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
Gregory Bell
PhD - McGill University
Yanis Bencheikh
Master's Research - HEC Montréal
Co-supervisor :
Tegan Maharaj
Website
Github
Anwesha Bhattacharya
PhD - McGill University
Titouan Brunel
Collaborating researcher - CentraleSupélec
Pedro Carneiro Carneiro
PhD - McGill University
Arthur Communal
Collaborating researcher - École Polytechnique Montréal
Liam Hodgson
PhD - McGill University
Ryan McPhedrain McPhedrain
Postdoctorate - McGill University
David Payette
Master's Research - McGill University
Sepehr Radmannia
Independent visiting researcher - McGill University
Karin Saltoun
PhD - McGill University
Chloé Savignac
PhD - McGill University
Github
Google Scholar
Jack Stanley
PhD - McGill University
Website
Github
Google Scholar
Praneet Suresh
PhD - McGill University
Google Scholar
Yutong Zheng
PhD - McGill University
Le Zhou
PhD - McGill University

Publications

Mapping gene transcription and neurocognition across human neocortex
Justine Y. Hansen
Ross D. Markello
Jacob W. Vogel
Jakob Seidlitz
Danilo Bzdok
Bratislav Misic
2021-03-24
Nature Human Behaviour (published)
doi.org
Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social … (see more)cognition, that define human interactions. Its putative domain-global role appears to tie into poorly understood differences between cognitive domains in both hemispheres. Across attentional, semantic, and social cognitive tasks, our study explored functional specialization within the IPL. The task specificity of IPL subregion activity was substantiated by distinct predictive signatures identified by multivariate pattern-learning algorithms. Moreover, the left and right IPL exerted domain-specific modulation of effective connectivity among their subregions. Task-evoked functional interactions of the anterior and posterior IPL subregions involved recruitment of distributed cortical partners. While anterior IPL subregions were engaged in strongly lateralized coupling links, both posterior subregions showed more symmetric coupling patterns across hemispheres. Our collective results shed light on how under-appreciated hemispheric specialization in the IPL supports some of the most distinctive human mental capacities.
2021-03-01
eLife (published)
doi.org
The neural correlates of ongoing conscious thought
Jonathan Smallwood
Adam Turnbull
Hao-Ting Wang
Nerissa S.P. Ho
Giulia L. Poerio
Theodoros Karapanagiotidis
Delali Konu
Brontë Mckeown
Meichao Zhang
Charlotte Murphy
Deniz Vatansever
Danilo Bzdok
Mahiko Konishi
Robert Leech
Paul Seli
Jonathan W. Schooler
Boris C Bernhardt
Daniel S. Margulies
Elizabeth Jefferies
2021-01-31
iScience (published)
doi.org
Author response: Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-01-20
(published)
doi.org
Deep learning identifies partially overlapping subnetworks in the human social brain
Hannah Kiesow
R. Nathan Spreng
Avram J. Holmes
Mallar Chakravarty
Andre Marquand
B.T. Thomas Yeo
Danilo Bzdok
2021-01-13
Communications Biology (published)
doi.org
Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function
Bo-yong Park
Reinder Vos de Wael
Casey Paquola
Sara Larivière
Oualid Benkarim
Jessica Royer
Shahin Tavakol
Raul R. Cruces
Qiongling Li
Sofie L. Valk
Daniel S. Margulies
Bratislav Misic
Danilo Bzdok
Jonathan Smallwood
Boris C. Bernhardt
2020-12-31
NeuroImage (published)
doi.org
Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism
Oualid Benkarim
Casey Paquola
Bo-yong Park
Seok-Jun Hong
Jessica Royer
Reinder Vos de Wael
Sara Larivière
Sofie Valk
Danilo Bzdok
Laurent Mottron
Boris Bernhardt
Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls… (see more) (TD) have yielded somewhat inconsistent patterns of results. The current work was centered on a novel approach to profile functional network idiosyncrasy, the inter-individual variability in the association between functional network organization and brain anatomy, and we tested the hypothesis that idiosyncrasy contributes to connectivity alterations in ASD. Studying functional network idiosyncrasy in a multi-centric dataset with 157 ASD and 172 TD, our approach revealed higher idiosyncrasy in ASD in the default mode, somatomotor and attention networks together with reduced idiosyncrasy in the lateral temporal lobe. Idiosyncrasy was found to increase with age in both ASD and TD, and was significantly correlated with symptom severity in the former group. Association analysis with structural and molecular brain features indicated that patterns of functional network idiosyncrasy were not correlated with ASD-related cortical thickness alterations, but closely with the spatial expression patterns of intracortical ASD risk genes. In line with our main hypothesis, we could demonstrate that idiosyncrasy indeed plays a strong role in the manifestation of connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between the heterogeneity of individuals with an autism diagnosis and group-level functional signatures, and help to consolidate prior research findings on the highly variable nature of the functional connectome in ASD. Our study promotes idiosyncrasy as a potential individualized diagnostic marker of atypical brain network development.
