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

Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence
Jakub Kopal
Kuldeep Kumar
Karin Saltoun
Claudia Modenato
Clara A. Moreau
Sandra Martin-Brevet
Guillaume Huguet
Martineau Jean-Louis
Charles-Olivier Martin
C.O. Martin
Zohra Saci
Nadine Younis
Petra Tamer
Elise Douard
Anne M. Maillard
Borja Rodriguez-Herreros
Aurélie Pain
Sonia Richetin
Leila Kushan
Ana I. Silva … (see 13 more)
Marianne B.M. van den Bree
David E.J. Linden
M. J. Owen
Jeremy Hall
Sarah Lippé
Bogdan Draganski
Ida E. Sønderby
Ole A. Andreassen
David C. Glahn
Paul M. Thompson
Carrie E. Bearden
Sébastien Jacquemont
Danilo Bzdok
2023-03-02
Nature human behaviour (published)
doi.org
Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity
Xiaoxuan Yan
Ru Kong
Aihuiping Xue
Qing Yang
Csaba Orban
Lijun An
Avram J. Holmes
Xing Qian
Jianzhong Chen
Xi-Nian Zuo
Juan Helen Zhou
Marielle V Fortier
Ai Peng Tan
Peter Gluckman
Yap Seng Chong
Michael J Meaney
Danilo Bzdok
Simon B. Eickhoff
B.T. Thomas Yeo
2023-02-25
NeuroImage (published)
doi.org
Social isolation is linked to classical risk factors of Alzheimer’s disease-related dementias
Kimia Shafighi
Sylvia Villeneuve
Pedro Rosa‐Neto
AmanPreet Badhwar
Judes Poirier
Vaibhav Sharma
Yasser Iturria-Medina
Patricia P. Silveira
Laurette Dubé
David C. Glahn
Danilo Bzdok
Alzheimer’s disease and related dementias is a major public health burden – compounding over upcoming years due to longevity. Recently, … (see more)clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals’ social capital and various indicators of Alzheimer’s disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them promising targets for preventive clinical action.
2023-02-01
PLOS ONE (published)
doi.org
Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion‐symptom mapping
Nick A. Weaver
Muhammad Hasnain Mamdani
Jae‐Sung Lim
J. Matthijs Biesbroek
Geert Jan Biessels
Irene M. C. Huenges Wajer
Yeonwook Kang
Beom Joon Kim
Byung‐Chul Lee
Keon‐Joo Lee
Kyung‐Ho Yu
Hee-Joon Bae
Danilo Bzdok
Hugo J. Kuijf
2023-01-20
Human Brain Mapping (published)
doi.org
Functional architecture of the aging brain
Roni Setton
Laetitia Mwilambwe-Tshilobo
Manesh Girn
Amber W. Lockrow
Giulia Baracchini
Alexander J. Lowe
Benjamin N. Cassidy
Jian Li
Wen-Ming Luh
Danilo Bzdok
Richard M. Leahy
Tian Ge
Daniel S. Margulies
Bratislav Mišić
Boris C Bernhardt
W. Dale Stevens
Felipe De Brigard
Prantik Kundu
Gary R. Turner
R. Nathan Spreng
The intrinsic functional connectome can reveal how a lifetime of learning and lived experience is represented in the functional architecture… (see more) of the aging brain. We investigated whether network dedifferentiation, a hallmark of brain aging, reflects a global shift in network dynamics, or comprises network-specific changes that reflect the changing landscape of aging cognition. We implemented a novel multi-faceted strategy involving multi-echo fMRI acquisition and de-noising, individualized cortical parcellation, and multivariate (gradient and edge-level) functional connectivity methods. Twenty minutes of resting-state fMRI data and cognitive assessments were collected in younger (n=181) and older (n=120) adults. Dimensionality in the BOLD signal was lower for older adults, consistent with global network dedifferentiation. Functional connectivity gradients were largely age-invariant. In contrast, edge-level connectivity showed widespread changes with age, revealing discrete, network-specific dedifferentiation patterns. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal regions showed greater coupling; and the dorsal attention network was less differentiated from transmodal regions. Associations with cognition suggest that the formation and preservation of integrated, large-scale brain networks supports complex cognitive abilities. However, into older adulthood, the connectome is dominated by large-scale network disintegration, global dedifferentiation and network-specific dedifferentiation associated with age-related cognitive change.
Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival
Julius M Kernbach
Daniel Delev
Georg Neuloh
Hans Clusmann
Danilo Bzdok
Simon B. Eickhoff
Victor E Staartjes
Flavio Vasella
Michael Weller
Luca Regli
Carlo Serra
Niklaus Krayenbühl
Kevin Akeret
2022-12-22
Brain Communications (published)
doi.org
APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer’s disease and related dementia
Chloé Savignac
Sylvia Villeneuve
AmanPreet Badhwar
Karin Saltoun
Kimia Shafighi
Chris Zajner
Vaibhav Sharma
Sarah A. Gagliano Taliun
Sali Farhan
Judes Poirier
Danilo Bzdok
2022-12-01
PLoS Biology (published)
doi.org
Age differences in functional brain networks associated with loneliness and empathy
Laetitia Mwilambwe-Tshilobo
Roni Setton
Danilo Bzdok
Gary R. Turner
R. Nathan Spreng
Abstract Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks i… (see more)n early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality—loneliness and empathic responding—and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.
2022-11-23
Network Neuroscience (published)
doi.org
Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles
Karin Saltoun
Ralph Adolphs
Lynn Paul
Vaibhav Sharma
Joern Diedrichsen
B.T. Thomas Yeo
Danilo Bzdok
2022-11-07
Nature Human Behaviour (published)
doi.org
Social isolation and the brain in the pandemic era
Danilo Bzdok
Robin I. M. Dunbar
2022-10-01
Nature Human Behaviour (published)
doi.org
Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity.
Benjamin Clemens
Jeremy Lefort-Besnard
Christoph Ritter
Elke Smith
Mikhail Votinov
Birgit Derntl
Ute Habel
Danilo Bzdok
BACKGROUND Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors… (see more). OBJECTIVE Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. METHODS  In both brain assessments, we used penalized logistic regression models and nonparametric permutation. RESULTS  We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. CONCLUSIONS  These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.
2022-09-14
Cerebral Cortex (published)
doi.org
From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning
Nahiyan Malik
Danilo Bzdok
2022-09-01
Neural Networks (published)
doi.org