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Maxime Descoteaux

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32 papers

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YNIMG Journal 2024 Journal Article

A unified filtering method for estimating asymmetric orientation distribution functions

Charles Poirier
Maxime Descoteaux

Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. In this work, we disentangle these many filtering methods proposed in the past and combine them into a novel, unified filtering equation. We also propose a self-supervised data-driven approach for calibrating the filtering parameter values. Our equation is implemented in an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Our method is applied on multi-shell multi-tissue fiber ODFs from the Human Connectome Project dataset (1. 25 mm 3 native resolution) and on single-shell single-tissue fiber ODFs from the Bilingualism and the Brain dataset (2. 0 mm 3 isotropic resolution) to evaluate the occurrence of asymmetric patterns on different spatial resolutions, representing cutting-edge and “clinical” research data. Asymmetry measures such as the asymmetric index (ASI) and our novel number of fiber directions (NuFiD) are then used to explain the behaviour of our method in these images. The contributions of this work are: (i) the disentanglement and unification of filtering methods for estimating asymmetric ODFs; (ii) a calibration method for automatically fixing the parameters governing the filtering; (iii) an open-source, efficient implementation of our unified filtering method for estimating asymmetric ODFs; (iv) a novel number of fiber directions (NuFiD) index for explaining asymmetric fiber configurations; and (v) a novel template of asymmetries, revealing that our filtering method estimates asymmetric configurations in at least 50% of the brain voxels ( ∼ 31 % of the white matter and ∼ 63 % of the gray matter).

YNIMG Journal 2023 Journal Article

FIESTA: Autoencoders for accurate fiber segmentation in tractography

Félix Dumais
Jon Haitz Legarreta
Carl Lemaire
Philippe Poulin
François Rheault
Laurent Petit
Muhamed Barakovic
Stefano Magon

White matter bundle segmentation is a cornerstone of modern tractography to study the brain’s structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework

YNICL Journal 2023 Journal Article

Impact of follow ups, time interval and study duration in diffusion & myelin MRI clinical study in MS

Manon Edde
Francis Houde
Guillaume Theaud
Matthieu Dumont
Guillaume Gilbert
Jean-Christophe Houde
Loïka Maltais
Antoine Théberge

It is currently unknown how quantitative diffusion and myelin MRI designs affect the results of a longitudinal study. We used two independent datasets containing 6 monthly MRI measurements from 20 healthy controls and 20 relapsing-remitting multiple sclerosis (RR-MS) patients. Six designs were tested, including 3 MRI acquisitions, either over 6 months or over a shorter study duration, with balanced (same interval) or unbalanced (different interval) time intervals between MRI acquisitions. First, we show that in RR-MS patients, the brain changes over time obtained with 3 MRI acquisitions were similar to those observed with 5 MRI acquisitions and that designs with an unbalanced time interval showed the highest similarity, regardless of study duration. No significant brain changes were found in the healthy controls over the same periods. Second, the study duration affects the sample size in the RR-MS dataset; a longer study requires more subjects and vice versa. Third, the number of follow-up acquisitions and study duration affect the sensitivity and specificity of the associations with clinical parameters, and these depend on the white matter bundle and MRI measure considered. Together, this suggests that the optimal design depends on the assumption of the dynamics of change in the target population and the accuracy required to capture these dynamics. Thus, this work provides a better understanding of key factors to consider in a longitudinal study and provides clues for better strategies in clinical trial design.

YNIMG Journal 2023 Journal Article

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Gabriel Girard
Jonathan Rafael-Patiño
Raphaël Truffet
Dogu Baran Aydogan
Nagesh Adluru
Veena A. Nair
Vivek Prabhakaran
Barbara B. Bendlin

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

YNIMG Journal 2022 Journal Article

An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI

Ahmed M. Radwan
Stefan Sunaert
Kurt Schilling
Maxime Descoteaux
Bennett A. Landman
Mathieu Vandenbulcke
Tom Theys
Patrick Dupont

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals.

