National Resource Center, Research Council of Norway, South-Eastern Norway Regional Health Authority, Norwegian Extra Foundation for Health and Rehabilitation, Sunnaas Rehabilitation Hospital HT, University of Oslo, and VA Rehabilitation Research and Development.
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Affiliation(s)
- Gilsoon Park
- Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Mahir H. Khan
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
| | - Justin W. Andrushko
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
| | - Nerisa Banaj
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Michael R. Borich
- Rehabilitation Medicine/Physical Therapy, Emory University, Atlanta, GA, United States
| | - Lara A. Boyd
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
| | - Amy Brodtmann
- Cognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Truman R. Brown
- Radiology, Medical University of South Carolina, Mount Pleasant, SC, United States
| | | | - Adriana B. Conforto
- LIM-44, Laboratory of Magnetic Resonance Imaging in Neuroradiology, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, SP, Brazil
- Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil
| | - Steven C. Cramer
- Department of Neurology, UCLA, Los Angeles, CA, United States
- California Rehabilitation Institute, Los Angeles, CA, United States
| | - Michael Dimyan
- UM Rehabilitation and Orthopaedic Institute, University of Maryland, Baltimore, MD, United States
| | - Martin Domin
- Core Unit Functional Imaging, University Medicine Greifswald, Greifswald, Deutschland, Germany
| | - Miranda R. Donnelly
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | | | - Elsa R. Ermer
- Neurology, University of Maryland Baltimore, Baltimore, MD, United States
| | - Wuwei Feng
- Neurology, Duke University School of Medicine, Durham, NC, United States
| | - Fatemeh Geranmayeh
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Colleen A. Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Brenton Hordacre
- Innovation, Implementation and Clinical Translation (IIMPACT) in Health, University of South Australia, Adelaide, South Australia, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Steven A. Kautz
- Department of Health Sciences and Research, Medical University of South Carolina, Charleston, SC, United States
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jingchun Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Martin Lotze
- Core Unit Functional Imaging, University Medicine Greifswald, Greifswald, Deutschland, Germany
| | | | - Feroze B. Mohamed
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | | | - Fabrizio Piras
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Kate P. Revill
- Facility for Education and Research in Neuroscience, Emory University, Atlanta, United States
| | - Andrew D. Robertson
- Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Christian Schranz
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
| | - Na Jin Seo
- Department of Health Sciences and Research, Medical University of South Carolina, Charleston, SC, United States
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Surjo R. Soekadar
- Clinical Neurotechnology, Charité - University Medicine Berlin, Berlin, Berlin, Germany
| | | | - Bethany P. Tavenner
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Gregory T. Thielman
- Department of Physical Therapy and Neuroscience, Saint Joseph’s University, Philadelphia, PA, USA
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Daniela Vecchio
- Department of Clinical Neuroscience and Neurorehabilitation, Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Emilio Werden
- Cognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | | | - Carolee J. Winstein
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
| | - George F. Wittenberg
- GRECC, VA Pittsburgh Healthcare System; Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Chunshui Yu
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sook-Lei Liew
- Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
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Schmitt JE, Alexander-Bloch A, Seidlitz J, Raznahan A, Neale MC. The genetics of spatiotemporal variation in cortical thickness in youth. Commun Biol 2024; 7:1301. [PMID: 39390064 PMCID: PMC11467331 DOI: 10.1038/s42003-024-06956-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/24/2024] [Indexed: 10/12/2024] Open
Abstract
Prior studies have shown strong genetic effects on cortical thickness (CT), structural covariance, and neurodevelopmental trajectories in childhood and adolescence. However, the importance of genetic factors on the induction of spatiotemporal variation during neurodevelopment remains poorly understood. Here, we explore the genetics of maturational coupling by examining 308 MRI-derived regional CT measures in a longitudinal sample of 677 twins and family members. We find dynamic inter-regional genetic covariation in youth, with the emergence of regional subnetworks in late childhood and early adolescence. Three critical neurodevelopmental epochs in genetically-mediated maturational coupling were identified, with dramatic network strengthening near eleven years of age. These changes are associated with statistically-significant (empirical p-value <0.0001) increases in network strength as measured by average clustering coefficient and assortativity. We then identify genes from the Allen Human Brain Atlas with similar co-expression patterns to genetically-mediated structural covariation in children. This set was enriched for genes involved in potassium transport and dendrite formation. Genetically-mediated CT-CT covariance was also strongly correlated with expression patterns for genes located in cells of neuronal origin.
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Affiliation(s)
- J Eric Schmitt
- Departments of Psychiatry and Radiology, Division of Neuroradiology, Brain Behavior Laboratory, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | - Aaron Alexander-Bloch
- Department of Psychiatry, CHOP-Penn Brain-Gene-Development Laboratory, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Seidlitz
- Department of Psychiatry, CHOP-Penn Brain-Gene-Development Laboratory, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institutes of Mental Health, Building 10, Room 4C110, 10 Center Drive, Bethesda, MD, USA
| | - Michael C Neale
- Departments of Psychiatry and Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
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8
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Kim BH, Seo SW, Park YH, Kim J, Kim HJ, Jang H, Yun J, Kim M, Kim JP. Clinical application of sparse canonical correlation analysis to detect genetic associations with cortical thickness in Alzheimer's disease. Front Neurosci 2024; 18:1428900. [PMID: 39381682 PMCID: PMC11458562 DOI: 10.3389/fnins.2024.1428900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 08/19/2024] [Indexed: 10/10/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cerebral cortex atrophy. In this study, we used sparse canonical correlation analysis (SCCA) to identify associations between single nucleotide polymorphisms (SNPs) and cortical thickness in the Korean population. We also investigated the role of the SNPs in neurological outcomes, including neurodegeneration and cognitive dysfunction. Methods We recruited 1125 Korean participants who underwent neuropsychological testing, brain magnetic resonance imaging, positron emission tomography, and microarray genotyping. We performed group-wise SCCA in Aβ negative (-) and Aβ positive (+) groups. In addition, we performed mediation, expression quantitative trait loci, and pathway analyses to determine the functional role of the SNPs. Results We identified SNPs related to cortical thickness using SCCA in Aβ negative and positive groups and identified SNPs that improve the prediction performance of cognitive impairments. Among them, rs9270580 was associated with cortical thickness by mediating Aβ uptake, and three SNPs (rs2271920, rs6859, rs9270580) were associated with the regulation of CHRNA2, NECTIN2, and HLA genes. Conclusion Our findings suggest that SNPs potentially contribute to cortical thickness in AD, which in turn leads to worse clinical outcomes. Our findings contribute to the understanding of the genetic architecture underlying cortical atrophy and its relationship with AD.
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Affiliation(s)
- Bo-Hyun Kim
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Yu Hyun Park
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - JiHyun Kim
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jihwan Yun
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Soonchunhyang University Bucheon Hospital, Gyeonggi-do, Republic of Korea
| | - Mansu Kim
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Jun Pyo Kim
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
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Vandewouw MM, Ye Y(J, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Arnold PD, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions. Hum Brain Mapp 2024; 45:e26815. [PMID: 39254138 PMCID: PMC11386318 DOI: 10.1002/hbm.26815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 07/08/2024] [Accepted: 07/29/2024] [Indexed: 09/11/2024] Open
Abstract
With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.
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Affiliation(s)
- Marlee M. Vandewouw
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoCanada
| | - Yifan (Julia) Ye
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Division of Engineering ScienceUniversity of TorontoTorontoCanada
| | - Jennifer Crosbie
- Department of PsychiatryUniversity of TorontoTorontoCanada
- Department of PsychiatryThe Hospital for Sick ChildrenTorontoOntarioCanada
| | - Russell J. Schachar
- Department of PsychiatryUniversity of TorontoTorontoCanada
- Department of PsychiatryThe Hospital for Sick ChildrenTorontoOntarioCanada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonCanada
| | | | - Elizabeth Kelley
- Department of PsychologyQueen's UniversityKingstonCanada
- Centre for Neuroscience StudiesQueen's UniversityKingstonCanada
- Department of PsychiatryQueen's UniversityKingstonCanada
| | - Muhammad Ayub
- Department of PsychiatryQueen's UniversityKingstonCanada
- Division of PsychiatryUniversity of College LondonLondonUK
| | - Jessica Jones
- Department of PsychologyQueen's UniversityKingstonCanada
- Centre for Neuroscience StudiesQueen's UniversityKingstonCanada
- Department of PsychiatryQueen's UniversityKingstonCanada
| | - Paul D. Arnold
- The Mathison Centre for Mental Health Research & Education, Cumming School of MedicineUniversity of CalgaryCalgaryCanada
| | - Margot J. Taylor
- Department of Diagnostic and Interventional RadiologyThe Hospital for Sick ChildrenTorontoCanada
- Program in Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
- Department of PsychologyUniversity of TorontoTorontoCanada
- Department of Medical ImagingUniversity of TorontoTorontoCanada
| | - Jason P. Lerch
- Program in Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Department of Medical BiophysicsUniversity of TorontoTorontoCanada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Program in Neurosciences and Mental HealthThe Hospital for Sick ChildrenTorontoCanada
- Institute of Medical ScienceUniversity of TorontoTorontoCanada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoCanada
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10
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Boelders SM, De Baene W, Postma E, Gehring K, Ong LL. Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space. Neuroinformatics 2024; 22:329-352. [PMID: 38900230 PMCID: PMC11329426 DOI: 10.1007/s12021-024-09671-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.
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Affiliation(s)
- S M Boelders
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - W De Baene
- Department of Cognitive Neuropsychology, Tilburg University Tilburg, Warandelaan 2, P. O. Box 90153, Tilburg, 5000 LE, The Netherlands
| | - E Postma
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - K Gehring
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands.
