Cognitive Science Program

Dipy, a library for the analysis of diffusion MRI data (2014)
Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stefan Van Der Walt, Maxime Descoteaux ...
Frontiers in neuroinformatics, 8 8

Abstract

Diffusion Imaging in Python (Dipy) is a free and open source software projectfor the analysis of data from diffusion magnetic resonance imaging (dMRI)experiments. dMRI is an application of MRI that can be used to measurestructural features of brain white matter. Many methods have been developed touse dMRI data to model the local configuration of white matter nerve fiberbundles and infer the trajectory of bundles connecting different parts of thebrain.Dipy gathers implementations of many different methods in dMRI, including:diffusion signal pre-processing; reconstruction of diffusion distributions inindividual voxels; fiber tractography and fiber track post-processing, analysisand visualization. Dipy aims to provide transparent implementations forall the different steps of dMRI analysis with a uniform programming interface.We have implemented classical signal reconstruction techniques, such as thediffusion tensor model and deterministic fiber tractography. In addition,cutting edge novel reconstruction techniques are implemented, such asconstrained spherical deconvolution and diffusion spectrum imaging withdeconvolution, as well as methods for probabilistic tracking and originalmethods for tractography clustering. Many additional utility functions areprovided to calculate various statistics, informative visualizations, as wellas file-handling routines to assist in the development and use of noveltechniques.In contrast to many other scientific software projects, Dipy is not beingdeveloped by a single research group. Rather, it is an open project thatencourages contributions from any scientist/developer through GitHub and opendiscussions on the …

The challenge of mapping the human connectome based on diffusion tractography (2017)
Klaus H Maier-Hein, Peter F Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong ...
Nature communications, 8 (1), 1349

Abstract

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography …

Tractometer: towards validation of tractography pipelines (2013)
Marc-Alexandre Côté, Gabriel Girard, Arnaud Boré, Eleftherios Garyfallidis, Jean-Christophe Houde and Maxime Descoteaux
Medical image analysis, 17 (7), 844-857

Abstract

We have developed the Tractometer: an online evaluation and validation system for tractography processing pipelines. One can now evaluate the results of more than 57,000 fiber tracking outputs using different acquisition settings (b-value, averaging), different local estimation techniques (tensor, q-ball, spherical deconvolution) and different tracking parameters (masking, seeding, maximum curvature, step size). At this stage, the system is solely based on a revised FiberCup analysis, but we hope that the community will get involved and provide us with new phantoms, new algorithms, third party libraries and new geometrical metrics, to name a few. We believe that the new connectivity analysis and tractography characteristics proposed can highlight limits of the algorithms and contribute in solving open questions in fiber tracking: from raw data to connectivity analysis. Overall, we show that (i) averaging improves …

Quickbundles, a method for tractography simplification (2012)
Eleftherios Garyfallidis, Matthew Brett, Marta Morgado Correia, Guy B Williams and Ian Nimmo-Smith
Frontiers in neuroscience, 6 175

Abstract

Diffusion MR data sets produce large numbers of streamlines which are hard to visualize, interact with, and interpret in a clinically acceptable time scale, despite numerous proposed approaches. As a solution we present a simple, compact, tailor-made clustering algorithm, QuickBundles (QB), that overcomes the complexity of these large data sets and provides informative clusters in seconds. Each QB cluster can be represented by a single centroid streamline; collectively these centroid streamlines can be taken as an effective representation of the tractography. We provide a number of tests to show how the QB reduction has good consistency and robustness. We show how the QB reduction can help in the search for similarities across several subjects.

Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI (2013)
Alessandro Daducci, Erick Jorge Canales-Rodrı, Maxime Descoteaux, Eleftherios Garyfallidis, Yaniv Gur, Ying-Chia Lin ...
IEEE transactions on medical imaging, 33 (2), 384-399

Abstract

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the “HARDI reconstruction challenge” organized in the context of the “ISBI 2012” conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations …

Extracting biomarkers of autism from MEG resting-state functional connectivity networks (2011)
Vassilis Tsiaras, Panagiotis G Simos, Roozbeh Rezaie, Bhavin R Sheth, Eleftherios Garyfallidis, Eduardo M Castillo ...
Computers in biology and medicine, 41 (12), 1166-1177

Abstract

The present study is a preliminary attempt to use graph theory for deriving distinct features of resting-state functional networks in young adults with autism spectrum disorder (ASD). Networks modeled neuromagnetic signal interactions between sensors using three alternative interdependence measures: (a) a non-linear measure of generalized synchronization (robust interdependence measure [RIM]), (b) mutual information (MI), and (c) partial directed coherence (PDC). To summarize the information contained in each network model we employed well-established global graph measures (average strength, assortativity, clustering, and efficiency) as well as graph measures (average strength of edges) tailored to specific hypotheses concerning the spatial distribution of abnormalities in connectivity among individuals with ASD. Graph measures then served as features in leave-one-out classification analyses …

Reduced brain white matter integrity in trichotillomania: a diffusion tensor imaging study (2010)
Samuel R Chamberlain, Adam Hampshire, Lara A Menzies, Eleftherios Garyfallidis, Jon E Grant, Brian L Odlaug ...
Archives of General Psychiatry, 67 (9), 965-971

