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

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 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

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… (voir plus)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 (publié)

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

(publié)

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 … (voir plus)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 (prépublication)

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 (publié)

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… (voir plus)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) (publié)

How does hemispheric specialization contribute to human-defining cognition?

Gesa Hartwigsen

2021-05-10

Neuron (publié)

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 … (voir plus)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 (publié)

Editorial: Social Interaction in Neuropsychiatry

Victoria Leong

Frieder M. Paulus

Kevin Pelphrey

Elizabeth Redcay

Leonhard Schilbach

2021-04-28

Frontiers in Psychiatry (publié)

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 … (voir plus)the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modelling to enable a hemisphere-aware analysis of 1080 acute ischaemic stroke patients with deep profiling ∼3 months after stroke. We show the relevance of the left hemisphere in the prediction of language and memory assessments and relevance of the right hemisphere in the prediction of visuospatial functioning. Global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left was particularly informative about lost naming and memory functions, while damage to these regions on the right was linked to lost visuospatial functioning. Global cognitive impairment was predominantly linked to lesioned tissue in the supramarginal and angular gyrus, the post-central 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 ischaemic brain tissue damage.

2021-03-31

Brain Communications (publié)

Age differences in the functional architecture of the human brain

Roni Setton

Laetitia Mwilambwe-Tshilobo

Manesh Girn

Amber W. Lockrow

Giulia Baracchini

Colleen Hughes

Alexander J. Lowe

Benjamin N. Cassidy

Jian Li

Wen-Ming Luh

Richard M. Leahy

Tian Ge

Daniel S. Margulies

Bratislav Misic

Boris C. Bernhardt

W. Dale Stevens

Felipe De Brigard

Prantik Kundu

Gary R. Turner … (voir 2 de plus)

R. Nathan Spreng

Montreal Neurological

The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, fr… (voir plus)om global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience dependent changes, or both, is uncertain. We employed a multi-method strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n=181) and older (n=120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant, while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multi-scale, multi-method approaches to characterize the architecture of functional brain aging.

2021-03-30

bioRxiv (prépublication)

Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients

András Maifeld

Hendrik Bartolomaeus

Ulrike Löber

Ellen G. Avery

Nico Steckhan

Lajos Markó

Nicola Wilck

Ibrahim Hamad

Urša Šušnjar

Anja Mähler

Christoph Hohmann

Chia-Yu Chen

Holger Cramer

Gustav Dobos

Till Robin Lesker

Till Strowig

Ralf Dechend

Markus Kleinewietfeld

Andreas Michalsen … (voir 2 de plus)

Dominik N. Müller

Sofia K. Forslund

Abstract Periods of fasting and refeeding may reduce cardiometabolic risk elevated by Western diet. We show that in hypertensive metabolic s… (voir plus)yndrome (MetS) patients (n=35), a 5-day fast followed by a modified DASH diet (Dietary Approach to Stop Hypertension) reduced systolic blood pressure (SBP), antihypertensive medication need, and body-mass index (BMI) at three months post intervention compared to a modified DASH diet alone (n=36). Fasting altered the gut microbiome, impacting bacterial taxa and gene modules associated with short-chain fatty acid production. Cross-system analyses revealed a positive correlation of circulating mucosa-associated invariant T (MAIT) cells, non-classical monocytes and CD4+ effector T cells with SBP. Furthermore, regulatory T cells (Tregs) positively correlated with BMI and weight. Machine learning could predict sustained SBP-responsiveness within the fasting group from baseline immunome data, identifying CD8+ effector T cells, Th17 cells and Tregs as important contributors to the model. The high-resolution multi-omics data highlights fasting as a promising non-pharmacological intervention in MetS.

2021-03-29

Nature Communications (publié)

Loneliness and Neurocognitive Aging

R. Nathan Spreng

2021-03-28

Advances in geriatric medicine and research (publié)