YNIMG Journal 2022 Journal Article

Bundle-o-graphy: improving structural connectivity estimation with adaptive microstructure-informed tractography

Matteo Battocchio
Simona Schiavi
Maxime Descoteaux
Alessandro Daducci

Tractography is a powerful tool for the investigation of the complex organization of the brain in vivo, as it allows inferring the macroscopic pathways of the major fiber bundles of the white matter based on non-invasive diffusion-weighted magnetic resonance imaging acquisitions. Despite this unique and compelling ability, some studies have exposed the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. In this work, we describe a novel method to readdress tractography reconstruction problem in a global manner by combining the strengths of so-called generative and discriminative strategies. Starting from an input tractogram, we parameterize the connections between brain regions following a bundle-based representation that allows to drastically reducing the number of parameters needed to model groups of fascicles. The parameters space is explored following an MCMC generative approach, while a discrimininative method is exploited to globally evaluate the set of connections which is updated according to Bayes' rule. Our results on both synthetic and real brain data show that the proposed solution, called bundle-o-graphy, allows improving the anatomical accuracy of the reconstructions while keeping the computational complexity similar to other state-of-the-art methods.

YNICL Journal 2022 Journal Article

Medial temporal lobe and obstructive sleep apnea: Effect of sex, age, cognitive status and free-water

Marie-Ève Martineau-Dussault
Claire André
Véronique Daneault
Andrée-Ann Baril
Katia Gagnon
Hélène Blais
Dominique Petit
Jacques Y. Montplaisir

Medial temporal structures, namely the hippocampus, the entorhinal cortex and the parahippocampal gyrus, are particularly vulnerable to Alzheimer's disease and hypoxemia. Here, we tested the associations between obstructive sleep apnea (OSA) severity and medial temporal lobe volumes in 114 participants aged 55-86 years (35 % women). We also investigated the impact of sex, age, cognitive status, and free-water fraction correction on these associations. Increased OSA severity was associated with larger hippocampal and entorhinal cortex volumes in women, but not in men. Greater OSA severity also correlated with increased hippocampal volumes in participants with amnestic mild cognitive impairment, but not in cognitively unimpaired participants, regardless of sex. Using free-water corrected volumes eliminated all significant associations with OSA severity. Therefore, the increase in medial temporal subregion volumes may possibly be due to edema. Whether these structural manifestations further progress to neuronal death in non-treated OSA patients should be investigated.

YNIMG Journal 2021 Journal Article

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Kurt G. Schilling
François Rheault
Laurent Petit
Colin B. Hansen
Vishwesh Nath
Fang-Cheng Yeh
Gabriel Girard
Muhamed Barakovic

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

YNIMG Journal 2020 Journal Article

Adaptive phase correction of diffusion-weighted images

Marco Pizzolato
Guillaume Gilbert
Jean-Philippe Thiran
Maxime Descoteaux
Rachid Deriche

Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines.

YNIMG Journal 2020 Journal Article

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Gabriel Girard
Roberto Caminiti
Alexandra Battaglia-Mayer
Etienne St-Onge
Karen S. Ambrosen
Simon F. Eskildsen
Kristine Krug
Tim B. Dyrby

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from “bottleneck” white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

YNIMG Journal 2020 Journal Article

Quantification of apparent axon density and orientation dispersion in the white matter of youth born with congenital heart disease

Kaitlyn Easson
Charles V. Rohlicek
Jean-Christophe Houde
Guillaume Gilbert
Christine Saint-Martin
Kimberly Fontes
Annette Majnemer
Ariane Marelli

Background White matter alterations have previously been demonstrated in adolescents born with congenital heart disease (CHD) using diffusion tensor imaging (DTI). However, due to the non-specific nature of DTI metrics, it is difficult to interpret these findings in terms of their microstructural implications. This study investigated the use of neurite orientation dispersion and density imaging (NODDI), which involves the acquisition of advanced multiple b-value data over two shells and provides proxy measures of apparent axon density and orientation dispersion within white matter, as a complement to classic DTI measures. Study design Youth aged 16 to 24 years born with complex CHD and healthy peers underwent brain magnetic resonance imaging. White matter tract volumes and tract-average values of DTI and NODDI metrics were compared between groups. Tract-average DTI and NODDI results were spatially confirmed using tract-based spatial statistics. Results There were widespread regions of lower tract-average neurite density index (NDI) in the CHD group as compared to the control group, particularly within long association tracts and in regions of the corpus callosum, accompanied by smaller white matter tract volumes and isolated clusters of lower fractional anisotropy (FA). There were no significant differences in orientation dispersion index (ODI) between groups. Conclusion Lower apparent density of axonal packing, but not altered axonal orientation, is a key microstructural factor in the white matter abnormalities observed in youth born with CHD. These impairments in axonal packing may be an enduring consequence of early life brain injury and dysmaturation and may explain some of the long-term neuropsychological difficulties experienced by this at-risk group.

