Portrait of Danilo Bzdok

Danilo Bzdok

Core Academic Member
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

Master's Research - McGill University
Postdoctorate - McGill University
PhD - McGill University
PhD - McGill University
PhD - McGill University
PhD - McGill University
PhD - McGill University
Master's Research - McGill University
Independent visiting researcher - McGill University
PhD - McGill University
PhD - McGill University
PhD - McGill University
PhD - McGill University
PhD - McGill University

Publications

Loneliness is linked to specific subregional alterations in hippocampus-default network covariation
Chris Zajner
Nathan Spreng
Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods
Taylor Bolt
Jason S. Nomi
Lucina Q. Uddin
Human brain anatomy reflects separable genetic and environmental components of socioeconomic status
H. Kweon
Gökhan Aydogan
Alain Dagher
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.
Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences
Galen Ballentine
Samuel Freesun Friedman
The default mode network in cognition: a topographical perspective
Jonathan Smallwood
Boris C Bernhardt
Robert Leech
Elizabeth Jefferies
Daniel S. Margulies
Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Catie Chang
B.T. Yeo
Lucina Q. Uddin
Shella Keilholz
A parsimonious description of global functional brain organization in three spatiotemporal patterns
Taylor Bolt
Jason S. Nomi
Jorge A. Salas
Catie Chang
B. T. Thomas Yeo
Lucina Q. Uddin
S. Keilholz
The Cost of Untracked Diversity in Brain-Imaging Prediction
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
Generative lesion pattern decomposition of cognitive impairment after stroke
Anna K. Bonkhoff
Jae‐Sung Lim
Hee-Joon Bae
Nick A. Weaver
Hugo J Kuijf
J. Matthijs Biesbroek
Natalia S Rost
Cognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in … (see more)the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modeling to enable hemisphere-aware analysis of 1080 subacute ischemic stroke patients with deep profiling ∼3 months after stroke. We show relevance of the left hemisphere in the prediction of language and memory assessments, while global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left were particularly informative about lost naming and memory function. Global cognitive impairment was predominantly linked to lesioned tissue in supramarginal and angular gyrus, the postcentral gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischemic brain tissue damage.
Publisher Correction: The default network of the human brain is associated with perceived social isolation
R. Nathan Spreng
Emile Dimas
Laetitia Mwilambwe-Tshilobo
Alain Dagher
Philipp Koellinger
Gideon Nave
Anthony Ong
Julius M Kernbach
Thomas V. Wiecki
Tian Ge
Avram J. Holmes
B.T. Thomas Yeo
Gary R. Turner
Robin I. M. Dunbar
Variability in Brain Structure and Function Reflects Lack of Peer Support
Matthias Schurz
Lucina Q. Uddin
Philipp Kanske
Claus Lamm
Jérôme Sallet
Boris C Bernhardt
Rogier B Mars
How does hemispheric specialization contribute to human-defining cognition?
Gesa Hartwigsen