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Cao P, Li R, Li Y, Dong Y, Tang Y, Xu G, Si Q, Chen C, Chen L, Liu W, Yao Y, Sui Y, Zhang J. Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging. J Affect Disord 2025; 383:20-31. [PMID: 40286928 DOI: 10.1016/j.jad.2025.04.135] [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: 01/14/2025] [Revised: 04/19/2025] [Accepted: 04/22/2025] [Indexed: 04/29/2025]
Abstract
BACKGROUND Cortical morphological abnormalities in schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) have been identified in past research. However, their potential as objective biomarkers to differentiate these disorders remains uncertain. Machine learning models may offer a novel diagnostic tool. METHODS Structural MRI (sMRI) of 220 SCZ, 220 MDD, 220 BD, and 220 healthy controls were obtained using a 3T scanner. Volume, thickness, surface area, and mean curvature of 68 cerebral cortices were extracted using FreeSurfer. 272 features underwent 3 feature selection techniques to isolate important variables for model construction. These features were incorporated into 3 classifiers for classification. After model evaluation and hyperparameter tuning, the best-performing model was identified, along with the most significant brain measures. RESULTS The univariate feature selection-Naive Bayes model achieved the best performance, with an accuracy of 0.66, macro-average AUC of 0.86, and sensitivities and specificities ranging from 0.58-0.86 to 0.81-0.93, respectively. Key features included thickness of right isthmus-cingulate cortex, area of left inferior temporal gyrus, thickness of right superior temporal gyrus, mean curvature of right pars orbitalis, thickness of left transverse temporal cortex, volume of left caudal anterior-cingulate cortex, area of right banks superior temporal sulcus, and thickness of right temporal pole. CONCLUSION The machine learning model based on sMRI data shows promise for aiding in the differential diagnosis of SCZ, MDD, and BD. Cortical features from the cingulate and temporal lobes may highlight distinct biological mechanisms underlying each disorder.
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Affiliation(s)
- Peiyu Cao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China
| | - Runda Li
- Duke University, 2080 Duke University Road, Durham, NC 27708, United States.
| | - Yuting Li
- Huzhou Third People's Hospital, China
| | | | - Yilin Tang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China
| | - Guoxin Xu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China
| | - Qi Si
- Huai'an No. 3 People's Hospital, China
| | | | - Lijun Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China
| | - Wen Liu
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, China
| | - Ye Yao
- Nanjing Medical University, China
| | - Yuxiu Sui
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China.
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Lab for Artificial Intelligence in Medical Imaging (LAIMI), School of Medical Imaging, Nanjing Medical University, Nanjing, Jiangsu, China.
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Kas MJH, Hyman S, Williams LM, Hidalgo-Mazzei D, Huys QJM, Hotopf M, Cuthbert B, Lewis CM, De Picker LJ, Lalousis PA, Etkin A, Modinos G, Marston HM. Towards a consensus roadmap for a new diagnostic framework for mental disorders. Eur Neuropsychopharmacol 2025; 90:16-27. [PMID: 39341044 DOI: 10.1016/j.euroneuro.2024.08.515] [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: 06/04/2024] [Revised: 08/17/2024] [Accepted: 08/20/2024] [Indexed: 09/30/2024]
Abstract
Current nosology claims to separate mental disorders into distinct categories that do not overlap with each other. This nosological separation is not based on underlying pathophysiology but on convention-based clustering of qualitative symptoms of disorders which are typically measured subjectively. Yet, clinical heterogeneity and diagnostic overlap in disease symptoms and dimensions within and across different diagnostic categories of mental disorders is huge. While diagnostic categories provide the basis for general clinical management, they do not describe the underlying neurobiology that gives rise to individual symptomatic presentations. The ability to incorporate neurobiology into the diagnostic framework and to stratify patients accordingly will be a critical step forward for the development of new treatments for mental disorders. Furthermore, it will also allow physicians to provide patients with a better understanding of their illness's complexities and management. To realize this ambition, a paradigm shift is needed to build an understanding of how neuropsychiatric conditions can be defined more precisely using quantitative (multimodal) biological processes and markers and thus to significantly improve treatment success. The ECNP New Frontiers Meeting 2024 set out to develop a consensus roadmap for building a new diagnostic framework for mental disorders by discussing its rationale, outlook, and consequences with all stakeholders involved. This framework would instantiate a set of principles and procedures by which research could continuously improve precision diagnostics while moving away from traditional nosology. In this meeting report, the speakers' summaries from their presentations are combined to address three key elements for generating such a roadmap, namely, the application of innovative technologies, understanding the biology of mental illness, and translating biological understanding into new approaches. In general, the meeting indicated a crucial need for a biology-informed framework to establish more precise diagnosis and treatment for mental disorders to facilitate bringing the right treatment to the right patient at the right time.
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Affiliation(s)
- Martien J H Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, the Netherlands.
| | - Steven Hyman
- Harvard University and Stanley Center, Broad Institute of MIT and Harvard, USA
| | - Leanne M Williams
- Stanford Center for Precision Mental Health and Wellness, Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive disorders unit, Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Quentin J M Huys
- Applied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry Psychology & Neuroscience, King's College London, London2, United Kingdom
| | - Bruce Cuthbert
- Contractor for the Research Domain Criteria project, National Institute of Mental Health (NIMH), USA
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Livia J De Picker
- Collaborative Antwerp Psychiatric Research Institute, University of Antwerp, Belgium; SINAPS, University Psychiatric Hospital Duffel, Belgium
| | - Paris A Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University of Munich, Munich, Germany
| | - Amit Etkin
- Alto Neuroscience Inc, Los Altos, CA, USA; Stanford University, Stanford, CA, USA
| | - Gemma Modinos
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Hugh M Marston
- CNS Discovery Research, Boehringer Ingelheim Pharma GmbH, Biberach, Germany
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Xie YT, Yang YJ. Research fronts and researchers of World Journal of Psychiatry in 2023: A visualization and analysis of mapping knowledge domains. World J Psychiatry 2024; 14:1118-1126. [PMID: 39050206 PMCID: PMC11262920 DOI: 10.5498/wjp.v14.i7.1118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/31/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND In the rapidly evolving landscape of psychiatric research, 2023 marked another year of significant progress globally, with the World Journal of Psychiatry (WJP) experiencing notable expansion and influence. AIM To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023. By delving into these publications, the aim is to determine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry. METHODS A selection process led to the inclusion of 107 papers from the WJP published in 2023, forming the dataset for the analysis. Employing advanced visualization techniques, this study mapped the knowledge domains represented in these papers. RESULTS The findings revealed a prevalent focus on key topics such as depression, mental health, anxiety, schizophrenia, and the impact of coronavirus disease 2019. Additionally, through keyword clustering, it became evident that these papers were predominantly focused on exploring mental health disorders, depression, anxiety, schizophrenia, and related factors. Noteworthy contributions hailed authors in regions such as China, the United Kingdom, United States, and Turkey. Particularly, the paper garnered the highest number of citations, while the American Psychiatric Association was the most cited reference. CONCLUSION It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry. Additionally, there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.
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Affiliation(s)
- Yun-Tian Xie
- Department of Applied Psychology, Changsha Normal University, Changsha 410100, Hunan Province, China
| | - Yu-Jing Yang
- Department of Applied Psychology, Changsha Normal University, Changsha 410100, Hunan Province, China
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Birdi A, Tomo S, Sharma M, Yadav P, Charan J, Sharma P, Yadav D. Association of Klotho with Neuropsychiatric Disorder: A Meta-Analysis. Indian J Clin Biochem 2024; 39:429-437. [PMID: 39005867 PMCID: PMC11239616 DOI: 10.1007/s12291-023-01132-5] [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: 12/01/2022] [Accepted: 03/08/2023] [Indexed: 04/07/2023]
Abstract
Neuropsychiatric disorders are mainly concerned with the behavioural, emotional and cognition symptoms that may be due to disturbed cerebral functions or extracerebral disease. Klotho protein is an antiaging protein that is mostly associated with cognitive changes in these disorders and thus this meta-analysis is conducted in order to find Klotho proteins association with these disorders. We searched related topics in pubmed, by using the key word i.e. Klotho and related disorder from neuropsychiatry e.g. Klotho levels and schizophrenia, Klotho levels and parkinsonism etc. Total 82 studies were found till 9th February 2021 after extensive search and 10 studies were selected for further analysis. The meta-analysis of studies was performed using the Random effect model. The forest plot represented each study in the meta-analysis, so as to make the comparison of SMD value across studies. The meta-analysis outcome demonstrated that overall schizophrenia had higher klotho levels as compared with bipolar disorder, psychosocial stress, parkinsonism, multiple sclerosis, depression, Alzheimer's disease, and healthy controls, followed by MS. The meta-analysis also found that bipolar disorder and Alzheimer's disease were associated with low klotho levels as compared to schizophrenia. The results indicate a significant association of the klotho levels and schizophrenia. Further studies are needed to characterize the potential biological roles of klotho levels in psychiatric disorders.
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Affiliation(s)
- Amandeep Birdi
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India
| | - Sojit Tomo
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India
| | - Monika Sharma
- Department of Bioscience and Bioengineering, IIT Jodhpur, Jodhpur, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, IIT Jodhpur, Jodhpur, India
| | - Jaykaran Charan
- Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, India
| | - Praveen Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India
| | - Dharmveer Yadav
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India
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Diotaiuti P, Corrado S, Tosti B, Spica G, Di Libero T, D’Oliveira A, Zanon A, Rodio A, Andrade A, Mancone S. Evaluating the effectiveness of neurofeedback in chronic pain management: a narrative review. Front Psychol 2024; 15:1369487. [PMID: 38770259 PMCID: PMC11104502 DOI: 10.3389/fpsyg.2024.1369487] [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: 01/12/2024] [Accepted: 03/28/2024] [Indexed: 05/22/2024] Open
Abstract
The prevalence and impact of chronic pain in individuals worldwide necessitate effective management strategies. This narrative review specifically aims to assess the effectiveness of neurofeedback, an emerging non-pharmacological intervention, on the management of chronic pain. The methodology adopted for this review involves a meticulous search across various scientific databases. The search was designed to capture a broad range of studies related to neurofeedback and chronic pain management. To ensure the quality and relevance of the included studies, strict inclusion and exclusion criteria were applied. These criteria focused on the study design, population, intervention type, and reported outcomes. The review synthesizes the findings from a diverse array of studies, including randomized controlled trials, observational studies, and case reports. Key aspects evaluated include the types of neurofeedback used (such as EEG biofeedback), the various chronic pain conditions addressed (like fibromyalgia, neuropathic pain, and migraines), and the methodologies employed in these studies. The review highlights the underlying mechanisms by which neurofeedback may influence pain perception and management, exploring theories related to neural plasticity, pain modulation, and psychological factors. The results of the review reveal a positive correlation between neurofeedback interventions and improved pain management. Several studies report significant reductions on pain intensity, improved quality of life, and decreased reliance on medication following neurofeedback therapy. The review also notes variations in the effectiveness of different neurofeedback protocols and individual responses to treatment. Despite the promising results, the conclusion of the review emphasizes the need for further research. It calls for larger, well-designed clinical trials to validate the findings, to understand the long-term implications of neurofeedback therapy, and to optimize treatment protocols for individual patients.
