Julien Cohen-Adad

Pierre Louis Benveniste

PhD - Polytechnique Montréal

Co-supervisor :

PhD - Polytechnique Montréal

Thomas Dagonneau

Master's Research - Polytechnique Montréal

Naga Karthik Enamundram

PhD - Polytechnique Montréal

Co-supervisor :

Sarath Chandar

emvnagakarthik@gmail.com

Master's Research - Polytechnique Montréal

Nilser Laines Medina

PhD - Polytechnique Montréal

Nathan Molinier

PhD - Polytechnique Montréal

Kalum Ost

Collaborating researcher

Julien Thouveny

Master's Research - Université de Montréal

Arthur Toulouse

Master's Research - Polytechnique Montréal

Postdoctorate - Polytechnique Montréal

Website

Google Scholar

Publications

Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study

Ali Khatibi

Shahabeddin Vahdat

Ovidiu Lungu

Jürgen Finsterbusch

Christian Büchel

V. Marchand-Pauvert

Julien Doyon

2022-06-01

NeuroImage (published)

Diffusion Kurtosis Imaging of the neonatal Spinal Cord: design and application of the first processing pipeline implemented in Spinal Cord Toolbox

Rosella Trò

Monica Roascio

Domenico Tortora

Mariasavina Severino

Andrea Rossi

Marco Massimo Fato

Gabriele Arnulfo

Diffusion Kurtosis Imaging (DKI) has undisputed advantages over more classical diffusion Magnetic Resonance Imaging (dMRI), as witnessed by … (see more)a fast-increasing number of clinical applications and software packages widely adopted in brain imaging domain. Despite its power in probing tissue microstructure compared to conventional MRI, DKI is still largely underutilized in Spinal Cord (SC) imaging because of its inherently demanding technological requirements. If state-of-the-art hardware advancements have recently allowed to make great strides in applying this emerging method to adult and child SC, the same does not apply to neonatal setting. Indeed, amplified technical issues related to SC district in this age range have made this field still unexplored. However, results arising from recent application of DKI to adult and child SC are promising enough to suggest how informative this technique would be in investigating newborns, too. Due to its extreme sensitivity to non-gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, and not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between SC region and the above brain, managing to apply such a method to neonatal cohort becomes of utmost importance. In this work, we illustrate the first semi-automated pipeline for handling with DKI data of neonatal SC, from acquisition setting to estimation of diffusion (DTI & DKI) measures, through accurate adjustment of processing algorithms customized for adult SC. Each processing step of this pipeline, built on Spinal Cord Toolbox (SCT) software, has undergone Quality Control check by supervision of an expert pediatric neuroradiologist, and the overall procedure has preliminarily been tested in a pilot clinical case study. Results of this application agree with findings achieved in a corresponding adult survey, thus confirming validity of adopted pipeline and diagnostic value of DKI in pediatrics. This novel tool hence paves the wave for extending its application also to other promising advanced dMRI models, such as Neurite Orientation Dispersion and Density Imaging (NODDI), and to a wider range of potential clinical applications concerning neonatal period.

2022-05-23

Frontiers in Radiology (published)

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

Marie-Hélène Bourget

Lee Kamentsky

Satrajit S. Ghosh

Giacomo Mazzamuto

Alberto Lazari

Christopher J. Markiewicz

Robert Oostenveld

Guiomar Niso

Yaroslav O. Halchenko

Ilona Lipp

Sylvain Takerkart

Paule-Joanne Toussaint

Ali R. Khan

Gustav Nilsonne

Filippo Maria Castelli

Stefan Ross Eric Franklin Anthony Rémi Christopher J. Taylor Appelhoff

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusab… (see more)le way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

2022-04-19

Frontiers in Neuroscience (published)

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

Marie-Hélène Bourget

L. Kamentsky

Satrajit S. Ghosh

Giacomo Mazzamuto

Alberto Lazari

Christopher J. Markiewicz

Robert Oostenveld

Guiomar Niso

Yaroslav O. Halchenko

Ilona Lipp

Sylvain Takerkart

P. Toussaint

Ali Raza Khan

Gustav Nilsonne

Filippo Maria Castelli

2022-04-19

Frontiers in Neuroscience (published)

NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

Vicente Enguix

Jeanette K. Kenley

David Luck

G. Lodygensky

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its … (see more)integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.

