Portrait of Danilo Bzdok

Danilo Bzdok

Core Academic Member
bzdokdan@mila.quebec
Canada CIFAR AI Chair
Associate Professor, McGill University, Department of Biomedical Engineering
Research Topics
Computational Biology
Deep Learning
Large Language Models (LLM)
Natural Language Processing

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

Shambhavi Aggarwal
Master's Research - McGill University
Github
Google Scholar
Badr Ait Hammou
Postdoctorate - McGill University
Karan Bali Bali
PhD - McGill University
Gregory Bell
PhD - McGill University
Anwesha Bhattacharya
PhD - McGill University
Pedro Carneiro Carneiro
PhD - McGill University
Github
Liam Hodgson
PhD - McGill University
Ryan McPhedrain McPhedrain
Postdoctorate - McGill University
ryan.mcphedrain@mail.mcgill.ca
David Payette
Master's Research - McGill University
Github
Sepehr Radmannia
Independent visiting researcher - McGill University
sradmannia@gmail.com
Github
Karin Saltoun
PhD - McGill University
Chloé Savignac
PhD - McGill University
Github
Google Scholar
Jack Stanley
PhD - McGill University
Website
Github
Praneet Suresh
PhD - McGill University
Le Zhou
PhD - McGill University

Publications

Phenotypical predictors of pregnancy-related restless legs syndrome and their association with basal ganglia and the limbic circuits
Natalia Chechko
Jeremy Lefort-Besnard
Tamme W. Goecke
Markus Frensch
Patricia Schnakenberg
Susanne Stickel
Danilo Bzdok
Restless legs syndrome (RLS) in pregnancy is a common disorder with a multifactorial etiology. A neurological and obstetrical cohort of 308 … (see more)postpartum women was screened for RLS within 1 to 6 days of childbirth and 12 weeks postpartum. Of the 308 young mothers, 57 (prevalence rate 19%) were identified as having been affected by RLS symptoms in the recently completed pregnancy. Structural and functional MRI was obtained from 25 of these 57 participants. A multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors of RLS. A decreased volume of the parietal, orbitofrontal and frontal areas shortly after delivery was found to be linked to persistent RLS symptoms up to 12 weeks postpartum, the symptoms' severity and intensity in the most recent pregnancy, and a history of RLS in previous pregnancies. The same negative relationship was observed between brain volume and not being married, not receiving any iron supplement and higher numbers of stressful life events. High cortisol levels, being married and receiving iron supplements, on the other hand, were found to be associated with increased volumes in the bilateral striatum. Investigating RLS symptoms in pregnancy within a brain-phenotype framework may help shed light on the heterogeneity of the condition.
2021-05-11
Scientific Reports (published)
doi.org
Phenotypical predictors of Restless Legs Syndrome in pregnancy and their association with basal ganglia and the limbic circuits
Natalia Chechko
Jeremy Lefort-Besnard
Tamme W. Goecke
Markus Frensch
Patricia Schnakenberg
Susanne Stickel
Danilo Bzdok
The pregnancy-related restless legs syndrome (RLS) is thought to have a multifactorial etiology. However, the reason behind the manifestatio… (see more)n of RLS during pregnancy remains largely elusive. A neurological and obstetrical cohort of 308 postpartum women was screened for RLS symptoms twice: 1 to 6 days (T0) and 12 weeks postpartum (T1). 57 participants were identified as affected by pregnancy-associated RLS. The clinical and anamnestic indicators of the condition were assessed by a pattern-learning classifier trained to predict the RLS status. Structural MRI was obtained from 25 of the 57 participants with RLS history in pregnancy. In this sample, a multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors. The RLS prevalence rate in our sample was 19% (n=57), with the women suffering from RLS being older, more often unmarried, affected by gestational diabetes and having been more exposed to stressful life events. A history of RLS and the severity and frequency of repetitive compulsive movements were found to be the strongest predictors of RLS manifestation. In the RLS group, high cortisol levels, being married and receiving iron supplements were found to be associated with increased volumes in the bilateral striatum. Investigating pregnancy-related RLS in a frame of brain phenotype modes may help shed light on the heterogeneity of the condition.
2021-05-11
Scientific Reports (published)
doi.org
Editorial: Social Interaction in Neuropsychiatry
Victoria Leong
Danilo Bzdok
Frieder M. Paulus
Kevin Pelphrey
Elizabeth Redcay
Leonhard Schilbach
2021-04-29
Frontiers in Psychiatry (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
Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients
András Maifeld
Hendrik Bartolomaeus
Ulrike Löber
Ellen G. Avery
Nico Steckhan
Lajos Markó
Nicola Wilck
Ibrahim Hamad
Urša Šušnjar
Anja Mähler
Christoph Hohmann
Chia-Yu Chen
Holger Cramer
Gustav Dobos
Till Robin Lesker
Till Strowig
Ralf Dechend
Danilo Bzdok
Markus Kleinewietfeld
Andreas Michalsen … (see 2 more)
Dominik N. Müller
Sofia K. Forslund
2021-03-30
Nature Communications (published)
doi.org
Loneliness and Neurocognitive Aging
R. Nathan Spreng
Danilo Bzdok
2021-03-29
Advances in geriatric medicine and research (published)
doi.org
Mapping gene transcription and neurocognition across human neocortex
Justine Y. Hansen
Ross D Markello
Jacob W. Vogel
Jakob Seidlitz
Danilo Bzdok
Bratislav Mišić
2021-03-25
Nature Human Behaviour (published)
doi.org
Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-03-02
eLife (published)
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
Prediction, Not Association, Paves the Road to Precision Medicine
Danilo Bzdok
Gael Varoquaux
Ewout W. Steyerberg
2021-02-01
JAMA Psychiatry (published)
doi.org
The neural correlates of ongoing conscious thought
Jonathan Smallwood
Adam Turnbull
Hao-Ting Wang
Nerissa S.P. Ho
Giulia L. Poerio
Theodoros Karapanagiotidis
Delali Konu
Brontë Mckeown
Meichao Zhang
Charlotte Murphy
Deniz Vatansever
Danilo Bzdok
Mahiko Konishi
Robert Leech
Paul Seli
Jonathan W. Schooler
Boris C Bernhardt
Daniel S. Margulies
Elizabeth Jefferies
2021-02-01
iScience (published)
doi.org
Author response: Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-01-21
(published)
doi.org