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
Data Mining
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
Gregory Bell
PhD - McGill University
Anwesha Bhattacharya
PhD - McGill University
Liam Hodgson
PhD - McGill University
Jakub Kopal
Postdoctorate - Université de Montréal
Google Scholar
Justin Marotta
Master's Research - McGill University
Anny Maza
Collaborating researcher - Universitat Politècnica
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
Zilong Wang
Master's Research - McGill University
Github
Google Scholar
Le Zhou
PhD - McGill University

Publications

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
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Oualid Benkarim
Casey Paquola
Bo-yong Park
Valeria Kebets
Seok-Jun Hong
Reinder Vos de Wael
Shaoshi Zhang
B.T. Thomas Yeo
Michael Eickenberg
Tian Ge
Jean-Baptiste Poline
Boris C Bernhardt
Danilo Bzdok
2022-04-29
PLoS Biology (published)
doi.org
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
L.Q.R. Ooi
Leon Qi Rong Ooi
Christopher L. Asplund
Scott Marek
Nico Dosenbach
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
2022-04-25
Nature Communications (published)
doi.org
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
L.Q.R. Ooi
Christopher L Asplund
Scott A. Marek
N. Dosenbach
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
2022-04-25
Nature Communications (published)
doi.org
Multivariate, Transgenerational Associations of the COVID-19 Pandemic Across Minoritized and Marginalized Communities.
Sarah W. Yip
Ayana Jordan
Robert J. Kohler
Avram J. Holmes
Danilo Bzdok
Importance The experienced consequences of the COVID-19 pandemic have diverged across individuals, families, and communities, resulting in i… (see more)nequity within a host of factors. There is a gap of quantitative evidence about the transgenerational impacts of these experiences and factors. Objective To identify baseline predictors of COVID-19 experiences, as defined by child and parent report, using a multivariate pattern-learning framework from the Adolescent Brain and Cognitive Development (ABCD) cohort. Design, Setting, and Participants ABCD is an ongoing prospective longitudinal study of child and adolescent development in the United States including 11 875 youths, enrolled at age 9 to 10 years. Using nationally collected longitudinal profiling data from 9267 families, a multivariate pattern-learning strategy was developed to identify factor combinations associated with transgenerational costs of the ongoing COVID-19 pandemic. ABCD data (release 3.0) collected from 2016 to 2020 and released between 2019 and 2021 were analyzed in combination with ABCD COVID-19 rapid response data from the first 3 collection points (May-August 2020). Exposures Social distancing and other response measures imposed by COVID-19, including school closures and shutdown of many childhood recreational activities. Main Outcomes and Measures Mid-COVID-19 experiences as defined by the ABCD's parent and child COVID-19 assessments. Results Deep profiles from 9267 youth (5681 female [47.8%]; mean [SD] age, 119.0 [7.5] months) and their caregivers were quantitatively examined. Enabled by a pattern-learning analysis, social determinants of inequity, including family structure, socioeconomic status, and the experience of racism, were found to be primarily associated with transgenerational impacts of COVID-19, above and beyond other candidate predictors such as preexisting medical or psychiatric conditions. Pooling information across more than 17 000 baseline pre-COVID-19 family indicators and more than 280 measures of day-to-day COVID-19 experiences, non-White (ie, families who reported being Asian, Black, Hispanic, other, or a combination of those choices) and/or Spanish-speaking families were found to have decreased resources (mode 1, canonical vector weight [CVW] = 0.19; rank 5 of 281), escalated likelihoods of financial worry (mode 1, CVW = -0.20; rank 4), and food insecurity (mode 1, CVW = 0.21; rank 2), yet were more likely to have parent-child discussions regarding COVID-19-associated health and prevention issues, such as handwashing (mode 1, CVW = 0.14; rank 9), conserving food or other items (mode 1, CVW = 0.21; rank 1), protecting elderly individuals (mode 1, CVW = 0.11; rank 21), and isolating from others (mode 1, CVW = 0.11; rank 23). In contrast, White families (mode 1, CVW = -0.07; rank 3), those with higher pre-COVID-19 income (mode 1, CVW = -0.07; rank 5), and presence of a parent with a postgraduate degree (mode 1, CVW = -0.06; rank 14) experienced reduced COVID-19-associated impact. In turn, children from families experiencing reduced COVID-19 impacts reported longer nighttime sleep durations (mode 1, CVW = 0.13; rank 14), less difficulties with remote learning (mode 2, CVW = 0.14; rank 7), and decreased worry about the impact of COVID-19 on their family's financial stability (mode 1, CVW = 0.134; rank 13). Conclusions and Relevance The findings of this study indicate that community-level, transgenerational intervention strategies may be needed to combat the disproportionate burden of pandemics on minoritized and marginalized racial and ethnic populations.
2022-04-01
JAMA Psychiatry (published)
doi.org
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
O. Benkarim
Casey Paquola
Bo-yong Park
Valeria Kebets
Seokjun Hong
Reinder Vos de Wael
Shaoshi Zhang
B.T. Thomas Yeo
Michael Eickenberg
Tian Ge
Jean-Baptiste Poline
B. Bernhardt
Danilo Bzdok
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the suc… (see more)cess of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
2022-04-01
PLoS Biology (published)
doi.org