Portrait de Danilo Bzdok

Danilo Bzdok

Membre académique principal
bzdokdan@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, McGill University, Département de génie biomédicale
Sujets de recherche
Apprentissage profond
Biologie computationnelle
Grands modèles de langage (LLM)
Traitement du langage naturel

Biographie

Danilo Bzdok est informaticien et médecin de formation. Il possède une double formation unique en neurosciences systémiques et en algorithmes d'apprentissage automatique. Après une formation à l'Université d'Aix-la-Chapelle (RWTH) (Allemagne), à l'Université de Lausanne (Suisse) et à la Harvard Medical School (États-Unis), il a obtenu un doctorat en neurosciences du Centre de recherche de Jülich (Allemagne) et un doctorat en informatique dans le domaine des statistiques d'apprentissage automatique à l'INRIA Saclay et à NeuroSpin (Paris, France). Il est actuellement professeur agrégé à la Faculté de médecine de l'Université McGill et titulaire d’une chaire en IA Canada-CIFAR à Mila – Institut québécois d'intelligence artificielle. Son activité de recherche interdisciplinaire est centrée sur la réduction des lacunes dans la connaissance des bases cérébrales des types de pensée qui définissent l'être humain, afin de découvrir les principes clés de conception computationnelle qui sous-tendent l'intelligence humaine.

Étudiants actuels

Karan Bali Bali
Doctorat - McGill
Gregory Bell
Doctorat - McGill
Yanis Bencheikh
Maîtrise recherche - HEC
Co-superviseur⋅e :
Tegan Maharaj
Site web
Github
Anwesha Bhattacharya
Doctorat - McGill
Titouan Brunel
Collaborateur·rice de recherche - CentraleSupélec
Pedro Carneiro Carneiro
Doctorat - McGill
Arthur Communal
Collaborateur·rice de recherche - École Polytechnique Paris
Liam Hodgson
Doctorat - McGill
Ryan McPhedrain McPhedrain
Postdoctorat - McGill
David Payette
Maîtrise recherche - McGill
Sepehr Radmannia
Visiteur de recherche indépendant - McGill
Karin Saltoun
Doctorat - McGill
Chloé Savignac
Doctorat - McGill
Github
Google Scholar
Jack Stanley
Doctorat - McGill
Site web
Github
Google Scholar
Praneet Suresh
Doctorat - McGill
Google Scholar
Yutong Zheng
Doctorat - McGill
Le Zhou
Doctorat - McGill

Publications

The meaning of significant mean group differences for biomarker discovery
Eva Loth
Jumana Ahmad
Chris Chatham
Beatriz López
Ben Carter
Daisy Crawley
Bethany Oakley
Hannah Hayward
Jennifer Cooke
Antonia San José Cáceres
Danilo Bzdok
Emily Jones
Tony Charman
Christian Beckmann
Thomas Bourgeron
Roberto Toro
Jan Buitelaar
Declan Murphy
Guillaume Dumas
Over the past decade, biomarker discovery has become a key goal in psychiatry to aid in the more reliable diagnosis and prognosis of heterog… (voir plus)eneous psychiatric conditions and the development of tailored therapies. Nevertheless, the prevailing statistical approach is still the mean group comparison between “cases” and “controls,” which tends to ignore within-group variability. In this educational article, we used empirical data simulations to investigate how effect size, sample size, and the shape of distributions impact the interpretation of mean group differences for biomarker discovery. We then applied these statistical criteria to evaluate biomarker discovery in one area of psychiatric research—autism research. Across the most influential areas of autism research, effect size estimates ranged from small (d = 0.21, anatomical structure) to medium (d = 0.36 electrophysiology, d = 0.5, eye-tracking) to large (d = 1.1 theory of mind). We show that in normal distributions, this translates to approximately 45% to 63% of cases performing within 1 standard deviation (SD) of the typical range, i.e., they do not have a deficit/atypicality in a statistical sense. For a measure to have diagnostic utility as defined by 80% sensitivity and 80% specificity, Cohen’s d of 1.66 is required, with still 40% of cases falling within 1 SD. However, in both normal and nonnormal distributions, 1 (skewness) or 2 (platykurtic, bimodal) biologically plausible subgroups may exist despite small or even nonsignificant mean group differences. This conclusion drastically contrasts the way mean group differences are frequently reported. Over 95% of studies omitted the “on average” when summarising their findings in their abstracts (“autistic people have deficits in X”), which can be misleading as it implies that the group-level difference applies to all individuals in that group. We outline practical approaches and steps for researchers to explore mean group comparisons for the discovery of stratification biomarkers.
2021-10-31
PLoS Computational Biology (publié)
doi.org
A cognitive fingerprint in human random number generation
Marc-Andre Schulz
Sebastian Baier
Benjamin Timmermann
Benjamin Timmermann
Danilo Bzdok
Karsten Witt

Most work in the neurosciences collapses data from multiple subjects to obtain robust statistical results. This research agenda ignores t… (voir plus)hat even in healthy subjects brain structure and function are known to be highly variable. Recently, Finn and colleagues showed that the brain's functional organisation is unique to each individual and can yield human-specific connectome fingerprints. This raises the question whether unique functional brain architecture may reflect a unique implementation of cognitive processes and problem solving - i.e. "Can we identify single individuals based on how they think?". The present study addresses the general question of interindividual differences in the specific context of human random number generation. We analyzed the deployment of recurrent patterns in the pseudorandom sequences to develop an identification scheme based on subject-specific volatility patterns. We demonstrate that individuals can be reliably identified based on how they how they generate randomness patterns alone. We moreover show that this phenomenon is driven by individual preference and inhibition of patterns, together forming a cognitive fingerprint.