Abstract

<h3 class="gsh_h3">Context</h3>Trichotillomania is an Axis I disorder characterized by repetitive, pathological hair pulling.<h3 class="gsh_h3">Objective</h3>To assess the integrity of white matter tracts in subjects with the disorder.<h3 class="gsh_h3">Design</h3>Between-group comparison using permutation cluster analysis, with stringent correction for multiple comparisons.<h3 class="gsh_h3">Setting</h3>Academic psychiatry department.<h3 class="gsh_h3">Participants</h3>Eighteen volunteers meetingDSM-IVcriteria for trichotillomania and 19 healthy control subjects.<h3 class="gsh_h3">Main Outcome Measures</h3>Fractional anisotropy (measured using diffusion tensor imaging), trichotillomania disease severity (Massachusetts General Hospital Hairpulling Scale score), and dysphoria (Montgomery-Asberg Depression Rating Scale score).<h3 class="gsh_h3">Results</h3>Subjects with trichotillomania exhibited significantly reduced fractional anisotropy in anterior cingulate, presupplementary motor area, and temporal cortices. Fractional anisotropy did not correlate significantly with …

Tractography-based connectomes are dominated by false-positive connections (2016)
Klaus Maier-Hein, Peter Neher, Jean-Christophe Houde, Marc-Alexandre Cote, Eleftherios Garyfallidis, Jidan Zhong ...
biorxiv, 84137

Abstract

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

Recognition of white matter bundles using local and global streamline-based registration and clustering (2018)
Eleftherios Garyfallidis, Marc-Alexandre Côté, Francois Rheault, Jasmeen Sidhu, Janice Hau, Laurent Petit ...
Academic Press. 170 283-295

Abstract

Virtual dissection of diffusion MRI tractograms is cumbersome and needs extensive knowledge of white matter anatomy. This virtual dissection often requires several inclusion and exclusion regions-of-interest that make it a process that is very hard to reproduce across experts. Having automated tools that can extract white matter bundles for tract-based studies of large numbers of people is of great interest for neuroscience and neurosurgical planning. The purpose of our proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform. This can help explore large tractograms from multiple persons directly in their native space. RecoBundles leverages latest state-of-the-art streamline-based registration and clustering to recognize and extract bundles using prior bundle models. RecoBundles uses bundle models as shape priors for detecting similar streamlines …

Dynamic changes in white matter abnormalities correlate with late improvement and deterioration following TBI: a diffusion tensor imaging study (2016)
Virginia FJ Newcombe, Marta M Correia, Christian Ledig, Maria G Abate, Joanne G Outtrim, Doris Chatfield ...
Neurorehabilitation and neural repair, 30 (1), 49-62

Abstract

Objective. Traumatic brain injury (TBI) is not a single insult with monophasic resolution, but a chronic disease, with dynamic processes that remain active for years. We aimed to assess patient trajectories over the entire disease narrative, from ictus to late outcome. Methods. Twelve patients with moderate-to-severe TBI underwent magnetic resonance imaging in the acute phase (within 1 week of injury) and twice in the chronic phase of injury (median 7 and 21 months), with some undergoing imaging at up to 2 additional time points. Longitudinal imaging changes were assessed using structural volumetry, deterministic tractography, voxel-based diffusion tensor analysis, and region of interest analyses (including corpus callosum, parasagittal white matter, and thalamus). Imaging changes were related to behavior. Results. Changes in structural volumes, fractional anisotropy, and mean diffusivity continued for months to …

Robust and efficient linear registration of white-matter fascicles in the space of streamlines (2015)
Eleftherios Garyfallidis, Omar Ocegueda, Demian Wassermann and Maxime Descoteaux
NeuroImage, 117 124-140

Abstract

The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines …

Towards an accurate brain tractography (2013)
Eleftherios Garyfallidis

A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles (2017)
Martin Cousineau, Pierre-Marc Jodoin, Eleftherios Garyfallidis, Marc-Alexandre Côté, Félix C Morency, Verena Rozanski ...
NeuroImage: Clinical, 16 222-233

Abstract

In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between …

Dipy–a novel software library for diffusion MR and tractography (2011)
Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Christopher Nguyen, Fang-Cheng Yeh, Y Halchenko ...
17th annual meeting of the organization for human brain mapping, 5-Jan

Abstract

Dipy [1] stands for diffusion imaging in python and it is a free, open source, python toolbox, which is growing, with an already extensive core. It provides a library or API of methods to give a full pathway from raw diffusion MR data to tractographies, with several novel algorithms for analysing and displaying tractographies.

Cleaning up the mess: tractography outlier removal using hierarchical QuickBundles clustering (2015)
Marc-Alexandre Côté, Eleftherios Garyfallidis, Hugo Larochelle and Maxime Descoteaux
Proceedings of: International Society of Magnetic Resonance in Medicine (ISMRM),

Abstract

Method Detecting outliers is performed by finding small clusters produced by a clustering algorithm. Most algorithms require that the user knows the true number of clusters beforehand (eg K-Means) because otherwise the computational complexity is too high to be applied on millions of streamlines (eg hierarchical clustering). QuickBundles performs tractography simplification in O (nm)(n:# streamlines, m:# clusters found) and does not require knowing the number of clusters a priori. Instead, a threshold parameter t influences the resulting number of clusters (t→ 0mm: one cluster per streamline; t→∞: one cluster for all streamlines). We rely on QB’s speed to recursively cluster streamlines in a hierarchy (Fig. A). We start by applying QB with a high distance threshold and recursively apply QB on each sub-cluster with a lowered threshold. This process goes on until each streamline is assigned to its own cluster. By …