- Department of Cognitive Neuropsychology, Tilburg University Tilburg, Warandelaan 2, P. O. Box 90153, Tilburg, 5000 LE, The Netherlands.
| | - L L Ong
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
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11
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Lee D, Jung YH, Kim S, Lee YI, Ku J, Yoon U, Choi SH. Alterations in cortical thickness of frontoparietal regions in patients with social anxiety disorder. Psychiatry Res Neuroimaging 2024; 340:111804. [PMID: 38460394 DOI: 10.1016/j.pscychresns.2024.111804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/26/2023] [Accepted: 02/20/2024] [Indexed: 03/11/2024]
Abstract
Although functional changes of the frontal and (para)limbic area for emotional hyper-reactivity and emotional dysregulation are well documented in social anxiety disorder (SAD), prior studies on structural changes have shown mixed results. This study aimed to identify differences in cortical thickness between SAD and healthy controls (CON). Thirty-five patients with SAD and forty-two matched CON underwent structural magnetic resonance imaging. A vertex-based whole brain and regional analyses were conducted for between-group comparison. The whole-brain analysis revealed increased cortical thickness in the left insula, left superior parietal lobule, left superior temporal gyrus, and left frontopolar cortex in patients with SAD compared to CON, as well as decreased thickness in the left superior/middle frontal gyrus and left fusiform gyrus in patients (after multiple-correction). The results from the ROI analysis did not align with these findings at the statistically significant level after multiple corrections. Changes in cortical thickness were not correlated with social anxiety symptoms. While consistent results were not obtained from different analysis methods, the results from the whole-brain analysis suggest that patients with SAD exhibit distinct neural deficits in areas involved in salience, attention, and socioemotional processing.
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Affiliation(s)
- Dasom Lee
- Department of Psychiatry, Seoul National University College of Medicine and Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Ye-Ha Jung
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Suhyun Kim
- Department of Biomedical Engineering, Daegu Catholic University, Gyeongsan-si, Gyeongbuk, Republic of Korea
| | - Yoonji Irene Lee
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jeonghun Ku
- Department of Biomedical Engineering, Keimyung University, Gyeongsan-si, Gyeongbuk, Republic of Korea
| | - Uicheul Yoon
- Department of Biomedical Engineering, Daegu Catholic University, Gyeongsan-si, Gyeongbuk, Republic of Korea.
| | - Soo-Hee Choi
- Department of Psychiatry, Seoul National University College of Medicine and Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
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12
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Park G, Jo H, Chai Y, Park HR, Lee H, Joo EY, Kim H. Static and dynamic brain morphological changes in isolated REM sleep behavior disorder compared to normal aging. Front Neurosci 2024; 18:1365307. [PMID: 38751861 PMCID: PMC11094219 DOI: 10.3389/fnins.2024.1365307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Objective/background To assess whether cerebral structural alterations in isolated rapid eye movement sleep behavior disorder (iRBD) are progressive and differ from those of normal aging and whether they are related to clinical symptoms. Patients/methods In a longitudinal study of 18 patients with iRBD (age, 66.1 ± 5.7 years; 13 males; follow-up, 1.6 ± 0.6 years) and 24 age-matched healthy controls (age, 67.0 ± 4.9 years; 12 males; follow-up, 2.0 ± 0.9 years), all participants underwent multiple extensive clinical examinations, neuropsychological tests, and magnetic resonance imaging at baseline and follow-up. Surface-based cortical reconstruction and automated subcortical structural segmentation were performed on T1-weighted images. We used mixed-effects models to examine the differences between the groups and the differences in anatomical changes over time. Results None of the patients with iRBD demonstrated phenoconversion during the follow-up. Patients with iRBD had thinner cortices in the frontal, occipital, and temporal regions, and more caudate atrophy, compared to that in controls. In similar regions, group-by-age interaction analysis revealed that patients with iRBD demonstrated significantly slower decreases in cortical thickness and caudate volume with aging than that observed in controls. Patients with iRBD had lower scores on the Korean version of the Mini-Mental Status Examination (p = 0.037) and frontal and executive functions (p = 0.049) at baseline than those in controls; however, no significant group-by-age interaction was identified. Conclusion Patients with iRBD show brain atrophy in the regions that are overlapped with the areas that have been documented to be affected in early stages of Parkinson's disease. Such atrophy in iRBD may not be progressive but may be slower than that in normal aging. Cognitive impairment in iRBD is not progressive.
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Affiliation(s)
- Gilsoon Park
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Hyunjin Jo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
- Medical Research Institute, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Yaqiong Chai
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Hea Ree Park
- Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, Republic of Korea
| | - Hanul Lee
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
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13
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Raguž M, Marčinković P, Chudy H, Orešković D, Lakić M, Dlaka D, Katavić N, Rački V, Vuletić V, Chudy D. Decreased brain volume may be associated with the occurrence of peri-lead edema in Parkinson's disease patients with deep brain stimulation. Parkinsonism Relat Disord 2024; 121:106030. [PMID: 38354427 DOI: 10.1016/j.parkreldis.2024.106030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/12/2024] [Accepted: 02/08/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Peri-lead edema (PLE) is a poorly understood complication of deep brain stimulation (DBS), which has been described in patients presenting occasionally with profound and often delayed symptoms with an incidence ranging from 0.4% up to even 100%. Therefore, our study aims to investigate the association of brain and brain compartment volumes on magnetic resonance imaging (MRI) with the occurrence of PLE in Parkinson's disease (PD) patients after DBS implantation in subthalamic nuclei (STN). METHODS This retrospective study included 125 consecutive PD patients who underwent STN DBS at the Department of Neurosurgery, Dubrava University Hospital from 2010 to 2022. Qualitative analysis was done on postoperative MRI T2-weighted sequence by two independent observers, marking PLE on midbrain, thalamus, and subcortical levels as mild, moderate, or severe. Quantitative volumetric analysis of brain and brain compartment volumes was conducted using an automated CIVET processing pipeline on preoperative MRI T1 MPRAGE sequences. In addition, observed PLE on individual hemispheres was delineated manually and measured using Analyze 14.0 software. RESULTS In our cohort, PLE was observed in 32.17%, mostly bilaterally. Mild PLE was observed in the majority of patients, regardless of the level observed. Age, sex, diabetes, hypertension, vascular disease, and the use of anticoagulant/antiplatelet therapy showed no significant association with the occurrence of PLE. Total grey matter volume showed a significant association with the PLE occurrence (r = -0.22, p = 0.04), as well as cortex volume (r = -0.32, p = 0.0005). Cortical volumes of hemispheres, overall hemisphere volumes, as well as hemisphere/total intracranial volume ratio showed significant association with the PLE occurrence. Furthermore, the volume of the cortex and total grey volume represent moderate indicators, while hemisphere volumes, cortical volumes of hemispheres, and hemisphere/total intracranial volume ratio represent mild to moderate indicators of possible PLE occurrence. CONCLUSION The results of our study suggest that the morphometric MRI measurements, as a useful tool, can provide relevant information about the structural status of the brain in patients with PD and represent moderate indicators of possible PLE occurrence. Identifying patients with greater brain atrophy, especially regarding grey matter before DBS implantation, will allow us to estimate the possible postoperative symptoms and intervene in a timely manner. Further studies are needed to confirm our findings and to investigate other potential predictors and risk factors of PLE occurrence.
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Affiliation(s)
- Marina Raguž
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia; School of Medicine, Catholic University of Croatia, Zagreb, Croatia.
| | - Petar Marčinković
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
| | - Hana Chudy
- Department of Neurology, Dubrava University Hospital, Zagreb, Croatia
| | - Darko Orešković
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
| | - Marin Lakić
- Department of Neurosurgery, Dubrovnik General Hospital, Dubrovnik, Croatia
| | - Domagoj Dlaka
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
| | - Nataša Katavić
- Department of Radiology and Interventional Radiology, Dubrava University Hospital, Zagreb, Croatia
| | - Valentino Rački
- Department of Neurology, University Hospital Centre, Rijeka, Croatia
| | - Vladimira Vuletić
- Department of Neurology, University Hospital Centre, Rijeka, Croatia
| | - Darko Chudy
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia; Department of Surgery, School of Medicine University of Zagreb, Zagreb, Croatia
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Shamir I, Assaf Y, Shamir R. Clustering the cortical laminae: in vivo parcellation. Brain Struct Funct 2024; 229:443-458. [PMID: 38193916 PMCID: PMC10917860 DOI: 10.1007/s00429-023-02748-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
The laminar microstructure of the cerebral cortex has distinct anatomical characteristics of the development, function, connectivity, and even various pathologies of the brain. In recent years, multiple neuroimaging studies have utilized magnetic resonance imaging (MRI) relaxometry to visualize and explore this intricate microstructure, successfully delineating the cortical laminar components. Despite this progress, T1 is still primarily considered a direct measure of myeloarchitecture (myelin content), rather than a probe of tissue cytoarchitecture (cellular composition). This study aims to offer a robust, whole-brain validation of T1 imaging as a practical and effective tool for exploring the laminar composition of the cortex. To do so, we cluster complex microstructural cortical datasets of both human (N = 30) and macaque (N = 1) brains using an adaptation of an algorithm for clustering cell omics profiles. The resulting cluster patterns are then compared to established atlases of cytoarchitectonic features, exhibiting significant correspondence in both species. Lastly, we demonstrate the expanded applicability of T1 imaging by exploring some of the cytoarchitectonic features behind various unique skillsets, such as musicality and athleticism.
