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

Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion‐symptom mapping

Nick A. Weaver

Muhammad Hasnain Mamdani

Jae‐Sung Lim

Johannes Matthijs Biesbroek

Geert Jan Biessels

Irene M. C. Huenges Wajer

Yeonwook Kang

Beom Joon Kim

Byung‐Chul Lee

Keon‐Joo Lee

Kyung‐Ho Yu

Hee‐Joon Bae

Hugo J. Kuijf

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion‐symptom mapping (LSM) … (see more)techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof‐of‐concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single‐outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well‐defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single‐outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single‐outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single‐outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.

2023-01-19

Human Brain Mapping (published)

Functional architecture of the aging brain

Roni Setton

Laetitia Mwilambwe-Tshilobo

Manesh Girn

Amber W. Lockrow

Giulia Baracchini

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

R. Nathan Spreng

The intrinsic functional connectome can reveal how a lifetime of learning and lived experience is represented in the functional architecture… (see more) of the aging brain. We investigated whether network dedifferentiation, a hallmark of brain aging, reflects a global shift in network dynamics, or comprises network-specific changes that reflect the changing landscape of aging cognition. We implemented a novel multi-faceted strategy involving multi-echo fMRI acquisition and de-noising, individualized cortical parcellation, and multivariate (gradient and edge-level) functional connectivity methods. Twenty minutes of resting-state fMRI data and cognitive assessments were collected in younger (n=181) and older (n=120) adults. Dimensionality in the BOLD signal was lower for older adults, consistent with global network dedifferentiation. Functional connectivity gradients were largely age-invariant. In contrast, edge-level connectivity showed widespread changes with age, revealing discrete, network-specific dedifferentiation patterns. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal regions showed greater coupling; and the dorsal attention network was less differentiated from transmodal regions. Associations with cognition suggest that the formation and preservation of integrated, large-scale brain networks supports complex cognitive abilities. However, into older adulthood, the connectome is dominated by large-scale network disintegration, global dedifferentiation and network-specific dedifferentiation associated with age-related cognitive change.

2022-12-31

(published)

www.semanticscholar.org

Home alone: A population neuroscience investigation of brain morphology substrates

MaryAnn Noonan

Chris Zajner

As a social species, ready exchange with peers is a pivotal asset - our “social capital”. Yet, single-person households have come to per… (see more)vade metropolitan cities worldwide, with unknown consequences in the long run. Here, we systematically explore the morphological manifestations associated with singular living in ∼40,000 UK Biobank participants. The uncovered population-level signature spotlights the highly associative default mode network, in addition to findings such as in the amygdala central, cortical and corticoamygdaloid nuclei groups, as well as the hippocampal fimbria and dentate gyrus. Sex-stratified analyses revealed male-specific neural substrates, including somatomotor, saliency and visual systems, while female-specific neural substrates centred on the dorsomedial prefrontal cortex. In line with our demographic profiling results, the discovered neural imprint of living alone is potentially linked to alcohol and tobacco consumption, anxiety, sleep quality as well as daily TV watching. The secular trend for solitary living will require new answers from public-health decision makers.

2022-12-31

NeuroImage (published)

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Xiaoxuan Yan

Ru Kong

Aihuiping Xue

Qing Yang

Csaba Orbán

Lijun An

Avram J. Holmes

Xing Qian

Jianzhong Chen

Xi-Nian Zuo

Juan Helen Zhou

Marielle V Fortier

Ai Peng Tan

Peter Gluckman

Yap Seng Chong

Michael J Meaney

Simon B. Eickhoff

B.T. Thomas Yeo

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human… (see more) neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

2022-12-31

NeuroImage (published)

Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study

Jianzhong Chen

Leon Qi Rong Ooi

Trevor Wei Kiat Tan

Shaoshi Zhang

Jingwei Li

Christopher L. Asplund

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation… (see more) of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

2022-12-31

NeuroImage (published)

Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival

Julius M. Kernbach

Daniel Delev

Georg Neuloh

Hans Clusmann

Simon B. Eickhoff

Victor E. Staartjes

Flavio Vasella

Michael Weller

Luca Regli

Carlo Serra

Niklaus Krayenbühl

Kevin Akeret

The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study … (see more)was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology’s unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical–clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.

2022-12-19

Brain Communications (published)

APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia

Chloé Savignac

Sylvia Villeneuve

AmanPreet Badhwar

Kimia Shafighi

Chris Zajner

Vaibhav Sharma

Sarah A. Gagliano Taliun

Sali Farhan

Judes Poirier

Alzheimer’s disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in th… (see more)e default network (DN). Here, we examined codependent structural variations between the MTL’s most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer’s disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer’s disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix’s fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.

2022-11-30

PLoS Biology (published)

Age differences in functional brain networks associated with loneliness and empathy

Laetitia Mwilambwe-Tshilobo

Roni Setton

Gary R. Turner

R. Nathan Spreng

Network Neuroscience

Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- … (see more)and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality—loneliness and empathic responding—and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.

2022-11-22

Network Neuroscience (published)

Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles

Ralph Adolphs

Lynn K. Paul

Vaibhav Sharma

Joern Diedrichsen

B. T. Thomas Yeo

2022-11-06

Nature Human Behaviour (unknown)

Social isolation and the brain in the pandemic era

Robin I. M. Dunbar

2022-10-17

Nature Human Behaviour (published)

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity

Benjamin Clemens

Jeremy Lefort-Besnard

Christoph Ritter

Elke Smith

Mikhail Votinov

Birgit Derntl

Ute Habel

Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors. Here, we… (see more) bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. In both brain assessments, we used penalized logistic regression models and nonparametric permutation. We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.

2022-09-13

Cerebral Cortex (unknown)

Endorsing Complexity Through Diversity: Computational Psychiatry Meets Big Data Analytics

2022-08-04

Biological Psychiatry (unknown)