2020-12-17
bioRxiv (preprint)
doi.org
Historical and cross-disciplinary trends in the biological and social sciences reveal an accelerating adoption of advanced analytics
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Lucina Q. Uddin
Methods for data analysis in the biomedical, life and social sciences are developing at a rapid pace. At the same time, there is increasing … (see more)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 research methods. Our survey of peer-reviewed literature screened ∼3.5 million openly available research articles to monitor the cross-disciplinary usage of research methods in the past decade. We applied data-driven text-mining analyses to the methods and materials section of a large subset of this corpus to identify method trends shared across disciplines, as well as those unique to each discipline. As a whole, usage of T -test, analysis of variance, and other classical regression-based methods has declined in the published literature over the past 10 years. Machine-learning approaches, such as artificial neural networks, have seen a significant increase in the total share of scientific publications. We find unique groupings of research methods associated with each biomedical, life and social science discipline, such as the use of structural equation modeling 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.
2020-12-02
bioRxiv (preprint)
doi.org
The default network of the human brain is associated with perceived social isolation
R. Nathan Spreng
Emile Dimas
Laetitia Mwilambwe-Tshilobo
Alain Dagher
Philipp Koellinger
Gideon Nave
Anthony Ong
Julius M. Kernbach
Thomas V. Wiecki
Tian Ge
Yue Li
Avram J. Holmes
B. T. Thomas Yeo
Gary R. Turner
Robin I. M. Dunbar
Danilo Bzdok
Humans survive and thrive through social exchange. Yet, social dependency also comes at a cost. Perceived social isolation, or loneliness, a… (see more)ffects physical and mental health, cognitive performance, overall life expectancy, and increases vulnerability to Alzheimer’s disease-related dementias. Despite severe consequences on behavior and health, the neural basis of loneliness remains elusive. Using the UK Biobank population imaging-genetics cohort (n = ~40,000, aged 40–69 years when recruited, mean age = 54.9), we test for signatures of loneliness in grey matter morphology, intrinsic functional coupling, and fiber tract microstructure. The loneliness-linked neurobiological profiles converge on a collection of brain regions known as the ‘default network’. This higher associative network shows more consistent loneliness associations in grey matter volume than other cortical brain networks. Lonely individuals display stronger functional communication in the default network, and greater microstructural integrity of its fornix pathway. The findings fit with the possibility that the up-regulation of these neural circuits supports mentalizing, reminiscence and imagination to fill the social void.
2020-11-30
Nature Communications (published)
doi.org
Inference and Prediction Diverge in Biomedicine
Danilo Bzdok
Denis Engemann
Bertrand Thirion
In the 20th century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. … (see more)In the early 21st century, ambitions toward precision medicine place a premium on detailed predictions for single individuals. The shift causes tension between traditional regression methods used to infer statistically significant group differences and burgeoning predictive analysis tools suited to forecast an individual's future. Our comparison applies linear models for identifying significant contributing variables and for finding the most predictive variable sets. In systematic data simulations and common medical datasets, we explored how variables identified as significantly relevant and variables identified as predictively relevant can agree or diverge. Across analysis scenarios, even small predictive performances typically coincided with finding underlying significant statistical relationships, but not vice versa. More complete understanding of different ways to define “important” associations is a prerequisite for reproducible research and advances toward personalizing medical care.
2020-10-31
Patterns (published)
doi.org
Neuroimaging: into the Multiverse
Jessica Dafflon
Pedro F. da Costa
František Váša
Ricardo Pio Monti
Danilo Bzdok
Peter J. Hellyer
Federico Turkheimer
Jonathan Smallwood
Emily J. H. Jones
Robert Leech
For most neuroimaging questions the huge range of possible analytic choices leads to the possibility that conclusions from any single analyt… (see more)ic approach may be misleading. Examples of possible choices include the motion regression approach used and smoothing and threshold factors applied during the processing pipeline. Although it is possible to perform a multiverse analysis that evaluates all possible analytic choices, this can be computationally challenging and repeated sequential analyses on the same data can compromise inferential and predictive power. Here, we establish how active learning on a low-dimensional space that captures the inter-relationships between analysis approaches can be used to efficiently approximate the whole multiverse of analyses. This approach balances the benefits of a multiverse analysis without the accompanying cost to statistical power, computational power and the integrity of inferences. We illustrate this approach with a functional MRI dataset of functional connectivity across adolescence, demonstrating how a multiverse of graph theoretic and simple pre-processing steps can be efficiently navigated using active learning. Our study shows how this approach can identify the subset of analysis techniques (i.e., pipelines) which are best able to predict participants’ ages, as well as allowing the performance of different approaches to be quantified.
2020-10-28
bioRxiv (preprint)
doi.org
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Marc-Andre Schulz
B. T. Thomas Yeo
Joshua T. Vogelstein
Janaina Mourao-Miranada
Jakob N. Kather
Konrad Kording
Blake Richards
Danilo Bzdok
Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep lear… (see more)ning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.
2020-08-24
Nature Communications (published)
doi.org