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Affiliation(s)
- Ittai Shamir
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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15
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Chen E, Barile B, Durand-Dubief F, Grenier T, Sappey-Marinier D. Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity. Front Neurosci 2024; 17:1268860. [PMID: 38304076 PMCID: PMC10830765 DOI: 10.3389/fnins.2023.1268860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/18/2023] [Indexed: 02/03/2024] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.
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Affiliation(s)
- Enyi Chen
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Berardino Barile
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Françoise Durand-Dubief
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
- Service de Sclérose en Plaques, des Pathologies de la Myéline et Neuro-Inflammation, Groupement Hospitalier Est, Hôpital Neurologique, Bron, France
| | - Thomas Grenier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Dominique Sappey-Marinier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
- CERMEP - Imagerie du Vivant, Université de Lyon, Bron, France
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DeKraker J, Palomero-Gallagher N, Kedo O, Ladbon-Bernasconi N, Muenzing SEA, Axer M, Amunts K, Khan AR, Bernhardt BC, Evans AC. Evaluation of surface-based hippocampal registration using ground-truth subfield definitions. eLife 2023; 12:RP88404. [PMID: 37956092 PMCID: PMC10642966 DOI: 10.7554/elife.88404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023] Open
Abstract
The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.
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Affiliation(s)
- Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Olga Kedo
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | | | - Sascha EA Muenzing
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Markus Axer
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Ali R Khan
- Robarts Research Institute, University of Western OntarioLondonCanada
| | - Boris C Bernhardt
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Alan C Evans
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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17
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Santamaria-Garcia H, Moguilner S, Rodriguez-Villagra OA, Botero-Rodriguez F, Pina-Escudero SD, O'Donovan G, Albala C, Matallana D, Schulte M, Slachevsky A, Yokoyama JS, Possin K, Ndhlovu LC, Al-Rousan T, Corley MJ, Kosik KS, Muniz-Terrera G, Miranda JJ, Ibanez A. The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations. GeroScience 2023; 45:2405-2423. [PMID: 36849677 PMCID: PMC10651610 DOI: 10.1007/s11357-023-00755-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.
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Affiliation(s)
- Hernando Santamaria-Garcia
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA.
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia.
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia.
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Felipe Botero-Rodriguez
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia
| | - Stefanie Danielle Pina-Escudero
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Gary O'Donovan
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia
| | - Cecilia Albala
- Instituto de Nutrición Y Tecnología de los Alimentos, Universidad de Chile, Avenida El Líbano 5524, Macul, Santiago, Chile
| | - Diana Matallana
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Mental Health Department, Hospital Universitario Fundación Santa Fe de Bogotá, Memory Clinic, Bogotá, Colombia
| | - Michael Schulte
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Andrea Slachevsky
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department - Institute of Biomedical Sciences (ICBM), Neurocience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago de Chile, Chile
- Geroscience Center for Brain Health and Metabolism, (GERO), Santiago de Chile, Chile
- Memory and Neuropsychiatric Center (CMYN), Memory Unit - Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago de Chile, Chile
- Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Santiago de Chile, Chile
| | - Jennifer S Yokoyama
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Katherine Possin
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Lishomwa C Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tala Al-Rousan
- Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
| | - Michael J Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Kenneth S Kosik
- Neuroscience Research Institute. Department of Molecular Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Department of Primary Care, Ohio University, Athens, USA
| | - J Jaime Miranda
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- The George Institute for Global Health, UNSW, Sydney, Australia
| | - Agustin Ibanez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA.
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina.
- Trinity College Dublin (TCD), Dublin, Ireland.
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18
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Juntunen A, Määttä S, Könönen M, Kallioniemi E, Niskanen E, Kaarre O, Kivimäki P, Vanninen R, Tolmunen T, Ferreri F, Kekkonen V. Cortical thickness is inversely associated with transcranial magnetic stimulation-evoked N45 potential among young adults whose heavy drinking began in adolescence. Alcohol Clin Exp Res 2023; 47:1341-1351. [PMID: 37526579 DOI: 10.1111/acer.15119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Adolescence is a particularly vulnerable stage of development in terms of the deleterious effects of alcohol. Both lower gray matter (GM) volume and greater GABAergic activity have been associated with chronic alcohol consumption during adolescence. However, the association between these measures has not been investigated. METHODS In this exploratory study, we compared 26 young adults with a 10year history of heavy alcohol consumption with 21 controls who used little or no alcohol. Simultaneous transcranial magnetic stimulation and electroencephalography were used to assess transcranial magnetic stimulation-evoked N45 potentials, reflecting a balance between GABAergic inhibition and N-methyl-D-aspartate (NMDA) receptor-mediated glutaminergic excitation in the brain. GM thickness was measured from magnetic resonance images and GM and N45 potentials were then correlated. RESULTS Cortical thickness was significantly lower in several brain regions in the heavy-drinking group than the light-drinking group. The N45 amplitude was significantly larger frontally in the heavy-drinking group. Among heavy drinkers, there were several statistically significant correlations between thinner GM and larger frontal N45 amplitudes that were not detectable in the light-drinking group. The strongest correlations were detected in the frontal and parietal lobes, especially in the left superior frontal gyrus and the left supramarginal gyrus, and in both hemispheres in the superior parietal lobes. CONCLUSIONS These findings show that a thinner cortex and greater inhibitory neurotransmission are correlated in certain brain regions among young, long-term heavy alcohol users. Studies are needed to explore the possible causal mechanisms underlying these effects.
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Affiliation(s)
- Anna Juntunen
- School of Medicine, Faculty of Health, University of Eastern Finland, Kuopio, Finland
| | - Sara Määttä
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Mervi Könönen
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
- Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Elisa Kallioniemi
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Eini Niskanen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Outi Kaarre
- School of Medicine, Faculty of Health, University of Eastern Finland, Kuopio, Finland
- Forensic Psychiatry Clinic of the University of Eastern Finland, Niuvanniemi Hospital, Kuopio, Finland
| | - Petri Kivimäki
- School of Medicine, Faculty of Health, University of Eastern Finland, Kuopio, Finland
- Vuosaari Psychiatric Outpatient Clinic, Vuosaari Health and Well-being Centre, City of Helsinki, Finland
| | - Ritva Vanninen
- Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Tommi Tolmunen
- School of Medicine, Faculty of Health, University of Eastern Finland, Kuopio, Finland
- Department of Adolescent Psychiatry, Kuopio University Hospital, Kuopio, Finland
| | - Florinda Ferreri
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
- Unit of Neurology, Unit of Clinical Neurophysiology and Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Virve Kekkonen
- School of Medicine, Faculty of Health, University of Eastern Finland, Kuopio, Finland
- Department of Adolescent Psychiatry, Kuopio University Hospital, Kuopio, Finland
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19
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Pretzsch CM, Ecker C. Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review. Front Neurosci 2023; 17:1172779. [PMID: 37457001 PMCID: PMC10347684 DOI: 10.3389/fnins.2023.1172779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.
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Affiliation(s)
- Charlotte M. Pretzsch
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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20
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Kim J, Song J, Kambari Y, Plitman E, Shah P, Iwata Y, Caravaggio F, Brown EE, Nakajima S, Chakravarty MM, De Luca V, Remington G, Graff-Guerrero A, Gerretsen P. Cortical thinning in relation to impaired insight into illness in patients with treatment resistant schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:27. [PMID: 37120642 PMCID: PMC10148890 DOI: 10.1038/s41537-023-00347-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 03/12/2023] [Indexed: 05/01/2023]
Abstract
Impaired insight into illness is a common element of schizophrenia that contributes to treatment nonadherence and negative clinical outcomes. Previous studies suggest that impaired insight may arise from brain abnormalities. However, interpretations of these findings are limited due to small sample sizes and inclusion of patients with a narrow range of illness severity and insight deficits. In a large sample of patients with schizophrenia, the majority of which were designated as treatment-resistant, we investigated the associations between impaired insight and cortical thickness and subcortical volumes. A total of 94 adult participants with a schizophrenia spectrum disorder were included. Fifty-six patients (60%) had treatment-resistant schizophrenia. The core domains of insight were assessed with the VAGUS insight into psychosis scale. We obtained 3T MRI T1-weighted images, which were analysed using CIVET and MAGeT-Brain. Whole-brain vertex-wise analyses revealed impaired insight, as measured by VAGUS average scores, was related to cortical thinning in left frontotemporoparietal regions. The same analysis in treatment-resistant patients showed thinning in the same regions, even after controlling for age, sex, illness severity, and chlorpromazine antipsychotic dose equivalents. No association was found in non-treatment-resistant patients. Region-of-interest analyses revealed impaired general illness awareness was associated with cortical thinning in the left supramarginal gyrus when controlling for covariates. Reduced right and left thalamic volumes were associated with VAGUS symptom attribution and awareness of negative consequences subscale scores, respectively, but not after correction for multiple testing. Our results suggest impaired insight into illness is related to cortical thinning in left frontotemporoparietal regions in patients with schizophrenia, particularly those with treatment resistance where insight deficits may be more chronic.
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Affiliation(s)
- Julia Kim
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Jianmeng Song
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Yasaman Kambari
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Eric Plitman
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Parita Shah
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Yusuke Iwata
- University of Yamanashi, Faculty of Medicine, Department of Neuropsychiatry, Yamanashi, Japan
| | - Fernando Caravaggio
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Eric E Brown
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, CAMH, Toronto, ON, Canada
- Geriatric Mental Health Division, CAMH, Toronto, ON, Canada
| | - Shinichiro Nakajima
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Vincenzo De Luca
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Gary Remington
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, CAMH, Toronto, ON, Canada
- Schizophrenia Division, CAMH, Toronto, ON, Canada
| | - Ariel Graff-Guerrero
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Geriatric Mental Health Division, CAMH, Toronto, ON, Canada
- Schizophrenia Division, CAMH, Toronto, ON, Canada
| | - Philip Gerretsen
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Geriatric Mental Health Division, CAMH, Toronto, ON, Canada.
