Publications

Connectivity alterations in autism reflect functional idiosyncrasy
Oualid Benkarim
Casey Paquola
Bo-yong Park
Seok-Jun Hong
Jessica Royer
Reinder Vos de Wael
Sara Larivière
Sofie Valk
Danilo Bzdok
Laurent Mottron
Boris C. Bernhardt
Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-develo… (voir plus)ping controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism. Benkarim et al devise an approach to profile inter-individual variability in functional network organization and test whether such idiosyncrasy contributes to the connectivity alterations found in Autism Spectrum Disorder. Their approach provides potential biomarkers to study atypical brain development and may be used to consolidate prior research findings on the variable nature of connectome level anomalies in autism.
2021-09-14
Communications Biology (publié)
doi.org
Haptics-based Curiosity for Sparse-Reward Tasks
Sai Rajeswar
Cyril Ibrahim
Nitin Surya
Florian Golemo
David Vázquez
Aaron Courville
Pedro O. Pinheiro
2021-09-12
robot-learning.org/CoRL/2021/Conference (poster)
proceedings.mlr.press
proceedings.mlr.press
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Mingde Zhao
Shuyuan Zhang
Xiao-Wen Chang
Doina Precup
2021-09-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge
Uzay Macar
Enamundram Naga Karthik
Charley Gros
Andréanne Lemay
Julien Cohen-Adad
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion … (voir plus)Segmentation. An overview of the data preprocessing steps applied is provided along with a brief description of the pipelines used, in terms of the architecture and the hyperparameters. Our code for this work can be found at: https://github.com/ivadomed/ms-challenge-2021.
2021-09-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Decision Models and Technology Can Help Psychiatry Develop Biomarkers
Daniel S. Barron
Justin T. Baker
Kristin S. Budde
Danilo Bzdok
Simon B. Eickhoff
Karl J. Friston
Peter T. Fox
Paul Geha
Stephen Heisig
Avram J. Holmes
Jukka-Pekka Onnela
Albert Powers
David Silbersweig
John H. Krystal
2021-09-08
Frontiers in Psychiatry (publié)
doi.org
Simple Video Generation using Neural ODEs
David Kanaa
Vikram Voleti
S Ebrahimi Kahou
Christopher Pal
Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely chall… (voir plus)enging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.
2021-09-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Social belonging: brain structure and function is linked to membership in sports teams, religious groups, and social clubs
Carolin Kieckhaefer
Leonhard Schilbach
Danilo Bzdok
Human behavior across the life span is driven by the psychological need to belong, right from kindergarten to bingo nights. Being part of so… (voir plus)cial groups constitutes a backbone for communal life and confers many benefits for the physical and mental health. Capitalizing on the neuroimaging and behavioral data from ∼40,000 participants from the UK Biobank population cohort, we used structural and functional analyses to explore how social participation is reflected in the human brain. Across 3 different types of social groups, structural analyses point toward the variance in ventromedial prefrontal cortex, fusiform gyrus, and anterior cingulate cortex as structural substrates tightly linked to social participation. Functional connectivity analyses not only emphasized the importance of default mode and limbic network but also showed differences for sports teams and religious groups as compared to social clubs. Taken together, our findings establish the structural and functional integrity of the default mode network as a neural signature of social belonging.
2021-09-06
bioRxiv (prépublication)
doi.org
Learning Neural Causal Models with Active Interventions
Nino Scherrer
Olexa Bilaniuk
Yashas Annadani
Anirudh Goyal
Patrick Schwab
Bernhard Schölkopf
Michael Curtis Mozer
Yoshua Bengio
Stefan Bauer
Nan Rosemary Ke
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing … (voir plus)scaling properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or interventional origin. In this work, we introduce an active intervention-targeting mechanism which enables quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting and is applicable for both discrete and continuous optimization formulations of learning the underlying directed acyclic graph (DAG) from data. We examine the proposed method across multiple frameworks in a wide range of settings and demonstrate superior performance on multiple benchmarks from simulated to real-world data.
2021-09-05
ArXiv (prépublication)
openreview.net
openreview.net
Proceedings 17th International Conference on Quantum Physics and Logic
Benoît Valiron
Shane Mansfield
Pablo Arrighi
Prakash Panangaden
This volume contains the proceedings of the 17th International Conference on Quantum Physics and Logic (QPL 2020), which was held June 2-6, … (voir plus)2020. Quantum Physics and Logic is an annual conference that brings together researchers working on mathematical foundations of quantum physics, quantum computing, and related areas, with a focus on structural perspectives and the use of logical tools, ordered algebraic and category-theoretic structures, formal languages, semantical methods, and other computer science techniques applied to the study of physical behavior in general. Work that applies structures and methods inspired by quantum theory to other fields (including computer science) is also welcome.
2021-09-05
Electronic Proceedings in Theoretical Computer Science (publié)
doi.org
arxiv.org
Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps
Fang Frank Yu
Susie Yi Huang
Thomas Witzel
Ashwin Kumar
Congyu Liao
Tanguy Duval
Julien Cohen-Adad
Berkin Bilgic
Purpose A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast… (voir plus) parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further. Methods Three adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. Results Subject #1’s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated. Conclusion We have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.
2021-09-03
Magnetic Resonance in Medicine (publié)
doi.org
Quantitative 7-Tesla Imaging of Cortical Myelin Changes in Early Multiple Sclerosis
Valeria Barletta
Elena Herranz
Constantina A. Treaba
Ambica Mehndiratta
Russell Ouellette
Gabriel Mangeat
Tobias Granberg
Jacob A. Sloane
Eric C Klawiter
Julien Cohen-Adad
Caterina Mainero
Cortical demyelination occurs early in multiple sclerosis (MS) and relates to disease outcome. The brain cortex has endogenous propensity fo… (voir plus)r remyelination as proven from histopathology study. In this study, we aimed at characterizing cortical microstructural abnormalities related to myelin content by applying a novel quantitative MRI technique in early MS. A combined myelin estimation (CME) cortical map was obtained from quantitative 7-Tesla (7T) T2* and T1 acquisitions in 25 patients with early MS and 19 healthy volunteers. Cortical lesions in MS patients were classified based on their myelin content by comparison with CME values in healthy controls as demyelinated, partially demyelinated, or non-demyelinated. At follow-up, we registered changes in cortical lesions as increased, decreased, or stable CME. Vertex-wise analysis compared cortical CME in the normal-appearing cortex in 25 MS patients vs. 19 healthy controls at baseline and investigated longitudinal changes at 1 year in 10 MS patients. Measurements from the neurite orientation dispersion and density imaging (NODDI) diffusion model were obtained to account for cortical neurite/dendrite loss at baseline and follow-up. Finally, CME maps were correlated with clinical metrics. CME was overall low in cortical lesions (p = 0.03) and several normal-appearing cortical areas (p 0.05) in the absence of NODDI abnormalities. Individual cortical lesion analysis revealed, however, heterogeneous CME patterns from extensive to partial or absent demyelination. At follow-up, CME overall decreased in cortical lesions and non-lesioned cortex, with few areas showing an increase (p 0.05). Cortical CME maps correlated with processing speed in several areas across the cortex. In conclusion, CME allows detection of cortical microstructural changes related to coexisting demyelination and remyelination since the early phases of MS, and shows to be more sensitive than NODDI and relates to cognitive performance.
2021-09-02
Frontiers in Neurology (publié)
doi.org
Sleep spindles track cortical learning patterns for memory consolidation
Marit Petzka
Alex Chatburn
Ian Charest
George M. Balanos
Bernhard P. Staresina
2021-09-02
Current Biology (publié)
doi.org