Bagrat Amirbekian

Dipy, a library for the analysis of diffusion MRI data

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

Connecting white matter injury and thalamic atrophy in clinically isolated syndromes

Previous studies suggest that thalamic degeneration is prominent in multiple sclerosis (MS) and even in pre-MS patients presenting with a clinically isolated syndrome (CIS). However, the relationships between white matter lesions and deep grey matter loss are not well understood. We analyzed the association between white matter lesions and the thalami in CIS patients to determine if connectivity is an important determinant. We studied 24 CIS patients and 18 normal controls with anatomical and diffusion tensor (DTI) MRI images. DTI fiber tracking was used to create probabilistic templates of the thalamocortical white matter and to define white matter connecting lesions and thalami. DTI metrics in the lesions and normal-appearing white matter (NAWM) regions were compared between CIS and controls, and correlated with thalamic volume changes estimated by voxel-based morphometry. There was 10 times higher density of lesions in thalamocortical compared to other brain white matter. Increased diffusivities and decreased fractional anisotropies were measured in the thalamocortical NAWM of CIS patients compared to controls. A step-wise regression analysis demonstrated that thalamocortical lesion volume and the mean diffusivity in track regions connecting lesion and thalami were significantly correlated with thalamic volumes in patients (Rsq=0.66, p<0.001), a finding not observed in regions outside the thalamocortical white matter. These results provide compelling evidence for a direct relationship between white matter lesions and thalamic atrophy in CIS patients.

Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods

Introduction Diffusion MRI tractography has been increasingly used to delineate white matter pathways in vivo for which the leading clinical application is presurgical mapping of eloquent regions. However, there is rare opportunity to quantify the accuracy or sensitivity of these approaches to delineate white matter fiber pathways in vivo due to the lack of a gold standard. Intraoperative electrical stimulation (IES) provides a gold standard for the location and existence of functional motor pathways that can be used to determine the accuracy and sensitivity of fiber tracking algorithms. In this study we used intraoperative stimulation from brain tumor patients as a gold standard to estimate the sensitivity and accuracy of diffusion tensor MRI (DTI) and q-ball models of diffusion with deterministic and probabilistic fiber tracking algorithms for delineation of motor pathways. Methods We used preoperative high angular resolution diffusion MRI (HARDI) data (55 directions, b = 2000 s/mm2) acquired in a clinically feasible time frame from 12 patients who underwent a craniotomy for resection of a cerebral glioma. The corticospinal fiber tracts were delineated with DTI and q-ball models using deterministic and probabilistic algorithms. We used cortical and white matter IES sites as a gold standard for the presence and location of functional motor pathways. Sensitivity was defined as the true positive rate of delineating fiber pathways based on cortical IES stimulation sites. For accuracy and precision of the course of the fiber tracts, we measured the distance between the subcortical stimulation sites and the tractography result. Positive predictive rate of the delineated tracts was assessed by comparison of subcortical IES motor function (upper extremity, lower extremity, face) with the connection of the tractography pathway in the motor cortex. Results We obtained 21 cortical and 8 subcortical IES sites from intraoperative mapping of motor pathways. Probabilistic q-ball had the best sensitivity (79%) as determined from cortical IES compared to deterministic q-ball (50%), probabilistic DTI (36%), and deterministic DTI (10%). The sensitivity using the q-ball algorithm (65%) was significantly higher than using DTI (23%) (p < 0.001) and the probabilistic algorithms (58%) were more sensitive than deterministic approaches (30%) (p = 0.003). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical stimulation sites. The offsets between diffusion fiber tracks and subcortical IES sites were increased significantly for those cases where the diffusion fiber tracks were visibly thinner than expected. There was perfect concordance between the subcortical IES function (e.g. hand stimulation) and the cortical connection of the nearest diffusion fiber track (e.g. upper extremity cortex). Discussion This study highlights the tremendous utility of intraoperative stimulation sites to provide a gold standard from which to evaluate diffusion MRI fiber tracking methods and has provided an object standard for evaluation of different diffusion models and approaches to fiber tracking. The probabilistic q-ball fiber tractography was significantly better than DTI methods in terms of sensitivity and accuracy of the course through the white matter. The commonly used DTI fiber tracking approach was shown to have very poor sensitivity (as low as 10% for deterministic DTI fiber tracking) for delineation of the lateral aspects of the corticospinal tract in our study. Effects of the tumor/edema resulted in significantly larger offsets between the subcortical IES and the preoperative fiber tracks. The provided data show that probabilistic HARDI tractography is the most objective and reproducible analysis but given the small sample and number of stimulation points a generalization about our results should be given with caution. Indeed our results inform the capabilities of preoperative diffusion fiber tracking and indicate that such data should be used carefully when making pre-surgical and intra-operative management decisions.