- Schizophrenia Division, CAMH, Toronto, ON, Canada.
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21
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Azzony S, Moria K, Alghamdi J. Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning. Brain Sci 2023; 13:brainsci13030487. [PMID: 36979297 PMCID: PMC10046408 DOI: 10.3390/brainsci13030487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/25/2023] [Accepted: 03/07/2023] [Indexed: 03/16/2023] Open
Abstract
Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain’s white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite’s pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.
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Affiliation(s)
- Sumayya Azzony
- Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
| | - Kawthar Moria
- Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Jamaan Alghamdi
- Diagnostic Radiology Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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22
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Kalantar-Hormozi H, Patel R, Dai A, Ziolkowski J, Dong HM, Holmes A, Raznahan A, Devenyi GA, Chakravarty MM. A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework. Neuroimage 2023; 268:119885. [PMID: 36657692 DOI: 10.1016/j.neuroimage.2023.119885] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 01/18/2023] Open
Abstract
Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.
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Affiliation(s)
- Hadis Kalantar-Hormozi
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada.
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Alyssa Dai
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada
| | - Justine Ziolkowski
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Department of Psychology, Yale University, New Haven, USA
| | - Avram Holmes
- Department of Psychology, Yale University, New Haven, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health (NIMH), Bethesda, MD, USA
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada
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23
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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24
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Kim JI, Bang S, Yang JJ, Kwon H, Jang S, Roh S, Kim SH, Kim MJ, Lee HJ, Lee JM, Kim BN. Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data. J Autism Dev Disord 2023; 53:25-37. [PMID: 34984638 DOI: 10.1007/s10803-021-05368-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2021] [Indexed: 02/03/2023]
Abstract
Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.
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Affiliation(s)
- Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Sungkyu Bang
- Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Jin-Ju Yang
- Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Heejin Kwon
- Department of Psychology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 02722, Republic of Korea
| | - Soomin Jang
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
- Department of Psychiatry, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Seok Hyeon Kim
- Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
- Department of Psychiatry, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Mi Jung Kim
- Department of Rehabilitation Medicine, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea.
| | - Bung-Nyun Kim
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea.
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25
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Zhang S, Wang C, Liu S, Wang Y, Lu S, Han S, Jiang H, Liu H, Yang Y. Effect of dietary phenylalanine on growth performance and intestinal health of triploid rainbow trout ( Oncorhynchus mykiss) in low fishmeal diets. Front Nutr 2023; 10:1008822. [PMID: 36960199 PMCID: PMC10028192 DOI: 10.3389/fnut.2023.1008822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
This study aimed to investigate the effects of phenylalanine on the growth, digestive capacity, antioxidant capability, and intestinal health of triploid rainbow trout (Oncorhynchus mykiss) fed a low fish meal diet (15%). Five isonitrogenous and isoenergetic diets with different dietary phenylalanine levels (1.82, 2.03, 2.29, 2.64, and 3.01%) were fed to triplicate groups of 20 fish (initial mean body weight of 36.76 ± 3.13 g). The weight gain rate and specific growth rate were significantly lower (p < 0.05) in the 3.01% group. The trypsin activity in the 2.03% group was significantly higher than that in the control group (p < 0.05). Amylase activity peaked in the 2.64% treatment group. Serum superoxide dismutase, catalase, and lysozyme had the highest values in the 2.03% treatment group. Liver superoxide dismutase and catalase reached their maximum values in the 2.03% treatment group, and lysozyme had the highest value in the 2.29% treatment group. Malondialdehyde levels in both the liver and serum were at their lowest in the 2.29% treatment group. Interleukin factors IL-1β and IL-6 both reached a minimum in the 2.03% group and were significantly lower than in the control group, while IL-10 reached a maximum in the 2.03% group (p < 0.05). The tight junction protein-related genes occludin, claudin-1, and ZO-1 all attained their highest levels in the 2.03% treatment group and were significantly higher compared to the control group (p < 0.05). The intestinal villi length and muscle layer thickness were also improved in the 2.03% group (p < 0.05). In conclusion, dietary phenylalanine effectively improved the growth, digestion, absorption capacity, antioxidant capacity, and intestinal health of O. mykiss. Using a quadratic curve model analysis based on WGR, the dietary phenylalanine requirement of triploid O. mykiss fed a low fish meal diet (15%) was 2.13%.
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Affiliation(s)
- Shuze Zhang
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
- College of Animal Science, Northeast Agricultural University, Harbin, China
| | - Chang’an Wang
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
- College of Animal Science, Northeast Agricultural University, Harbin, China
- *Correspondence: Chang’an Wang,
| | - Siyuan Liu
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
- College of Life Science, Dalian Ocean University, Dalian, China
| | - Yaling Wang
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
- College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Shaoxia Lu
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
| | - Shicheng Han
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
| | - Haibo Jiang
- College of Animal Science, Guizhou University, Guiyang, China
| | - Hongbai Liu
- Key Laboratory of Aquatic Animal Diseases and Immune Technology of Heilongjiang Province, Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin, China
- Hongbai Liu,
| | - Yuhong Yang
- College of Animal Science, Northeast Agricultural University, Harbin, China
- Yuhong Yang,
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26
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Schmitt JE, DeBevits JJ, Roalf DR, Ruparel K, Gallagher RS, Gur RC, Alexander-Bloch A, Eom TY, Alam S, Steinberg J, Akers W, Khairy K, Crowley TB, Emanuel B, Zakharenko SS, McDonald-McGinn DM, Gur RE. A Comprehensive Analysis of Cerebellar Volumes in the 22q11.2 Deletion Syndrome. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:79-90. [PMID: 34848384 PMCID: PMC9162086 DOI: 10.1016/j.bpsc.2021.11.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 10/12/2021] [Accepted: 11/08/2021] [Indexed: 01/17/2023]
Abstract
BACKGROUND The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically. METHODS High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models. RESULTS Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p < .0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p < .0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS. CONCLUSIONS The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.
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Affiliation(s)
- J Eric Schmitt
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania; Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - John J DeBevits
- Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - David R Roalf
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Kosha Ruparel
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - R Sean Gallagher
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Aaron Alexander-Bloch
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Tae-Yeon Eom
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Shahinur Alam
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Jeffrey Steinberg
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Walter Akers
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Khaled Khairy
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - T Blaine Crowley
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Beverly Emanuel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Stanislav S Zakharenko
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Donna M McDonald-McGinn
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania; Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
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27
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Praveenkumar S, Kalaiselvi T, Somasundaram K. Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review. Crit Rev Biomed Eng 2023; 51:1-40. [PMID: 37581349 DOI: 10.1615/critrevbiomedeng.2023047606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.
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Affiliation(s)
| | - T Kalaiselvi
- Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram 624302, Tamil Nadu, India
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28
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Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders. Neuroimage 2022; 264:119753. [PMID: 36400380 DOI: 10.1016/j.neuroimage.2022.119753] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022] Open
Abstract
Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.
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29
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Nian R, Gao M, Zhang S, Yu J, Gholipour A, Kong S, Wang R, Sui Y, Velasco-Annis C, Tomas-Fernandez X, Li Q, Lv H, Qian Y, Warfield SK. Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite. Cereb Cortex 2022; 33:5082-5096. [PMID: 36288912 DOI: 10.1093/cercor/bhac401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.
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Affiliation(s)
- Rui Nian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Mingshan Gao
- Citigroup Services and Technology Limited, 1000 Chenhi Road, Shanghai, China
| | | | - Junjie Yu
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ali Gholipour
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Shuang Kong
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ruirui Wang
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yao Sui
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Clemente Velasco-Annis
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Qiuying Li
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Hangyu Lv
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yuqi Qian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Simon K Warfield
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
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30
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Barile B, Ashtari P, Stamile C, Marzullo A, Maes F, Durand-Dubief F, Van Huffel S, Sappey-Marinier D. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome. Front Robot AI 2022; 9:926255. [PMID: 36313252 PMCID: PMC9608344 DOI: 10.3389/frobt.2022.926255] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (E g ), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed. Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8. Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.
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Affiliation(s)
- Berardino Barile
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Pooya Ashtari
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | | | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Frederik Maes
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Françoise Durand-Dubief
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Hôpital Neurologique, Service de Neurologie, Hospices Civils de Lyon, Bron, France
| | | | - Dominique Sappey-Marinier
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- CERMEP–Imagerie du Vivant, Université de Lyon, Lyon, France
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31
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Ma X, Chen N, Wang F, Zhang C, Dai J, Ding H, Yan C, Shen W, Yang S. Surface-based functional metrics and auditory cortex characteristics in chronic tinnitus. Heliyon 2022; 8:e10989. [PMID: 36276740 PMCID: PMC9582700 DOI: 10.1016/j.heliyon.2022.e10989] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 06/11/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Abnormal auditory cortex (AC) neuronal activity is thought to be a primary cause of the auditory disturbances perceived by individuals suffering from tinnitus. The present study was designed to test that possibility by evaluating auditory cortical characteristics (volume, curvature, surface area, thickness) and surface-based functional metrics in chronic tinnitus patients. In total, 63 chronic tinnitus patients and 36 age-, sex- and education level-matched healthy control (HC) patients were enrolled in this study. Hearing levels in these two groups were comparable, and following magnetic resonance imaging (MRI) of these individuals, the DPABISurf software was used to compute cerebral cortex curvature, thickness, and surface area as well as surface-based functional metrics. The Tinnitus Handicap Inventory (THI), Tinnitus Handicap Questionary (THQ), and Visual Analogue Scales (VAS) were used to gauge participant tinnitus severity, while correlation analyses were conducted to evaluate associations between these different analyzed parameters. A significant increase in the regional homogeneity (ReHo) of the right secondary AC was detected in the tinnitus group relative to the HC group. There were also significant reductions in the cortical volume and surface area of the right secondary AC in the tinnitus group relative to the HC group (all P < 0.05). In addition, significant negative correlations between tinnitus pitch and the cortical area and volume of the right secondary AC were observed in the tinnitus group.
