• 1.5 T
• 3 T
• RS-fMRI
• Reliability
• Sensitivity
• Task fMRI

• Humans
• Magnetic Resonance Imaging/methods
• Magnetic Resonance Imaging/standards
• Transcranial Magnetic Stimulation/methods
• Transcranial Magnetic Stimulation/standards
• Male
• Female
• Reproducibility of Results
• Adult
• Brain/physiology
• Brain/diagnostic imaging
• Young Adult
• Brain Mapping/methods
• Image Processing, Computer-Assisted/methods

• 3D printing technology
• neuroanatomy
• surgical planning
• surgical simulation
• virtual reality

• BOLD fMRI
• Brain tumors
• DTI
• Functional brain mapping
• Neurosurgery
• Tractography

We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients’ caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed.

• Glioma
• Language
• Resting-state fMRI
• Seed-based functional connectivity
• Task-based fMRI

Deep-seated brain tumours represent a unique neurosurgical challenge as they are often surrounded by eloquent structures. We describe a minimally invasive technique using tubular retractors and intraoperative neurophysiology monitoring for open biopsy of a deep-seated lesion surrounded by the corticospinal tract. We used preoperative functional mapping with diffusion tensor imaging tractography and navigated transcranial magnetic stimulation to identify a safe surgical corridor. We also used 5-Aminolevulinic Acid induced fluorescence to identify the lesion intraoperatively and optimize tissue samples obtained for histopathological diagnosis. We found the use of these tools improved the safety of surgery and reduced the risk of surgical morbidity.

• brain mapping
• data-driven analysis
• language assessment
• resting-state fMRI
• task-based fMRI

• DTI
• arcuate fasciculus
• corticospinal tract
• mb-DTI
• s-DTI

• DTI = diffusion tensor imaging
• IQR = interquartile range
• brain mapping
• clinician survey
• fMRI = functional MRI
• functional MRI
• neurooncology
• neurosurgery

• 3D T1-weighted magnetization-prepared rapid gradient echo (T1-MPRAGE)
• Double inversion recovery (DIR)
• Gray-white matter junction
• Motor cortex
• fMRI

• Blood Oxygenation Level-Dependent (BOLD) signal
• HyperBaric Oxygen Therapy (HBOT)
• chronic cerebral stroke
• functional Magnetic Resonance Imaging (fMRI)

• Adult
• Aged
• Brain Ischemia/diagnostic imaging
• Brain Ischemia/therapy
• Humans
• Hyperbaric Oxygenation
• Language
• Magnetic Resonance Imaging
• Male
• Middle Aged
• Retrospective Studies
• Stroke/diagnostic imaging
• Stroke/therapy

Functional MRI (fMRI) is gaining importance in the preoperative assessment of language. Selecting the appropriate language to test by fMRI in trilingual patients is intricate. Our objective is to compare fMRI maps for all three languages in left- and right-handed trilingual subjects. 15 right- and 15 left-handed trilingual volunteers were included in the study. We performed fMRI for each volunteer with a visual responsive naming paradigm that was repeated three times, once in each language. The activated areas and the laterality indices were calculated and correlation with the age of acquisition and proficiency of each language was determined. Strong statistical correlation was found between the Laterality Index (LI) of the three languages, in both the right and left-handed groups. Discordant lateralization of language was only observed in four left-handed subjects who demonstrated bilateral and left-lateralization. In right-handed subjects, the activation maps for the first and the second acquired language were similar. The largest activation was seen with the last acquired language. Irrespective of language proficiency and age of acquisition, the language lateralization might change for left-handed subjects. In right-handed subjects, there is no change and the last acquired language results in the largest activation. fMRI performed for a single language can accurately determine language lateralization in right-handed subjects, whereas in left-handed subjects, it is mandatory to test all languages.

• Adult
• Brain/physiology
• Brain Mapping/methods
• Cohort Studies
• Female
• Functional Laterality/physiology
• Humans
• Language
• Magnetic Resonance Imaging/methods
• Male
• Middle Aged
• Neurosurgery/methods
• Young Adult

• Wellcome Trust
• 203148/Z/16/Z Wellcome Trust
• WT 203148/Z/16/Z Wellcome Trust

• brain mapping
• functional connectivity
• language network
• presurgical planning
• resting-state fMRI

• Adult
• Brain Mapping/methods
• Cerebral Cortex/physiology
• Connectome
• Female
• Humans
• Language
• Magnetic Resonance Imaging/methods
• Male
• Nerve Net/physiology
• Pattern Recognition, Visual/physiology
• Psychomotor Performance/physiology
• Reading
• Young Adult

• Fundação para a Ciência e a Tecnologia

• Brain metastases
• Independent component analysis
• Resting state networks
• Resting-state functional MRI

In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.

