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

Shambhavi Aggarwal
Master's Research - McGill University
Github
Google Scholar
Badr Ait Hammou
Postdoctorate - McGill University
Karan Bali Bali
PhD - McGill University
Gregory Bell
PhD - McGill University
Anwesha Bhattacharya
PhD - McGill University
Pedro Carneiro Carneiro
PhD - McGill University
Github
Liam Hodgson
PhD - McGill University
Ryan McPhedrain McPhedrain
Postdoctorate - McGill University
ryan.mcphedrain@mail.mcgill.ca
David Payette
Master's Research - McGill University
Github
Sepehr Radmannia
Independent visiting researcher - McGill University
sradmannia@gmail.com
Github
Karin Saltoun
PhD - McGill University
Chloé Savignac
PhD - McGill University
Github
Google Scholar
Jack Stanley
PhD - McGill University
Website
Github
Praneet Suresh
PhD - McGill University
Le Zhou
PhD - McGill University

Publications

Myeloarchitecture gradients in the human insula: Histological underpinnings and association to intrinsic functional connectivity
Jessica Royer
Casey Paquola
Sara Lariviere
Reinder Vos de Wael
Shahin Tavakol
Alexander J. Lowe
Oualid Benkarim
Alan C. Evans
Danilo Bzdok
Jonathan Smallwood
Birgit Frauscher
Boris C Bernhardt
2020-04-20
NeuroImage (published)
doi.org
Neuropsychiatric copy number variants exert shared effects on human brain structure
Claudia Modenato
Kuldeep Kumar
Clara A. Moreau
Sandra Martin-Brevet
Guillaume Huguet
Catherine Schramm
Jean-Louis Martineau
Charles-Olivier Martin
C.O. Martin
Nadine Younis
Petra Tamer
Elise Douard
Fanny Thébault-Dagher
Valérie Côté
Audrey-Rose Charlebois
Florence Deguire
Anne M. Maillard
Borja Rodriguez-Herreros
Aurélie Pain
Sonia Richetin … (see 15 more)
16p11.2 European Consortium
Simons Variation in Individuals Project Consortium
Ana I. Silva
Leila Kushan
Lester Melie-Garcia
Marianne B.M. van den Bree
David E.J. Linden
M. J. Owen
Jeremy Hall
Sarah Lippé
Mallar Chakravarty
Danilo Bzdok
Carrie E. Bearden
Bogdan Draganski
Sébastien Jacquemont
2020-04-17
medRxiv (preprint)
doi.org
10,000 social brains: Sex differentiation in human brain anatomy
Hannah Kiesow
Robin I. M. Dunbar
Joseph W. Kable
Tobias Kalenscher
Kai Vogeley
Leonhard Schilbach
Andre Marquand
Thomas V. Wiecki
Danilo Bzdok
2020-03-18
Science Advances (published)
doi.org
Not one model fits all: unfairness in RSFC-based prediction of behavioral data in African American
Jingwei Li
Danilo Bzdok
Avram J. Holmes
Thomas B.t. Yeo
Sarah Genon
14 Helmholtz AI kick-off meeting 5 Mar 2020, 14:17:33 Page 1/1 Abstract #14 | Poster Not one model fits all: unfairness in RSFC-based predic… (see more)tion of behavioral data in African American J. Li , D. Bzdok, A. Holmes, T. Yeo, S. Genon 1 Forschungszentrum Julich, Institute of Neuroscience and Medicine, Jülich, Germany 2 McGill University, Department of Biomedical Imaging, Montreal, Canada 3 National University of Singapore, ECE, CSC, CIRC, N.1 & MNP, Singapore, Singapore 4 Yale University, New Haven, United States of America While predictive models are expected to play a major role in personalized medicine approaches in the future, biases towards specific population groups have been evidenced, hence raising concerns about the risks of unfairness of machine learning algorithms. As great hopes and intense work have been invested recently in the prediction of behavioral phenotypes based on brain resting-state functional connectivity (RSFC), we here examined potential differences in RSFC-based predictive models of behavioral data between African American (AA) and White American (WA) samples matched for the main demographic, anthropometric, behavioral and in-scanner motion variables. We used resting-fMRI data with 58 behavioral measures of 953 subjects comprising 130 African American (AA) and 724 White American (WA). For each subject, a 419 x 419 matrix summarizing connectivity of 419 brain regions was computed. Matching between AA and WA was performed at the subject level by creating 102 pairs of AA and WA subjects, matched for 6 types of variables (age, sex, intracranial volume, education, in-scanner motion and behavioral scores). We performed 10-fold nested cross-validation by randomly splitting the 102 pairs across 10 sets. The remaining 749 subjects were also divided across the 10 sets. A predictive model was built for each behavioral variable by using kernel ridge regression. All analyses focused on the 102 matched AA and WA groups. After FDR correction (q 0.05), no significant difference was found between the matched AA and WA groups for the matching variables. Out of 58 behavioral variables, 38 showed significantly above chance prediction accuracies (based on permutation test, FDR corrected). Overall, average prediction performance for these variables was higher in the WA group than in the AA group. Furthermore, significant differences in prediction performance between the two groups were found in 35 behavioral variables (FDR corrected; q 0.05). Our results suggest that RSFC-based prediction models of behavioral phenotype trained on the entire HCP population show different prediction performance in different subsets of the population. This suggest that one model might not fit all that, in some cases, RSFC-based predictive models might have poorer prediction accuracies for African Americans compared to matched White Americans. Future work should evaluate the factors contributing to these discrepancies and the potential consequences, as well as possible recommendations.
Analysing brain networks in population neuroscience: a case for the Bayesian philosophy
Danilo Bzdok
Dorothea L. Floris
Andre Marquand
Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widel… (see more)y available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here, we contemplate the advantages of analysing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena, provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner, usher us towards avenues to go beyond binary statements on existence versus non-existence of an effect, and afford credibility estimates around all model parameters at play which thus enable single-subject predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
2020-02-24
Philosophical Transactions of the Royal Society of London. Biological Sciences (published)
doi.org
Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments
Jeremy Lefort-Besnard
Kai Vogeley
Leonhard Schilbach
Gael Varoquaux
Bertrand Thirion
Guillaume Dumas
Danilo Bzdok
2019-09-27
Translational Psychiatry (published)
doi.org