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Affiliation(s)
- Xiaoyan Ma
- The First Affiliated Hospital of Xi'an, Jiaotong University, Shanxi, China,Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Ningxuan Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China
| | - Fangyuan Wang
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Chi Zhang
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Jing Dai
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Haina Ding
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China,Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, NY, USA,Corresponding author.
| | - Weidong Shen
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China,Corresponding author.
| | - Shiming Yang
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China,Corresponding author.
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32
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Kang K, Jeong SY, Park K, Hahm MH, Kim J, Lee H, Kim C, Yun E, Han J, Yoon U, Lee S. Distinct cerebral cortical perfusion patterns in idiopathic normal-pressure hydrocephalus. Hum Brain Mapp 2022; 44:269-279. [PMID: 36102811 PMCID: PMC9783416 DOI: 10.1002/hbm.25974] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/29/2022] [Accepted: 05/12/2022] [Indexed: 02/05/2023] Open
Abstract
The aims of the study are to evaluate idiopathic normal-pressure hydrocephalus (INPH)-related cerebral blood flow (CBF) abnormalities and to investigate their relation to cortical thickness in INPH patients. We investigated cortical CBF utilizing surface-based early-phase 18 F-florbetaben (E-FBB) PET analysis in two groups: INPH patients and healthy controls. All 39 INPH patients and 20 healthy controls were imaged with MRI, including three-dimensional volumetric images, for automated surface-based cortical thickness analysis across the entire brain. A subgroup with 37 participants (22 INPH patients and 15 healthy controls) that also underwent 18 F-fluorodeoxyglucose (FDG) PET imaging was further analyzed. Compared with age- and gender-matched healthy controls, INPH patients showed statistically significant hyperperfusion in the high convexity of the frontal and parietal cortical regions. Importantly, within the INPH group, increased perfusion correlated with cortical thickening in these regions. Additionally, significant hypoperfusion mainly in the ventrolateral frontal cortex, supramarginal gyrus, and temporal cortical regions was observed in the INPH group relative to the control group. However, this hypoperfusion was not associated with cortical thinning. A subgroup analysis of participants that also underwent FDG PET imaging showed that increased (or decreased) cerebral perfusion was associated with increased (or decreased) glucose metabolism in INPH. A distinctive regional relationship between cerebral cortical perfusion and cortical thickness was shown in INPH patients. Our findings suggest distinct pathophysiologic mechanisms of hyperperfusion and hypoperfusion in INPH patients.
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Affiliation(s)
- Kyunghun Kang
- Department of Neurology, School of MedicineKyungpook National UniversityDaeguSouth Korea
| | - Shin Young Jeong
- Department of Nuclear Medicine, School of MedicineKyungpook National UniversityDaeguSouth Korea
| | - Ki‐Su Park
- Department of Neurosurgery, School of MedicineKyungpook National UniversityDaeguSouth Korea
| | - Myong Hun Hahm
- Department of Radiology, School of MedicineKyungpook National UniversityDaeguSouth Korea
| | - Jaeil Kim
- School of Computer Science and EngineeringKyungpook National UniversityDaeguSouth Korea
| | - Ho‐Won Lee
- Department of Neurology, School of MedicineKyungpook National UniversityDaeguSouth Korea,Brain Science and Engineering InstituteKyungpook National UniversityDaeguSouth Korea
| | - Chi‐Hun Kim
- Department of NeurologyHallym University Sacred Heart HospitalAnyangSouth Korea
| | - Eunkyeong Yun
- Department of Biomedical EngineeringDaegu Catholic UniversityGyeongsan‐siSouth Korea
| | - Jaehwan Han
- Department of Biomedical EngineeringDaegu Catholic UniversityGyeongsan‐siSouth Korea
| | - Uicheul Yoon
- Department of Biomedical EngineeringDaegu Catholic UniversityGyeongsan‐siSouth Korea
| | - Sang‐Woo Lee
- Department of Nuclear Medicine, School of MedicineKyungpook National UniversityDaeguSouth Korea
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Nowinski WL. On the definition, construction, and presentation of the human cerebral sulci: A morphology-based approach. J Anat 2022; 241:789-808. [PMID: 35638263 PMCID: PMC9358745 DOI: 10.1111/joa.13695] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 04/25/2022] [Accepted: 05/17/2022] [Indexed: 11/29/2022] Open
Abstract
Although the term sulcus is known for almost four centuries, its formal, precise, consistent, constructive, and quantitative definition is practically lacking. As the cerebral sulci (and gyri) are vital in cortical anatomy which, in turn, is central in neuroeducation and neuroimage processing, a new sulcus definition is needed. The contribution of this work is threefold, namely to (1) propose a new, morphology-based definition of the term sulcus (and consequently that of gyrus), (2) formulate a constructive method for sulcus calculation, and (3) provide a novel way for the presentation of sulci. The sulcus is defined here as a volumetric region on the cortical mantle between adjacent gyri separated from them at the levels of their gyral white matter crest lines. Consequently, the sulcal inner surface is demarcated by the crest lines of the gyral white matter of its adjacent gyri. Correspondingly, the gyrus is defined as a volumetric region on the cortical mantle separated from its adjacent sulci at the level of its gyral white matter crest line. This volumetric sulcus definition is conceptually simple, anatomy-based, educationally friendly, quantitative, and constructive. Considering the sulcus as a volumetric object is a major differentiation from other works. Based on the introduced sulcus definition, a method for volumetric sulcus construction is proposed in two, conceptually straightforward, steps, namely, sulcal intersection formation followed by its propagation which steps are to be repeated for every sulcal segment. These sulcal and gyral constructions can be automated by applying existing methods and public tools. As a volumetric sulcus forms an imprint into the white matter, this enables prominent sulcus presentation. Since this type of presentation is novel yet unfamiliar to the reader, also a dual surface presentation was proposed here by employing the spatially co-registered white matter and cortical surfaces. The results were presented as dual surface labeled sulci on eight standard orthogonal views, anterior, left lateral, posterior, right lateral, superior, inferior, medial left, and medial right by using a 3D brain atlas. Moreover, additional 108 labeled images were created with sulcus-oriented views for 27 individual left and right sulci forming 54 dual white matter-cortical surface images strengthening in this way the educational value of the proposed approach. These images were included for public use in the NOWinBRAIN neuroimage repository with over 7700 3D images available at www.nowinbrain.org. The results demonstrated the superiority of white matter surface sulci presentation over the standard cortical surface and cross-sectional presentations in terms of sulcal course, continuity, size, shape, width, depth, side branches, and pattern. To my best knowledge, this is the first work ever presenting the labeling of sulci on all cerebral white matter surfaces as well as on dual white matter-cortical surfaces. Additionally to neuroeducation, three other applications of the proposed approach were discussed, sulcal reference maps, sulcus quantification in terms of new parameters introduced here (sulcal volume, wall skewness, and the number of white matter basins), and an atlas-assisted tool for exploration and studying of cerebral sulci and gyri .
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Affiliation(s)
- Wieslaw L. Nowinski
- School of Medicine, University of Cardinal Stefan WyszynskiWarsawPoland
- Nowinski Brain FoundationLomiankiPoland
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34
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Moon SY, Kim S, Choi SH, Hong CH, Park YK, Na HR, Song HS, Park HK, Choi M, Lee SM, Chun BO, Lee JM, Jeong JH. Impact of Multidomain Lifestyle Intervention on Cerebral Cortical Thickness and Serum Brain-Derived Neurotrophic Factor: the SUPERBRAIN Exploratory Sub-study. Neurotherapeutics 2022; 19:1514-1525. [PMID: 35915368 PMCID: PMC9606175 DOI: 10.1007/s13311-022-01276-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 11/26/2022] Open
Abstract
In the SoUth Korean study to PrEvent cognitive impaiRment and protect BRAIN health through lifestyle intervention in at-risk elderly people (SUPERBRAIN), we evaluated the impact of a 24-week facility-based multidomain intervention (FMI) and home-based MI (HMI) on cortical thickness, brain volume, and the serum brain-derived neurotrophic factor (BDNF). Totally, 152 participants, aged 60-79 years without dementia but with ≥ 1 modifiable dementia risk factor, were randomly assigned to the FMI, HMI, or control groups. Among them, 55 participants (20 FMI, 19 HMI, and 16 controls) underwent brain MRI at baseline and 24 weeks. We compared changes in global/regional mean cortical thickness at the region-of-interest (ROI) between the intervention and control groups. The changes in the total cortical gray matter volume and global mean cortical thickness were compared using analysis of covariance with age, sex, and education as covariates. ComBat site harmonization was applied for cortical thickness values across the scanners. ROI-based analysis was controlled for multiple comparisons, with a false discovery rate threshold of p < 0.05. Serum BDNF levels were significantly higher in the FMI group than in the control group (p = 0.029). Compared with the control group, the mean global cortical thickness increased in the FMI group (0.033 ± 0.070 vs. - 0.003 ± 0.040, p = 0.013); particularly, cortical thickness of the bilateral frontotemporal lobes, cingulate gyri, and insula increased. The increase in cortical thickness and serum BDNF in the FMI group suggests that group preventive strategies at the facility may be beneficial through structural neuroplastic changes in brain areas, which facilitates learning and neurotrophic factors.
