Danilo Bzdok

Core Academic Member

Canada CIFAR AI Chair

Associate Professor, McGill University, Department of Biomedical Engineering

Biography

Danilo Bzdok is a computer scientist and medical doctor by training with a unique dual background in systems neuroscience and machine learning algorithms. After training at RWTH Aachen University (Germany), Université de Lausanne (Switzerland) and Harvard Medical School, Bzdok completed two doctoral degrees, one in neuroscience at Forschungszentrum Jülich in Germany, and another in computer science (machine learning statistics) at INRIA–Saclay and the Neurospin brain imaging centre in Paris.

Danilo is currently an associate professor at McGill University’s Faculty of Medicine and a Canada CIFAR AI Chair at Mila – Quebec Artificial Intelligence Institute. His interdisciplinary research centres around narrowing knowledge gaps in the brain basis of human-defining types of thinking in order to uncover key computational design principles underlying human intelligence.

Current Students

Anny Maza

Collaborating researcher - Universitat Politècnica

anny.maza@mila.quebec

Anwesha Bhattacharya

PhD - McGill University

anwesha.bhattacharya@mila.quebec

Badr Ait Hammou

Postdoctorate - McGill University

badr.aithammou@mila.quebec

Chloé Savignac

PhD - McGill University

chloe.savignac@mila.quebec

Github

Google Scholar

Gregory Bell

PhD - McGill University

gregory.bell@mila.quebec

Jack Stanley

PhD - McGill University

jack.stanley@mila.quebec

Website

Github

Jakub Kopal

Postdoctorate - Université de Montréal

jakub.kopal@mila.quebec

Google Scholar

Justin Marotta

Master's Research - McGill University

justin.marotta@mila.quebec

Karam Ghanem

Master's Research - McGill University

karam.ghanem@mila.quebec

Karin Saltoun

PhD - McGill University

karin.saltoun@mila.quebec

Le Zhou

PhD - McGill University

le.zhou@mila.quebec

Liam Hodgson

PhD - McGill University

liam.hodgson@mila.quebec

Nicole Osayande

Master's Research - McGill University

nicole.osayande@mila.quebec

Shambhavi Aggarwal

Master's Research - McGill University

shambhavi.aggarwal@mila.quebec

Github

Zilong Wang

Master's Research - McGill University

zilong.wang@mila.quebec

Github

Google Scholar

Blog Posts

September 8, 2020

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

Danilo Bzdok

Read the article

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… (see more)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… (see more)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