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

Cognitive health mediates the effect of hippocampal volume on COVID‐19‒related knowledge or anxiety change during the COVID‐19 pandemic
Min Su Kang
Julie Ottoy
Stijn Servaes
Firoza Z Lussier
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Danilo Bzdok
Suzanne King
Serge Gauthier
Pedro Rosa‐Neto
2021-12-01
Alzheimer's & Dementia (published)
doi.org
Tau‐load in the lingual gyrus impacts anxiety levels during the COVID‐19 pandemic in participants of longitudinal observational studies in aging
Stijn Servaes
Firoza Z Lussier
Gleb Bezgin
Yi‐Ting Wang
Jenna Stevenson
Cécile Tissot
Guillaume Elgbeili
Jaime Fernandez Arias
Joseph Therriault
Andréa Lessa Benedet
Mira Chamoun
Tharick A. Pascoal
Danilo Bzdok
Suzanne King
Serge Gauthier
Pedro Rosa‐Neto
2021-12-01
Alzheimer's & Dementia (published)
doi.org
Tau‐PET is associated with knowledge of COVID‐19, COVID‐19‐related distress, and change in sleep quality during the pandemic
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Guillaume Elgbeili
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
While the global COVID‐19 pandemic has hindered many human research operations, it has allowed for the investigation of novel scientific q… (see more)uestions. Particularly, the effects of the pandemic and its resulting social isolation on elderly individuals and their association with Alzheimer’s disease biomarkers remains a broad and open question. Here, we sought to investigate whether knowledge of COVID‐19, pandemic‐related distress, and changes in sleep quality were associated with in vivo tau deposition in an AD‐enriched cohort.
2021-12-01
Alzheimer's & Dementia (published)
doi.org
The meaning of significant mean group differences for biomarker discovery
Eva Loth
Jumana Ahmad
Christopher H. Chatham
Beatriz López
Ben Carter
Daisy Crawley
Beth Oakley
Hannah Hayward
Jennifer Cooke
Antonia San José Cáceres
Danilo Bzdok
Emily J. H. Jones
Tony Charman
Christian Beckmann
Thomas Bourgeron
Roberto Toro
Jan K. Buitelaar
Declan Murphy
Guillaume Dumas
2021-11-18
PLoS Computational Biology (published)
doi.org
A cognitive fingerprint in human random number generation
Marc-Andre Schulz
Sebastian Baier
Benjamin Timmermann
Benjamin Böhme
Danilo Bzdok
Karsten Witt
2021-10-12
Scientific Reports (published)
doi.org
Diagnosing as autistic people increasingly distant from prototypes lead neither to clinical benefit nor to the advancement of knowledge
Laurent Mottron
Danilo Bzdok
2021-10-12
Molecular Psychiatry (published)
doi.org
Population modeling with machine learning can enhance measures of mental health
Kamalaker Dadi
Gael Varoquaux
Josselin Houenou
Danilo Bzdok
Bertrand Thirion
Denis-Alexander Engemann
Background Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. Instead, individual differences in mental fun… (see more)ction are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures when modeling from brain signals or sociodemographic data, capturing multiple health-related constructs. Conclusions Population modeling with machine learning can derive measures of mental health from brain signals and questionnaire data, which may complement or even substitute for psychometric assessments in clinical populations. Key Points We applied machine learning on more than 10.000 individuals from the general population to define empirical approximations of health-related psychological measures that do not require human judgment. We found that machine-learning enriched the given psychological measures via approximation from brain and sociodemographic data: Resulting proxy measures related as well or better to real-world health behavior than the original measures. Model comparisons showed that sociodemographic information contributed most to characterizing psychological traits beyond aging.
2021-09-24
bioRxiv (preprint)
doi.org
Connectivity alterations in autism reflect functional idiosyncrasy
Oualid Benkarim
Casey Paquola
Bo-yong Park
Seok-Jun Hong
Jessica Royer
Reinder Vos de Wael
Sara Lariviere
Sofie Valk
Danilo Bzdok
Laurent Mottron
Boris C Bernhardt
2021-09-15
Communications Biology (published)
doi.org
Social isolation is linked to classical risk factors of Alzheimer’s disease-related dementias
Kimia Shafighi
Sylvia Villeneuve
P. 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.
2021-09-15
bioRxiv (preprint)
doi.org
Decision Models and Technology Can Help Psychiatry Develop Biomarkers
Daniel S. Barron
Justin T. Baker
Kristin S. Budde
Danilo Bzdok
Simon B. Eickhoff
Karl J. Friston
Peter T. Fox
Paul Geha
Stephen Heisig
Avram J. Holmes
Jukka-Pekka Onnela
Albert Powers
David Silbersweig
John H. Krystal
2021-09-09
Frontiers in Psychiatry (published)
doi.org
Social belonging: Brain structure and function is linked to membership in sports teams, religious groups and social clubs
Carolin Kieckhaefer
Leonhard Schilbach
Danilo Bzdok
2021-09-07
bioRxiv (preprint)
doi.org
Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns
Chris Zajner
Nathan Spreng
Danilo Bzdok
Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominatin… (see more)g factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40,000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3, and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC-DN co-variation followed a clear segregation divide into the left and right brain hemispheres. Separable regimes of structural HC-DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.
2021-08-19
bioRxiv (preprint)
doi.org