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Affiliation(s)
- So Young Moon
- Department of Neurology, Ajou University School of Medicine, Suwon, Korea
| | - Sohui Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Korea
| | - Chang Hyung Hong
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Yoo Kyoung Park
- Department of Medical Nutrition, Graduate School of East-West Medical Nutrition, Kyung Hee University, Suwon, Korea
| | - Hae Ri Na
- Department of Neurology, Bobath Memorial Hospital, Seongnam, Korea
| | - Hong-Sun Song
- Department of Sports Sciences, Korea Institute of Sports Science, Seoul, Korea
| | - Hee Kyung Park
- Department of Neurology, Ewha Womans University School of Medicine, 260 Gonghang-daero, Gangseo-gu, Seoul, 07804, Korea
| | - Muncheong Choi
- Department of Physical Education, Kookmin University, Seoul, Korea
- , Exercowork, Seoul, Korea
| | - Sun Min Lee
- Department of Neurology, Ajou University School of Medicine, Suwon, Korea
| | - Buong-O Chun
- Department of Sports Sciences, Korea Institute of Sports Science, Seoul, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Sanhak-kisulkwan Bldg, #319, 222 Wangsipri-ro, Sungdong-gu, Seoul, 133-791, Republic of Korea.
| | - Jee Hyang Jeong
- Department of Neurology, Ewha Womans University School of Medicine, 260 Gonghang-daero, Gangseo-gu, Seoul, 07804, Korea.
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35
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Xiong RM, Xie T, Zhang H, Li T, Gong G, Yu X, He Y. The pattern of cortical thickness underlying disruptive behaviors in Alzheimer's disease. PSYCHORADIOLOGY 2022; 2:113-120. [PMID: 38665603 PMCID: PMC10917178 DOI: 10.1093/psyrad/kkac017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/23/2022] [Accepted: 11/02/2022] [Indexed: 04/28/2024]
Abstract
Background Disruptive behaviors, including agitation, disinhibition, irritability, and aberrant motor behaviors, are commonly observed in patients with Alzheimer's disease (AD). However, the neuroanatomical basis of these disruptive behaviors is not fully understood. Objective To confirm the differences in cortical thickness and surface area between AD patients and healthy controls and to further investigate the features of cortical thickness and surface area associated with disruptive behaviors in patients with AD. Methods One hundred seventy-four participants (125 AD patients and 49 healthy controls) were recruited from memory clinics at the Peking University Institute of Sixth Hospital. Disruptive behaviors, including agitation/aggression, disinhibition, irritability/lability, and aberrant motor activity subdomain scores, were evaluated using the Neuropsychiatry Inventory. Both whole-brain vertex-based and region-of-interest-based cortical thickness and surface area analyses were automatically conducted with the CIVET pipeline based on structural magnetic resonance images. Both group-based statistical comparisons and brain-behavior association analyses were performed using general linear models, with age, sex, and education level as covariables. Results Compared with healthy controls, the AD patients exhibited widespread reduced cortical thickness, with the most significant thinning located in the medial and lateral temporal and parietal cortex, and smaller surface areas in the left fusiform and left inferior temporal gyrus. High total scores of disruptive behaviors were significantly associated with cortical thinning in several regions that are involved in sensorimotor processing, language, and expression functions. The total score of disruptive behaviors did not show significant associations with surface areas. Conclusion We highlight that disruptive behaviors in patients with AD are selectively associated with cortical thickness abnormalities in sensory, motor, and language regions, which provides insights into neuroanatomical substrates underlying disruptive behaviors. These findings could lead to sensory, motor, and communication interventions for alleviating disruptive behaviors in patients with AD.
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Affiliation(s)
- Raymond M Xiong
- Experimental High School Attached to Beijing Normal University, Beijing 100032, China
| | - Teng Xie
- Dementia Care & Research Center, Peking University Institute of Mental Health & National Clinical Research Center for Mental Disorders, Beijing 100191, China
| | - Haifeng Zhang
- Dementia Care & Research Center, Peking University Institute of Mental Health & National Clinical Research Center for Mental Disorders, Beijing 100191, China
| | - Tao Li
- Dementia Care & Research Center, Peking University Institute of Mental Health & National Clinical Research Center for Mental Disorders, Beijing 100191, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xin Yu
- Dementia Care & Research Center, Peking University Institute of Mental Health & National Clinical Research Center for Mental Disorders, Beijing 100191, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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36
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The Dorsolateral Prefrontal Cortex Presents Structural Variations Associated with Empathy and Emotion Regulation in Psychotherapists. Brain Topogr 2022; 35:613-626. [DOI: 10.1007/s10548-022-00910-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 08/09/2022] [Indexed: 11/02/2022]
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37
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Kwon H, Kim JI, Son SY, Jang YH, Kim BN, Lee HJ, Lee JM. Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels. Front Neurosci 2022; 16:935431. [PMID: 35873817 PMCID: PMC9301472 DOI: 10.3389/fnins.2022.935431] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.
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Affiliation(s)
- Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, South Korea
| | - Seung-Yeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
| | - Yong Hun Jang
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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38
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Raucher-Chéné D, Lavigne KM, Makowski C, Lepage M. Altered Surface Area Covariance in the Mentalizing Network in Schizophrenia: Insight Into Theory of Mind Processing. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:706-715. [PMID: 32919946 DOI: 10.1016/j.bpsc.2020.06.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Theory of mind (ToM), the cognitive capacity to attribute mental states to self and others, is robustly affected in schizophrenia. The neural substrates of ToM impairment have been largely studied with functional imaging, but little is known about structural abnormalities. We compared structural covariance (between-subjects correlations of brain regional measures) of magnetic resonance imaging-based cortical surface area between patients with schizophrenia and healthy control subjects and between schizophrenia subgroups based on the patients' ToM ability to examine ToM-specific effects on structural covariance in schizophrenia. METHODS T1-weighted structural images were acquired on a 3T magnetic resonance imaging scanner, and ToM was assessed with the Hinting Task for 104 patients with schizophrenia and 69 healthy control subjects. The sum of surface area was computed for 12 regions of interest selected and compared between groups to examine structural covariance within the often reported mentalizing network: rostral and caudal middle frontal gyrus, inferior parietal lobule, precuneus, and middle and superior temporal gyrus. High and low ToM groups were defined using a median split on the Hinting Task. RESULTS Cortical surface contraction was observed in the schizophrenia group, predominantly in temporoparietal regions. Patients with schizophrenia also exhibited significantly stronger covariance between the right rostral middle frontal gyrus and the right superior temporal gyrus than control subjects (r = 4.015; p < .001). Direct comparisons between high and low ToM subgroups revealed stronger contralateral frontotemporal covariances in the low ToM group. CONCLUSIONS Our results provide evidence for structural changes underlying ToM impairments in schizophrenia that need to be confirmed to develop new therapeutic perspectives.
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Affiliation(s)
- Delphine Raucher-Chéné
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada; Cognition, Health, and Society Laboratory EA 6291, University of Reims Champagne-Ardenne, Reims, France; Academic Department of Psychiatry, University Hospital of Reims, Etablissement Public de Santé Mentale de la Marne, Reims, France
| | - Katie M Lavigne
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada; McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, California; Department of Radiology, University of California, San Diego School of Medicine, La Jolla, California
| | - Martin Lepage
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
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39
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Demirci N, Holland MA. Cortical thickness systematically varies with curvature and depth in healthy human brains. Hum Brain Mapp 2022; 43:2064-2084. [PMID: 35098606 PMCID: PMC8933257 DOI: 10.1002/hbm.25776] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
Cortical thickness varies throughout the cortex in a systematic way. However, it is challenging to investigate the patterns of cortical thickness due to the intricate geometry of the cortex. The cortex has a folded nature both in radial and tangential directions which forms not only gyri and sulci but also tangential folds and intersections. In this article, cortical curvature and depth are used to characterize the spatial distribution of the cortical thickness with much higher resolution than conventional regional atlases. To do this, a computational pipeline was developed that is capable of calculating a variety of quantitative measures such as surface area, cortical thickness, curvature (mean curvature, Gaussian curvature, shape index, intrinsic curvature index, and folding index), and sulcal depth. By analyzing 501 neurotypical adult human subjects from the ABIDE-I dataset, we show that cortex has a very organized structure and cortical thickness is strongly correlated with local shape. Our results indicate that cortical thickness consistently increases along the gyral-sulcal spectrum from concave to convex shape, encompassing the saddle shape along the way. Additionally, tangential folds influence cortical thickness in a similar way as gyral and sulcal folds; outer folds are consistently thicker than inner.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
| | - Maria A. Holland
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
- Department of Aerospace and Mechanical EngineeringUniversity of Notre DameNotre DameIndianaUSA
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40
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Ren J, Hu Q, Wang W, Zhang W, Hubbard CS, Zhang P, An N, Zhou Y, Dahmani L, Wang D, Fu X, Sun Z, Wang Y, Wang R, Li L, Liu H. Fast cortical surface reconstruction from MRI using deep learning. Brain Inform 2022; 9:6. [PMID: 35262808 PMCID: PMC8907118 DOI: 10.1186/s40708-022-00155-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 02/25/2022] [Indexed: 11/23/2022] Open
Abstract
Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the “level set representation”. A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject’s cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test–retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.
