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

Inferring disease subtypes from clusters in explanation space
Marc-Andre Schulz
Matt Chapman-Rounds
Manisha Verma
Danilo Bzdok
Konstantinos Georgatzis
2020-07-30
Scientific Reports (publié)
doi.org
Molecular signatures of cognition and affect
Justine Y. Hansen
Ross D Markello
Jacob W. Vogel
Jakob Seidlitz
Danilo Bzdok
Bratislav Mišić
2020-07-16
bioRxiv (prépublication)
doi.org
Hemispheric specialization within the inferior parietal lobe 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 … (voir plus)cognition that define human interactions. Its putative domain-global role appears to tie into poorly understood functional differences between both hemispheres. Across attentional, semantic, and social cognitive experiments, our study explored hemispheric 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 each anterior IPL subregion was 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 lateralization effects within the IPL support some of the most distinctive human mental capacities.
2020-07-01
bioRxiv (prépublication)
doi.org
Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment.
Anna K. Bonkhoff
Thomas Hope
Danilo Bzdok
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… (voir plus)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.
2020-06-29
Brain (publié)
doi.org
Shared and unique brain network features predict cognition, personality and mental health in childhood
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
Leon Qi
Leon Qi Rong Ooi
Scott Marek
Nico Dosenbach
Simon B. Eickhoff
Danilo Bzdok
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… (voir plus) 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.
2020-06-24
bioRxiv (prépublication)
doi.org
Dark control: The default mode network as a reinforcement learning agent
Elvis Dohmatob
Guillaume Dumas
Danilo Bzdok
2020-06-05
Human Brain Mapping (publié)
doi.org
The Neurobiology of Social Distance
Danilo Bzdok
Robin I. M. Dunbar
2020-06-03
Trends in Cognitive Sciences (publié)
doi.org
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
Danilo Bzdok
2020-06-01
Social Cognitive and Affective Neuroscience (publié)
doi.org
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
Danilo Bzdok
Sarah Lippé
Mallar Chakravarty
Bogdan Draganski
Sébastien Jacquemont
2020-05-01
Biological Psychiatry (publié)
doi.org
What Can Machine Learning Do for Psychiatry?
Daniel S. Barron
Danilo Bzdok
John H. Krystal
R. Todd Constable
2020-05-01
Biological Psychiatry (publié)
doi.org
Autism spectrum heterogeneity: fact or artifact?
Laurent Mottron
Danilo Bzdok
2020-04-30
Molecular Psychiatry (publié)
doi.org
CNN to detect differences in cerebral cortical anatomy of left- and right- handers
Lisa Meyer-Baese
Erik Roecher
Lucas Moesch
Danilo Bzdok
Klaus Mathiak
Handedness is one of the most obvious functional asymmetries, but its relation to anatomical asymmetry in the brain has not yet been clearly… (voir plus) demonstrated. However, there is no significant evidence to prove or disprove this structure-function correlation, thus left-handed patients are often excluded from magnetic resonance imaging (MRI) studies. MRI classification of left and right hemispheres is a difficult task on its own due to the complexity of the images and the structural similarities between the two halves. We demonstrate a deep artificial neural network approach in connection with a detailed preprocessing pipeline for the classification of lateralization in T1-weighted MR images of the human brain. Preprocessing includes bias field correction and registration on the MNI template. Our classifier is a convolutional neural network (CNN) that was trained on 287 images. Each image was duplicated and mirrored on the mid-sagittal plane. The best model reached an accuracy of 97.594% with a mean of 95.42% and standard deviation of 1.37%. Additionally, our model’s performance was evaluated on an independent set of 118 images and reached a classification accuracy of 97%. In a larger study we tested the model on grey-matter images of 927 left and 927 right-handed patients from the UK Biobank. Here all right-handed images and all left-handed images were classified as belonging to one class. The results suggest that there is no structural difference in grey-matter between the two hemispheres that can be distinguished by the deep learning classifier.
2020-04-27
Real-Time Image Processing and Deep Learning 2020 (publié)
doi.org