YNIMG Journal 2020 Journal Article

TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

Guillaume Theaud
Jean-Christophe Houde
Arnaud Boré
François Rheault
Felix Morency
Maxime Descoteaux

Diffusion MRI tractography processing pipeline requires a large number of steps (typically 20+ steps). If parameters of these steps, number of threads, and random seed generators are not carefully controlled, the resulting tractography can easily be non-reproducible and non-replicable, even in test-test experiments. To handle these issues, we developed TractoFlow. TractoFlow is fully automatic from raw diffusion weighted images to tractography. The pipeline also outputs classical diffusion tensor imaging measures and several fiber orientation distribution function measures. TractoFlow supports the recent Brain Imaging Data Structure (BIDS) format as input and is based on two engines: Nextflow and Singularity. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100%, depending on parameter choices. Moreover, it is easy to use for non-technical users, with little to no installation requirements. TractoFlow is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

YNIMG Journal 2019 Journal Article

Bundle-specific tractography with incorporated anatomical and orientational priors

Francois Rheault
Etienne St-Onge
Jasmeen Sidhu
Klaus Maier-Hein
Nathalie Tzourio-Mazoyer
Laurent Petit
Maxime Descoteaux

Anatomical white matter bundles vary in shape, size, length, and complexity, making diffusion MRI tractography reconstruction of some bundles more difficult than others. As a result, bundles reconstruction often suffers from a poor spatial extent recovery. To fill-up the white matter volume as much and as best as possible, millions of streamlines can be generated and filtering techniques applied to address this issue. However, well-known problems and biases are introduced such as the creation of a large number of false positives and over-representation of easy-to-track parts of bundles and under-representation of hard-to-track. To address these challenges, we developed a Bundle-Specific Tractography (BST) algorithm. It incorporates anatomical and orientational prior knowledge during the process of streamline tracing to increase reproducibility, sensitivity, specificity and efficiency when reconstructing certain bundles of interest. BST outperforms classical deterministic, probabilistic, and global tractography methods. The increase in anatomically plausible streamlines, with larger spatial coverage, helps to accurately represent the full shape of bundles, which could greatly enhance and robustify tract-based and connectivity-based neuroimaging studies.

YNIMG Journal 2019 Journal Article

Cortical distance, not cancellation, dominates inter-subject EEG gamma rhythm amplitude

Russell Butler
Pierre-Michel Bernier
Gregory W. Mierzwinski
Maxime Descoteaux
Guillaume Gilbert
Kevin Whittingstall

The neurophysiological response to visual stimulation in both humans and animals is characterized by an increase in high frequency amplitude peaking in the gamma range (40–100Hz) and a suppression of low frequency amplitude peaking in the alpha range (10–16Hz). Due to the large number of studies linking amplitude and peak frequency to perception and neurological disorders, there is great interest in understanding the basis of inter-subject variability in gamma and alpha responses. To address this, we measured gamma and alpha amplitude and peak frequency of response to visual stimulation in 42 healthy humans. Using FMRI to delineate active cortical tissue in the same subjects, we correlated these neurophysiological metrics with two structural metrics: distance from active cortex to electrode, and dipole cancellation over active cortex. We find that distance strongly predicted inter-subject gamma amplitude, but had little effect on alpha amplitude, while cancellation had little effect on gamma or alpha amplitude. Neither alpha peak frequency nor gamma peak frequency correlated with our structural metrics. These results suggest that inter-subject variability in gamma amplitude may reflect gross morphology rather than neurophysiological variability, and should be interpreted with caution, while peak frequency may serve as a more sensitive metric of differences in neuronal activity across subjects.

