Portrait de Danilo Bzdok

Danilo Bzdok

Membre académique principal

bzdokdan@mila.quebec

Chaire en IA Canada-CIFAR

Professeur agrégé, McGill University, Département de génie biomédicale

Sujets de recherche

Apprentissage profond

Biologie computationnelle

Grands modèles de langage (LLM)

Traitement du langage naturel

Biographie

Danilo Bzdok est informaticien et médecin de formation. Il possède une double formation unique en neurosciences systémiques et en algorithmes d'apprentissage automatique. Après une formation à l'Université d'Aix-la-Chapelle (RWTH) (Allemagne), à l'Université de Lausanne (Suisse) et à la Harvard Medical School (États-Unis), il a obtenu un doctorat en neurosciences du Centre de recherche de Jülich (Allemagne) et un doctorat en informatique dans le domaine des statistiques d'apprentissage automatique à l'INRIA Saclay et à NeuroSpin (Paris, France). Il est actuellement professeur agrégé à la Faculté de médecine de l'Université McGill et titulaire d’une chaire en IA Canada-CIFAR à Mila – Institut québécois d'intelligence artificielle. Son activité de recherche interdisciplinaire est centrée sur la réduction des lacunes dans la connaissance des bases cérébrales des types de pensée qui définissent l'être humain, afin de découvrir les principes clés de conception computationnelle qui sous-tendent l'intelligence humaine.

Étudiants actuels

Karan Bali Bali

Doctorat - McGill

Doctorat - McGill

Yanis Bencheikh

Maîtrise recherche - HEC

Co-superviseur⋅e :

Anwesha Bhattacharya

Doctorat - McGill

Collaborateur·rice de recherche - CentraleSupélec

Pedro Carneiro Carneiro

Doctorat - McGill

Arthur Communal

Collaborateur·rice de recherche - École Polytechnique Paris

Doctorat - McGill

Ryan McPhedrain McPhedrain

Postdoctorat - McGill

Maîtrise recherche - McGill

Sepehr Radmannia

Visiteur de recherche indépendant - McGill

Doctorat - McGill

Chloé Savignac

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Billets de blogue

a Blury picture of a brain representation on a blue background

7 avril 2026

L'IA de Mila propulse la plus grande étude au monde sur les psychédéliques

par

A tab representation of the Pre trained CLIP Vision Transformer

8 octobre 2025

Pourquoi les modèles d'IA hallucinent et comment y remédier

par

25 mars 2025

Utiliser les LLMs pour mieux comprendre le diagnostic de l'autisme

par

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

8 octobre 2020

Vers une médecine personnalisée : comprendre la divergence entre l’inférence et la prédiction en biomédecine

par

Publications

On the Neurobiological Basis of Chronotype: Insights from a Multimodal Population Neuroscience Study

Julie Carrier

Kai-Florian Storch

Robin Dunbar

Abstract

The rapid shifts of society have brought about changes in human behavioral patterns, with increased eveni… (voir plus)ng activities, increased screen time, and postponed sleep schedules. As an explicit manifestation of circadian rhythms, chronotype is closely intertwined with both physical and mental health. Night owls often exhibit more unhealthy lifestyle habits, are more susceptible to mood disorders, and have poorer physical fitness. Although individual differences in chronotype yield varying consequences, their neurobiological underpinnings remain elusive. Here we carry out a pattern-learning analysis, and capitalize on a vast array of ~ 1,000 phenome-wide phenotypes with three brain-imaging modalities (region volume of gray matter, whiter-matter fiber tracts, and functional connectivity) in 27,030 UK Biobank participants. The resulting multi-level depicts of brain images converge on the basal ganglia, limbic system, hippocampus, as well as cerebellum vermis, thus implicating key nodes in habit formation, emotional regulation and reward processing. Complementary by comprehensive investigations of in-deep phenotypic collections, our population study offers evidence of behavioral pattern disparities linked to distinct chronotype-related behavioral tendencies in our societies.

2024-04-09

Research Square (prépublication)

Structural covariation between cerebellum and neocortex intrinsic structural covariation links cerebellum subregions to the cerebral cortex

Jörn Diedrichsen

Christopher Steele

Sheeba Rani Arnold-Anteraper

B. T. Thomas Yeo

Jeremy D. Schmahmann

Cerebellum’s association with the entire cerebral cortex has not been holistically studied in a unified way. Here, we conjointly character… (voir plus)ize the population-level cortical-cerebellar structural covariation patterns leveraging ∼40,000 UK Biobank participants whole brain structural scans and ∼1,000 phenotypes. We revitalize the previous hypothesis of an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel ipsilateral cerebral-cerebellar associations. Phenome-wide association (PheWAS) revealed real-world implications of the structural covariation patterns.

2024-02-19

bioRxiv (prépublication)

Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data

Pansheng Chen

Lijun An

Naren Wulan

Chen Zhang

Shaoshi Zhang

Leon Qi Rong Ooi

Ru Kong

Jianzhong Chen

Jianxiao Wu

Sidhant Chopra

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantl… (voir plus)y improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a “meta-matching” approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated a large improvement over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants (“meta-matching with dataset stacking” and “multilayer meta-matching”) to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original “meta-matching with stacking” approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available athttps://github.com/ThomasYeoLab/Meta_matching_models/tree/main/rs-fMRI/v2.0.

