Danilo Bzdok

Membre académique principal

bzdokdan@mila.quebec

Chaire en IA Canada-CIFAR

Professeur agrégé, McGill University, Département de génie biomédicale

Sujets de recherche

Apprentissage profond

Biologie computationnelle

Grands modèles de langage (LLM)

Traitement du langage naturel

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

Shambhavi Aggarwal

Maîtrise recherche - McGill

Github

Google Scholar

Badr Ait Hammou

Postdoctorat - McGill

Gregory Bell

Doctorat - McGill

Anwesha Bhattacharya

Doctorat - McGill

Liam Hodgson

Doctorat - McGill

Jakub Kopal

Postdoctorat - UdeM

Google Scholar

Justin Marotta

Maîtrise recherche - McGill

Anny Maza

Collaborateur·rice de recherche - Universitat Politècnica

Ryan McPhedrain McPhedrain

Postdoctorat - McGill

ryan.mcphedrain@mail.mcgill.ca

David Payette

Maîtrise recherche - McGill

Github

Sepehr Radmannia

Visiteur de recherche indépendant - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Zilong Wang

Maîtrise recherche - McGill

Github

Google Scholar

Le Zhou

Doctorat - McGill

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

Lire l'article

Publications

Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns

Chris Zajner

Nathan Spreng

Danilo Bzdok

Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominatin… (voir plus)g factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40,000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3, and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC-DN co-variation followed a clear segregation divide into the left and right brain hemispheres. Separable regimes of structural HC-DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.

2021-08-19

bioRxiv (preprint)

doi.org

Human brain anatomy reflects separable genetic and environmental components of socioeconomic status

H. Kweon

Gökhan Aydogan

Alain Dagher

Danilo Bzdok

C. Ruff

Gideon Nave

Martha J Farah

Philipp Koellinger

Recent studies report that socioeconomic status (SES) correlates with brain structure. Yet, such findings are variable and little is known a… (voir plus)bout underlying causes. We present a well-powered voxel-based analysis of grey matter volume (GMV) across levels of SES, finding many small SES effects widely distributed across the brain, including cortical, subcortical and cerebellar regions. We also construct a polygenic index of SES to control for the additive effects of common genetic variation related to SES, which attenuates observed SES-GMV relations, to different degrees in different areas. Remaining variance, which may be attributable to environmental factors, is substantially accounted for by body mass index, a marker for lifestyle related to SES. In sum, SES affects multiple brain regions through measurable genetic and environmental effects. One-sentence Summary Socioeconomic status is linked with brain anatomy through a varying balance of genetic and environmental influences.

2021-07-29

bioRxiv (preprint)

doi.org

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