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

Prediction, Not Association, Paves the Road to Precision Medicine

Ewout W. Steyerberg

2020-08-11

JAMA Psychiatry (published)

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data

Tong He

Lijun An

Jiashi Feng

Avram J Holmes

Simon B. Eickhoff

B.T. Thomas Yeo

There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most … (see more)prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework – “meta-matching” – to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.

2020-08-10

bioRxiv (preprint)

Hidden population modes in social brain morphology: Its parts are more than its sum

Hannah Kiesow

R. Nathan Spreng

Avram J. Holmes

M. Mallar Chakravarty

Andre F. Marquand

B.T. Thomas Yeo

The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map … (see more)perspective taking’, ‘empathy’, and other canonical psychological constructs to distinguishable brain circuits. This predominant research paradigm was seldom complemented by bottom-up studies of the unknown sources of variation that add up to measures of social brain structure; perhaps due to a lack of large population datasets. We aimed at a systematic de-construction of social brain morphology into its elementary building blocks in the UK Biobank cohort (n=~10,000). Coherent patterns of structural co-variation were explored within a recent atlas of social brain locations, enabled through translating autoencoder algorithms from deep learning. The artificial neural networks learned rich subnetwork representations that became apparent from social brain variation at population scale. The learned subnetworks carried essential information about the co-dependence configurations between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as feelings of loneliness. Our population-level evidence indicates that hidden subsystems of the social brain underpin interindividual variation in dissociable aspects of social lifestyle.

2020-08-06

bioRxiv (preprint)

Inferring disease subtypes from clusters in explanation space

Marc-Andre Schulz

Matt Chapman-Rounds

Manisha Verma

Konstantinos Georgatzis

Identification of disease subtypes and corresponding biomarkers can substantially improve clinical diagnosis and treatment selection. Discov… (see more)ering these subtypes in noisy, high dimensional biomedical data is often impossible for humans and challenging for machines. We introduce a new approach to facilitate the discovery of disease subtypes: Instead of analyzing the original data, we train a diagnostic classifier (healthy vs. diseased) and extract instance-wise explanations for the classifier’s decisions. The distribution of instances in the explanation space of our diagnostic classifier amplifies the different reasons for belonging to the same class–resulting in a representation that is uniquely useful for discovering latent subtypes. We compare our ability to recover subtypes via cluster analysis on model explanations to classical cluster analysis on the original data. In multiple datasets with known ground-truth subclasses, particularly on UK Biobank brain imaging data and transcriptome data from the Cancer Genome Atlas, we show that cluster analysis on model explanations substantially outperforms the classical approach. While we believe clustering in explanation space to be particularly valuable for inferring disease subtypes, the method is more general and applicable to any kind of sub-type identification.

2020-07-29

Scientific Reports (published)

Molecular signatures of cognition and affect

Justine Y. Hansen

Ross D. Markello

Jacob W. Vogel

Jakob Seidlitz

Bratislav Misic

Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activ… (see more)ity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial-dorsolateral gradient of gene assemblies that separate affective and cognitive domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation, and intrinsic network affiliation. In addition, this molecular-psychological signature is related to individual differences in cognitive performance, strengthens over neurodevelopment, and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.

2020-07-15

bioRxiv (preprint)

Shared and unique brain network features predict cognition, personality and mental health in childhood

Jianzhong Chen

Angela Tam

Valeria Kebets

Csaba Orban

Leon Qi Rong Ooi

Leon Qi Rong Ooi

Scott Marek

Nico Dosenbach

Simon Eickhoff

Avram J Holmes

B.T. Thomas Yeo

The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental… (see more) question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains.

2020-06-23

bioRxiv (preprint)

Dark control: The default mode network as a reinforcement learning agent

Elvis Dohmatob

Guillaume Dumas

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to oth… (see more)er brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.

2020-06-04

Human Brain Mapping (unknown)

The Neurobiology of Social Distance

Robin I. M. Dunbar

2020-06-02

Trends in Cognitive Sciences (published)

Population variability in social brain morphology for social support, household size and friendship satisfaction

Arezoo Taebi

Hannah Kiesow

Kai Vogeley

Leonhard Schilbach

Boris C. Bernhardt

The social brain hypothesis proposes that the complexity of human brains has coevolved with increasing complexity of social interactions in … (see more)primate societies. The present study explored the possible relationships between brain morphology and the richness of more intimate ‘inner’ and wider ‘outer’ social circles by integrating Bayesian hierarchical modeling with a large cohort sample from the UK Biobank resource (n = 10 000). In this way, we examined population volume effects in 36 regions of the ‘social brain’, ranging from lower sensory to higher associative cortices. We observed strong volume effects in the visual sensory network for the group of individuals with satisfying friendships. Further, the limbic network displayed several brain regions with substantial volume variations in individuals with a lack of social support. Our population neuroscience approach thus showed that distinct networks of the social brain show different patterns of volume variations linked to the examined social indices.

2020-05-31

Social Cognitive and Affective Neuroscience (published)

Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment

Anna K Bonkhoff

Thomas Hope

Adrian G Guggisberg

Rachel L Hawe

Sean P Dukelow

Anne K Rehme

Gereon R Fink

Christian Grefkes

Howard Bowman

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More tha… (see more)n 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies—one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called ‘fitters’, pointed to a combination of proportional to lost function and constant recovery. ‘Proportional to lost’ here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, ‘fitters’ and ‘non-fitters’, a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

2020-04-30

Brain (published)

General Principles of Gene Dosage Effects on Brain Structure

Claudia Modenato

Kuldeep Kumar

Clara A. Moreau

Catherine Schramm

Guillaume Huguet

Sandra Martin-Brevet

Aurélie Pain

Anne M. Maillard

Sonia Richetin

Borja Rodriguez-Herreros

Lester Melie-Garcia

Ana Dos Santos Silva

Marianne B.M. van den Bree

David E.J. Linden

Carrie E. Bearden

Sarah Lippé

Mallar Chakravarty

Bogdan Draganski

Sébastien Jacquemont

2020-04-30

Biological Psychiatry (published)

What Can Machine Learning Do for Psychiatry?

Daniel S. Barron

John H. Krystal

R. Todd Constable

2020-04-30

Biological Psychiatry (published)