YNIMG Journal 2019 Journal Article

Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain

Maxime Chamberland
Erika P. Raven
Sila Genc
Kate Duffy
Maxime Descoteaux
Greg D. Parker
Chantal M.W. Tax
Derek K. Jones

Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.

YNIMG Journal 2019 Journal Article

Limits to anatomical accuracy of diffusion tractography using modern approaches

Kurt G. Schilling
Vishwesh Nath
Colin Hansen
Prasanna Parvathaneni
Justin Blaber
Yurui Gao
Peter Neher
Dogu Baran Aydogan

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

YNICL Journal 2019 Journal Article

The Canadian Dementia Imaging Protocol: Harmonization validity for morphometry measurements

Olivier Potvin
Isabelle Chouinard
Louis Dieumegarde
Robert Bartha
Pierre Bellec
D. Louis Collins
Maxime Descoteaux
Rick Hoge

The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of CDIP parameters compliance on total brain volume variance using 86 scans of the same individual acquired on various scanners. Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. For images acquired from Philips scanners, lower variance in brain volumes were observed when the stated CDIP resolution was set. For images acquired from GE scanners, lower variance in brain volumes were noticed when TE/TR values were within 5% of the CDIP protocol, compared to values farther from that criteria. Together, these results suggest that a harmonized protocol like the CDIP may help to reduce neuromorphometric measurement variability in multi-centric studies.

YNIMG Journal 2019 Journal Article

White matter information flow mapping from diffusion MRI and EEG

Samuel Deslauriers-Gauthier
Jean-Marc Lina
Russell Butler
Kevin Whittingstall
Guillaume Gilbert
Pierre-Michel Bernier
Rachid Deriche
Maxime Descoteaux

The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.

YNICL Journal 2018 Journal Article

Cortical thickness analysis in operculo-insular epilepsy

Sami Obaid
Alan Tucholka
Jimmy Ghaziri
Pierre-Marc Jodoin
Félix Morency
Maxime Descoteaux
Alain Bouthillier
Dang K. Nguyen

Background: In temporal lobe epilepsy (TLE), advanced neuroimaging techniques reveal anomalies extending beyond the temporal lobe such as thinning of fronto-central cortices. Operculo-insular epilepsy (OIE) is an under-recognized and poorly characterized condition with the potential of mimicking TLE. In this work, we investigated insular and extra-insular cortical thickness (CT) changes in OIE. Methods: -test (threshold of p < 0.05 after correction for multiple comparisons; cut-off threshold of 250 voxels) between (i) patients with OIE vs TLE, and (ii) patients with OIE vs healthy controls. Results: Significant widespread thinning was observed in OIE patients as compared with healthy controls mainly in the ipsilateral insula, peri-rolandic region, orbito-frontal area, mesiotemporal structures and lateral temporal neocortex. Contralateral cortical shrinkage followed a similar albeit milder and less diffuse pattern.The CT of OIE patients was equal or reduced relative to the TLE group for every cortical region analyzed. Thinning was observed diffusely in OIE patients, predominantly inboth insulae and the ipsilateral occipito-temporal area. Conclusion: Our results reveal structural anomalies extending beyond the operculo-insular area in OIE.

YNIMG Journal 2018 Journal Article

High-resolution 3D diffusion tensor MRI of anesthetized rhesus macaque brain at 3T

Slimane Tounekti
Thomas Troalen
Yann Bihan-Poudec
Mathilda Froesel
Franck Lamberton
Valéry Ozenne
Justine Cléry
Nathalie Richard