Quantifying accuracy and precision of diffusion MR tractography of the corticospinal tract in brain tumors

OBJECT The aim of this paper was to validate the diffusion tensor imaging (DTI) model for delineation of the corticospinal tract using cortical and subcortical white matter electrical stimulation for the location of functional motor pathways. METHODS The authors compare probabilistic versus deterministic DTI fiber tracking by reconstructing the pyramidal fiber tracts on preoperatively acquired DTI in patients with brain tumors. They determined the accuracy and precision of these 2 methods using subcortical stimulation points and the sensitivity using cortical stimulation points. The authors further explored the reliability of these methods by estimation of the potential that the found connections were due to a random chance using a novel neighborhood permutation method. RESULTS The probabilistic tracking method delineated tracts that were significantly closer to the stimulation points and was more sensitive than deterministic DTI fiber tracking to define the tracts directed to the motor sites. However, both techniques demonstrated poor sensitivity to finding lateral motor regions. CONCLUSIONS This study highlights the importance of the validation and quantification of preoperative fiber tracking with the aid of electrophysiological data during the surgery. The poor sensitivity of DTI to delineate lateral motor pathways reported herein suggests that DTI fiber tracking must be used with caution and only as adjunctive data to established methods for motor mapping.

Q-Ball of Inferior Fronto-Occipital Fasciculus and Beyond

The inferior fronto-occipital fasciculus (IFOF) is historically described as the longest associative bundle in the human brain and it connects various parts of the occipital cortex, temporo-basal area and the superior parietal lobule to the frontal lobe through the external/extreme capsule complex. The exact functional role and the detailed anatomical definition of the IFOF are still under debate within the scientific community. In this study we present a fiber tracking dissection of the right and left IFOF by using a q-ball residual-bootstrap reconstruction of High-Angular Resolution Diffusion Imaging (HARDI) data sets in 20 healthy subjects. By defining a single seed region of interest on the coronal fractional anisotropy (FA) color map of each subject, we investigated all the pathways connecting the parietal, occipital and posterior temporal cortices to the frontal lobe through the external/extreme capsule. In line with recent post-mortem dissection studies we found more extended anterior-posterior association connections than the “classical” fronto-occipital representation of the IFOF. In particular the pathways we evidenced showed: a) diffuse projections in the frontal lobe, b) fronto-parietal lobes connections trough the external capsule in almost all the subjects and c) widespread connections in the posterior regions. Our study represents the first consistent in vivo demonstration across a large group of individuals of these novel anterior and posterior terminations of the IFOF detailed described only by post-mortem anatomical dissection. Furthermore our work establishes the feasibility of consistent in vivo mapping of this architecture with independent in vivo methodologies. In conclusion q-ball tractography dissection supports a more complex definition of IFOF, which includes several subcomponents likely underlying specific function.

Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas

OBJECT Diffusion MRI has uniquely enabled in vivo delineation of white matter tracts, which has been applied to the segmentation of eloquent pathways for intraoperative mapping. The last decade has also seen the development from earlier diffusion tensor models to higher-order models, which take advantage of high angular resolution diffusion-weighted imaging (HARDI) techniques. However, these advanced methods have not been widely implemented for routine preoperative and intraoperative mapping. The authors report on the application of residual bootstrap q-ball fiber tracking for routine mapping of potentially functional language pathways, the development of a system for rating tract injury to evaluate the impact on clinically assessed language function, and initial results predicting long-term language deficits following glioma resection. METHODS The authors have developed methods for the segmentation of 8 putative language pathways including dorsal phonological pathways and ventral semantic streams using residual bootstrap q-ball fiber tracking. Furthermore, they have implemented clinically feasible preoperative acquisition and processing of HARDI data to delineate these pathways for neurosurgical application. They have also developed a rating scale based on the altered fiber tract density to estimate the degree of pathway injury, applying these ratings to a subset of 35 patients with pre- and postoperative fiber tracking. The relationships between specific pathways and clinical language deficits were assessed to determine which pathways are predictive of long-term language deficits following surgery. RESULTS This tracking methodology has been routinely implemented for preoperative mapping in patients with brain gliomas who have undergone awake brain tumor resection at the University of California, San Francisco (more than 300 patients to date). In this particular study the authors investigated the white matter structure status and language correlation in a subcohort of 35 subjects both pre- and postsurgery. The rating scales developed for fiber pathway damage were found to be highly reproducible and provided significant correlations with language performance. Preservation of the left arcuate fasciculus (AF) and the temporoparietal component of the superior longitudinal fasciculus (SLF-tp) was consistent in all patients without language deficits (p < 0.001) at the long-term follow-up. Furthermore, in patients with short-term language deficits, the AF and/or SLF-tp were affected, and damage to these 2 pathways was predictive of a long-term language deficit (p = 0.005). CONCLUSIONS The authors demonstrated the successful application of q-ball tracking in presurgical planning for language pathways in brain tumor patients and in assessing white matter tract integrity postoperatively to predict long-term language dysfunction. These initial results predicting long-term language deficits following tumor resection indicate that postoperative injury to dorsal language pathways may be prognostic for long-term clinical language deficits. Study results suggest the importance of dorsal stream tract preservation to reduce language deficits in patients undergoing glioma resection, as well as the potential prognostic value of assessing postoperative injury to dorsal language pathways to predict long-term clinical language deficits.

Cluster Confidence Index: A Streamline-Wise Pathway Reproducibility Metric for Diffusion-Weighted MRI Tractography

Diffusion‐weighted magnetic resonance imaging tractography can be used to create models of white matter fascicles. Anatomical and pathological variability between subjects can drastically alter the tractography output, so standardizing results across a cohort is nontrivial. Furthermore, tractography methods have inherently low reproducibility due to stochasticity (for probabilistic methods) and subjective decisions, since the final fascicle model often requires a manual intervention step performed by an expert human operator to control both outliers and systematic false‐positive pathways, as defined by prior knowledge of anatomy.

Investigating The Functional Consequence Of White Matter Damage: An Automatic Pipeline To Create Longitudinal Disconnection Tractograms

Neurosurgical resection is one of the few opportunities researchers have to image the human brain both prior to and following focal damage. One of the challenges associated with studying brains undergoing surgical resection is that they often do not fit the brain templates most image-processing methodologies are based on, so manual intervention is required to reconcile the pathology and the most extreme cases must be excluded. Manual intervention requires significant time investment and introduces reproducibility concerns. We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration in the patient space, which aligns images using white matter contrast. Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection. Abbreviations DTI Diffusion Tensor Imaging IOS Intra-Operative Stimulation VLSM Voxel-Based Lesion-Symptom Mapping MD mean diffusivity FA fractional anisotropy B0 minimally diffusion-weighted image AP anisotropic power ASAP automatic segmentation of anisotropic power changes HARDI High Angular Resolution Diffusion Imaging MRI Magnetic Resonance Imaging FSL FMRIB Software Library Dipy Diffusion Imaging in Python APM Anisotropic Power Map was calculated DTI-TK Diffusion Tensor Imaging ToolKit TFCE Threshold-Free-Cluster-Enhancement ROI Region of Interest CCI Cluster Confidence Index AF arcuate Fascicle SLF II and SLF III components 2 and 3 of the SLF SLF-tp temporo-parietal component of the SLF IFOF inferior fronto-occipital Fascicle UF uncinate Fascicle ILF inferior longitudinal Fascicle Md-LF middle longitudinal Fascicle CST corticospinal tract OR optic radiation QC quality-control Funding This work was supported by the National Institutes of Health [5R01NS066654-05]; KJ was supported by the Department of Defense (DoD) [National Defense Science & Engineering Graduate Fellowship (NDSEG) Program].