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Affiliation(s)
- Jianxun Ren
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Qingyu Hu
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China
| | | | - Wei Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100080, China
| | - Catherine S Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Ning An
- Neural Galaxy, Beijing, 102206, China
| | - Ying Zhou
- Neural Galaxy, Beijing, 102206, China
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Xiaoxuan Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.,Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA.,State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300401, China
| | | | | | - Ruiqi Wang
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China. .,Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China. .,IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, 100084, China. .,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA. .,Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA.
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41
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Cherkasova MV, Fu JF, Jarrett M, Johnson P, Abel S, Tam R, Rauscher A, Sossi V, Kolind S, Li DKB, Sadovnick AD, Machan L, Girard JM, Emond F, Vosoughi R, Traboulsee A, Stoessl AJ. Cortical morphology predicts placebo response in multiple sclerosis. Sci Rep 2022; 12:732. [PMID: 35031632 PMCID: PMC8760243 DOI: 10.1038/s41598-021-04462-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/22/2021] [Indexed: 11/27/2022] Open
Abstract
Despite significant insights into the neural mechanisms of acute placebo responses, less is known about longer-term placebo responses, such as those seen in clinical trials, or their interactions with brain disease. We examined brain correlates of placebo responses in a randomized trial of a then controversial and now disproved endovascular treatment for multiple sclerosis. Patients received either balloon or sham extracranial venoplasty and were followed for 48 weeks. Venoplasty had no therapeutic effect, but a subset of both venoplasty- and sham-treated patients reported a transient improvement in health-related quality of life, suggesting a placebo response. Placebo responders did not differ from non-responders in total MRI T2 lesion load, count or location, nor were there differences in normalized brain volume, regional grey or white matter volume or cortical thickness (CT). However, responders had higher lesion activity. Graph theoretical analysis of CT covariance showed that non-responders had a more small-world-like CT architecture. In non-responders, lesion load was inversely associated with CT in somatosensory, motor and association areas, precuneus, and insula, primarily in the right hemisphere. In responders, lesion load was unrelated to CT. The neuropathological process in MS may produce in some a cortical configuration less capable of generating sustained placebo responses.
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Affiliation(s)
- Mariya V Cherkasova
- Department of Psychology, University of British Columbia, Vancouver, Canada. .,Department of Psychology, West Virginia University, 2128 Life Science Building, Morgantown, WV, 26506, USA.
| | - Jessie F Fu
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Michael Jarrett
- Population Data BC, University of British Columbia, Vancouver, BC, Canada
| | - Poljanka Johnson
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada.,School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Rauscher
- Depatment of Pediatrics (Division of Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Shannon Kolind
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - A Dessa Sadovnick
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Lindsay Machan
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - J Marc Girard
- Centre Hospitalier de L'Université de Montréal, Montréal, QC, Canada
| | - Francois Emond
- CHU de Québec-Université Laval, Hôpital de L'Enfant-Jésus, Québec, Canada
| | - Reza Vosoughi
- Department of Internal Medicine (Neurology), University of Manitoba, Winnipeg, Canada
| | - Anthony Traboulsee
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - A Jon Stoessl
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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42
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Gao K, Fan Z, Su J, Zeng LL, Shen H, Zhu J, Hu D. Deep Transfer Learning for Cerebral Cortex Using Area-Preserving Geometry Mapping. Cereb Cortex 2021; 32:2972-2984. [PMID: 34791082 DOI: 10.1093/cercor/bhab394] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/01/2021] [Accepted: 10/03/2021] [Indexed: 01/17/2023] Open
Abstract
Limited sample size hinders the application of deep learning in brain image analysis, and transfer learning is a possible solution. However, most pretrained models are 2D based and cannot be applied directly to 3D brain images. In this study, we propose a novel framework to apply 2D pretrained models to 3D brain images by projecting surface-based cortical morphometry into planar images using computational geometry mapping. Firstly, 3D cortical meshes are reconstructed from magnetic resonance imaging (MRI) using FreeSurfer and projected into 2D planar meshes with topological preservation based on area-preserving geometry mapping. Then, 2D deep models pretrained on ImageNet are adopted and fine-tuned for cortical image classification on morphometric shape metrics. We apply the framework to sex classification on the Human Connectome Project dataset and autism spectrum disorder (ASD) classification on the Autism Brain Imaging Data Exchange dataset. Moreover, a 2-stage transfer learning strategy is suggested to boost the ASD classification performance by using the sex classification as an intermediate task. Our framework brings significant improvement in sex classification and ASD classification with transfer learning. In summary, the proposed framework builds a bridge between 3D cortical data and 2D models, making 2D pretrained models available for brain image analysis in cognitive and psychiatric neuroscience.
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Affiliation(s)
- Kai Gao
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zhipeng Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Jubo Zhu
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.,Pazhou Lab, Guangzhou 510330, China
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Chaddock-Heyman L, Weng T, Loui P, Kienzler C, Weisshappel R, Drollette ES, Raine LB, Westfall D, Kao SC, Pindus DM, Baniqued P, Castelli DM, Hillman CH, Kramer AF. Brain network modularity predicts changes in cortical thickness in children involved in a physical activity intervention. Psychophysiology 2021; 58:e13890. [PMID: 34219221 PMCID: PMC8419073 DOI: 10.1111/psyp.13890] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 05/30/2021] [Accepted: 06/09/2021] [Indexed: 11/26/2022]
Abstract
Individual differences in brain network modularity at baseline can predict improvements in cognitive performance after cognitive and physical interventions. This study is the first to explore whether brain network modularity predicts changes in cortical brain structure in 8- to 9-year-old children involved in an after-school physical activity intervention (N = 62), relative to children randomized to a wait-list control group (N = 53). For children involved in the physical activity intervention, brain network modularity at baseline predicted greater decreases in cortical thickness in the anterior frontal cortex and parahippocampus. Further, for children involved in the physical activity intervention, greater decrease in cortical thickness was associated with improvements in cognitive efficiency. The relationships among baseline modularity, changes in cortical thickness, and changes in cognitive performance were not present in the wait-list control group. Our exploratory study has promising implications for the understanding of brain network modularity as a biomarker of intervention-related improvements with physical activity.
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Affiliation(s)
- Laura Chaddock-Heyman
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Timothy Weng
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Psyche Loui
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Caitlin Kienzler
- Department of Psychology, University of Colorado, Denver, CO, USA
| | - Robert Weisshappel
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Eric S. Drollette
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
| | - Lauren B. Raine
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Daniel Westfall
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Shih-Chun Kao
- Health and Kinesiology, Purdue University, West Lafayette, IN, USA
| | - Dominika M. Pindus
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Pauline Baniqued
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Brain and Creativity Institute, University of Southern California, Los Angeles, CA, USA
| | - Darla M. Castelli
- Department of Kinesiology and Health Education, The University of Texas at Austin, USA
| | - Charles H. Hillman
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Physical Therapy, Movement, & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Arthur F. Kramer
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
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Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl K, DeKraker J, Toussaint PJ, Valk SL, Collins L, Khan AR, Amunts K, Evans AC, Dickscheid T, Bernhardt B. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 2021; 10:e70119. [PMID: 34431476 PMCID: PMC8445620 DOI: 10.7554/elife.70119] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Lindsay B Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontrealCanada
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College LondonLondonUnited Kingdom
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Brain and Mind Institute, University of Western OntarioOntarioCanada
| | - Paule-J Toussaint
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum JülichJülichGermany
| | - Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
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45
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Park J, Kim TJ, Song JH, Jang H, Kim JS, Kang SH, Kim HR, Hwangbo S, Shin HY, Na DL, Seo SW, Kim HJ, Kim JJ. Helicobacter Pylori Infection Is Associated with Neurodegeneration in Cognitively Normal Men. J Alzheimers Dis 2021; 82:1591-1599. [PMID: 34180413 DOI: 10.3233/jad-210119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND An association between Helicobacter pylori (H. pylori) infection and dementia was reported in previous studies; however, the evidence is inconsistent. OBJECTIVE In the present study, the association between H. pylori infection and brain cortical thickness as a biomarker of neurodegeneration was investigated. METHODS A cross-sectional study of 822 men who underwent a medical health check-up, including an esophagogastroduodenoscopy and 3.0 T magnetic resonance imaging, was performed. H. pylori infection status was assessed based on histology. Multiple linear regression analyses were conducted to evaluate the relationship between H. pylori infection and brain cortical thickness. RESULTS Men with H. pylori infection exhibited overall brain cortical thinning (p = 0.022), especially in the parietal (p = 0.008) and occipital lobes (p = 0.050) compared with non-infected men after adjusting for age, educational level, alcohol intake, smoking status, and intracranial volume. 3-dimentional topographical analysis showed that H. pylori infected men had cortical thinning in the bilateral lateral temporal, lateral frontal, and right occipital areas compared with non-infected men with the same adjustments (false discovery rate corrected, Q < 0.050). The association remained significant after further adjusting for inflammatory marker (C-reactive protein) and metabolic factors (obesity, dyslipidemia, fasting glucose, and blood pressure). CONCLUSION Our results indicate H. pylori infection is associated with neurodegenerative changes in cognitive normal men. H. pylori infection may play a pathophysiologic role in the neurodegeneration and further studies are needed to validate this association.