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Affiliation(s)
- Pierluigi Diotaiuti
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Stefano Corrado
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Beatrice Tosti
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Giuseppe Spica
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Tommaso Di Libero
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Anderson D’Oliveira
- Department of Physical Education, CEFID, Santa Catarina State University, Florianopolis, Santa Catarina, Brazil
| | - Alessandra Zanon
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Angelo Rodio
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
| | - Alexandro Andrade
- Department of Physical Education, CEFID, Santa Catarina State University, Florianopolis, Santa Catarina, Brazil
| | - Stefania Mancone
- Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Lazio, Italy
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Wang Y, Yang Y, Xu W, Yao X, Xie X, Zhang L, Sun J, Wang L, Hua Q, He K, Tian Y, Wang K, Ji GJ. Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission. Schizophr Bull 2024; 50:545-556. [PMID: 38253437 PMCID: PMC11059819 DOI: 10.1093/schbul/sbae003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
BACKGROUND AND HYPOTHESIS There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network. STUDY DESIGN We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions. STUDY RESULTS We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission. CONCLUSIONS Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.
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Affiliation(s)
- Yingru Wang
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Yinian Yang
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Wenqiang Xu
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaoqing Yao
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaohui Xie
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Long Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qiang Hua
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kongliang He
- Department of Psychiatry, Fourth People’s Hospital of Hefei, Anhui Mental Health Center, Hefei, China
| | - Yanghua Tian
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
| | - Gong-Jun Ji
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
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Grot S, Smine S, Potvin S, Darcey M, Pavlov V, Genon S, Nguyen H, Orban P. Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110950. [PMID: 38266867 DOI: 10.1016/j.pnpbp.2024.110950] [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: 06/02/2023] [Revised: 11/11/2023] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them. METHODS To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders. RESULTS Hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD. CONCLUSIONS Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.
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Affiliation(s)
- Stéphanie Grot
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada; Department of Psychiatry and Addictology, University of Montreal, Montréal, Québec, Canada
| | - Salima Smine
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada
| | - Stéphane Potvin
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada; Department of Psychiatry and Addictology, University of Montreal, Montréal, Québec, Canada
| | - Maëliss Darcey
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada
| | - Vilena Pavlov
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Hien Nguyen
- School of Mathematics and Physics, University of Queensland, St. Lucia, Queensland, Australia; Department of Mathematics and Statistics, Latrobe University, Melbourne, Victoria, Australia
| | - Pierre Orban
- Research Center, Montreal University Institute for Mental Health, Montréal, Québec, Canada; Department of Psychiatry and Addictology, University of Montreal, Montréal, Québec, Canada.
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Romeo Z, Biondi M, Oltedal L, Spironelli C. The Dark and Gloomy Brain: Grey Matter Volume Alterations in Major Depressive Disorder-Fine-Grained Meta-Analyses. Depress Anxiety 2024; 2024:6673522. [PMID: 40226746 PMCID: PMC11919126 DOI: 10.1155/2024/6673522] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 12/09/2023] [Accepted: 02/16/2024] [Indexed: 04/15/2025] Open
Abstract
Background While the brain correlates of major depressive disorder (MDD) have been extensively studied, there is no consensus conclusion so far. Various meta-analyses tried to determine the most consistent findings, but the results are often discordant for grey matter volume (GMV) atrophy and hypertrophy. Applying rigorous and stringent inclusion criteria and controlling for confounding factors, such as the presence of anxiety comorbidity, we carried out two novel meta-analyses on the existing literature to unveil MDD signatures. Methods A systematic literature search was performed up to January 2023. Seventy-three studies on MDD patients reporting GMV abnormalities were included in the first meta-analysis, for a total of 6167 patients and 6237 healthy controls (HC). To test the effects of anxiety comorbidity, we conducted a second meta-analysis, by adding to the original pure MDD sample a new cohort of MDD patients with comorbid anxiety disorders (308 patients and 342 HC). An activation likelihood estimation (ALE) analysis and a coordinate-based mapping approach separate for atrophy and hypertrophy were used to identify common brain structural alterations among patients. Results The pure MDD sample exhibited atrophy in the left insula, as well as hypertrophy in the bilateral amygdala and parahippocampal gyri. When we added patients with comorbid anxiety to the original sample, bilateral insula atrophy emerged, whereas the hypertrophy results were not replicated. Conclusions Our findings revealed important structural alterations in pure MDD patients, particularly in the insula and amygdala, which play key roles in sensory input integration and in emotional processing, respectively. Additionally, the amygdala and parahippocampal gyrus hypertrophy may be related to MDD functional overactivation to emotional stimuli, rumination, and overactive self-referential thinking. Conversely, the presence of anxiety comorbidity revealed separate effects which were not seen in the pure MDD sample, underscoring the importance of strict inclusion criteria for investigations of disorder-specific effects.
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Affiliation(s)
- Zaira Romeo
- Department of General Psychology, University of Padova, 35131 Padova, Italy
| | - Margherita Biondi
- Department of General Psychology, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Leif Oltedal
- Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Chiara Spironelli
- Department of General Psychology, University of Padova, 35131 Padova, Italy
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
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Parker N, Cheng W, Hindley GFL, O'Connell KS, Karthikeyan S, Holen B, Shadrin AA, Rahman Z, Karadag N, Bahrami S, Lin A, Steen NE, Ueland T, Aukrust P, Djurovic S, Dale AM, Smeland OB, Frei O, Andreassen OA. Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts. Biol Psychiatry 2024; 95:62-71. [PMID: 37348803 PMCID: PMC11684752 DOI: 10.1016/j.biopsych.2023.06.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 06/02/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND For many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels). METHODS Linear regression was used to assess phenotypic associations in 30,823 UK Biobank participants. Genome-wide and local genetic correlations were assessed using linkage disequilibrium score regression and local analysis of covariance annotation. The number of shared trait-influencing genetic variants was estimated using MiXeR. Shared genetic architecture was assessed using a conjunctional false discovery rate framework, and mapped genes were included in gene-set enrichment analyses. RESULTS Cortical structure and blood immune markers exhibited predominantly inverse phenotypic associations. There were modest genome-wide genetic correlations, the strongest of which were for C-reactive protein levels (rg_surface_area = -0.13, false discovery rate-corrected p = 4.17 × 10-3; rg_thickness = -0.13, false discovery rate-corrected p = 4.00 × 10-2). Meanwhile, local genetic correlations showed a mosaic of positive and negative associations. White blood cells shared on average 46.24% and 38.64% of trait-influencing genetic variants with surface area and thickness, respectively. Additionally, surface area shared 55 unique loci with the blood immune markers while thickness shared 15. Overall, monocyte count exhibited the largest genetic overlap with cortical brain structure. A series of gene enrichment analyses implicated neuronal-, astrocytic-, and schizophrenia-associated genes. CONCLUSIONS The findings indicate shared genetic underpinnings for cortical brain structure and blood immune markers, with implications for neurodevelopment and understanding the etiology of brain-related disorders.
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Affiliation(s)
- Nadine Parker
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandeep Karthikeyan
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway; KG Jebsen Thrombosis Research and Expertise Centre, University of Tromsø, Tromsø, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Section of Clinical Immunology and Infectious Disease, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California; Department of Psychiatry, University of California, San Diego, La Jolla, California; Department of Neurosciences, University of California San Diego, La Jolla, California; Department of Radiology, University of California San Diego, La Jolla, California
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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10
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Medina-Rodríguez JC. Exploring Neuropsychiatry: Contemporary Challenges, Breakthroughs, and Philosophical Perspectives. Cureus 2023; 15:e49404. [PMID: 38149145 PMCID: PMC10749795 DOI: 10.7759/cureus.49404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2023] [Indexed: 12/28/2023] Open
Abstract
This editorial provides a concise and updated overview of neuropsychiatry, emphasizing its definitional challenges and profound implications for education, training, research, and the integration of phenomenology and philosophy of mind. Neuropsychiatry, situated at the crossroads of neurology and psychiatry, grapples with complex definitional issues that impede research progress. Establishing a unified conceptual framework is essential for focused research and delving into fundamental questions regarding topics like "consciousness." Nevertheless, the integration of philosophical perspectives into neuropsychiatry, while valuable, faces hurdles due to conceptual ambiguities and the fluid boundaries of the field. These obstacles disrupt research and hinder progress in effectively addressing neuropsychiatric conditions. This editorial advocates for a systematic approach to defining neuropsychiatry to mitigate these concerns. Additionally, as neuropsychiatry evolves, it necessitates an integrative approach. Recent advancements in neuroscience, propelled by technologies like artificial intelligence and advanced neuroimaging, reshape our comprehension of brain-behavior interactions, offering potential biomarkers and comprehensive treatment approaches.
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Affiliation(s)
- José Carlos Medina-Rodríguez
- Department of Neurology & Neuropsychiatry, National Institute of Psychiatry, Mexico City, MEX
- Postgraduate Studies Division, National Autonomous University of Mexico, Mexico City, MEX
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11
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Gil-Rivas A, de Pascual-Teresa B, Ortín I, Ramos A. New Advances in the Exploration of Esterases with PET and Fluorescent Probes. Molecules 2023; 28:6265. [PMID: 37687094 PMCID: PMC10488407 DOI: 10.3390/molecules28176265] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 09/10/2023] Open
Abstract
Esterases are hydrolases that catalyze the hydrolysis of esters into the corresponding acids and alcohols. The development of fluorescent probes for detecting esterases is of great importance due to their wide spectrum of biological and industrial applications. These probes can provide a rapid and sensitive method for detecting the presence and activity of esterases in various samples, including biological fluids, food products, and environmental samples. Fluorescent probes can also be used for monitoring the effects of drugs and environmental toxins on esterase activity, as well as to study the functions and mechanisms of these enzymes in several biological systems. Additionally, fluorescent probes can be designed to selectively target specific types of esterases, such as those found in pathogenic bacteria or cancer cells. In this review, we summarize the recent fluorescent probes described for the visualization of cell viability and some applications for in vivo imaging. On the other hand, positron emission tomography (PET) is a nuclear-based molecular imaging modality of great value for studying the activity of enzymes in vivo. We provide some examples of PET probes for imaging acetylcholinesterases and butyrylcholinesterases in the brain, which are valuable tools for diagnosing dementia and monitoring the effects of anticholinergic drugs on the central nervous system.