Diffusion Magnetic Resonance Imaging (dMRI) has been widely used to investigate human brain microstructure and connectivity and its abnormalities in a variety of brain deficits, whether acute, neurodevelopmental or neurodegenerative. However, the biological interpretation and validation of dMRI data modelling is still a crucial challenge in the field. In this respect, achieving high spatial resolution in-vivo dMRI in the non-human primate to compare these observations both with human dMRI on the one hand and 'ground truth' microstructural and histological data on the other hand is of outmost importance. Here, we developed a dMRI pulse sequence based on 3D-multishot Echo Planar Imaging (3D-msEPI) on a 3T human clinical scanner. We demonstrate the feasibility of cerebral dMRI at an isotropic resolution of 0. 5 mm in 4 anesthetized macaque monkeys. The added value of the high-resolution dMRI is illustrated by focusing on two aspects. First, we show an enhanced descriptive power of the fine substructure of the hippocampus. Second, we show a more physiological description of the interface between cortex grey matter, superficial and deep white matter. Overall, the high spatial resolution dMRI acquisition method proposed in this study is a significant achievement with respect to the state of the art of dMRI on anesthetized monkeys. This study highlights also the potential of very high-resolution dMRI to precisely capture the microstructure of thin cerebral structures such as the hippocampus and superficial white matter.

YNIMG Journal 2018 Journal Article

Mapping population-based structural connectomes

Zhengwu Zhang
Maxime Descoteaux
Jingwen Zhang
Gabriel Girard
Maxime Chamberland
David Dunson
Anuj Srivastava
Hongtu Zhu

Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects’ brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects.

YNIMG Journal 2018 Journal Article

Recognition of white matter bundles using local and global streamline-based registration and clustering

Eleftherios Garyfallidis
Marc-Alexandre Côté
Francois Rheault
Jasmeen Sidhu
Janice Hau
Laurent Petit
David Fortin
Stephen Cunanne

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 and bundles in tractograms. RecoBundles is 100% streamline-based, is efficient to work with millions of streamlines and, most importantly, is robust and adaptive to incomplete data and bundles with missing components. It is also robust to pathological brains with tumors and deformations. We evaluated our results using multiple bundles and showed that RecoBundles is in good agreement with the neuroanatomical experts and generally produced more dense bundles. Across all the different experiments reported in this paper, RecoBundles was able to identify the core parts of the bundles, independently from tractography type (deterministic or probabilistic) or size. Thus, RecoBundles can be a valuable method for exploring tractograms and facilitating tractometry studies.

YNIMG Journal 2018 Journal Article

Surface-enhanced tractography (SET)

Etienne St-Onge
Alessandro Daducci
Gabriel Girard
Maxime Descoteaux

In this work, we exploit the T1 weighted image in conjunction with cortical surface boundary to improve the precision of tractography under the cortex. We show that utilizing the cortical interface and a surface flow, to model the superficial white matter streamlines, enhance and improve tractography trajectory near the cortex. Our novel surface-enhanced tractography reduces the gyral bias, the length bias and the amount of false positive streamlines produced by tractography. This method improves the reproducibility and the cortical surface coverage of tractograms which are crucial for connectomics studies. The usage of cortical surfaces, extracted from the standardly acquired 1 mm isotropic T1, is a straightforward and effective way to improve existing tractography processing pipelines and structural connectivity studies.

YNICL Journal 2017 Journal Article

A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

Martin Cousineau
Pierre-Marc Jodoin
Eleftherios Garyfallidis
Marc-Alexandre Côté
Félix C. Morency
Verena Rozanski
Marilyn Grand’Maison
Barry J. Bedell

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 Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.

YNIMG Journal 2017 Journal Article

Fiber tractography using machine learning

Peter F. Neher
Marc-Alexandre Côté
Jean-Christophe Houde
Maxime Descoteaux
Klaus H. Maier-Hein

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i. e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

YNIMG Journal 2016 Journal Article

Optimal DSI reconstruction parameter recommendations: Better ODFs and better connectivity

Michael Paquette
Guillaume Gilbert
Maxime Descoteaux

Diffusion Spectrum Imaging (DSI) has been used for tractography in several publicly available software and a number of recent high impact publications. However, there are several important theoretical, numerical and practical considerations that are often ignored. We revisit the theoretical and state-of-the-art processing steps necessary to go from the DSI signal to the diffusion orientation distribution function (dODF) used by tractography. We show that the parameters in the reconstruction have huge impact on the reconstruction quality and that, while there is no consensus about what they should be, the parameters we most often see in the literature are not optimal. We provide applicable recommendations that improve the accuracy of extracted local orientations and improve accuracy of global connectivity as measured by the Tractometer, a tractography online evaluation system. These recommendations come for “free” as they are applicable to all existing DSI data and do not require a significant increase in computation time. Hence, this paper highlights the do's and dont's of DSI reconstruction.

