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

Shambhavi Aggarwal
Maîtrise recherche - McGill
Github
Google Scholar
Karan Bali Bali
Doctorat - McGill
Gregory Bell
Doctorat - McGill
Anwesha Bhattacharya
Doctorat - McGill
Pedro Carneiro Carneiro
Doctorat - McGill
Github
Liam Hodgson
Doctorat - McGill
Github
Ryan McPhedrain McPhedrain
Postdoctorat - McGill
ryan.mcphedrain@mail.mcgill.ca
David Payette
Maîtrise recherche - McGill
Github
Sepehr Radmannia
Visiteur de recherche indépendant - McGill
sradmannia@gmail.com
Github
Karin Saltoun
Doctorat - McGill
Chloé Savignac
Doctorat - McGill
Github
Google Scholar
Jack Stanley
Doctorat - McGill
Site web
Github
Praneet Suresh
Doctorat - McGill
Le Zhou
Doctorat - McGill

Publications

Author response: Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-01-21
(publié)
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
2021-01-14
Communications Biology (publié)
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 Lariviere
Sofie Valk
Danilo Bzdok
Laurent Mottron
Boris C Bernhardt
Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls… (voir plus) (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.
2020-12-18
bioRxiv (prépublication)
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
2020-12-03
bioRxiv (prépublication)
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
2020-12-01
Nature Communications (publié)
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… (voir plus)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.
2020-10-29
bioRxiv (prépublication)
doi.org
Inference and Prediction Diverge in Biomedicine
Danilo Bzdok
Denis-Alexander Engemann
Bertrand Thirion
2020-10-08
Patterns (publié)
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 Lariviere
Oualid Benkarim
Jessica Royer
Shahin Tavakol
Raul Rodriguez-cruces
Qiongling Li
Sofie Valk
Daniel S. Margulies
Bratislav Mišić
Danilo Bzdok
Jonathan Smallwood
Boris C Bernhardt
2020-10-07
NeuroImage (publié)
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 Paul Kording
Blake Richards
Danilo Bzdok
2020-08-25
Nature Communications (publié)
doi.org
Meta-matching: a simple framework to translate phenotypic predictive models from big to small data
Tong He
Lijun An
Jiashi Feng
Danilo Bzdok
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 … (voir plus)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.
2020-08-11
bioRxiv (prépublication)
doi.org
Hidden population modes in social brain morphology: Its parts are more than its sum
Hannah Kiesow
R. Nathan Spreng
Avram J. Holmes
Mallar Chakravarty
Andre Marquand
B.T. Thomas Yeo
Danilo Bzdok
The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map … (voir plus)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.
2020-08-07
bioRxiv (prépublication)
doi.org
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists
Hao-Ting Wang
Jonathan Smallwood
Janaina Mourão Miranda
Cedric Huchuan Xia
Theodore D. Satterthwaite
Danielle S. Bassett
Danilo Bzdok
2020-08-01
NeuroImage (publié)
doi.org