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Affiliation(s)
- Alba Gil-Rivas
- Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28668 Boadilla del Monte, Spain
| | - Beatriz de Pascual-Teresa
- Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28668 Boadilla del Monte, Spain
| | - Irene Ortín
- Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28668 Boadilla del Monte, Spain
| | - Ana Ramos
- Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28668 Boadilla del Monte, Spain
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12
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Thankarajan E, Oz S, Saady A, Kulbitski K, Kompanets MO, Eisen MS, Berlin S. SNAP-Tag-Targeted MRI-Fluorescent Multimodal Probes. Chembiochem 2023; 24:e202300172. [PMID: 37092744 DOI: 10.1002/cbic.202300172] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 04/25/2023]
Abstract
Magnetic resonance imaging (MRI) is a powerful imaging modality, widely employed in research and clinical settings. However, MRI images suffer from low signals and a lack of target specificity. We aimed to develop a multimodal imaging probe to detect targeted cells by MRI and fluorescence microscopy. We synthesized a trifunctional imaging probe consisting of a SNAP-tag substrate for irreversible and specific labelling of cells, cyanine dyes for bright fluorescence, and a chelated GdIII molecule for enhancing MRI contrast. Our probes exhibit specific and efficient labelling of genetically defined cells (expressing SNAP-tag at their membrane), bright fluorescence and MRI signal. Our synthetic approach provides a versatile platform for the production of multimodal imaging probes, particularly for light microscopy and MRI.
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Affiliation(s)
- Ebaston Thankarajan
- Department of Neuroscience, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, 3525422, Israel
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Shimrit Oz
- Department of Neuroscience, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, 3525422, Israel
| | - Abed Saady
- Department of Neuroscience, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, 3525422, Israel
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
- Present address: School of Chemistry, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
| | - Kseniya Kulbitski
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Mykhail O Kompanets
- L.M. Litvinenko Institute of Physico-Organic Chemistry and Coal Chemistry, National Academy of Sciences of Ukraine, Kyiv, 02660, Ukraine
| | - Moris S Eisen
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Shai Berlin
- Department of Neuroscience, Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, 3525422, Israel
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13
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Menon V, Palaniyappan L, Supekar K. Integrative Brain Network and Salience Models of Psychopathology and Cognitive Dysfunction in Schizophrenia. Biol Psychiatry 2023; 94:108-120. [PMID: 36702660 DOI: 10.1016/j.biopsych.2022.09.029] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
Brain network models of cognitive control are central to advancing our understanding of psychopathology and cognitive dysfunction in schizophrenia. This review examines the role of large-scale brain organization in schizophrenia, with a particular focus on a triple-network model of cognitive control and its role in aberrant salience processing. First, we provide an overview of the triple network involving the salience, frontoparietal, and default mode networks and highlight the central role of the insula-anchored salience network in the aberrant mapping of salient external and internal events in schizophrenia. We summarize the extensive evidence that has emerged from structural, neurochemical, and functional brain imaging studies for aberrancies in these networks and their dynamic temporal interactions in schizophrenia. Next, we consider the hypothesis that atypical striatal dopamine release results in misattribution of salience to irrelevant external stimuli and self-referential mental events. We propose an integrated triple-network salience-based model incorporating striatal dysfunction and sensitivity to perceptual and cognitive prediction errors in the insula node of the salience network and postulate that dysregulated dopamine modulation of salience network-centered processes contributes to the core clinical phenotype of schizophrenia. Thus, a powerful paradigm to characterize the neurobiology of schizophrenia emerges when we combine conceptual models of salience with large-scale cognitive control networks in a unified manner. We conclude by discussing potential therapeutic leads on restoring brain network dysfunction in schizophrenia.
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Affiliation(s)
- Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California.
| | - Lena Palaniyappan
- Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California
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14
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Rutherford S, Barkema P, Tso IF, Sripada C, Beckmann CF, Ruhe HG, Marquand AF. Evidence for embracing normative modeling. eLife 2023; 12:e85082. [PMID: 36912775 PMCID: PMC10036120 DOI: 10.7554/elife.85082] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.
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Affiliation(s)
- Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical CentreNijmegenNetherlands
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
- Department of Psychiatry, University of Michigan-Ann ArborAnn ArborUnited States
| | - Pieter Barkema
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
| | - Ivy F Tso
- Department of Psychiatry, University of Michigan-Ann ArborAnn ArborUnited States
- Department of Psychology, University of Michigan-Ann ArborAnn ArborUnited States
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan-Ann ArborAnn ArborUnited States
- Department of Philosophy, University of Michigan-Ann ArborAnn ArborUnited States
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical CentreNijmegenNetherlands
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
- Center for Functional MRI of the Brain (FMRIB), Nuffield Department for Clinical Neuroscience, Welcome Centre for Integrative Neuroimaging, Oxford UniversityOxfordUnited Kingdom
| | - Henricus G Ruhe
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
- Department of Psychiatry, Radboud University Nijmegen Medical CentreNijmegenNetherlands
| | - Andre F Marquand
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical CentreNijmegenNetherlands
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
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15
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Chavanne AV, Paillère Martinot ML, Penttilä J, Grimmer Y, Conrod P, Stringaris A, van Noort B, Isensee C, Becker A, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Nees F, Papadopoulos Orfanos D, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Martinot JL, Artiges E. Anxiety onset in adolescents: a machine-learning prediction. Mol Psychiatry 2023; 28:639-646. [PMID: 36481929 PMCID: PMC9908534 DOI: 10.1038/s41380-022-01840-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 09/09/2022] [Accepted: 10/10/2022] [Indexed: 12/13/2022]
Abstract
Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18-23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with the caudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4-8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction in adolescents.
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Grants
- MRF_MRF-058-0004-RG-DESRI MRF
- MR/R00465X/1 Medical Research Council
- R01 MH085772 NIMH NIH HHS
- U54 EB020403 NIBIB NIH HHS
- R56 AG058854 NIA NIH HHS
- MR/W002418/1 Medical Research Council
- MR/S020306/1 Medical Research Council
- MRF_MRF-058-0009-RG-DESR-C0759 MRF
- MR/N000390/1 Medical Research Council
- R01 DA049238 NIDA NIH HHS
- This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), the Medical Research Council Grant 'c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministerium für Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC- Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from: - the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. The INSERM, and the Strasbourg University and SATT CONECTUS, provided sponsorship (PI: Jean-Luc Martinot).
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Affiliation(s)
- Alice V Chavanne
- Université Paris-Saclay, Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Centre Borelli, Gif-sur-Yvette, France
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marie Laure Paillère Martinot
- Université Paris-Saclay, Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Centre Borelli, Gif-sur-Yvette, France
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, AP-HP, Sorbonne Université, Paris, France
| | - Jani Penttilä
- Department of Social and Health Care, Psychosocial Services Adolescent Outpatient Clinic Kauppakatu 14, Lahti, Finland
| | - Yvonne Grimmer
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Patricia Conrod
- Department of Psychiatry, CHU Sainte-Justine Hospital, University of Montréal, Montreal, QC, Canada
| | | | - Betteke van Noort
- Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy, Campus CharitéMitte, Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Corinna Isensee
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Andreas Becker
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Department of Psychiatry and Neuroscience, Faculty of Medicine, CHU Sainte-Justine Research Center, Population Neuroscience Laboratory, University of Montreal, Montreal, QC, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Section of Systems Neuroscience, Medical Faculty, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Section of Systems Neuroscience, Medical Faculty, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University Shanghai and Department of Psychiatry and Neuroscience, Charité University Medicine, Berlin, Germany
| | - Jean-Luc Martinot
- Université Paris-Saclay, Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Centre Borelli, Gif-sur-Yvette, France.
| | - Eric Artiges
- Université Paris-Saclay, Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Centre Borelli, Gif-sur-Yvette, France
- Department of Psychiatry, EPS Barthélémy Durand, Etampes, France
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16
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Zhang D, Yu L, Chen Y, Shen J, Du L, Lin L, Wu J. Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study. Cereb Cortex 2023:6992943. [PMID: 36657794 DOI: 10.1093/cercor/bhac531] [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: 10/31/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 01/21/2023] Open
Abstract
Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.
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Affiliation(s)
- Die Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China.,Department of Radiology, Shenzhen Third People's Hospital, Shenzhen 518000, China
| | - Lan Yu
- Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 211166,China
| | - Yingying Chen
- Department of Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen 518172, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lina Du
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lin Lin
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
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17
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Li R, Hosseini H, Saggar M, Balters SC, Reiss AL. Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry. NEUROPHOTONICS 2023; 10:013505. [PMID: 36777700 PMCID: PMC9904322 DOI: 10.1117/1.nph.10.1.013505] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/13/2023] [Indexed: 05/19/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.
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Affiliation(s)
- Rihui Li
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Hadi Hosseini
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Manish Saggar
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Stephanie Christina Balters
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Allan L. Reiss
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
- Stanford University, Department of Radiology and Pediatrics, Stanford, California, United States
- Stanford University, Department of Pediatrics, Stanford, California, United States
- Address all correspondence to Allan L. Reiss,
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18
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67:23TR01. [PMID: 36279868 DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brainin vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, People's Republic of China
- Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu 610041, Sichuan, People's Republic of China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, People's Republic of China
- Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu 610041, Sichuan, People's Republic of China
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, United States of America
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, People's Republic of China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, People's Republic of China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, People's Republic of China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, United States of America
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19
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Yuan L, Ma X, Li D, Ouyang L, Fan L, Li C, He Y, Chen X. Alteration of a brain network with stable and strong functional connections in subjects with schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:91. [PMID: 36333328 PMCID: PMC9636375 DOI: 10.1038/s41537-022-00305-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
It is widely accepted that there are some common network patterns in the human brain. However, the existence of stable and strong functional connections in the human brain and whether they change in schizophrenia is still a question. By setting 1% connections with the smallest coefficient of variation, we found a widespread brain functional network (frame network) in healthy people(n = 380, two datasets from public databases). We then explored the alterations in a medicated group (60 subjects with schizophrenia vs 71 matched controls) and a drug-naive first-episode group (68 subjects with schizophrenia vs 45 matched controls). A linear support vector classifier (SVC) was constructed to distinguish patients and controls using the medicated patients' frame network. We found most frame connections of healthy people had high strength, which were symmetrical and connected the left and right hemispheres. Conversely, significant differences in frame connections were observed in both patient groups, which were positively correlated with negative symptoms (mainly language dysfunction). Additionally, patients' frame network were more left-lateralized, concentrating on the left frontal lobe, and was quite accurate at distinguishing medicated patients from controls (classifier accuracy was 78.63%, sensitivity was 86.67%, specificity was 76.06%, and the area under the curve (AUC) was 0.83). Furthermore, the results were repeated in the drug-naive set (accuracy was 84.96%, sensitivity was 85.29%, specificity was 88.89%, and AUC was 0.93). These findings indicate that the abnormal pattern of frame network in subjects with schizophrenia might provide new insights into the dysconnectivity in schizophrenia.