2021-10-11
Scientific Reports (publié)
doi.org
Diagnosing as autistic people increasingly distant from prototypes lead neither to clinical benefit nor to the advancement of knowledge
Laurent Mottron
Danilo Bzdok
2021-10-11
Molecular Psychiatry (publié)
doi.org
Population modeling with machine learning can enhance measures of mental health
Kamalaker Dadi
Gael Varoquaux
Josselin Houenou
Danilo Bzdok
Bertrand Thirion
Denis Engemann
We applied machine learning on more than 10.000 individuals from the general population to define empirical approximations of health-related… (voir plus) psychological measures that do not require human judgment.We found that machine-learning enriched the given psychological measures via approximation from brain and sociodemographic data: Resulting proxy measures related as well or better to real-world health behavior than the original measures.Model comparisons showed that sociodemographic information contributed most to characterizing psychological traits beyond aging.
2021-09-23
bioRxiv (prépublication)
doi.org
Connectivity alterations in autism reflect functional idiosyncrasy
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 C. Bernhardt
Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-develo… (voir plus)ping controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with 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 inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism. Benkarim et al devise an approach to profile inter-individual variability in functional network organization and test whether such idiosyncrasy contributes to the connectivity alterations found in Autism Spectrum Disorder. Their approach provides potential biomarkers to study atypical brain development and may be used to consolidate prior research findings on the variable nature of connectome level anomalies in autism.
2021-09-14
Communications Biology (publié)
doi.org
Decision Models and Technology Can Help Psychiatry Develop Biomarkers
Daniel S. Barron
Justin T. Baker
Kristin S. Budde
Danilo Bzdok
Simon B. Eickhoff
Karl J. Friston
Peter T. Fox
Paul Geha
Stephen Heisig
Avram J. Holmes
Jukka-Pekka Onnela
Albert Powers
David Silbersweig
John H. Krystal
2021-09-08
Frontiers in Psychiatry (publié)
doi.org
Social belonging: brain structure and function is linked to membership in sports teams, religious groups, and social clubs
Carolin Kieckhaefer
Leonhard Schilbach
Danilo Bzdok
Human behavior across the life span is driven by the psychological need to belong, right from kindergarten to bingo nights. Being part of so… (voir plus)cial groups constitutes a backbone for communal life and confers many benefits for the physical and mental health. Capitalizing on the neuroimaging and behavioral data from ∼40,000 participants from the UK Biobank population cohort, we used structural and functional analyses to explore how social participation is reflected in the human brain. Across 3 different types of social groups, structural analyses point toward the variance in ventromedial prefrontal cortex, fusiform gyrus, and anterior cingulate cortex as structural substrates tightly linked to social participation. Functional connectivity analyses not only emphasized the importance of default mode and limbic network but also showed differences for sports teams and religious groups as compared to social clubs. Taken together, our findings establish the structural and functional integrity of the default mode network as a neural signature of social belonging.
2021-09-06
bioRxiv (prépublication)
doi.org
Loneliness is linked to specific subregional alterations in hippocampus-default network co-variation
Chris Zajner
R. Nathan Spreng
Danilo Bzdok
Social interaction complexity makes humans unique. But in times of social deprivation this strength risks to expose important vulnerabilitie… (voir plus)s. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically co-vary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By co-decomposition using structural brain scans of ∼40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex signatures coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white-matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN co-variation had distinct associations with the genetic predisposition for loneliness at the population level.
2021-08-18
bioRxiv (prépublication)
doi.org
Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences
Galen Ballentine
Samuel Freesun Friedman
Danilo Bzdok
Psychedelics probably alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Indiv… (voir plus)idual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6850 free-form testimonials about 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to three-dimensional coordinates in the brain via their gene transcription levels from invasive tissue probes. Despite high interindividual variability, our pattern-learning approach delineated how drug-induced changes of conscious awareness are linked to cortex-wide anatomical distributions of receptor density proxies. Each discovered receptor-experience factor spanned between a higher-level association pole and a sensory input pole, which may relate to the previously reported collapse of hierarchical order among large-scale networks. Coanalyzing many psychoactive molecules and thousands of natural language descriptions of drug experiences, our analytical framework finds the underlying semantic structure and maps it directly to the brain.
2021-07-13
bioRxiv (prépublication)
doi.org
The default mode network in cognition: a topographical perspective
Jonathan Smallwood
Boris C Bernhardt
Robert Leech
Danilo Bzdok
Elizabeth Jefferies
Daniel S. Margulies
2021-07-04
Nature Reviews Neuroscience (publié)
doi.org
Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Lucina Q. Uddin
Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is incr… (voir plus)easing 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 analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the “Methods” and “Results” sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) 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.
2021-06-30
PLoS Biology (publié)
doi.org
Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Catie Chang
B.T. Yeo
Lucina Q. Uddin
Shella Keilholz
2021-06-19
(publié)
doi.org