• diffusion tensor imaging
• glioma
• intracranial electrical stimulation
• resection
• tractography

• Adult
• Brain Mapping/methods
• Brain Mapping/standards
• Diffusion Tensor Imaging/methods
• Diffusion Tensor Imaging/standards
• Epilepsy, Temporal Lobe/diagnostic imaging
• Epilepsy, Temporal Lobe/surgery
• Female
• Humans
• Language
• Male
• Middle Aged
• Neurosurgical Procedures
• Preoperative Care
• White Matter/diagnostic imaging

• Wellcome Trust
• MR/M00841X/1 Medical Research Council

• Bayesian model
• Spatially varying coefficients
• functional magnetic resonance imaging
• multiresolution

• MRI
• Presurgical planning
• Tractography
• Trigonal lesions

Transcranial magnetic stimulation (TMS) is a useful technique to help localize motor function prior to neurosurgical procedures. Adequate modelling of the effect of TMS on the brain is a prerequisite to obtain reliable data.

Twelve patients were included with perirolandic tumors to undergo TMS-based motor mapping. Several models were developed to analyze the mapping data, from a projection to the nearest brain surface to motor evoked potential (MEP) amplitude informed weighted average of the induced electric fields over a multilayer detailed individual head model. The probability maps were compared with direct cortical stimulation (DCS) data in all patients for the hand and in three for the foot. The gold standard was defined as the results of the DCS sampling (with on average 8 DCS-points per surgery) extrapolated over the exposed cortex (of the tailored craniotomy), and the outcome parameters were based on the similarity of the probability maps with this gold standard.

All models accurately gauge the location of the motor cortex, with point-cloud based mapping algorithms having an accuracy of 83–86%, with similarly high specificity. To delineate the whole area of the motor cortex representation, the model based on the weighted average of the induced electric fields calculated with a realistic head model performs best. The optimal single threshold to visualize the field based maps is 40% of the maximal value for the anisotropic model and 50% for the isotropic model, but dynamic thresholding adds information for clinical practice.

The method with which TMS mapping data are analyzed clearly affects the predicted area of the primary motor cortex representation. Realistic electric field based modelling is feasible in clinical practice and improves delineation of the motor cortex representation compared to more simple point-cloud based methods.

•

Probability maps of the motor cortex representation were created from a TMS mapping.

• Electrical field modelling
• Motor cortex
• Presurgical functional mapping
• Transcranial magnetic stimulation

• binocular disparity
• extrastriate cortex
• fMRI
• macaque
• single unit recording

• functional connectivity
• language mapping
• motor mapping
• preoperative setting
• resting-state fMRI
• task-based fMRI

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

• Brain Tumor
• Computational Neuroscience
• Connectome
• Functional Connectivity
• Graph Theory
• Neuroinformatics

• Functional magnetic resonance imaging
• Language template
• Normal population
• Presurgical planning

• Adult
• Brain/physiology
• Brain Mapping
• Female
• Healthy Volunteers
• Humans
• Image Processing, Computer-Assisted
• Language
• Magnetic Resonance Imaging
• Male
• Neurosurgical Procedures/methods
• Patient Care Planning
• Quality of Life
• Reference Values
• Semantics
• Sex Characteristics
• Young Adult

• Brain/blood supply
• Brain/diagnostic imaging
• Brain/surgery
• Brain Mapping/instrumentation
• Brain Mapping/methods
• Humans
• Image Processing, Computer-Assisted
• Magnetic Resonance Imaging
• Oxygen/blood
• Preoperative Care/instrumentation
• Preoperative Care/methods

• P41 RR019703 NCRR NIH HHS
• P41 EB015898 NIBIB NIH HHS
• R21 NS075728 NINDS NIH HHS
• R25 CA089017 NCI NIH HHS
• R21 CA198740 NCI NIH HHS

• brain networks
• fMRI
• functional connectivity
• individual variability
• myelin mapping

• Adult
• Analysis of Variance
• Brain/diagnostic imaging
• Brain/physiology
• Brain Mapping
• Connectome
• Female
• Humans
• Image Processing, Computer-Assisted
• Individuality
• Magnetic Resonance Imaging
• Male
• Models, Neurological
• Myelin Sheath/physiology
• Neural Pathways/diagnostic imaging
• Neural Pathways/physiology
• Oxygen/blood
• Reproducibility of Results
• Rest
• Young Adult

• K23 NS088590 NINDS NIH HHS
• F30 MH100872 NIMH NIH HHS
• P30 NS098577 NINDS NIH HHS
• T32 DA007261 NIDA NIH HHS
• UL1 TR000448 NCATS NIH HHS
• UL1 TR002345 NCATS NIH HHS
• U54 HD087011 NICHD NIH HHS
• K01 MH104592 NIMH NIH HHS

• Alzheimer disease
• Brain tumor
• Dementia
• Glioma
• PET/MR imaging

We propose a method to calculate field maps from the phase of each EPI in an fMRI time series. These field maps can be used to correct the corresponding magnitude images for distortion caused by inhomogeneity in the static magnetic field. In contrast to conventional static distortion correction, in which one ‘snapshot’ field map is applied to all subsequent fMRI time points, our method also captures dynamic changes to B₀ which arise due to motion and respiration. The approach is based on the assumption that the non-B₀-related contribution to the phase measured by each radio-frequency coil, which is dominated by the coil sensitivity, is stable over time and can therefore be removed to yield a field map from EPI.