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Affiliation(s)
- Liu Yuan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Xiaoqian Ma
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - David Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Lijun Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Lejia Fan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Chunwang Li
- Department of Radiology, Hunan Children's Hospital, Changsha, China
| | - Ying He
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China.
| | - Xiaogang Chen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China.
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20
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Lalousis PA, Schmaal L, Wood SJ, Reniers RLEP, Barnes NM, Chisholm K, Griffiths SL, Stainton A, Wen J, Hwang G, Davatzikos C, Wenzel J, Kambeitz-Ilankovic L, Andreou C, Bonivento C, Dannlowski U, Ferro A, Lichtenstein T, Riecher-Rössler A, Romer G, Rosen M, Bertolino A, Borgwardt S, Brambilla P, Kambeitz J, Lencer R, Pantelis C, Ruhrmann S, Salokangas RKR, Schultze-Lutter F, Schmidt A, Meisenzahl E, Koutsouleris N, Dwyer D, Upthegrove R. Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes. Biol Psychiatry 2022; 92:552-562. [PMID: 35717212 PMCID: PMC10128104 DOI: 10.1016/j.biopsych.2022.03.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/04/2022] [Accepted: 03/01/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.
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Affiliation(s)
- Paris Alexandros Lalousis
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom.
| | - Lianne Schmaal
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen J Wood
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Renate L E P Reniers
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom; Institute of Clinical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Nicholas M Barnes
- Institute of Clinical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Department of Psychology, Aston University, Birmingham, United Kingdom
| | - Sian Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Alexandra Stainton
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Junhao Wen
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | | | - Carolina Bonivento
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Adele Ferro
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Theresa Lichtenstein
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | | | - Georg Romer
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Paolo Brambilla
- Department of Psychiatry, University of Basel, Basel, Switzerland; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephan Ruhrmann
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | | | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany; University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | - André Schmidt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom; Birmingham Early Interventions Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom
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21
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Vij R, Arora S. A systematic survey of advances in retinal imaging modalities for Alzheimer's disease diagnosis. Metab Brain Dis 2022; 37:2213-2243. [PMID: 35290546 DOI: 10.1007/s11011-022-00927-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 02/04/2022] [Indexed: 01/06/2023]
Abstract
Recent advances in retinal imaging pathophysiology have shown a new function for biomarkers in Alzheimer's disease diagnosis and prognosis. The significant improvements in Optical coherence tomography (OCT) retinal imaging have led to significant clinical translation, particularly in Alzheimer's disease detection. This systematic review will provide a comprehensive overview of retinal imaging in clinical applications, with a special focus on biomarker analysis for use in Alzheimer's disease detection. Articles on OCT retinal imaging in Alzheimer's disease diagnosis were identified in PubMed, Google Scholar, IEEE Xplore, and Research Gate databases until March 2021. Those studies using simultaneous retinal imaging acquisition were chosen, while those using sequential techniques were rejected. "Alzheimer's disease" and "Dementia" were searched alone and in combination with "OCT" and "retinal imaging". Approximately 1000 publications were searched, and after deleting duplicate articles, 145 relevant studies focused on the diagnosis of Alzheimer's disease utilizing retinal imaging were chosen for study. OCT has recently been demonstrated to be a valuable technique in clinical practice as according to this survey, 57% of the researchers employed optical coherence tomography, 19% used ocular fundus imaging, 13% used scanning laser ophthalmoscopy, and 11% have used multimodal imaging to diagnose Alzheimer disease. Retinal imaging has become an important diagnostic technique for Alzheimer's disease. Given the scarcity of available literature, it is clear that future prospective trials involving larger and more homogeneous groups are necessary, and the work can be expanded by evaluating its significance utilizing a machine-learning platform rather than simply using statistical methodologies.
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Affiliation(s)
- Richa Vij
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India
| | - Sakshi Arora
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India.
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22
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Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology. Commun Biol 2022; 5:1024. [PMID: 36168040 PMCID: PMC9515219 DOI: 10.1038/s42003-022-03963-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/07/2022] [Indexed: 02/06/2023] Open
Abstract
It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.
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23
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Tadd K, Rego T, Gaillard F, Malpas CB, Walterfang M, Velakoulis D, Farrand S. Neuroimaging in the Acute Psychiatric Setting: Associations With Neuropsychiatric Risk Factors. J Neuropsychiatry Clin Neurosci 2022; 35:184-191. [PMID: 36128679 DOI: 10.1176/appi.neuropsych.21110269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The appropriateness and clinical utility of neuroimaging in psychiatric populations has been long debated, and the ambiguity of guideline recommendations is well established. Most of the literature is focused on first-episode psychosis. The investigators aimed to review ordering practices and identify risk factors associated with neuroradiological MRI abnormalities and their clinical utility in a general psychiatric population. METHODS A retrospective file review was undertaken for 100 consecutive brain MRI scans for adult psychiatric inpatients who received scanning as part of their clinical care in an Australian hospital. RESULTS Brain MRI was abnormal in 79.0% of scans; in these cases, 72.2% of patients required further investigation or follow-up, with 17.7% requiring urgent referral within days to weeks, despite only 3.7% of admitted patients undergoing MRI during the study period. Psychiatrically relevant abnormalities were found in 32.0% of scans. Abnormalities were more likely to be found in the presence of cognitive impairment, older age, and longer duration of psychiatric disorder. Psychiatrically relevant abnormalities had further associations with older age at onset of the psychiatric disorder and a weak association with abnormal neurological examination. Multiple indications for imaging were present in 57.0% of patients; the most common indications were physical, neurological, and cognitive abnormalities. CONCLUSIONS Brain MRI is a useful part of psychiatric management in the presence of certain neuropsychiatric risk factors. The present findings suggest that treating teams can judiciously tailor radiological investigations while limiting excessive imaging. Future research in larger cohorts across multiple centers may contribute to shaping more consistent neuroimaging guidelines in psychiatry.
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Affiliation(s)
- Katelyn Tadd
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Thomas Rego
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Frank Gaillard
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Charles B Malpas
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Mark Walterfang
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Dennis Velakoulis
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Sarah Farrand
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
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24
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Abdelrahman HAF, Ubukata S, Ueda K, Fujimoto G, Oishi N, Aso T, Murai T. Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects. Neuropsychiatr Dis Treat 2022; 18:1801-1814. [PMID: 36039160 PMCID: PMC9419894 DOI: 10.2147/ndt.s354265] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 07/01/2022] [Indexed: 11/23/2022] Open
Abstract
Aim Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls. Methods Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers. Results Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09. Conclusion These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.
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Affiliation(s)
| | - Shiho Ubukata
- Kyoto University Graduate School of Medicine-Medical Innovation Center, Kyoto, 606-8507, Japan
| | - Keita Ueda
- Kyoto University Graduate School of Medicine-Department of Psychiatry, Kyoto, 606-8507, Japan
| | - Gaku Fujimoto
- Kyoto University Graduate School of Medicine-Department of Psychiatry, Kyoto, 606-8507, Japan
| | - Naoya Oishi
- Kyoto University Graduate School of Medicine-Medical Innovation Center, Kyoto, 606-8507, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, 650-0047, Japan
| | - Toshiya Murai
- Kyoto University Graduate School of Medicine-Department of Psychiatry, Kyoto, 606-8507, Japan
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Hawco C, Dickie EW, Herman G, Turner JA, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. A longitudinal multi-scanner multimodal human neuroimaging dataset. Sci Data 2022; 9:332. [PMID: 35701471 PMCID: PMC9198098 DOI: 10.1038/s41597-022-01386-3] [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: 04/13/2021] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of "travelling human phantoms" had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Erin W Dickie
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Gabrielle Herman
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychology & Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Miklos Argyelan
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Maith O, Dinkelbach HÜ, Baladron J, Vitay J, Hamker FH. BOLD Monitoring in the Neural Simulator ANNarchy. Front Neuroinform 2022; 16:790966. [PMID: 35392282 PMCID: PMC8981038 DOI: 10.3389/fninf.2022.790966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 02/08/2022] [Indexed: 01/13/2023] Open
Abstract
Multi-scale network models that simultaneously simulate different measurable signals at different spatial and temporal scales, such as membrane potentials of single neurons, population firing rates, local field potentials, and blood-oxygen-level-dependent (BOLD) signals, are becoming increasingly popular in computational neuroscience. The transformation of the underlying simulated neuronal activity of these models to simulated non-invasive measurements, such as BOLD signals, is particularly relevant. The present work describes the implementation of a BOLD monitor within the neural simulator ANNarchy to allow an on-line computation of simulated BOLD signals from neural network models. An active research topic regarding the simulation of BOLD signals is the coupling of neural processes to cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2). The flexibility of ANNarchy allows users to define this coupling with a high degree of freedom and thus, not only allows to relate mesoscopic network models of populations of spiking neurons to experimental BOLD data, but also to investigate different hypotheses regarding the coupling between neural processes, CBF and CMRO2 with these models. In this study, we demonstrate how simulated BOLD signals can be obtained from a network model consisting of multiple spiking neuron populations. We first demonstrate the use of the Balloon model, the predominant model for simulating BOLD signals, as well as the possibility of using novel user-defined models, such as a variant of the Balloon model with separately driven CBF and CMRO2 signals. We emphasize how different hypotheses about the coupling between neural processes, CBF and CMRO2 can be implemented and how these different couplings affect the simulated BOLD signals. With the BOLD monitor presented here, ANNarchy provides a tool for modelers who want to relate their network models to experimental MRI data and for scientists who want to extend their studies of the coupling between neural processes and the BOLD signal by using modeling approaches. This facilitates the investigation and model-based analysis of experimental BOLD data and thus improves multi-scale understanding of neural processes in humans.
