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
Anwesha Bhattacharya
Doctorat - McGill
Titouan Brunel
Baccalauréat - 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
Audric Trarieux
Collaborateur·rice de recherche - Aix-Marseille Université
LinkedIn
Yutong Zheng
Doctorat - McGill
Le Zhou
Doctorat - McGill

Publications

Title: Functional architecture of the aging brain
Roni Setton
Laetitia Mwilambwe-Tshilobo
Manesh Girn
Amber W. Lockrow
Giulia Baracchini
Alexander J. Lowe
Benjamin N. Cassidy
Jian Li
Wen-Ming Luh
Danilo Bzdok
Richard M. Leahy
Tian Ge
Daniel S. Margulies
Bratislav Misic
Boris C Bernhardt
W. Dale Stevens
Felipe De Brigard
Prantik Kundu
Richard S. Gary
Gary R. Turner … (voir 1 de plus)
R. Nathan Spreng
The intrinsic functional connectome can reveal how a lifetime of learning and lived experience is represented in the functional architecture… (voir plus) of the aging brain. We investigated whether network dedifferentiation, a hallmark of brain aging, reflects a global shift in network dynamics, or comprises network-specific changes that reflect the changing landscape of aging cognition. We implemented a novel multi-faceted strategy involving multi-echo fMRI acquisition and de-noising, individualized cortical parcellation, and multivariate (gradient and edge-level) functional connectivity methods. Twenty minutes of resting-state fMRI data and cognitive assessments were collected in younger (n=181) and older (n=120) adults. Dimensionality in the BOLD signal was lower for older adults, consistent with global network dedifferentiation. Functional connectivity gradients were largely age-invariant. In contrast, edge-level connectivity showed widespread changes with age, revealing discrete, network-specific dedifferentiation patterns. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal regions showed greater coupling; and the dorsal attention network was less differentiated from transmodal regions. Associations with cognition suggest that the formation and preservation of integrated, large-scale brain networks supports complex cognitive abilities. However, into older adulthood, the connectome is dominated by large-scale network disintegration, global dedifferentiation and network-specific dedifferentiation associated with age-related cognitive change.
2021-12-31
(publié)
www.semanticscholar.org
Tau‐PET is associated with knowledge of COVID‐19, COVID‐19‐related distress, and change in sleep quality during the pandemic
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Guillaume Elgbeili
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
While the global COVID‐19 pandemic has hindered many human research operations, it has allowed for the investigation of novel scientific q… (voir plus)uestions. Particularly, the effects of the pandemic and its resulting social isolation on elderly individuals and their association with Alzheimer’s disease biomarkers remains a broad and open question. Here, we sought to investigate whether knowledge of COVID‐19, pandemic‐related distress, and changes in sleep quality were associated with in vivo tau deposition in an AD‐enriched cohort.
2021-12-30
Alzheimer's & Dementia (publié)
doi.org
Recovery after stroke: the severely impaired are a distinct group
Anna K. Bonkhoff
Thomas Hope
Danilo Bzdok
Adrian G. Guggisberg
Rachel L. Hawe
Sean P. Dukelow
François Chollet
David J. Lin
Christian Grefkes
Howard Bowman
Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both sha… (voir plus)red and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.
2021-12-21
Journal of Neurology Neurosurgery & Psychiatry (publié)
doi.org
Adapting to the COVID‐19 pandemic in cohort studies: Validation of online assessments of cognition and neuropsychiatric symptoms in an aging population
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
The occurrence of the COVID‐19 pandemic has had a significant impact on cohort studies, particularly those whose subjects are at higher ri… (voir plus)sk of developing complications from the virus. As such, assessment methods must be adapted to minimize COVID‐19 exposure risk. The TRIAD (Translational Biomarkers of Aging and Dementia) cohort assessed N=292 individuals during initial COVID‐19 lockdown measures by telephone interview to rate cognition, neuropsychiatric symptoms, and impact of the pandemic. To increase speed and efficiency of data collection, we aim to follow these individuals by means of online survey. Here, we present a validation of our online assessment tools by comparing data obtained through both methods (phone interview and online survey) in the same subjects.
2021-11-30
Alzheimer's & Dementia (publié)
doi.org
Cognitive health mediates the effect of hippocampal volume on COVID‐19‒related knowledge or anxiety change during the COVID‐19 pandemic
Min Su Kang
Julie Ottoy
Stijn Servaes
Firoza Z Lussier
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Danilo Bzdok
Suzanne King
Serge Gauthier
Pedro Rosa‐Neto
Our finding highlights the poorer knowledge of COVID19 and related risks in individuals with cognitive/memory impairments; the CDRSOB, indic… (voir plus)ative of cognitive health, significantly mediated the effect of hippocampal volume on the rate of change in anxiety or knowledge on COVID19 in our cohort. This study urges for a more effective strategy and policy about informing and educating the individual with cognitive/memory impairment on COVID19 and related risks.
2021-11-30
Alzheimer's & Dementia (publié)
doi.org
Tau‐load in the lingual gyrus impacts anxiety levels during the COVID‐19 pandemic in participants of longitudinal observational studies in aging
Stijn Servaes
Firoza Z Lussier
Gleb Bezgin
Yi‐Ting Wang
Jenna Stevenson
Cécile Tissot
Guillaume Elgbeili
Jaime Fernandez Arias
Joseph Therriault
Andréa Lessa Benedet
Mira Chamoun
Tharick A. Pascoal
Danilo Bzdok
Suzanne King
Serge Gauthier
Pedro Rosa‐Neto
By obtaining a better grasp on the impact of the COVID‐19 pandemic on individuals with cognitive impairment, this knowledge could be used … (voir plus)to improve the delivery of information to this particular group. We aimed to assess the relationship between tau deposition and the change in anxiety levels, before and during the pandemic. We hypothesized that since the pandemic, higher tau loads would lower the change in anxiety. Furthermore, we expected these anxiety levels not to be associated with COVID‐19 related stress in participants with cognitive decline. 63 participants of the Translational Biomarker of Aging and Dementia (TRIAD) cohort (cognitively healthy, N=38; cognitively impaired, N=25, of which 7 had dementia due to Alzheimer’s disease), were assessed to evaluate their individual change in anxiety levels (GAD‐7). This was done at three different timepoints, of which the latest fell during the COVID‐19 lockdown period. Two rates of change, one before and one during the pandemic, were determined using the following definition: (next timepoint – current timepoint)/time difference. In addition, at the latest timepoint, subjective stress due to COVID‐19 was measured using the Montreal Assessment of Stress related to COVID‐19 (MASC). To assess the levels of tau, standard uptake value ratios (SUVR) from previously obtained [18F]MK‐6240 PET‐scans were used. [18F]MK‐6240 tracer binding in the lingual gyrus was negatively associated with the rate of change in GAD‐7 scores after correcting for age, sex, years of education and the presence of APOE ε4, but only in cognitively impaired individuals during the pandemic (fig 1A). In addition, the GAD‐7 score at the latest timepoint was associated with stress related to COVID‐19, but only in cognitively healthy individuals (fig 1B and 1C). The presence of tau in the lingual gyrus negatively affected the rate of change in GAD‐7 scores during the COVID‐19 pandemic in individuals with cognitive impairment. This could indicate that information pertaining to the pandemic does not reach these individuals in an efficient manner. The missing association between COVID‐19 induced stress and the latest GAD‐7 scores in these individuals is a further indication of this.
2021-11-30
Alzheimer's & Dementia (inconnu)
doi.org
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
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