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

Karan Bali Bali

PhD - McGill University

PhD - McGill University

Anwesha Bhattacharya

PhD - McGill University

Undergraduate - CentraleSupélec

Pedro Carneiro Carneiro

PhD - McGill University

Arthur Communal

Collaborating researcher - École Polytechnique Montréal Paris

PhD - McGill University

Ryan McPhedrain McPhedrain

Postdoctorate - McGill University

Master's Research - McGill University

Sepehr Radmannia

Independent visiting researcher - McGill University

PhD - McGill University

Chloé Savignac

PhD - McGill University

PhD - McGill University

PhD - McGill University

Audric Trarieux

Collaborating researcher - Aix-Marseille Université

PhD - McGill University

PhD - McGill University

Blog Posts

a Blury picture of a brain representation on a blue background

April 7, 2026

Mila’s AI drives world’s largest study on psychedelics

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A tab representation of the Pre trained CLIP Vision Transformer

October 8, 2025

Why AI Models Hallucinate and How to Fix Them

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March 25, 2025

Using LLMs to better understand autism diagnosis

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Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

September 8, 2020

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

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Publications

Social belonging: brain structure and function is linked to membership in sports teams, religious groups, and social clubs

Carolin Kieckhaefer

Leonhard Schilbach

Human behavior across the life span is driven by the psychological need to belong, right from kindergarten to bingo nights. Being part of so… (see more)cial groups constitutes a backbone for communal life and confers many benefits for the physical and mental health. Capitalizing on the neuroimaging and behavioral data from ∼40,000 participants from the UK Biobank population cohort, we used structural and functional analyses to explore how social participation is reflected in the human brain. Across 3 different types of social groups, structural analyses point toward the variance in ventromedial prefrontal cortex, fusiform gyrus, and anterior cingulate cortex as structural substrates tightly linked to social participation. Functional connectivity analyses not only emphasized the importance of default mode and limbic network but also showed differences for sports teams and religious groups as compared to social clubs. Taken together, our findings establish the structural and functional integrity of the default mode network as a neural signature of social belonging.

2021-09-06

bioRxiv (preprint)

Loneliness is linked to specific subregional alterations in hippocampus-default network co-variation

Chris Zajner

R. Nathan Spreng

Social interaction complexity makes humans unique. But in times of social deprivation this strength risks to expose important vulnerabilitie… (see more)s. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically co-vary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By co-decomposition using structural brain scans of ∼40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex signatures coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white-matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN co-variation had distinct associations with the genetic predisposition for loneliness at the population level.

2021-08-18

bioRxiv (preprint)

Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences

Galen Ballentine

Samuel Freesun Friedman

Psychedelics probably alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Indiv… (see more)idual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6850 free-form testimonials about 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to three-dimensional coordinates in the brain via their gene transcription levels from invasive tissue probes. Despite high interindividual variability, our pattern-learning approach delineated how drug-induced changes of conscious awareness are linked to cortex-wide anatomical distributions of receptor density proxies. Each discovered receptor-experience factor spanned between a higher-level association pole and a sensory input pole, which may relate to the previously reported collapse of hierarchical order among large-scale networks. Coanalyzing many psychoactive molecules and thousands of natural language descriptions of drug experiences, our analytical framework finds the underlying semantic structure and maps it directly to the brain.

2021-07-13

bioRxiv (preprint)

The default mode network in cognition: a topographical perspective

Jonathan Smallwood

Boris C Bernhardt

Robert Leech

Elizabeth Jefferies

Daniel S. Margulies

2021-07-04

Nature Reviews Neuroscience (published)

Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods

Taylor Bolt

Jason S. Nomi

Lucina Q. Uddin

Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is incr… (see more)easing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the “Methods” and “Results” sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.

2021-06-30

PLoS Biology (published)

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

2021-06-19

(published)

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 Bernhardt

Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success … (see more)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 stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two 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 to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

2021-06-16

bioRxiv (preprint)

Publisher Correction: The default network of the human brain is associated with perceived social isolation

R. Nathan Spreng

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

2021-05-20

Nature Communications (published)

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

Humans are a highly social species. Complex interactions for mutual support range from helping neighbors to building social welfare institut… (see more)ions. During times of distress or crisis, sharing life experiences within one’s social circle is critical for well-being. By translating pattern-learning algorithms to the UK Biobank imaging-genetics cohort (n = ~40 000 participants), we have delineated manifestations of regular social support in multimodal whole-brain measurements. In structural brain variation, we identified characteristic volumetric signatures in the salience and limbic networks for high- versus low-social support individuals. In patterns derived from functional coupling, we also located interindividual differences in social support in action–perception circuits related to binding sensory cues and initiating behavioral responses. In line with our demographic profiling analysis, the uncovered neural substrates have potential implications for loneliness, substance misuse, and resilience to stress.

2021-05-12

Cerebral Cortex (New York, NY) (published)

How does hemispheric specialization contribute to human-defining cognition?

Gesa Hartwigsen

2021-05-10

Neuron (published)

Phenotypical predictors of pregnancy-related restless legs syndrome and their association with basal ganglia and the limbic circuits

Natalia Chechko

Jeremy Lefort-Besnard

Tamme W. Goecke

Markus Frensch

Patricia Schnakenberg

Susanne Stickel

Restless legs syndrome (RLS) in pregnancy is a common disorder with a multifactorial etiology. A neurological and obstetrical cohort of 308 … (see more)postpartum women was screened for RLS within 1 to 6 days of childbirth and 12 weeks postpartum. Of the 308 young mothers, 57 (prevalence rate 19%) were identified as having been affected by RLS symptoms in the recently completed pregnancy. Structural and functional MRI was obtained from 25 of these 57 participants. A multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors of RLS. A decreased volume of the parietal, orbitofrontal and frontal areas shortly after delivery was found to be linked to persistent RLS symptoms up to 12 weeks postpartum, the symptoms' severity and intensity in the most recent pregnancy, and a history of RLS in previous pregnancies. The same negative relationship was observed between brain volume and not being married, not receiving any iron supplement and higher numbers of stressful life events. High cortisol levels, being married and receiving iron supplements, on the other hand, were found to be associated with increased volumes in the bilateral striatum. Investigating RLS symptoms in pregnancy within a brain-phenotype framework may help shed light on the heterogeneity of the condition.

2021-05-10

Scientific Reports (published)

Editorial: Social Interaction in Neuropsychiatry

Victoria Leong

Frieder M. Paulus

Kevin Pelphrey

Elizabeth Redcay

Leonhard Schilbach

2021-04-28

Frontiers in Psychiatry (published)