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Affiliation(s)
| | | | | | | | - Fred H. Hamker
- Department of Computer Science, Chemnitz University of Technology, Chemnitz, Germany
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Grecucci A, Lapomarda G, Messina I, Monachesi B, Sorella S, Siugzdaite R. Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach. Front Psychiatry 2022; 13:804440. [PMID: 35295769 PMCID: PMC8918568 DOI: 10.3389/fpsyt.2022.804440] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
Previous morphometric studies of Borderline Personality Disorder (BPD) reported inconsistent alterations in cortical and subcortical areas. However, these studies have investigated the brain at the voxel level using mass univariate methods or region of interest approaches, which are subject to several artifacts and do not enable detection of more complex patterns of structural alterations that may separate BPD from other clinical populations and healthy controls (HC). Multiple Kernel Learning (MKL) is a whole-brain multivariate supervised machine learning method able to classify individuals and predict an objective diagnosis based on structural features. As such, this method can help identifying objective biomarkers related to BPD pathophysiology and predict new cases. To this aim, we applied MKL to structural images of patients with BPD and matched HCs. Moreover, to ensure that results are specific for BPD and not for general psychological disorders, we also applied MKL to BPD against a group of patients with bipolar disorder, for their similarities in affective instability. Results showed that a circuit, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC (80%). Notably, this circuit positively correlates with the affective sector of the Zanarini questionnaire, thus indicating an involvement of this circuit with affective disturbances. Moreover, by contrasting BPD with BD, the spurious regions were excluded, and a specific circuit for BPD was outlined. These results support that BPD is characterized by anomalies in a cortico-subcortical circuit related to affective instability and that this circuit discriminates BPD from controls and from other clinical populations.
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Affiliation(s)
- Alessandro Grecucci
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Center for Medical Sciences - CISMed, University of Trento, Trento, Italy
| | - Gaia Lapomarda
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Department of Psychology, Science Division, New York University of Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Irene Messina
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Universitas Mercatorum, Rome, Italy
| | - Bianca Monachesi
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Sara Sorella
- Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Roma Siugzdaite
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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Fovet T, Yger P, Lopes R, de Pierrefeu A, Duchesnay E, Houenou J, Thomas P, Szaffarczyk S, Domenech P, Jardri R. Decoding Activity in Broca's Area Predicts the Occurrence of Auditory Hallucinations Across Subjects. Biol Psychiatry 2022; 91:194-201. [PMID: 34742546 DOI: 10.1016/j.biopsych.2021.08.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/19/2021] [Accepted: 08/31/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Functional magnetic resonance imaging (fMRI) capture aims at detecting auditory-verbal hallucinations (AVHs) from continuously recorded brain activity. Establishing efficient capture methods with low computational cost that easily generalize between patients remains a key objective in precision psychiatry. To address this issue, we developed a novel automatized fMRI-capture procedure for AVHs in patients with schizophrenia (SCZ). METHODS We used a previously validated but labor-intensive personalized fMRI-capture method to train a linear classifier using machine learning techniques. We benchmarked the performances of this classifier on 2320 AVH periods versus resting-state periods obtained from SCZ patients with frequent symptoms (n = 23). We characterized patterns of blood oxygen level-dependent activity that were predictive of AVH both within and between subjects. Generalizability was assessed with a second independent sample gathering 2000 AVH labels (n = 34 patients with SCZ), while specificity was tested with a nonclinical control sample performing an auditory imagery task (840 labels, n = 20). RESULTS Our between-subject classifier achieved high decoding accuracy (area under the curve = 0.85) and discriminated AVH from rest and verbal imagery. Optimizing the parameters on the first schizophrenia dataset and testing its performance on the second dataset led to an out-of-sample area under the curve of 0.85 (0.88 for the converse test). We showed that AVH detection critically depends on local blood oxygen level-dependent activity patterns within Broca's area. CONCLUSIONS Our results demonstrate that it is possible to reliably detect AVH states from fMRI blood oxygen level-dependent signals in patients with SCZ using a multivariate decoder without performing complex preprocessing steps. These findings constitute a crucial step toward brain-based treatments for severe drug-resistant hallucinations.
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Affiliation(s)
- Thomas Fovet
- Plasticity & SubjectivitY team, Lille Neuroscience & Cognition Research Centre, University of Lille, INSERM U1172, Lille, France; CURE platform, Psychiatry Department, Fontan Hospital, Centre Hospitalier Universitaire de Lille, Lille, France; Centre National de Ressources et de Résilience Lille-Paris, France
| | - Pierre Yger
- Plasticity & SubjectivitY team, Lille Neuroscience & Cognition Research Centre, University of Lille, INSERM U1172, Lille, France; Institut de la Vision, Sorbonne Université, INSERM, Centre national de la recherche scientifique, Paris, France
| | - Renaud Lopes
- Vascular & Cognitive Deficits team, Lille Neuroscience & Cognition Research Centre, University of Lille, INSERM U1172, Lille, France; In-vivo Imaging and Functions core facility, Neuroradiology Department, Centre Hospitalier Universitaire de Lille, Lille, France
| | | | | | - Josselin Houenou
- NeuroSpin, Univ Paris Saclay, CEA, Gif-sur-Yvette, France; Neurosurgery, Psychiatry and Addictology Departments, Groupe Hospitalier Universitaire Henri-Mondor, AP-HP, Créteil, France; Faculté de Santé UPEC, Université Paris Est Créteil, Créteil, France
| | - Pierre Thomas
- Plasticity & SubjectivitY team, Lille Neuroscience & Cognition Research Centre, University of Lille, INSERM U1172, Lille, France; CURE platform, Psychiatry Department, Fontan Hospital, Centre Hospitalier Universitaire de Lille, Lille, France
| | - Sébastien Szaffarczyk
- Plasticity & SubjectivitY team, Lille Neuroscience & Cognition Research Centre, University of Lille, INSERM U1172, Lille, France
| | - Philippe Domenech
- Institut du Cerveau et de la Moelle épinière, Sorbonne Université, INSERM, Centre national de la recherche scientifique, Paris, France; Neurosurgery, Psychiatry and Addictology Departments, Groupe Hospitalier Universitaire Henri-Mondor, AP-HP, Créteil, France; Faculté de Santé UPEC, Université Paris Est Créteil, Créteil, France
| | - Renaud Jardri
- Plasticity & SubjectivitY team, Lille Neuroscience & Cognition Research Centre, University of Lille, INSERM U1172, Lille, France; CURE platform, Psychiatry Department, Fontan Hospital, Centre Hospitalier Universitaire de Lille, Lille, France.
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Liu M, Wang Y, Zhang H, Yang Q, Shi F, Zhou Y, Shen D. OUP accepted manuscript. Cereb Cortex 2022; 32:4641-4656. [PMID: 35136966 PMCID: PMC9627024 DOI: 10.1093/cercor/bhab507] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/12/2022] Open
Abstract
Subcortical ischemic vascular disease could induce subcortical vascular cognitive impairments (SVCIs), such as amnestic mild cognitive impairment (aMCI) and non-amnestic MCI (naMCI), or sometimes no cognitive impairment (NCI). Previous SVCI studies focused on focal structural lesions such as lacunes and microbleeds, while the functional connectivity networks (FCNs) from functional magnetic resonance imaging are drawing increasing attentions. Considering remarkable variations in structural lesion sizes, we expect that seeking abnormalities in the multiscale hierarchy of brain FCNs could be more informative to differentiate SVCI patients with varied outcomes (NCI, aMCI, and naMCI). Driven by this hypothesis, we first build FCNs based on the atlases at multiple spatial scales for group comparisons and found distributed FCN differences across different spatial scales. We then verify that combining multiscale features in a prediction model could improve differentiation accuracy among NCI, aMCI, and naMCI. Furthermore, we propose a graph convolutional network to integrate the naturally emerged multiscale features based on the brain network hierarchy, which significantly outperforms all other competing methods. In addition, the predictive features derived from our method consistently emphasize the limbic network in identifying aMCI across the different scales. The proposed analysis provides a better understanding of SVCI and may benefit its clinical diagnosis.
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Affiliation(s)
| | | | - Han Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Qing Yang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yan Zhou
- Address correspondence to Dinggang Shen, School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China. . Yan Zhou, Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Dinggang Shen
- Address correspondence to Dinggang Shen, School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China. . Yan Zhou, Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
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Lund MJ, Alnæs D, de Lange AMG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin 2021; 33:102921. [PMID: 34959052 PMCID: PMC8718718 DOI: 10.1016/j.nicl.2021.102921] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health. METHODS We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance. RESULTS Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN. DISCUSSION Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.
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Affiliation(s)
- Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway.
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany.
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Wagh VV, Vyas P, Agrawal S, Pachpor TA, Paralikar V, Khare SP. Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review. Front Genet 2021; 12:736483. [PMID: 34721526 PMCID: PMC8548640 DOI: 10.3389/fgene.2021.736483] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 08/31/2021] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.
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Affiliation(s)
- Vipul Vilas Wagh
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
| | - Parin Vyas
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
| | - Suchita Agrawal
- The Psychiatry Unit, KEM Hospital and KEM Hospital Research Centre, Pune, India
| | | | - Vasudeo Paralikar
- The Psychiatry Unit, KEM Hospital and KEM Hospital Research Centre, Pune, India
| | - Satyajeet P Khare
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
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Kirkpatrick RH, Munoz DP, Khalid-Khan S, Booij L. Methodological and clinical challenges associated with biomarkers for psychiatric disease: A scoping review. J Psychiatr Res 2021; 143:572-579. [PMID: 33221025 DOI: 10.1016/j.jpsychires.2020.11.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/20/2020] [Accepted: 11/09/2020] [Indexed: 12/20/2022]
Abstract
Over the past decade, psychiatric research has been on an important hunt for biomarkers of psychiatric disease. In psychiatry, the term "biomarker" is a broad umbrella term used to identify any biological variable that can be objectively measured and applied to a diagnosis; this includes genetic and epigenetic assessments, hormone levels, measures of neuro-anatomy and many other scientific modalities. However, despite hundreds of studies on the topic being published yearly and other medical specialties having success in discovering biomarkers, clinical psychiatric practice has not had the same success. This paper aims to consolidate the many opinions on the search for psychiatric biomarkers to suggest key methodological and clinical challenges that psychiatric biomarker research faces. Psychiatry as a specialty has many fundamental differences compared to other medical specialties in methods of diagnosing, underlying etiology and disease pathologies that may be limiting the success of biomarker research in itself and puts strict requirements on the research being conducted. The academic and clinical environment in which the research is being conducted also heavily influences the translation of the findings. Finally, once biomarkers are identified, more often than not they are inapplicable to clinical settings, unable to integrate into clinical practice and fail to outperform current diagnostic practices and guidelines. We also make six recommendations for more promising future research in psychiatric biomarkers.
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Affiliation(s)
- Ryan H Kirkpatrick
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada; School of Medicine, Queen's University, Kingston, Canada.
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada; School of Medicine, Queen's University, Kingston, Canada; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada; Department of Psychology, Queen's University, Kingston, Canada
| | - Sarosh Khalid-Khan
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada; School of Medicine, Queen's University, Kingston, Canada; Department of Psychology, Queen's University, Kingston, Canada; Department of Psychiatry, Queen's University, Kingston, Canada
| | - Linda Booij
- Department of Psychology, Queen's University, Kingston, Canada; Department of Psychology, Concordia University, Montréal, Canada; CHU Sainte-Justine Hospital, University of Montréal, Montréal, Canada; Department of Psychiatry, McGill University, Montréal, Canada.
