Diffusion Tensor Imaging Datasets Research Articles

Gray matter parcellation constrained full brain fiber bundling with diffusion tensor imaging

Purpose: Studying white matter fibers from diffusion tensor imaging (DTI) often requires them to be grouped into bundles that correspond to coherent anatomic structures, particularly bundles that connect cortical/subcortical basic units. However, traditional fiber clustering algorithms usually generate bundles with poor anatomic correspondence as they do not incorporate brain anatomic information into the clustering process. On the other hand, image registration‐based bundling methods segment fiber bundles by referring to a coregistered atlas or template with prelabeled anatomic information, but these approaches suffer from the uncertainties introduced from misregistration and fiber tracking errors and thus the resulting bundles usually have poor coherence. In this work, a bundling algorithm is proposed to overcome the above issues. Methods: The proposed algorithm combines clustering‐ and registration‐based approaches so that the bundle coherence and the consistency with brain anatomy are simultaneously achieved. Moreover, based on this framework, a groupwise fiber bundling method is further proposed to leverage a group of DTI data for reducing the effect of the uncertainties in a single DTI data set and improving cross‐subject bundle consistency. Results: Using the Montreal Neurological Institute template, the proposed methods are applied to building a full brain bundle network that connects cortical/subcortical basic units. Based on several proposed metrics, the resulting bundles show promising bundle coherence and anatomic consistency as well as improved cross‐subject consistency for the groupwise bundling. Conclusions: A fiber bundling algorithm has been proposed in this paper to cluster a set of whole brain fibers into coherent bundles that are consistent to the brain anatomy.

Assessment of Global and Regional Diffusion Changes along White Matter Tracts in Parkinsonian Disorders by MR Tractography

PurposeThe aim of the study was to determine the usefulness of diffusion tensor tractography (DTT) in parkinsonian disorders using a recently developed method for normalization of diffusion data and tract size along white matter tracts. Furthermore, the use of DTT in selected white matter tracts for differential diagnosis was assessed.MethodsWe quantified global and regional diffusion parameters in major white matter tracts in patients with multiple system atrophy (MSA), progressive nuclear palsy (PSP), idiopathic Parkinson’s disease (IPD) and healthy controls). Diffusion tensor imaging data sets with whole brain coverage were acquired at 3 T using 48 diffusion encoding directions and a voxel size of 2×2×2 mm3. DTT of the corpus callosum (CC), cingulum (CG), corticospinal tract (CST) and middle cerebellar peduncles (MCP) was performed using multiple regions of interest. Regional evaluation comprised projection of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and the apparent area coefficient (AAC) onto a calculated mean tract and extraction of their values along each structure.ResultsThere were significant changes of global DTT parameters in the CST (MSA and PSP), CC (PSP) and CG (PSP). Consistent tract-specific variations in DTT parameters could be seen along each tract in the different patient groups and controls. Regional analysis demonstrated significant changes in the anterior CC (MD, RD and FA), CST (MD) and CG (AAC) of patients with PSP compared to controls. Increased MD in CC and CST, as well as decreased AAC in CG, was correlated with a diagnosis of PSP compared to IPD.ConclusionsDTT can be used for demonstrating disease-specific regional white matter changes in parkinsonian disorders. The anterior portion of the CC was identified as a promising region for detection of neurodegenerative changes in patients with PSP, as well as for differential diagnosis between PSP and IPD.

Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer’s Disease Using Machine Learning Classification of Multicenter DTI Data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.

White matter differences in euthymic bipolar I disorder: a combined magnetic resonance imaging and diffusion tensor imaging voxel‐based study

A broad range of subtle and markedly heterogenous neuroanatomical abnormalities of grey matter and white matter have been reported in bipolar disorder. Euthymic bipolar disorder patients represent a clinically homogenous group in which to identify trait-based biomarkers of bipolar disorder. In this study, we sought to clarify the nature and extent of neuroanatomical differences in a large, clinically homogeneous group of euthymic bipolar disorder patients. Structural magnetic resonance imaging (sMRI) was obtained for 60 patients with prospectively confirmed euthymic bipolar I disorder and 60 individually age- and gender-matched healthy volunteers. High angular resolution diffusion tensor imaging (DTI) scans were obtained for a subset of this sample comprising 35 patients and 43 controls. Voxel-based analysis of both sMRI and DTI data sets was performed. Bipolar disorder patients displayed global reductions in white matter volume and fractional anisotropy reductions in the corpus callosum, posterior cingulum, and prefrontal white matter compared with controls. There were corresponding increases in radial diffusivity in the callosal splenium in patients compared with controls. No significant group differences were detected in grey matter. In patients, lithium was associated with a bilateral increase in grey matter volume in the temporal lobes, but not with any DTI parameter. Euthymic bipolar I disorder is characterized by both diffuse global white matter deficits and potential regional disorganization in interhemispheric and longitudinal tracts, while grey matter appears to be preserved.

