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

Anwesha Bhattacharya

Doctorat - McGill

Baccalauréat - 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

Audric Trarieux

Collaborateur·rice de recherche - Aix-Marseille Université

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

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

Andrea I. Luppi

S. Parker Singleton

Justine Y. Hansen

Keith W. Jamison

Amy Kuceyeski

Richard F. Betzel

Bratislav Misic

The mechanisms linking the brain’s network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveragi… (voir plus)ng principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

2024-08-04

Nature Biomedical Engineering (publié)

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Nicole Osayande

Shambhavi Aggarwal

Avram Holmes

Sarah W. Yip

Population-level analyses are inherently complex due to a myriad of latent confounding effects that underlie the interdisciplinary topics of… (voir plus) research interest. Despite the mounting demand for generative population models, the limited generalizability to underrepresented groups hinders their widespread adoption in downstream applications. Interpretability and reliability are essential for clinicians and policymakers, while accuracy and precision are prioritized from an engineering standpoint. Thus, in domains such as population neuroscience, the challenge lies in determining a suitable approach to model population data effectively. Notably, the traditional strata-agnostic nature of existing methods in this field reveals a pertinent gap in quantitative techniques that directly capture major sources of population stratification. The emergence of population-scale cohorts, like the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, provides unparalleled opportunities to explore and characterize neurobehavioral and sociodemographic relationships comprehensively. We propose diversity-aware population modeling, a framework poised to standardize systematic incorporation of diverse attributes, structured with respect to intrinsic population stratification to obtain holistic insights. Here, we leverage Bayesian multilevel regression and poststratification, to elucidate inter-individual differences in the relationships between socioeconomic status (SES) and cognitive development. We constructed 14 varying-intercepts and varying-slopes models to investigate 3 cognitive phenotypes and 5 sociodemographic variables (SDV), across 17 US states and 5 race subgroups. SDVs exhibited systemic socio-spatial effects that served as fundamental drivers of variation in cognitive outcomes. Low SES was disproportionately associated with cognitive development among Black and Hispanic children, while high SES was a robust predictor of cognitive development only among White and Asian children, consistent with the minorities’ diminished returns (MDRs) theory. Notably, adversity-susceptible subgroups demonstrated an expressive association with fluid cognition compared to crystallized cognition. Poststratification proved effective in correcting group attribution biases, particularly in Pennsylvania, highlighting sampling discrepancies in US states with the highest percentage of marginalized participants in the ABCD Study©. Our collective analyses underscore the inextricable link between race and geographic location within the US. We emphasize the importance of diversity-aware population models that consider the intersectional composition of society to derive precise and interpretable insights across applicable domains.

2024-07-29

Research Square (publié)

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.

2024-07-16

Imaging Neuroscience (publié)

Estimating Unknown Population Sizes Using the Hypergeometric Distribution

The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into mult… (voir plus)iple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the sizes of its constituent categories are unknown. Here, we propose a novel solution using the hypergeometric likelihood to solve this estimation challenge, even in the presence of severe under-sampling. We develop our approach to account for a data generating process where the ground-truth is a mixture of distributions conditional on a continuous latent variable, such as with collaborative filtering, using the variational autoencoder framework. Empirical data simulation demonstrates that our method outperforms other likelihood functions used to model count data, both in terms of accuracy of population size estimate and in its ability to learn an informative latent space. We demonstrate our method's versatility through applications in NLP, by inferring and estimating the complexity of latent vocabularies in text excerpts, and in biology, by accurately recovering the true number of gene transcripts from sparse single-cell genomics data.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology

Guillaume Huguet

Shambhavi Aggarwal

Nicole Osayande

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B.T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging genetics field has st… (voir plus)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, environment, human health, daily living skills and outcomes. Hence, we interrogated the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and corresponding implications on cognitive, behavioral, psychosocial, and socioeconomic traits. Specifically, we designed a holistic pattern-learning algorithm that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 962 behavioral variables spanning 20 categories in 7,657 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks, and specific parameters of the family well-being (increased parental and child stress, anxiety and depression) or neighborhood dynamics (decreased safety); effects extending beyond the impairment of cognitive ability or language capacity, dominantly reported in the CNV literature. Our investigation thus spotlights a far-reaching interplay between genetic variation and subjective life quality in adolescents and their families.