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Rahaman MA, Rodrigue A, Glahn D, Turner J, Calhoun V. Shared sets of correlated polygenic risk scores and voxel-wise grey matter across multiple traits identified via bi-clustering. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2201-2206. [PMID: 34891724 DOI: 10.1109/embc46164.2021.9630825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuropsychiatric disorders involve complex polygenic determinants as well as brain alterations. The combination of genetic inheritance and neuroimaging approaches could advance our understanding of psychiatric disorders. However, cross-disorder overlap is a current issue since psychiatric conditions share some neurogenetic correlates, symptoms, and brain effects. Exploring the impact of genetic risk on the brain across disorders could help understand commonalities across multiple psychopathologies. To do this, we first compute the linear relationship between PRS and voxel-wise grey matter volume to generate brain maps for five psychiatric and three control traits. Next, we use the biclustering approach to identify regions of the brain associated with polygenic risk scores in one or more traits. Our results demonstrate a significant overlap in brain regions connected to polygenic risk across psychiatric traits. Moreover, such brain domains are highly allied with the polygenic risk for non-psychiatric control traits. This multi-trait overlap characterizes the nonspecific relationship between neural anatomy and inherited risk factors in psychiatric conditions, and in some cases, the overlap in neural features linked to genetic risk for non-psychiatric attributes.Clinical Relevance-This study presents biclusters of multiple psychiatric and control traits. The analysis reported various brain regions, including cerebellum, cuneus, precuneus, fusiform, supplementary motor area, that show significant correlation with polygenic risk scores across diverse groups of psychiatric conditions and non-psychiatric control traits.
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Roth JG, Huang MS, Li TL, Feig VR, Jiang Y, Cui B, Greely HT, Bao Z, Paşca SP, Heilshorn SC. Advancing models of neural development with biomaterials. Nat Rev Neurosci 2021; 22:593-615. [PMID: 34376834 PMCID: PMC8612873 DOI: 10.1038/s41583-021-00496-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2021] [Indexed: 12/12/2022]
Abstract
Human pluripotent stem cells have emerged as a promising in vitro model system for studying the brain. Two-dimensional and three-dimensional cell culture paradigms have provided valuable insights into the pathogenesis of neuropsychiatric disorders, but they remain limited in their capacity to model certain features of human neural development. Specifically, current models do not efficiently incorporate extracellular matrix-derived biochemical and biophysical cues, facilitate multicellular spatio-temporal patterning, or achieve advanced functional maturation. Engineered biomaterials have the capacity to create increasingly biomimetic neural microenvironments, yet further refinement is needed before these approaches are widely implemented. This Review therefore highlights how continued progression and increased integration of engineered biomaterials may be well poised to address intractable challenges in recapitulating human neural development.
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Affiliation(s)
- Julien G Roth
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michelle S Huang
- Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Thomas L Li
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vivian R Feig
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yuanwen Jiang
- Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Bianxiao Cui
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Henry T Greely
- Stanford Law School, Stanford University, Stanford, CA, USA
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Sergiu P Paşca
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sarah C Heilshorn
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
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35
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Uzbekov MG. Monoamine Oxidase as a Potential Biomarker of the Efficacy of Treatment of Mental Disorders. BIOCHEMISTRY (MOSCOW) 2021; 86:773-783. [PMID: 34225599 DOI: 10.1134/s0006297921060146] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The review summarizes the results of our own studies and published data on the biological markers of psychiatric disorders, with special emphasis on the activity of platelet monoamine oxidase. Pharmacotherapy studies in patients with the mixed anxiety-depressive disorder and first episode of schizophrenia have shown that the activity of platelet monoamine oxidase could serve as a potential biomarker of the efficacy of therapeutic interventions in these diseases.
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Affiliation(s)
- Marat G Uzbekov
- Moscow Research Institute of Psychiatry, Branch of Serbsky National Medical Research Center for Psychiatry and Narcology, Moscow, 107076, Russia.
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36
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Yamaguchi H, Hashimoto Y, Sugihara G, Miyata J, Murai T, Takahashi H, Honda M, Hishimoto A, Yamashita Y. Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a "Diagnostic Label-Free" Approach: Application to Schizophrenia Datasets. Front Neurosci 2021; 15:652987. [PMID: 34305514 PMCID: PMC8294943 DOI: 10.3389/fnins.2021.652987] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 06/07/2021] [Indexed: 01/17/2023] Open
Abstract
There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS) and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets, including 71 SZ patients and 71 HS. We created 16 3D-CAE models with different channels and convolutions to explore the effective range of hyperparameters for psychiatric brain imaging. The number of blocks containing two convolutional layers and one pooling layer was set, ranging from 1 block to 4 blocks. The number of channels in the extraction layer varied from 1, 4, 16, and 32 channels. The proposed 3D-CAEs were successfully reproduced into 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relation to clinical information. We explored the appropriate hyperparameter range of 3D-CAE, and it was suggested that a model with 3 blocks may be related to extracting features for predicting the dose of medication and symptom severity in schizophrenia.
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Affiliation(s)
- Hiroyuki Yamaguchi
- Department of Information Medicine, National Center of Neurology and Psychiatry, National Institute of Neuroscience, Tokyo, Japan.,Department of Psychiatry, School of Medicine, Yokohama City University, Yokohama, Japan
| | - Yuki Hashimoto
- Department of Information Medicine, National Center of Neurology and Psychiatry, National Institute of Neuroscience, Tokyo, Japan
| | - Genichi Sugihara
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Manabu Honda
- Department of Information Medicine, National Center of Neurology and Psychiatry, National Institute of Neuroscience, Tokyo, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, School of Medicine, Yokohama City University, Yokohama, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Center of Neurology and Psychiatry, National Institute of Neuroscience, Tokyo, Japan
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37
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Rathje M, Waxman H, Benoit M, Tammineni P, Leu C, Loebrich S, Nedivi E. Genetic variants in the bipolar disorder risk locus SYNE1 that affect CPG2 expression and protein function. Mol Psychiatry 2021; 26:508-523. [PMID: 30610203 PMCID: PMC6609516 DOI: 10.1038/s41380-018-0314-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 11/14/2018] [Accepted: 11/15/2018] [Indexed: 12/28/2022]
Abstract
Bipolar disorder (BD) is a common mood disorder characterized by recurrent episodes of mania and depression. Both genetic and environmental factors have been implicated in BD etiology, but the biological underpinnings remain elusive. Recently, genome-wide association studies (GWAS) of neuropsychiatric disorders have identified a risk locus for BD containing the SYNE1 gene, a large gene encoding multiple proteins. The BD association signal spans, almost exclusively, the part of SYNE1 encoding CPG2, a brain-specific protein localized to excitatory postsynaptic sites, where it regulates glutamate receptor internalization. Here we show that CPG2 protein levels are significantly decreased in postmortem brain tissue from BD patients, as compared to control subjects, as well as schizophrenia and depression patients. We identify genetic variants within the postmortem brains that map to the CPG2 promoter region, and show that they negatively affect gene expression. We also identify missense single nucleotide polymorphisms (SNPs) in CPG2 coding regions that affect CPG2 expression, localization, and synaptic function. Our findings link genetic variation in the CPG2 region of SYNE1 with a mechanism for glutamatergic synapse dysfunction that could underlie susceptibility to BD in some individuals. Few GWAS hits in human genetics for neuropsychiatric disorders to date have afforded such mechanistic clues. Further, the potential for genetic distinction of susceptibility to BD from other neuropsychiatric disorders with overlapping clinical traits holds promise for improved diagnostics and treatment of this devastating illness.
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Affiliation(s)
- Mette Rathje
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hannah Waxman
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marc Benoit
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Prasad Tammineni
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA,Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA,Institute of Neurology, University College London, London, United Kingdom
| | - Sven Loebrich
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elly Nedivi
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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38
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Chestnykh DA, Amato D, Kornhuber J, Müller CP. Pharmacotherapy of schizophrenia: Mechanisms of antipsychotic accumulation, therapeutic action and failure. Behav Brain Res 2021; 403:113144. [PMID: 33515642 DOI: 10.1016/j.bbr.2021.113144] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 12/13/2022]
Abstract
Schizophrenia is a multi-dimensional disorder with a complex and mostly unknown etiology, leading to a severe decline in life quality. Antipsychotic drugs (APDs) remain beneficial interventions in the treatment of the disorder, but vary significantly in binding profile, clinical effects and adverse reactions. The present review summarizes the main principles of APD mechanisms of action with a particular focus on recent findings in APD accumulation and its role in the therapeutic efficacy and treatment failure. High and low doses of APDs were shown to be effective in different dimensions of antipsychotic-like behaviour in rodent models. Efficacy of the APDs correlates with high dopamine D2 receptor occupancy, which occurs quickly after drug administration. However, onset and peak of action are delayed up to several days or weeks. APD accumulation via acidic trapping in synaptic vesicles is considered to underlie the time course of APD action. Use-dependent exocytosis, co-release with dopamine and serotonin and inhibition of ion channels impact on the neuronal transmission and determine effects of APDs. Disruption in accumulating properties leads to diminished APD effects. In addition, long-term APD administration at therapeutic doses leads to treatment failure both in animal models and in humans. APD failure was associated with treatment induced neuroadaptations, including a decline in extracellular dopamine levels, dopamine transporter upregulation, and altered neuronal firing. However, enhanced synaptic vesicle release has also been reported. APD loss of efficacy may be reversed through inhibition of the dopamine transporter or switching the administration regimen from continuous to intermittent. Thus, manipulating the accumulation properties of APDs, changes in the administration regimen and doses, or co-administration with dopamine transporter inhibitors may be considered to yield benefits in the development of new effective strategies in the treatment of schizophrenia.
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Affiliation(s)
- Daria A Chestnykh
- Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Davide Amato
- Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054, Erlangen, Germany; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Christian P Müller
- Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054, Erlangen, Germany.
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39
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Eslami T, Almuqhim F, Raiker JS, Saeed F. Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey. Front Neuroinform 2021; 14:575999. [PMID: 33551784 PMCID: PMC7855595 DOI: 10.3389/fninf.2020.575999] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/07/2020] [Indexed: 12/15/2022] Open
Abstract
Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.