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Affiliation(s)
- Jaehong Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Tae Jun Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Joo Hye Song
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Hang-Rai Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
| | - Song Hwangbo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Hee Young Shin
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Jae J Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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46
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Fernández-Andújar M, Morales-García E, García-Casares N. Obesity and Gray Matter Volume Assessed by Neuroimaging: A Systematic Review. Brain Sci 2021; 11:brainsci11080999. [PMID: 34439618 PMCID: PMC8391982 DOI: 10.3390/brainsci11080999] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/19/2021] [Accepted: 07/26/2021] [Indexed: 12/03/2022] Open
Abstract
Obesity has become a major public and individual health problem due to its high worldwide prevalence and its relation with comorbid conditions. According to previous studies, obesity is related to an increased risk of cognitive impairment and dementia. This systematic review aims to further examine the present state of the art about the association between obesity and gray matter volume (GMV) as assessed by magnetic resonance imaging (MRI). A search was conducted in Pubmed, SCOPUS and Cochrane of those studies released before 1 February 2021 including MRIs to assess the GMVs in obese participants. From this search, 1420 results were obtained, and 34 publications were finally included. Obesity was mainly measured by the body mass index, although other common types of evaluations were used (e.g., waist circumference, waist-to-hip ratio and plasma leptin levels). The selected neuroimaging analysis methods were voxel-based morphometry (VBM) and cortical thickness (CT), finding 21 and 13 publications, respectively. There were 30 cross-sectional and 2 prospective longitudinal studies, and 2 articles had both cross-sectional and longitudinal designs. Most studies showed a negative association between obesity and GMV. This would have important public health implications, as obesity prevention could avoid a potential risk of GMV reductions, cognitive impairment and dementia.
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Affiliation(s)
| | - Ester Morales-García
- Servicio de Neurología, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain;
| | - Natalia García-Casares
- Department of Medicine, Faculty of Medicine, University of Malaga, 29010 Malaga, Spain
- Centro de Investigaciones Médico-Sanitarias (C.I.M.E.S), University of Malaga, 29010 Malaga, Spain
- Área de Enfermedades cardiovasculares, obesidad y diabetes, Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Malaga, Spain
- Correspondence: ; Tel.: +34-952-137-354
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47
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OmidYeganeh M, Khalili-Mahani N, Bermudez P, Ross A, Lepage C, Vincent RD, Jeon S, Lewis LB, Das S, Zijdenbos AP, Rioux P, Adalat R, Van Eede MC, Evans AC. A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines. Front Neuroinform 2021; 15:665560. [PMID: 34381348 PMCID: PMC8350777 DOI: 10.3389/fninf.2021.665560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/07/2021] [Indexed: 11/25/2022] Open
Abstract
In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset) and in a within-subject pre-/post-lesion design [using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS)]. The simulation method is sensitive to partial volume effect and lesion size. Comparisons between pipelines illustrate the ability of this method to uncover differences in sensitivity and specificity of lesion detection. We propose that this method be adopted in the workflow of software development and release.
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Affiliation(s)
- Mona OmidYeganeh
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,PERFORM Centre, Concordia University, Montreal, QC, Canada
| | - Patrick Bermudez
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Alison Ross
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Claude Lepage
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Robert D Vincent
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - S Jeon
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Lindsay B Lewis
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - S Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Alex P Zijdenbos
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | | | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
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48
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Guo Y, Ortug A, Sadberry R, Rezayev A, Levman J, Shiohama T, Takahashi E. Symptom-Related Differential Neuroimaging Biomarkers in Children with Corpus Callosum Abnormalities. Cereb Cortex 2021; 31:4916-4932. [PMID: 34289021 DOI: 10.1093/cercor/bhab131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 01/23/2023] Open
Abstract
We aimed to identify symptom-related neuroimaging biomarkers for patients with dysgenesis of the corpus callosum (dCC) by summarizing neurological symptoms reported in clinical evaluations and correlating them with retrospectively collected structural/diffusion brain magnetic resonance imaging (MRI) measures from 39 patients/controls (mean age 8.08 ± 3.98). Most symptoms/disorders studied were associated with CC abnormalities. Total brain (TB) volume was related to language, cognition, muscle tone, and metabolic/endocrine abnormalities. Although white matter (WM) volume was not related to symptoms studied, gray matter (GM) volume was related to cognitive, behavioral, and metabolic/endocrine disorders. Right hemisphere (RH) cortical thickness (CT) was linked to language abnormalities, while left hemisphere (LH) CT was linked to epilepsy. While RH gyrification index (GI) was not related to any symptoms studied, LH GI was uniquely related to cognitive disorders. Between patients and controls, GM volume and LH/RH CT were significantly greater in dCC patients, while WM volume and LH/RH GI were significantly greater in controls. TB volume and diffusion indices for tissue microstructures did not show differences between the groups. In summary, our brain MRI-based measures successfully revealed differential links to many symptoms. Specifically, LH GI abnormality can be a predictor for dCC patients, which is uniquely associated with the patients' symptom. In addition, patients with CC abnormalities had normal TB volume and overall tissue microstructures, with potentially deteriorated mechanisms to expand/fold the brain, indicated by GI.
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Affiliation(s)
- Yurui Guo
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Alpen Ortug
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Rodney Sadberry
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Behavioral Neuroscience, Northeastern University, Boston, MA 02215, USA
| | - Arthur Rezayev
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Biology, Boston University, Boston, MA 02215, USA
| | - Jacob Levman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
| | - Tadashi Shiohama
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatrics, Chiba University Hospital, Chiba 2608670, Japan
| | - Emi Takahashi
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
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49
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Min KD, Kim JS, Park YH, Shin HY, Kim C, Seo SW, Kim SY. New assessment for residential greenness and the association with cortical thickness in cognitively healthy adults. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 778:146129. [PMID: 33714817 DOI: 10.1016/j.scitotenv.2021.146129] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 01/26/2021] [Accepted: 02/22/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Recent evidence suggests that neurological health could be improved with the intervention of local green space. A few studies adopted cortical thickness, as an effective biomarker for neurodegenerative disorder, to investigate the association with residential greenness. However, they relied on limited data sources, definitions or applications to assess residential greenness. Our cross-sectional study assessed individual residential greenness using an alternative measure, which provides a more realistic definition of local impact and application based on the type of area, and investigated the association with cortical thickness. METHODS The study population included 2542 subjects who participated in the medical check-up program at the Health Promotion Center of the Samsung Medical Center in Seoul, Korea, from 2008 to 2014. The cortical thickness was calculated by each of the four and global lobes from brain MRI. For greenness, we used the enhanced vegetation index (EVI) that detects canopy structural variation by adjusting background noise based on satellite imagery data. To assess individual exposure to residential greenness, we computed the maximum annual EVI before the date of a medical check-up and averaged it within 750 m from subjects' homes to represent an average walking distance. Finally, we assessed the association with cortical thickness by urban and non-urban populations using multiple linear regression adjusting for individual characteristics. RESULTS The average global cortical thickness and EVI were 3.05 mm (standard deviation = 0.1 mm) and 0.31 (0.1), respectively. An interquartile range increase in EVI was associated with 11 μm (95% confidence interval = 3-20) and 9 μm (1-16) increases in cortical thickness of the parietal and occipital regions among the urban population. We did not find associations in non-urban subjects. CONCLUSIONS Our findings confirm the association between residential greenness and neurological health using alternative exposure assessments, indicating that high exposure to residential greenness can prevent neurological disorders.
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Affiliation(s)
- Kyung-Duk Min
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea; Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hee Young Shin
- Health Promotion Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Changsoo Kim
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
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50
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Fulong X, Karen S, Xiaosong D, Zhaolong C, Jun Z, Fang H. Morphological and Age-Related Changes in the Narcolepsy Brain. Cereb Cortex 2021; 31:5460-5469. [PMID: 34165139 DOI: 10.1093/cercor/bhab171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/03/2021] [Accepted: 05/24/2021] [Indexed: 11/12/2022] Open
Abstract
Morphological changes in the cortex of narcolepsy patients were investigated by surface-based morphometry analysis in this study. Fifty-one type 1 narcolepsy patients and 60 demographically group-matched healthy controls provided resting-state functional and high-resolution 3T anatomical magnetic resonance imaging scans. Vertex-level cortical thickness (CT), gyrification, and voxel-wise functional connectivity were calculated. Adolescent narcolepsy patients showed decreased CT in bilateral frontal cortex and left precuneus. Adolescent narcolepsy demonstrated increased gyrification in left occipital lobe, left precuneus, and right fusiform but decreased gyrification in left postcentral gyrus, whereas adult narcolepsy exhibited increased gyrification in left temporal lobe and right frontal cortex. Furthermore, sleepiness severity was associated with altered CT and gyrification. Increased gyrification was associated with reduced long-range functional connectivity. In adolescent patients, those with more severe sleepiness showed increased right postcentral gyrification. Decreased frontal and occipital gyrification was found in cases with hallucination. In adult patients, a wide range of regions showed reduced gyrification in those with adolescence-onset compared adult-onset narcolepsy patients. Particularly the frontal lobes showed altered brain morphology, being a thinner cortex and more gyri. The impact of narcolepsy on age-related brain morphological changes may remain from adolescence to young adulthood, and it was especially exacerbated in adolescence.
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Affiliation(s)
- Xiao Fulong
- Department of General Internal Medicine, Peking University People's Hospital, Beijing 100044, People's Republic of China
| | - Spruyt Karen
- Lyon Neuroscience Research Center, INSERM, U1028-CNRS UMR 5292, School of Medicine, University Claude Bernard, Lyon, France
| | - Dong Xiaosong
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, People's Republic of China
| | - Cao Zhaolong
- Department of General Internal Medicine, Peking University People's Hospital, Beijing 100044, People's Republic of China
| | - Zhang Jun
- Department of Neurology, Peking University People's Hospital, Beijing 100044, People's Republic of China
| | - Han Fang
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, People's Republic of China
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