2024-05-21

medRxiv (prépublication)

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology

Guillaume Huguet

Shambhavi Aggarwal

Nicole Osayande

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B.T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging genetics field has st… (voir plus)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, environment, human health, daily living skills and outcomes. Hence, we interrogated the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and corresponding implications on cognitive, behavioral, psychosocial, and socioeconomic traits. Specifically, we designed a holistic pattern-learning algorithm that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 962 behavioral variables spanning 20 categories in 7,657 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks, and specific parameters of the family well-being (increased parental and child stress, anxiety and depression) or neighborhood dynamics (decreased safety); effects extending beyond the impairment of cognitive ability or language capacity, dominantly reported in the CNV literature. Our investigation thus spotlights a far-reaching interplay between genetic variation and subjective life quality in adolescents and their families.

2024-05-21

medRxiv : the preprint server for health sciences (publié)

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

Yasser Iturria-Medina

Jo Anne Stratton

Smita Krishnaswamy

David A. Bennett

Late onset Alzheimer’s disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface… (voir plus). AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain’s major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject’s progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci. Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.

2024-05-16

Communications Biology (publié)

Longitudinal microstructural changes in 18 amygdala nuclei resonate with cortical circuits and phenomics

Aparna Suvrathan

Bogdan Draganski

The amygdala nuclei modulate distributed neural circuits that most likely evolved to respond to environmental threats and opportunities. So … (voir plus)far, the specific role of unique amygdala nuclei in the context processing of salient environmental cues lacks adequate characterization across neural systems and over time. Here, we present amygdala nuclei morphometry and behavioral findings from longitudinal population data (>1400 subjects, age range 40-69 years, sampled 2-3 years apart): the UK Biobank offers exceptionally rich phenotyping along with brain morphology scans. This allows us to quantify how 18 microanatomical amygdala subregions undergo plastic changes in tandem with coupled neural systems and delineating their associated phenome-wide profiles. In the context of population change, the basal, lateral, accessory basal, and paralaminar nuclei change in lockstep with the prefrontal cortex, a region that subserves planning and decision-making. The central, medial and cortical nuclei are structurally coupled with the insular and anterior-cingulate nodes of the salience network, in addition to the MT/V5, basal ganglia, and putamen, areas proposed to represent internal bodily states and mediate attention to environmental cues. The central nucleus and anterior amygdaloid area are longitudinally tied with the inferior parietal lobule, known for a role in bodily awareness and social attention. These population-level amygdala-brain plasticity regimes in turn are linked with unique collections of phenotypes, ranging from social status and employment to sleep habits and risk taking. The obtained structural plasticity findings motivate hypotheses about the specific functions of distinct amygdala nuclei in humans.

2024-04-17

Communications Biology (publié)

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)

Data science opportunities of large language models for neuroscience and biomedicine

Andrew Thieme

Oleksiy Levkovskyy

Paul Wren

Thomas Ray

2024-02-29

Neuron (publié)

Intrinsic structural covariation links cerebellum subregions to the cerebral cortex

Jörn Diedrichsen

Christopher Steele

Sheeba Rani Arnold-Anteraper

B.T. Thomas Yeo

Jeremy Schmahmann

The human cerebellum is increasingly recognized to be involved in non-motor and higher-order cognitive functions. Yet, its ties with the ent… (voir plus)ire cerebral cortex have not been holistically studied in a whole-brain exploration with a unified analytical framework. Here, we characterized disso-ciable cortical-cerebellar structural covariation patterns across the brain in n=38,527 UK Bio-bank participants. Our results invigorate previous observations in that important shares of corti-cal-cerebellar structural covariation are described as i) a dissociation between the higher-level cognitive system and lower-level sensorimotor system, as well as ii) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neurosci-ence view paves the way for future studies to explore the implications of these structural covaria-tions, potentially illuminating new pathways in our understanding of neurological and cognitive disorders.

2024-02-19

BioRxiv (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)