Danilo Bzdok

Membre académique principal

Chaire en IA Canada-CIFAR

Professeur agrégé, McGill University, Département de génie biomédicale

Biographie

Danilo Bzdok est informaticien et médecin de formation. Il possède une double formation unique en neurosciences systémiques et en algorithmes d'apprentissage automatique. Après une formation à l'Université d'Aix-la-Chapelle (RWTH) (Allemagne), à l'Université de Lausanne (Suisse) et à la Harvard Medical School (États-Unis), il a obtenu un doctorat en neurosciences du Centre de recherche de Jülich (Allemagne) et un doctorat en informatique dans le domaine des statistiques d'apprentissage automatique à l'INRIA Saclay et à NeuroSpin (Paris, France). Il est actuellement professeur agrégé à la Faculté de médecine de l'Université McGill et titulaire d’une chaire en IA Canada-CIFAR à Mila – Institut québécois d'intelligence artificielle. Son activité de recherche interdisciplinaire est centrée sur la réduction des lacunes dans la connaissance des bases cérébrales des types de pensée qui définissent l'être humain, afin de découvrir les principes clés de conception computationnelle qui sous-tendent l'intelligence humaine.

Étudiants actuels

Anny Maza

Collaborateur·rice de recherche - Universitat Politècnica

anny.maza@mila.quebec

Anwesha Bhattacharya

Doctorat - McGill University

anwesha.bhattacharya@mila.quebec

Badr Ait Hammou

Postdoctorat - McGill University

badr.aithammou@mila.quebec

Chloé Savignac

Doctorat - McGill University

chloe.savignac@mila.quebec

Github

Google Scholar

Gregory Bell

Doctorat - McGill University

gregory.bell@mila.quebec

Jack Stanley

Doctorat - McGill University

jack.stanley@mila.quebec

Site web

Github

Jakub Kopal

Postdoctorat - Université de Montréal

jakub.kopal@mila.quebec

Google Scholar

Justin Marotta

Maîtrise recherche - McGill University

justin.marotta@mila.quebec

Karam Ghanem

Maîtrise recherche - McGill University

karam.ghanem@mila.quebec

Karin Saltoun

Doctorat - McGill University

karin.saltoun@mila.quebec

Le Zhou

Doctorat - McGill University

le.zhou@mila.quebec

Liam Hodgson

Doctorat - McGill University

liam.hodgson@mila.quebec

Nicole Osayande

Maîtrise recherche - McGill University

nicole.osayande@mila.quebec

Shambhavi Aggarwal

Maîtrise recherche - McGill University

shambhavi.aggarwal@mila.quebec

Github

Zilong Wang

Maîtrise recherche - McGill University

zilong.wang@mila.quebec

Github

Google Scholar

Billets de blogue

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

8 octobre 2020

Vers une médecine personnalisée : comprendre la divergence entre l’inférence et la prédiction en biomédecine

par

Danilo Bzdok

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Publications

A parsimonious description of global functional brain organization in three spatiotemporal patterns

Taylor Bolt

Jason S. Nomi

Danilo Bzdok

Jorge A. Salas

Catie Chang

B. T. Thomas Yeo

Lucina Q. Uddin

S. Keilholz

2021-06-20

Nature Neuroscience (published)

doi.org

Multi-tract multi-symptom relationships in pediatric concussion

Guido Ivan Guberman

Sonja Stojanovski

Eman Nishat

Alain Ptito

Danilo Bzdok

A. Wheeler

Maxime Descoteaux

The heterogeneity of white matter damage and symptoms in concussions has been identified as a major obstacle to therapeutic innovation. In c… (voir plus)ontrast, the vast majority of diffusion MRI studies on concussion have traditionally employed group-comparison approaches. Such studies do not consider heterogeneity of damage and symptoms in concussion. To parse concussion heterogeneity, the present study combines diffusion MRI (dMRI) and multivariate statistics to investigate multi-tract multi-symptom relationships. Using dMRI data from a sample of 306 children ages 9 and 10 with a history of concussion from the Adolescent Brain Cognitive Development Study (ABCD study), we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first capturing a mixture of patterns suggestive of microstructural complexity, the second representing almost exclusively axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 behavioural measures that capture well-known symptoms of concussions. We found idiosyncratic symptom-specific multi-tract connectivity features, which would not be captured in traditional univariate analyses. Multivariable connectome-symptom correspondences were stronger than all single-tract/single-symptom associations. Multi-tract connectivity features were also expressed equally across different sociodemographic strata and their expression was not accounted for by injury-related variables. In a replication dataset, the expression of multi-tract connectivity features predicted adverse psychiatric outcomes after accounting for other psychopathology-related variables. By defining cross-demographic multi-tract multi-symptom relationships to parse concussion heterogeneity, the present study can pave the way for the development of improved stratification strategies that may contribute to the success of future clinical trials and the improvement of concussion management.

2021-04-07

medRxiv (preprint)

doi.org

Interacting brains revisited: A cross‐brain network neuroscience perspective

Christian Gerloff

Kerstin Konrad

Danilo Bzdok

Christina Büsing

Vanessa Reindl

Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to exten… (voir plus)d the isolated perspective on the human brain by considering interacting persons’ brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions’ roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

2021-02-20

bioRxiv (preprint)

doi.org

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Marc-Andre Schulz

B.T. Thomas Yeo

Joshua T. Vogelstein

Janaina Mourao-Miranada

Jakob N. Kather

Konrad Paul Kording

Blake Richards

Danilo Bzdok