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Affiliation(s)
- Taban Eslami
- Department of Computer Science, Western Michigan University, Kalamazoo, MI, United States
| | - Fahad Almuqhim
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
| | - Joseph S. Raiker
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Fahad Saeed
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
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40
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Abstract
Time and Body promotes the application of phenomenological psychopathology and embodied research to a broad spectrum of mental disorders. In a new and practical way, it integrates the latest research on the temporal and intersubjective constitution of the body, self and its mental disorders from phenomenological, embodied and interdisciplinary research perspectives. The authors investigate how temporal processes apply to the contribution of embodiment and selfhood, as well as to their destabilization, such as in eating disorders and borderline personality disorders, schizophrenia, depression, social anxiety or dementia. The chapters demonstrate the applicability of phenomenological psychopathology to a range of illnesses and its relevance to treatment and clinical practice.
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41
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Deng ZD, Luber B, Balderston NL, Velez Afanador M, Noh MM, Thomas J, Altekruse WC, Exley SL, Awasthi S, Lisanby SH. Device-Based Modulation of Neurocircuits as a Therapeutic for Psychiatric Disorders. Annu Rev Pharmacol Toxicol 2020; 60:591-614. [PMID: 31914895 DOI: 10.1146/annurev-pharmtox-010919-023253] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Device-based neuromodulation of brain circuits is emerging as a promising new approach in the study and treatment of psychiatric disorders. This work presents recent advances in the development of tools for identifying neurocircuits as therapeutic targets and in tools for modulating neurocircuits. We review clinical evidence for the therapeutic efficacy of circuit modulation with a range of brain stimulation approaches, including subthreshold, subconvulsive, convulsive, and neurosurgical techniques. We further discuss strategies for enhancing the precision and efficacy of neuromodulatory techniques. Finally, we survey cutting-edge research in therapeutic circuit modulation using novel paradigms and next-generation devices.
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Affiliation(s)
- Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA; .,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Bruce Luber
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Nicholas L Balderston
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Melbaliz Velez Afanador
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Michelle M Noh
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Jeena Thomas
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - William C Altekruse
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Shannon L Exley
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Shriya Awasthi
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Sarah H Lisanby
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA; .,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA
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42
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Shared Versus Disorder-Specific Brain Morphometric Features of Major Psychiatric Disorders in Adulthood. Biol Psychiatry 2020; 88:e41-e43. [PMID: 33032695 PMCID: PMC9924243 DOI: 10.1016/j.biopsych.2020.07.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 07/27/2020] [Indexed: 01/26/2023]
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43
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Opel N, Goltermann J, Hermesdorf M, Berger K, Baune BT, Dannlowski U. Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium. Biol Psychiatry 2020; 88:678-686. [PMID: 32646651 DOI: 10.1016/j.biopsych.2020.04.027] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 01/21/2023]
Abstract
BACKGROUND Neuroimaging studies have consistently reported similar brain structural abnormalities across different psychiatric disorders. Yet, the extent and regional distribution of shared morphometric abnormalities between disorders remains unknown. METHODS Here, we conducted a cross-disorder analysis of brain structural abnormalities in 6 psychiatric disorders based on effect size estimates for cortical thickness and subcortical volume differences between healthy control subjects and psychiatric patients from 11 mega- and meta-analyses from the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta Analysis) consortium. Correlational and exploratory factor analyses were used to quantify the relative overlap in brain structural effect sizes between disorders and to identify brain regions with disorder-specific abnormalities. RESULTS Brain structural abnormalities in major depressive disorder, bipolar disorder, schizophrenia, and obsessive-compulsive disorder were highly correlated (r = .443 to r = .782), and one shared latent underlying factor explained between 42.3% and 88.7% of the brain structural variance of each disorder. The observed shared morphometric signature of these disorders showed little similarity with brain structural patterns related to physiological aging. In contrast, patterns of brain structural abnormalities independent of all other disorders were observed in both attention-deficit/hyperactivity disorder and autism spectrum disorder. Brain regions showing high proportions of independent variance were identified for each disorder to locate disorder-specific morphometric abnormalities. CONCLUSIONS Taken together, these results offer novel insights into transdiagnostic as well as disorder-specific brain structural abnormalities across 6 major psychiatric disorders. Limitations comprise the uncertain contribution of risk factors, comorbidities, and medication effects to the observed pattern of results that should be clarified by future research.
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Affiliation(s)
- Nils Opel
- Department of Psychiatry, University of Münster, Münster, Germany; Interdisciplinary Centre for Clinical Research, University of Münster, Münster, Germany.
| | - Janik Goltermann
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Marco Hermesdorf
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Parkville, Victoria, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
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Behfar Q, Behfar SK, von Reutern B, Richter N, Dronse J, Fassbender R, Fink GR, Onur OA. Graph Theory Analysis Reveals Resting-State Compensatory Mechanisms in Healthy Aging and Prodromal Alzheimer's Disease. Front Aging Neurosci 2020; 12:576627. [PMID: 33192468 PMCID: PMC7642892 DOI: 10.3389/fnagi.2020.576627] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/29/2020] [Indexed: 01/20/2023] Open
Abstract
Several theories of cognitive compensation have been suggested to explain sustained cognitive abilities in healthy brain aging and early neurodegenerative processes. The growing number of studies investigating various aspects of task-based compensation in these conditions is contrasted by the shortage of data about resting-state compensatory mechanisms. Using our proposed criterion-based framework for compensation, we investigated 45 participants in three groups: (i) patients with mild cognitive impairment (MCI) and positive biomarkers indicative of Alzheimer's disease (AD); (ii) cognitively normal young adults; (iii) cognitively normal older adults. To increase reliability, three sessions of resting-state functional magnetic resonance imaging for each participant were performed on different days (135 scans in total). To elucidate the dimensions and dynamics of resting-state compensatory mechanisms, we used graph theory analysis along with volumetric analysis. Graph theory analysis was applied based on the Brainnetome atlas, which provides a connectivity-based parcellation framework. Comprehensive neuropsychological examinations including the Rey Auditory Verbal Learning Test (RAVLT) and the Trail Making Test (TMT) were performed, to relate graph measures of compensatory nodes to cognition. To avoid false-positive findings, results were corrected for multiple comparisons. First, we observed an increase of degree centrality in cognition related brain regions of the middle frontal gyrus, precentral gyrus and superior parietal lobe despite local atrophy in MCI and healthy aging, indicating a resting-state connectivity increase with positive biomarkers. When relating the degree centrality measures to cognitive performance, we observed that greater connectivity led to better RAVLT and TMT scores in MCI and, hence, might constitute a compensatory mechanism. The detection and improved understanding of the compensatory dynamics in healthy aging and prodromal AD is mandatory for implementing and tailoring preventive interventions aiming at preserved overall cognitive functioning and delayed clinical onset of dementia.
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Affiliation(s)
- Qumars Behfar
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | - Stefan Kambiz Behfar
- Laboratory for Innovation Science at Harvard (LISH), Harvard University, Cambridge, MA, United States
| | - Boris von Reutern
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Richter
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | - Julian Dronse
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | - Ronja Fassbender
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | - Oezguer A Onur
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Research Centre Jülich, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
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45
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Zich C, Johnstone N, Lührs M, Lisk S, Haller SP, Lipp A, Lau JY, Kadosh KC. Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females. Neuroimage 2020; 220:117053. [PMID: 32574803 PMCID: PMC7573536 DOI: 10.1016/j.neuroimage.2020.117053] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 01/01/2023] Open
Abstract
Research has shown that difficulties with emotion regulation abilities in childhood and adolescence increase the risk for developing symptoms of mental disorders, e.g anxiety. We investigated whether functional magnetic resonance imaging (fMRI)-based neurofeedback (NF) can modulate brain networks supporting emotion regulation abilities in adolescent females. We performed three experiments (Experiment 1: N = 18; Experiment 2: N = 30; Experiment 3: N = 20). We first compared different NF implementations regarding their effectiveness of modulating prefrontal cortex (PFC)-amygdala functional connectivity (fc). Further we assessed the effects of fc-NF on neural measures, emotional/metacognitive measures and their associations. Finally, we probed the mechanism underlying fc-NF by examining concentrations of inhibitory and excitatory neurotransmitters. Results showed that NF implementations differentially modulate PFC-amygdala fc. Using the most effective NF implementation we observed important relationships between neural and emotional/metacognitive measures, such as practice-related change in fc was related with change in thought control ability. Further, we found that the relationship between state anxiety prior to the MRI session and the effect of fc-NF was moderated by GABA concentrations in the PFC and anterior cingulate cortex. To conclude, we were able to show that fc-NF can be used in adolescent females to shape neural and emotional/metacognitive measures underlying emotion regulation. We further show that neurotransmitter concentrations moderate fc–NF–effects.
Neurofeedback implementations differentially modulate PFC-amygdala connectivity. Functional connectivity neurofeedback affect measures of emotion regulation. Neurotransmitter concentrations moderate neurofeedback effects.
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Affiliation(s)
- Catharina Zich
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
| | - Nicola Johnstone
- School of Psychology, University of Surrey, Guildford, GU2 7XH, UK
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, 6211 LK, the Netherlands; Department of Research and Development, Brain Innovation, Maastricht, 6229 EV, the Netherlands
| | - Stephen Lisk
- Psychology Department, Institute of Psychiatry, King's College London, London, SE5 8AF, UK
| | - Simone Pw Haller
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK
| | - Annalisa Lipp
- School of Psychology, University of Surrey, Guildford, GU2 7XH, UK
| | - Jennifer Yf Lau
- Psychology Department, Institute of Psychiatry, King's College London, London, SE5 8AF, UK
| | - Kathrin Cohen Kadosh
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK; School of Psychology, University of Surrey, Guildford, GU2 7XH, UK
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46
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Vittala A, Murphy N, Maheshwari A, Krishnan V. Understanding Cortical Dysfunction in Schizophrenia With TMS/EEG. Front Neurosci 2020; 14:554. [PMID: 32547362 PMCID: PMC7270174 DOI: 10.3389/fnins.2020.00554] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/05/2020] [Indexed: 12/16/2022] Open
Abstract
In schizophrenia and related disorders, a deeper mechanistic understanding of neocortical dysfunction will be essential to developing new diagnostic and therapeutic techniques. To this end, combined transcranial magnetic stimulation and electroencephalography (TMS/EEG) provides a non-invasive tool to simultaneously perturb and measure neurophysiological correlates of cortical function, including oscillatory activity, cortical inhibition, connectivity, and synchronization. In this review, we summarize the findings from a variety of studies that apply TMS/EEG to understand the fundamental features of cortical dysfunction in schizophrenia. These results lend to future applications of TMS/EEG in understanding the pathophysiological mechanisms underlying cognitive deficits in schizophrenia.
