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

Yanis Bencheikh

Master's Research - HEC Montréal

Co-supervisor :

Anwesha Bhattacharya

PhD - McGill University

Collaborating researcher - CentraleSupélec

Pedro Carneiro Carneiro

PhD - McGill University

Arthur Communal

Collaborating researcher - École Polytechnique Montréal

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

PhD - McGill University

PhD - McGill University

Blog Posts

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

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

Jianzhong Chen

Leon Qi Rong Ooi

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

2022-09-30

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)

A Parsimonious Description of Global Functional Brain Organization in Three Spatiotemporal Patterns

Taylor Bolt

Jason S. Nomi

Jorge A. Salas

Catie Chang

B.T. Thomas Yeo

Lucina Q. Uddin

Shella D. Keilholz

Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-… (see more)scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.

2022-07-27

Nature Neuroscience (published)

Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets

Mariam Zabihi

Seyed Mostafa Kia

Thomas Wolfers

Stijn de Boer

Charlotte Fraza

Sourena Soheili-Nezhad

Richard Dinga

Alberto Llera Arenas

Christian F. Beckmann

Andre Marquand

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral ma… (see more)pping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

2022-07-26

bioRxiv (preprint)

Global fMRI signal topography differs systematically across the lifespan

Jason S. Nomi

Jingwei Li

Taylor Bolt

Catie Chang

Salome Kornfeld

Zachary T. Goodman

B.T. Thomas Yeo

R. Nathan Spreng

Lucina Q. Uddin

The global signal (GS) in resting-state fMRI, known to contain artifacts and non-neuronal physiological signals, also contains important neu… (see more)ral information related to individual state and trait characteristics. Here we show distinct linear and curvilinear lifespan patterns of GS topography in a cross-sectional lifespan sample, demonstrating its importance for consideration in studies of development and aging. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across the lifespan. The thalamus has stronger coupling in older-age individuals compared with younger-aged individuals, while the putamen has stronger coupling in younger individuals compared with older individuals. The subcortical nucleus basalis shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where coupling with the GS is stronger at early and old age, with weaker coupling in middle age. This differentiation in coupling strength between subcortical and cortical brain activity across the lifespan supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on the lifespan stage of individuals. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.

2022-07-26

bioRxiv (preprint)

From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation

Laurette Dubé

Patricia P. Silveira

Daiva E. Nielsen

Spencer Moore

Catherine Paquet

J. Miguel Cisneros-Franco

Gina Kemp

Bärbel Knauper

Yu Ma

Mehmood Khan

Gillian Bartlett-Esquilant

Alan C. Evans

Lesley K. Fellows

Jorge L. Armony

R. Nathan Spreng

Jian-Yun Nie

Shawn T. Brown

Georg Northoff

2022-07-04

Frontiers in Public Health (published)