Out‐of‐atlas likelihood estimation using multi‐atlas segmentation

Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures that are anatomically consistent across a large population of potential target subjects (i.e., multi-atlas segmentation is limited to "in-atlas" applications). Herein, the authors propose a technique to determine the likelihood that a multi-atlas segmentation estimate is representative of the problem at hand, and, therefore, identify anomalous regions that are not well represented within the atlases. The authors derive a technique to estimate the out-of-atlas (OOA) likelihood for every voxel in the target image. These estimated likelihoods can be used to determine and localize the probability of an abnormality being present on the target image. Using a collection of manually labeled whole-brain datasets, the authors demonstrate the efficacy of the proposed framework on two distinct applications. First, the authors demonstrate the ability to accurately and robustly detect malignant gliomas in the human brain-an aggressive class of central nervous system neoplasms. Second, the authors demonstrate how this OOA likelihood estimation process can be used within a quality control context for diffusion tensor imaging datasets to detect large-scale imaging artifacts (e.g., aliasing and image shading). The proposed OOA likelihood estimation framework shows great promise for robust and rapid identification of brain abnormalities and imaging artifacts using only weak dependencies on anomaly morphometry and appearance. The authors envision that this approach would allow for application-specific algorithms to focus directly on regions of high OOA likelihood, which would (1) reduce the need for human intervention, and (2) reduce the propensity for false positives. Using the dual perspective, this technique would allow for algorithms to focus on regions of normal anatomy to ascertain image quality and adapt to image appearance characteristics.

Accelerated MR diffusion tensor imaging using distributed compressed sensing

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion-weighted images. Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k-space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion-weighted images were reconstructed by solving an l2-l1 norm minimization problem. Reconstruction performance with varied signal-to-noise ratio and acceleration factors were evaluated by root-mean-square error and maps of reconstructed DTI indices. Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal-to-noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4. Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically.

Mathematical modelling of glioma growth: The use of Diffusion Tensor Imaging (DTI) data to predict the anisotropic pathways of cancer invasion

The nonuniform growth of certain forms of cancer can present significant complications for their treatment, a particularly acute problem in gliomas. A number of experimental results have suggested that invasion is facilitated by the directed movement of cells along the aligned neural fibre tracts that form a large component of the white matter. Diffusion tensor imaging (DTI) provides a window for visualising this anisotropy and gaining insight on the potential invasive pathways. In this paper we develop a mesoscopic model for glioma invasion based on the individual migration pathways of invading cells along the fibre tracts. Via scaling we obtain a macroscopic model that allows us to explore the overall growth of a tumour. To connect DTI data to parameters in the macroscopic model we assume that directional guidance along fibre tracts is described by a bimodal von Mises–Fisher distribution (a normal distribution on a unit sphere) and parametrised according to the directionality and degree of anisotropy in the diffusion tensors. We demonstrate the results in a simple model for glioma growth, exploiting both synthetic and genuine DTI datasets to reveal the potentially crucial role of anisotropic structure on invasion.

An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease

We introduce a novel image-processing framework for tracking longitudinal changes in white matter microstructure using diffusion tensor imaging (DTI). Charting the trajectory of such temporal changes offers new insight into disease progression but to do so accurately faces a number of challenges. Recent developments have highlighted the importance of processing each subject's data at multiple time points in an unbiased way. In this paper, we aim to highlight a different challenge critical to the processing of longitudinal DTI data, namely the approach to image alignment. Standard approaches in the literature align DTI data by registering the corresponding scalar-valued fractional anisotropy (FA) maps. We propose instead a DTI registration algorithm that leverages full tensor information to drive improved alignment. This proposed pipeline is evaluated against the standard FA-based approach using a DTI dataset from an ongoing study of Alzheimer's disease (AD). The dataset consists of subjects scanned at two time points and at each time point the DTI acquisition consists of two back-to-back repeats in the same scanning session. The repeated scans allow us to evaluate the specificity of each pipeline, using a test-retest design, and assess precision, using bootstrap-based method. The results show that the tensor-based pipeline achieves both higher specificity and precision than the standard FA-based approach. Tensor-based registration for longitudinal processing of DTI data in clinical studies may be of particular value in studies assessing disease progression.