Our solution addresses imaging with multi-channel coils at ultra-high field (7 T), where phase offsets vary rapidly in space, phase processing is non-trivial and distortions are comparatively large. We propose using dual-echo gradient echo reference scan for the phase offset calculation, which yields estimates with high signal-to-noise ratio. An extrapolation method is proposed which yields reliable estimates for phase offsets even where motion is large and a tailored phase unwrapping procedure for EPI is suggested which gives robust results in regions with disconnected tissue or strong signal decay.

Phase offsets are shown to be stable during long measurements (40 min) and for large head motions. The dynamic distortion correction proposed here is found to work accurately in the presence of large motion (up to 8.1°), whereas a conventional method based on single field map fails to correct or even introduces distortions (up to 11.2 mm). Finally, we show that dynamic unwarping increases the temporal stability of EPI in the presence of motion.

Our approach can be applied to any EPI measurements without the need for sequence modification.

• Dynamic distortion correction
• EPI
• Field mapping
• Ultra-high field
• fMRI

• fMRI analysis
• loss function
• pre-surgical mapping
• spatially adaptive CAR models

• R01 NS075066 NINDS NIH HHS

Functional magnetic resonance imaging (fMRI) continues to develop as a clinical tool for patients with brain cancer, offering data that may directly influence surgical decisions. Unfortunately, routine integration of preoperative fMRI has been limited by concerns about reliability. Many pertinent studies have been undertaken involving healthy controls, but work involving brain tumor patients has been limited. To develop fMRI fully as a clinical tool, it will be critical to examine these reliability issues among patients with brain tumors. The present work is the first to extensively characterize differences in activation map quality between brain tumor patients and healthy controls, including the effects of tumor grade and the chosen behavioral testing paradigm on reliability outcomes.

Test-retest data were collected for a group of low-grade (n = 6) and high-grade glioma (n = 6) patients, and for matched healthy controls (n = 12), who performed motor and language tasks during a single fMRI session. Reliability was characterized by the spatial overlap and displacement of brain activity clusters, BOLD signal stability, and the laterality index. Significance testing was performed to assess differences in reliability between the patients and controls, and low-grade and high-grade patients; as well as between different fMRI testing paradigms.

There were few significant differences in fMRI reliability measures between patients and controls. Reliability was significantly lower when comparing high-grade tumor patients to controls, or to low-grade tumor patients. The motor task produced more reliable activation patterns than the language tasks, as did the rhyming task in comparison to the phonemic fluency task.

In low-grade glioma patients, fMRI data are as reliable as healthy control subjects. For high-grade glioma patients, further investigation is required to determine the underlying causes of reduced reliability. To maximize reliability outcomes, testing paradigms should be carefully selected to generate robust activation patterns.

• Adult
• Aged
• Brain Neoplasms/diagnosis
• Case-Control Studies
• Cohort Studies
• Female
• Glioma/diagnosis
• Humans
• Magnetic Resonance Imaging
• Male
• Middle Aged
• Preoperative Care
• Reproducibility of Results
• Task Performance and Analysis
• Young Adult

• Brain mapping
• Functional imaging
• Hemispheric dominance
• Language lateralization
• Middle frontal gyrus

• Brain Mapping/methods
• Brain Neoplasms/physiopathology
• Broca Area/physiopathology
• Dominance, Cerebral
• Female
• Humans
• Language
• Magnetic Resonance Imaging/methods
• Male
• Middle Aged
• Nerve Net/physiopathology
• Prefrontal Cortex/physiopathology

• P30 CA008748 NCI NIH HHS

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way.

• fMRI language mapping
• functional MRI
• independent component analysis
• neurosurgery
• resting-state fMRI

• diffusion tensor imaging tractography
• facial nerve
• vestibular schwannoma

• CNS
• Functional magnetic resonance imaging (fMRI)
• brain
• surgery

• Advanced MR imaging
• Diffusion tensor imaging
• Functional MR imaging
• High-field imaging
• MR perfusion
• MR spectroscopy
• Susceptibility weighted imaging
• Time-resolved MRA

• Arteriovenous malformation
• Brain mapping
• Cancer
• Epilepsy
• Functional MR imaging
• Human
• Visual cortex

• Motivation
• Nucleus accumbens
• Parallel imaging
• Somatosensori motor cortex
• Spatial specificity
• fMRI

Evaluating language has been a long-standing application in functional magnetic resonance imaging (fMRI) studies, both in research and clinical circumstances, and still provides challenges. Localization of eloquent areas is important in neurosurgical cases, so that there is least possible damage to these areas during surgery, maintaining their function postoperatively, therefore providing good quality of life to the patient. Preoperative fMRI study is a non-invasive tool to localize the eloquent areas, including language, with other traditional methods generally used being invasive and at times perilous. In this article, we describe methods and various paradigms to study the language areas, in clinical neurosurgical cases, along with illustrations of cases from our institute.

• Functional magnetic resonance imaging
• language
• neurosurgical

• 7 Tesla
• Savitzky-Golay filtering
• blood oxygenation level dependent (BOLD) contrast
• functional magnetic resonance imaging
• high resolution imaging
• phase regression

• attention-shift network
• auditory
• fMRI
• functional mapping
• language
• visual