2023-12-31

Imaging Neuroscience (publié)

Performance reserves in brain-imaging-based phenotype prediction

Marc-Andre Schulz

Stefan Haufe

John-Dylan Haynes

Kerstin Ritter

This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. … (voir plus)Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.

2023-12-31

Cell Reports (publié)

Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients

Justin Böhmer

Pablo Reinhardt

Maria Garbusow

Michael Marxen

Michael N. Smolka

U. Zimmermann

Andreas Heinz

Eva Friedel

Johann Kruschwitz

Henrik Walter

Alcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidatin… (voir plus)g neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long-lasting abstinence. In the present study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified AD patients (n = 93) who were followed-up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age and gender matched controls (CON, n = 83). Our results suggest increased whole-brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared to CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared to CON, encompassing regions associated with higher-order executive functions, sensory and reward processing. Among AD patients, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow-up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.

2023-10-01

Addiction Biology (publié)

Bayesian modelling disentangles language versus executive control disruption in stroke

Gesa Hartwigsen

Jae-Sung Lim

Hee-Joon Bae

Kyung-Ho Yu

Hugo J. Kuijf

Nick A. Weaver

J. Matthijs Biesbroek

Stroke is the leading cause of long-term disability worldwide. Incurred brain damage disrupts cognition, often with persisting deficits in l… (voir plus)anguage and executive capacities. Despite their clinical relevance, the commonalities, and differences of language versus executive control impairments remain under-specified. We tailored a Bayesian hierarchical modeling solution in a largest-of-its-kind cohort (1080 stroke patients) to deconvolve language and executive control in the brain substrates of stroke insults. Four cognitive factors distinguished left- and right-hemispheric contributions to ischemic tissue lesion. One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized control and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two factors: executive speech functions and verbal memory. Impairments on both were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.

2023-08-06

bioRxiv (prépublication)

Multivariate analytical approaches for investigating brain-behavior relationships

E. Leighton Durham

Andrew J. Stier

Carlos Cardenas-Iniguez

Gabrielle E. Reimann

Hee Jung Jeong

Randolph M. Dupont

Xiaoyu Dong

Tyler M. Moore

Marc G. Berman

Benjamin B. Lahey

Antonia N. Kaczkurkin

2023-07-30

Frontiers in Neuroscience (publié)

The default network dominates neural responses to evolving movie stories

Filip Milisav

Avram J. Holmes

Georgios D. Mitsis

Bratislav Misic

Emily S. Finn

Neuroscientific studies exploring real-world dynamic perception often overlook the influence of continuous changes in narrative content. In … (voir plus)our research, we utilize machine learning tools for natural language processing to examine the relationship between movie narratives and neural responses. By analyzing over 50,000 brain images of participants watching Forrest Gump from the studyforrest dataset, we find distinct brain states that capture unique semantic aspects of the unfolding story. The default network, associated with semantic information integration, is the most engaged during movie watching. Furthermore, we identify two mechanisms that underlie how the default network liaises with the amygdala and hippocampus. Our findings demonstrate effective approaches to understanding neural processes in everyday situations and their relation to conscious awareness.

2023-07-13

Nature Communications (publié)

Distinctive Whole-brain Cell-Types Predict Tissue Damage Patterns in Thirteen Neurodegenerative Conditions

Veronika Pak

Quadri Adewale

Mahsa Dadar

Yashar Zeighami

Yasser Iturria-Medina

Abstract For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most whole-brain neu… (voir plus)rodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell-types’ contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell-types extensively predicts tissue damage in eleven neurodegenerative disorders, including early- and late-onset Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, frontotemporal dementia, and tauopathies. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell-types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, on spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorders pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.

2023-06-08

bioRxiv (prépublication)

156. Modeling Eye Gaze to Videos Using Dynamic Trajectory Variability Analysis

Qianying Wu

Na Yeon Kim

Jasmin Turner

Umit Keles

Lynn Paul

Ralph Adolphs

2023-04-30

Biological Psychiatry (publié)

Using rare genetic mutations to revisit structural brain asymmetry

Kuldeep Kumar

Kimia Shafighi

Claudia Modenato

Clara A. Moreau

Guillaume Huguet

Martineau Jean-Louis

Charles-Olivier Martin

Charles-Olivier Martin

Zohra Saci

Nadine Younis

Elise Douard

Khadije Jizi

Alexis Beauchamp-Chatel

Leila Kushan

Ana I. Silva

Marianne B. M. van den Bree

David E. J. Linden

Michael J. Owen … (voir 11 de plus)

Jeremy Hall

Sarah Lippé

Bogdan Draganski

Ida E. Sønderby

Ole A. Andreassen

David C. Glahn

Paul M. Thompson

Carrie E. Bearden

Robert Zatorre

Sébastien Jacquemont

Asymmetry between the left and right brain is a key feature of brain organization. Hemispheric functional specialization underlies some of t… (voir plus)he most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variant studies, which typically exert small effects on brain phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We quantitatively dissected the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior mapping highlights the consequences of genetically controlled brain lateralization on human-defining cognitive traits.

2023-04-17

bioRxiv (prépublication)

Genesis, modelling and methodological remedies to autism heterogeneity

Juliette Rabot

Eya‐mist Rødgaard

Ridha Joober

Guillaume Dumas

Boris C Bernhardt

Sébastien Jacquemont

Laurent Mottron

2023-03-31

Neuroscience and Biobehavioral Reviews (publié)