2022-04-08

ArXiv (preprint)

arxiv.org

Rapid, automated nerve histomorphometry through open-source artificial intelligence

Simeon Christian Daeschler

Marie-Hélène Bourget

Dorsa Derakhshan

Vasudev Sharma

Stoyan Ivaylov Asenov

Tessa Gordon

Gregory Howard Borschel

2022-04-08

Scientific Reports (published)

Rapid, automated nerve histomorphometry through open-source artificial intelligence

S. Daeschler

Marie-Hélène Bourget

Dorsa Derakhshan

Vasudev Sharma

Stoyan Ivaylov Asenov

Tessa Gordon

G. Borschel

2022-04-08

Scientific Reports (published)

Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach

Reza Azad

Moein Heidari

Ehsan Adeli

Dorit Merhof

Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related disea… (see more)ses such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce

2022-04-06

ArXiv (preprint)

arxiv.org

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

Eva Alonso‐Ortiz

Stephanie Alley

Maria Marcella Lagana

Francesca Baglio

Signe Johanna Vannesjo

Haleh Karbasforoushan

Maryam Seif

Alan C. Seifert

Junqian Xu

Joo‐Won Kim

René Labounek

Lubomír Vojtíšek

Marek Dostál

Rebecca S. Samson

Francesco Grussu

Marco Battiston

Claudia A. M. Gandini Wheeler-Kingshott

Marios C. Yiannakas … (see 4 more)

Guillaume Gilbert

Torben Schneider

Brian Johnson

Ferran Prados

Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (see more) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.

2022-04-05

Magnetic Resonance in Medicine (published)

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

Eva Alonso‐Ortiz

Stephanie Alley

M. M. Laganá

Francesca Baglio

S. Vannesjo

Haleh Karbasforoushan

Maryam Seif

A. Seifert

Junqian Xu

Joo-won Kim

René Labounek

Lubomír Vojtíšek

Marek Dostál

Rebecca Sara Samson

Francesco Grussu

Marco Battiston

C. G. Gandini Wheeler-Kingshott

Marios C. Yiannakas … (see 4 more)

Guillaume Gilbert

Torben Schneider

Brian Johnson

Ferran Prados

2022-04-05

Magnetic Resonance in Medicine (published)

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

Eva Alonso‐Ortiz

Stephanie Alley

Maria Marcella Lagana

Francesca Baglio

Signe Johanna Vannesjo

Haleh Karbasforoushan

Maryam Seif

Alan C. Seifert

Junqian Xu

Joo‐Won Kim

René Labounek

Lubomír Vojtíšek

Marek Dostál

Rebecca S. Samson

Francesco Grussu

Marco Battiston

Claudia A. M. Gandini Wheeler-Kingshott

Marios C. Yiannakas … (see 4 more)

Guillaume Gilbert

Torben Schneider

Brian Johnson

Ferran Prados

2022-04-05

Magnetic Resonance in Medicine (published)

Reproducibility and Evolution of Diffusion Mri Measurements Within the Cervical Spinal Cord in Multiple Sclerosis

Haykel Snoussi

Emmanuel Caruyer

Benoit Combes

Olivier Commowick

Elise Bannier

Anne Kerbrat

Christian Barillot

In Multiple Sclerosis (MS), there is a large discrepancy between the clinical observations and how the pathology is exhibited on brain image… (see more)s, this is known as the clinical-radiological paradox. One of the hypotheses is that the clinical deficit may be more related to the spinal cord damage than the number or location of lesions in the brain. Therefore, investigating how the spinal cord is damaged becomes an acute challenge to better understand and overcome this paradox. Diffusion MRI is known to provide quantitative figures of neuronal degeneration and axonal loss, in the brain as well as in the spinal cord. In this paper, we propose to investigate how diffusion MRI metrics vary in the different cervical regions with the progression of the disease. We first study the reproducibility of diffusion MRI on healthy volunteers with a test-retest procedure using both standard diffusion tensor imaging (DTI) and multi-compartment Ball-and-Stick models. Then, based on the test re-test quantitative calibration, we provide quantitative figures of pathology evolution between M0 and M12 in the cervical spine on a set of 31 MS patients, exhibiting how the pathology damage spans in the cervical spinal cord.

2022-03-28

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (published)