Effects of b-value and echo time on magnetic resonance diffusion tensor imaging-derived parameters at 1.5 t: A voxel-wise study

The diffusion weighting factor (b-value) and echo time (TE) are two important parameters that influence quantitative measurements of diffusion tensor imaging (DTI). The present study employs a voxel-wise post-processing scheme to investigate the effects of b-value and TE on the reproducibility and accuracy of DTI-derived indices based on actual human brain data with commonly used imaging parameters. Five repeated DTI datasets of six b-values and five TE values were acquired from a healthy subject. The reproducibility and accuracy of axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), fractional anisotropy (FA), and the principal eigenvector (PEV) are assessed by calculating the voxel-wise standard deviation, coefficient of variance, and difference of mean values from the reference DTI dataset acquired with number of excitations (NEX) = 15. The results show that the reproducibility and accuracy of AD, RD, MD, FA, and PEV are significantly impacted by b-value and TE, and that there is a significant difference between gray and white matter tissues. Therefore, the reproducibility and accuracy of DTI-derived indices depends on the b-value and TE for both gray and white matter.

A framework for modelling and clustering randomly structured white matter fibre tracts in diffusion tensor imaging

Reliable modelling and clustering of white matter (WM) fibre tracts are essential for studies using diffusion tensor imaging (DTI) tractography. This paper presents a novel scheme for modelling and clustering randomly structured WM fibre tracts reconstructed from DTI tractography. In this study, the mathematical representation of WM fibre tracts is formed by incorporating the diffusion orientation information and geometric characteristics of fibre tracts into the model. The quantitative measurements are achieved by calculating the pairwise affinity score between every two WM fibre tracts. This affinity score is sensitive to the shape, location and length of WM fibre tracts. A matching scheme is developed for finding piece-wise correspondences between two random WM fibre tracts. Real DTI datasets are used to assess the proposed approach. Experimental results show that this technique can effectively separate multiple fascicles, which do not have equal length and a common region of interest (ROI), into plausible bundles.

Diffusion Anisotropy Color‐Coded Map of Cerebral White Matter: Quantitative Comparison between Orthogonal Anisotropic Diffusion‐Weighted Imaging and Diffusion Tensor Imaging

Diffusion anisotropy color-coded maps of cerebral white matter can be generated from orthogonal anisotropic diffusion-weighted imaging (DWI) using the three-dimensional anisotropy contrast (3DAC) technique, but its precision has not been fully validated. Hence, we attempted to determine whether 3DAC is comparable to a diffusion tensor imaging (DTI) color map. We examined 15 healthy individuals and generated color-coded maps using 3DAC as well as using primary eigenvector (e1) and fractional anisotropy (FA) from identical DTI datasets. The difference in the direction of the 3DAC vector from e1 (θ) in cerebral white matter was evaluated. Correlations between θ and FA or obliqueness of e1 were also examined. In cerebral white matter, θ had significantly negative and positive correlations with FA values and e1 obliqueness, respectively. Among white matter tracts, the pyramidal tract, cingulum, and corpus callosum, which had significantly high FA and/or low obliqueness, exhibited similar coloration and significantly smaller θ (4.4° ± 1.6°, 9.3° ± 2.8°, and 11.2° ± 1.1°, respectively) than the entire white matter (13.9° ± 1.1°). The 3DAC could visualize directional information of white matter tracts as precisely DTI-based color maps did, particularly when FA was large and/or e1 directions were orthogonal.