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Affiliation(s)
- Aadith Vittala
- Department of Biosciences, Rice University, Houston, TX, United States
| | - Nicholas Murphy
- Department of Psychiatry and Behavioral Science, Baylor College of Medicine, Houston, TX, United States
| | - Atul Maheshwari
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States.,Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States
| | - Vaishnav Krishnan
- Department of Psychiatry and Behavioral Science, Baylor College of Medicine, Houston, TX, United States.,Department of Neurology, Baylor College of Medicine, Houston, TX, United States.,Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States
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47
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Liu M, Liu X, Hildebrandt A, Zhou C. Individual Cortical Entropy Profile: Test-Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation. Cereb Cortex Commun 2020; 1:tgaa015. [PMID: 34296093 PMCID: PMC8153045 DOI: 10.1093/texcom/tgaa015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 04/24/2020] [Accepted: 05/01/2020] [Indexed: 12/19/2022] Open
Abstract
The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test-retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.
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Affiliation(s)
- Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Xinyang Liu
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Department of Physics, Zhejiang University, 310000 Hangzhou, China
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48
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Dwyer DB, Kalman JL, Budde M, Kambeitz J, Ruef A, Antonucci LA, Kambeitz-Ilankovic L, Hasan A, Kondofersky I, Anderson-Schmidt H, Gade K, Reich-Erkelenz D, Adorjan K, Senner F, Schaupp S, Andlauer TFM, Comes AL, Schulte EC, Klöhn-Saghatolislam F, Gryaznova A, Hake M, Bartholdi K, Flatau-Nagel L, Reitt M, Quast S, Stegmaier S, Meyers M, Emons B, Haußleiter IS, Juckel G, Nieratschker V, Dannlowski U, Yoshida T, Schmauß M, Zimmermann J, Reimer J, Wiltfang J, Reininghaus E, Anghelescu IG, Arolt V, Baune BT, Konrad C, Thiel A, Fallgatter AJ, Figge C, von Hagen M, Koller M, Lang FU, Wigand ME, Becker T, Jäger M, Dietrich DE, Scherk H, Spitzer C, Folkerts H, Witt SH, Degenhardt F, Forstner AJ, Rietschel M, Nöthen MM, Mueller N, Papiol S, Heilbronner U, Falkai P, Schulze TG, Koutsouleris N. An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings: The PsyCourse Study. JAMA Psychiatry 2020; 77:523-533. [PMID: 32049274 PMCID: PMC7042925 DOI: 10.1001/jamapsychiatry.2019.4910] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
IMPORTANCE Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. OBJECTIVE To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. DESIGN, SETTING, AND PARTICIPANTS This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. MAIN OUTCOMES AND MEASURES A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. RESULTS Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial η2 = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. CONCLUSIONS AND RELEVANCE Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups.
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Affiliation(s)
- Dominic B. Dwyer
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Janos L. Kalman
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,International Max Planck Research School (IMPRS-TP), Munich, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Joseph Kambeitz
- Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Linda A. Antonucci
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | | | - Alkomiet Hasan
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Ivan Kondofersky
- Institute of Computational Biology, Helmholtz Zentrum Munich, Oberschleißheim, Germany,Department of Mathematics, Technical University of Munich Garching, Garching, Germany
| | - Heike Anderson-Schmidt
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Department of Psychiatry and Psychotherapy, University Medical Center Gottingen, Gottingen, Germany
| | - Katrin Gade
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Department of Psychiatry and Psychotherapy, University Medical Center Gottingen, Gottingen, Germany
| | - Daniela Reich-Erkelenz
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Kristina Adorjan
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Fanny Senner
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Sabrina Schaupp
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Department of Psychiatry and Psychotherapy, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Till F. M. Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ashley L. Comes
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,International Max Planck Research School (IMPRS-TP), Munich, Germany
| | - Eva C. Schulte
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Farah Klöhn-Saghatolislam
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Anna Gryaznova
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Maria Hake
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Kim Bartholdi
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Laura Flatau-Nagel
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Markus Reitt
- Department of Psychiatry and Psychotherapy, University Medical Center Gottingen, Gottingen, Germany
| | - Silke Quast
- Department of Psychiatry and Psychotherapy, University Medical Center Gottingen, Gottingen, Germany
| | - Sophia Stegmaier
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Milena Meyers
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, Germany
| | - Barbara Emons
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, Germany
| | - Ida Sybille Haußleiter
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, Germany
| | - Georg Juckel
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, Germany
| | - Vanessa Nieratschker
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Tomoya Yoshida
- Department of Physiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Max Schmauß
- Department of Psychiatry and Psychotherapy, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Jörg Zimmermann
- Psychiatrieverbund Oldenburger Land gGmbH, Karl-Jaspers-Klinik, Bad Zwischenahn, Germany
| | - Jens Reimer
- Department of Psychiatry, Klinikum Bremen-Ost, Bremen, Germany,Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Gottingen, Gottingen, Germany,German Center for Neurodegenerative Diseases (DZNE), Gottingen, Germany,Institute of BioMedicine (iBiMED), Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Eva Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | | | - Volker Arolt
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Bernhard T. Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany,Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia,The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Carsten Konrad
- Department of Psychiatry and Psychotherapy, Agaplesion Diakonieklinikum, Rotenburg, Germany
| | - Andreas Thiel
- Department of Psychiatry and Psychotherapy, Agaplesion Diakonieklinikum, Rotenburg, Germany
| | - Andreas J. Fallgatter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Christian Figge
- Karl-Jaspers Clinic, European Medical School Oldenburg-Groningen, Oldenburg, Germany
| | - Martin von Hagen
- Clinic for Psychiatry and Psychotherapy, Clinical Center Werra-Meißner, Eschwege, Germany
| | | | - Fabian U. Lang
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Moritz E. Wigand
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Thomas Becker
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Markus Jäger
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Detlef E. Dietrich
- AMEOS Clinical Center Hildesheim, Hildesheim, Germany,Center for Systems Neuroscience, Hannover, Germany,Burghof-Klinik Rinteln, Rinteln, Germany
| | | | - Carsten Spitzer
- Department of Psychosomatics and Psychotherapeutic Medicine, University Medical Center Rostock, Rostock, Germany
| | - Here Folkerts
- Department of Psychiatry, Psychotherapy and Psychosomatics, Clinical Center Wilhelmshaven, Wilhelmshaven, Germany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Andreas J. Forstner
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany,Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland,Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Nikola Mueller
- Institute of Computational Biology, Helmholtz Zentrum Munich, Oberschleißheim, Germany
| | - Sergi Papiol
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Thomas G. Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany,International Max-Planck Research School for Translational Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany
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Tahmasian M, Sepehry AA, Samea F, Khodadadifar T, Soltaninejad Z, Javaheripour N, Khazaie H, Zarei M, Eickhoff SB, Eickhoff CR. Practical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders. Hum Brain Mapp 2019; 40:5142-5154. [PMID: 31379049 PMCID: PMC6865620 DOI: 10.1002/hbm.24746] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 07/09/2019] [Accepted: 07/16/2019] [Indexed: 02/04/2023] Open
Abstract
Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual neuroimaging studies, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To consolidate the emergent findings toward clinically useful insight, meta-analyses have been developed to integrate the results of studies and identify areas that are consistently involved in pathophysiology of particular neuropsychiatric disorders. However, it should be considered that the results of meta-analyses could also be divergent due to heterogeneity in search strategy, selection criteria, imaging modalities, behavioral tasks, number of experiments, data organization methods, and statistical analysis with different multiple comparison thresholds. Following an introduction to the problem and the concepts of quantitative summaries of neuroimaging findings, we propose practical recommendations for clinicians and researchers for conducting transparent and methodologically sound neuroimaging meta-analyses. This should help to consolidate the search for convergent regional brain abnormality in neuropsychiatric disorders.
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Affiliation(s)
- Masoud Tahmasian
- Institute of Medical Science and TechnologyShahid Beheshti UniversityTehranIran
| | - Amir A. Sepehry
- Clinical and Counselling Psychology ProgramAdler UniversityVancouverBritish ColumbiaCanada
| | - Fateme Samea
- Institute of Cognitive and Brain SciencesShahid Beheshti UniversityTehranIran
| | - Tina Khodadadifar
- School of Cognitive SciencesInstitute for Research in Fundamental SciencesTehranIran
| | - Zahra Soltaninejad
- Institute of Cognitive and Brain SciencesShahid Beheshti UniversityTehranIran
| | | | - Habibolah Khazaie
- Sleep Disorders Research CenterKermanshah University of Medical SciencesKermanshahIran
| | - Mojtaba Zarei
- Institute of Medical Science and TechnologyShahid Beheshti UniversityTehranIran
| | - Simon B. Eickhoff
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐1, INM‐7)Research Center JülichJülichGermany
| | - Claudia R. Eickhoff
- Institute of Neuroscience and Medicine (INM‐1, INM‐7)Research Center JülichJülichGermany
- Institute of Clinical Neuroscience and Medical PsychologyHeinrich Heine University DüsseldorfDüsseldorfGermany
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50
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Shen X, Adams MJ, Ritakari TE, Cox SR, McIntosh AM, Whalley HC. White Matter Microstructure and Its Relation to Longitudinal Measures of Depressive Symptoms in Mid- and Late Life. Biol Psychiatry 2019; 86:759-768. [PMID: 31443934 PMCID: PMC6906887 DOI: 10.1016/j.biopsych.2019.06.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/04/2019] [Accepted: 06/04/2019] [Indexed: 12/27/2022]
Abstract
BACKGROUND Studies of white matter microstructure in depression typically show alterations in individuals with depression, but they are frequently limited by small sample sizes and the absence of longitudinal measures of depressive symptoms. Depressive symptoms are dynamic, however, and understanding the neurobiology of different trajectories could have important clinical implications. METHODS We examined associations between current and longitudinal measures of depressive symptoms and white matter microstructure (fractional anisotropy and mean diffusivity [MD]) in the UK Biobank Imaging Study. Depressive symptoms were assessed on two to four occasions over 5.9 to 10.7 years (n = 18,959 individuals on at least two occasions, n = 4444 on four occasions), from which we derived four measures of depressive symptomatology: cross-sectional measure at the time of scan and three longitudinal measures, namely trajectory and mean and intrasubject variance over time. RESULTS Decreased white matter microstructure in the anterior thalamic radiation demonstrated significant associations across all four measures of depressive symptoms (MD: βs = .020-.029, pcorr < .030). The greatest effect sizes were seen between white matter microstructure and longitudinal progression (MD: βs = .030-.040, pcorr < .049). Cross-sectional symptom severity was particularly associated with decreased white matter integrity in association fibers and thalamic radiations (MD: βs = .015-.039, pcorr < .041). Greater mean and within-subject variance were mainly associated with decreased white matter microstructure within projection fibers (MD: βs = .019-.029, pcorr < .044). CONCLUSIONS These findings indicate shared and differential neurobiological associations with severity, course, and intrasubject variability of depressive symptoms. This enriches our understanding of the neurobiology underlying dynamic features of the disorder.
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Affiliation(s)
- Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Tuula E Ritakari
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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