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

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.
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
Endorsing Complexity Through Diversity: Computational Psychiatry Meets Big Data Analytics
Jakub Kopal
Danilo Bzdok
2022-08-01
Biological Psychiatry (published)
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
Shella Keilholz
2022-07-28
Nature Neuroscience (published)
doi.org
Global fMRI signal topography differs systematically across the lifespan
Jason S. Nomi
Danilo Bzdok
Jingwei Li
Taylor Bolt
Catie Chang
Salome Kornfeld
Zachary T. Goodman
B.T. Thomas Yeo
R. Nathan Spreng
Lucina Q. Uddin
2022-07-27
bioRxiv (preprint)
doi.org
From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation
Laurette Dubé
Patricia P. Silveira
Daiva E. Nielsen
Spencer Moore
Catherine Paquet
J. Miguel Cisneros-Franco
Gina Kemp
Bärbel Knauper
Yu Ma
Mehmood Khan
Gillian Bartlett-Esquilant
Alan C. Evans
Lesley K. Fellows
Jorge L. Armony
R. Nathan Spreng
Jian-Yun Nie
Shawn T. Brown
Georg Northoff
Danilo Bzdok
Citation: Dubé L, Silveira PP, Nielsen DE, Moore S, Paquet C, Cisneros-Franco JM, Kemp G, Knauper B, Ma Y, Khan M, Bartlett-Esquilant G, Ev… (see more)ans AC, Fellows LK, Armony JL, Spreng RN, Nie J-Y, Brown ST, Northoff G and Bzdok D (2022) From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation. Front. Public Health 10:720117. doi: 10.3389/fpubh.2022.720117 From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation
2022-07-05
Frontiers in Public Health (published)
doi.org
Interacting brains revisited: A cross‐brain network neuroscience perspective
Christian Gerloff
Kerstin Konrad
Danilo Bzdok
Christina Büsing
Vanessa Reindl
2022-06-06
Human Brain Mapping (published)
doi.org
Human brain anatomy reflects separable genetic and environmental components of socioeconomic status
Hyeokmoon Kweon
Gökhan Aydogan
Alain Dagher
Danilo Bzdok
Christian 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… (see more)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.
2022-05-18
Science Advances (published)
doi.org
Multi-tract multi-symptom relationships in pediatric concussion
Guido I Guberman
Sonja Stojanovski
Eman Nishat
Alain Ptito
Danilo Bzdok
Anne L 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.
2022-05-17
eLife (published)
doi.org