Role of standardized and study‐specific human brain diffusion tensor templates in inter‐subject spatial normalization

To investigate the effect of standardized and study-specific human brain diffusion tensor templates on the accuracy of spatial normalization, without ignoring the important roles of data quality and registration algorithm effectiveness. Two groups of diffusion tensor imaging (DTI) datasets, with and without visible artifacts, were normalized to two standardized diffusion tensor templates (IIT2, ICBM81) as well as study-specific templates, using three registration approaches. The accuracy of inter-subject spatial normalization was compared across templates, using the most effective registration technique for each template and group of data. It was demonstrated that, for DTI data with visible artifacts, the study-specific template resulted in significantly higher spatial normalization accuracy than standardized templates. However, for data without visible artifacts, the study-specific template and the standardized template of higher quality (IIT2) resulted in similar normalization accuracy. For DTI data with visible artifacts, a carefully constructed study-specific template may achieve higher normalization accuracy than that of standardized templates. However, as DTI data quality improves, a high-quality standardized template may be more advantageous than a study-specific template, because in addition to high normalization accuracy, it provides a standard reference across studies, as well as automated localization/segmentation when accompanied by anatomical labels.

Model‐based reconstruction of undersampled diffusion tensor k‐space data

The practical utility of diffusion tensor imaging, especially for 3D high-resolution spin warp experiments of ex vivo specimens, has been hampered by long acquisition times. To accelerate the acquisition, a compressed sensing framework that uses a model-based formulation to reconstruct diffusion tensor fields from undersampled k-space data was presented and evaluated. Accuracies in brain specimen white matter fiber orientation, fractional anisotropy, and mean diffusivity mapping were compared with alternative methods achievable using the same scan time via reduced image resolution, fewer diffusion encoding directions, standard compressed sensing, or asymmetrical sampling reconstruction. The efficiency of the proposed approach was also compared with fully sampled cases across a range of the number of diffusion encoding directions. In general, the proposed approach was found to reduce the image blurring and noise and to provide more accurate fiber orientation, fractional anisotropy, and mean diffusivity measurements compared with the alternative methods. Moreover, depending on the degree of undersampling used and the diffusion tensor imaging parameter examined, the measurement accuracy of the proposed scheme was equivalent to fully sampled diffusion tensor imaging datasets that consist of 33-67% more encoding directions and require proportionally longer scan times. The findings show model-based compressed sensing to be promising for improving the resolution, accuracy, or scan time of diffusion tensor imaging.

Assessment of bias in experimentally measured diffusion tensor imaging parameters using SIMEX

Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the micrometer level. Parameters are derived through an estimation process that is susceptible to noise and artifacts. Estimated parameters (e.g., fractional anisotropy) exhibit both variability and bias relative to the true parameter value estimated from a hypothetical noise-free acquisition. Herein, we present the use of the simulation and extrapolation (SIMEX) approach for post hoc assessment of bias in a massively univariate imaging setting and evaluate the potential of a SIMEX-based bias correction. Using simulated data with known truth models, spatially varying fractional anisotropy bias error maps are evaluated on two independent and highly differentiated case studies. The stability of SIMEX and its distributional properties are further evaluated on 42 empirical diffusion tensor imaging datasets. Using gradient subsampling, an empirical experiment with a known true outcome is designed and SIMEX performance is compared to the original estimator. With this approach, we find SIMEX bias estimates to be highly accurate offering significant reductions in parameter bias for individual datasets and greater accuracy in averaged population-based estimates.

Voxelized Computational Model for Convection-Enhanced Delivery in the Rat Ventral Hippocampus: Comparison with In Vivo MR Experimental Studies

Convection-enhanced delivery (CED) is a promising local delivery technique for overcoming the blood-brain barrier (BBB) and treating diseases of the central nervous system (CNS). For CED, therapeutics are infused directly into brain tissue and the drug agent is spread through the extracellular space, considered to be highly tortuous porous media. In this study, 3D computational models developed using magnetic resonance (MR) diffusion tensor imaging data sets were used to predict CED transport in the rat ventral hippocampus using a voxelized modeling previously developed by our group. Predicted albumin tracer distributions were compared with MR-measured distributions from in vivo CED in the ventral hippocampus up to 10 μL of Gd-DTPA albumin tracer infusion. Predicted and measured tissue distribution volumes and distribution patterns after 5 and 10 μL infusions were found to be comparable. Tracers were found to occupy the underlying landmark structures with preferential transport found in regions with less fluid resistance such as the molecular layer of the dentate gyrus. Also, tracer spread was bounded by high fluid resistance layers such as the granular cell layer and pyramidal cell layer of dentate gyrus. Leakage of tracers into adjacent CSF spaces was observed towards the end of infusions.

VBM–DTI Correlates of Verbal Intelligence: A Potential Link to Broca's Area

Human brain lesion studies first investigated the biological roots of cognitive functions including language in the late 1800s. Neuroimaging studies have reported correlation findings with general intelligence predominantly in fronto-parietal cortical areas. However, there is still little evidence about the relationship between verbal intelligence and structural properties of the brain. We predicted that verbal performance is related to language regions of Broca's and Wernicke's areas. Verbal intelligence quotient (vIQ) was assessed in 30 healthy young subjects. T1-weighted MRI and diffusion tensor imaging data sets were acquired. Voxel-wise regression analyses were used to correlate fractional anisotropy (FA) and mean diffusivity values with vIQ. Moreover, regression analyses of regional brain volume with vIQ were performed adopting voxel-based morphometry (VBM) and ROI methodology. Our analyses revealed a significant negative correlation between vIQ and FA and a significant positive correlation between vIQ and mean diffusivity in the left-hemispheric Broca's area. VBM regression analyses did not show significant results, whereas a subsequent ROI analysis of Broca's area FA peak cluster demonstrated a positive correlation of gray matter volume and vIQ. These findings suggest that cortical thickness in Broca's area contributes to verbal intelligence. Diffusion parameters predicted gray matter ratio in Broca's area more sensitive than VBM methodology.

Modeling the Field Distribution in Deep Brain Stimulation: The Influence of Anisotropy of Brain Tissue

The neurosurgical method of deep brain stimulation (DBS) is used to treat symptoms of movement disorders like Parkinson's disease by implanting stimulation electrodes in deep brain areas. The aim of this study was to examine the field distribution in DBS and the role of heterogeneous and anisotropic material properties in the brain areas where stimulation is applied. Finite element models of the human brain were developed comprising tissue heterogeneity and anisotropy. The tissue data were derived from averaged magnetic resonance imaging and diffusion tensor imaging datasets. Unilateral stimulation of the subthalamic nucleus (STN) was computed using an accurate model of an electrode used in clinical treatment of DBS extended with an encapsulation layer around the electrode body. Computations of anisotropic and isotropic brain models, which consider resistive tissue properties for unipolar and bipolar electrode configurations, were carried out. Electrode position was varied within an area around the stimulation center. Results have shown a deviation of 2% between anisotropic and isotropic field distributions in the vicinity of the STN. The sensitivity of this deviation referring to the electrode position remained small, but increased when the electrode position approached areas of high anisotropy.

Quantitative evaluation of white matter tract DTI parameter changes in gliomas using nonlinear registration

Diffusion tensor imaging (DTI) has been used extensively to investigate white matter architecture in the brain. In the context of neurological disease, quantification of DTI data sets enables objective characterisation of the associated pathological changes. The aim of this study is to propose a method of evaluating DTI parameter changes in gliomas in the internal capsule using nonlinear registration to delineate the white matter and enable quantitative assessment of DTI derived parameters. 20 patients selected pre-operatively with probable grade 2 or grade 3 glioma on structural MRI along with ten normal volunteers were included in this study. DTI fractional anisotropy (FA) maps were used to define a common segmented FA skeleton that was projected back onto the original individual FA maps. Objective segment classification as normal or abnormal was achieved by comparison to prediction intervals of FA and mean diffusivity (MD) defined in normal subjects. The internal capsules of each patient were segmented into 10 regions of interest (ROI) with 20 and 16 segments across the group having significantly increased or decreased FA and MD values respectively. Seven glioma patients had abnormal DTI parameters in the internal capsule. We show that the classification of tract segments was consistent with disruption, oedema or compression. The results suggest that this method could be used to detect changes in eloquent white matter tracts in individual patients.

Open Neuroscience Solutions for the Connectome-wide Association Era

The neuroimaging community is at a crossroads. Long characterized by individualism, the data and computational and analytic needs of the connectome-wide association era necessitate cultural reform. Emerging initiatives have demonstrated the feasibility and utility of adopting an open neuroscience model to accelerate the pace and success of scientific discovery.