YNIMG Journal 2015 Journal Article

A new compression format for fiber tracking datasets

Caroline Presseau
Pierre-Marc Jodoin
Jean-Christophe Houde
Maxime Descoteaux

A single diffusion MRI streamline fiber tracking dataset may contain hundreds of thousands, and often millions of streamlines and can take up to several gigabytes of memory. This amount of data is not only heavy to compute, but also difficult to visualize and hard to store on disk (especially when dealing with a collection of brains). These problems call for a fiber-specific compression format that simplifies its manipulation. As of today, no fiber compression format has yet been adopted and the need for it is now becoming an issue for future connectomics research. In this work, we propose a new compression format, .zfib, for streamline tractography datasets reconstructed from diffusion magnetic resonance imaging (dMRI). Tracts contain a large amount of redundant information and are relatively smooth. Hence, they are highly compressible. The proposed method is a processing pipeline containing a linearization, a quantization and an encoding step. Our pipeline is tested and validated under a wide range of DTI and HARDI tractography configurations (step size, streamline number, deterministic and probabilistic tracking) and compression options. Similar to JPEG, the user has one parameter to select: a worst-case maximum tolerance error in millimeter (mm). Overall, we find a compression factor of more than 96% for a maximum error of 0. 1mm without any perceptual change or change of diffusion statistics (mean fractional anisotropy and mean diffusivity) along bundles. This opens new opportunities for connectomics and tractometry applications.

YNIMG Journal 2015 Journal Article

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Eleftherios Garyfallidis
Omar Ocegueda
Demian Wassermann
Maxime Descoteaux

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. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis.

YNIMG Journal 2014 Journal Article

Towards quantitative connectivity analysis: reducing tractography biases

Gabriel Girard
Kevin Whittingstall
Rachid Deriche
Maxime Descoteaux

Diffusion MRI tractography is often used to estimate structural connections between brain areas and there is a fast-growing interest in quantifying these connections based on their position, shape, size and length. However, a portion of the connections reconstructed with tractography is biased by their position, shape, size and length. Thus, connections reconstructed are not equally distributed in all white matter bundles. Quantitative measures of connectivity based on the streamline distribution in the brain such as streamline count (density), average length and spatial extent (volume) are biased by erroneous streamlines produced by tractography algorithms. In this paper, solutions are proposed to reduce biases in the streamline distribution. First, we propose to optimize tractography parameters in terms of connectivity. Then, we propose to relax the tractography stopping criterion with a novel probabilistic stopping criterion and a particle filtering method, both based on tissue partial volume estimation maps calculated from a T1-weighted image. We show that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution. These tractography biases are quantitatively reported using in-vivo and synthetic data. This is a critical step towards producing tractography results for quantitative structural connectivity analysis.

YNIMG Journal 2013 Journal Article

Collaborative patch-based super-resolution for diffusion-weighted images

Pierrick Coupé
José V. Manjón
Maxime Chamberland
Maxime Descoteaux
Bassem Hiba

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms of improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images are presented at 0. 6×0. 6×0. 6mm3 and 0. 4×0. 4×0. 4mm3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.

YNIMG Journal 2011 Journal Article

Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom

Pierre Fillard
Maxime Descoteaux
Alvina Goh
Sylvain Gouttard
Ben Jeurissen
James Malcolm
Alonso Ramirez-Manzanares
Marco Reisert

As it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the “Fiber Cup”. 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http: //www. lnao. fr/spip. php? rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to fibercup09@gmail. com and updates will be published on the webpage.

YNIMG Journal 2008 Journal Article

Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI

Peter Savadjiev
Jennifer S.W. Campbell
Maxime Descoteaux
Rachid Deriche
G. Bruce Pike
Kaleem Siddiqi

Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm.