Danilo Bzdok

Google Scholar

Badr Ait Hammou

Postdoctorat - McGill

Karan Bali Bali

Doctorat - McGill

Gregory Bell

Doctorat - McGill

Anwesha Bhattacharya

Doctorat - McGill

Pedro Carneiro Carneiro

Doctorat - McGill

Liam Hodgson

Doctorat - McGill

ryan.mcphedrain@mail.mcgill.ca

Ryan McPhedrain McPhedrain

Postdoctorat - McGill

David Payette

Maîtrise recherche - McGill

Sepehr Radmannia

Visiteur de recherche indépendant - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Visiteur de recherche indépendant - Yale University

Utiliser les LLMs pour mieux comprendre le diagnostic de l'autisme

Le Zhou

Doctorat - McGill

Billets de blogue

25 mars 2025

par

Jack Stanley

Danilo Bzdok

Lire l'article

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

8 octobre 2020

Vers une médecine personnalisée : comprendre la divergence entre l’inférence et la prédiction en biomédecine

par

Danilo Bzdok

Lire l'article

Publications

Population variation in social brain morphology: Links to socioeconomic status and health disparity

Nathania Suryoputri

Hannah Kiesow

2022-05-04

Social Neuroscience (publié)

Using Population Datasets to Identify the Brain Basis of Social Isolation

2022-05-01

Biological Psychiatry (publié)

Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging

Oualid Benkarim

Casey Paquola

Bo-yong Park

Valeria Kebets

Seok-Jun Hong

Reinder Vos de Wael

Shaoshi Zhang

B.T. Thomas Yeo

Michael Eickenberg

Tian Ge

Jean-Baptiste Poline

Boris C Bernhardt

2022-04-29

PLoS Biology (publié)

Rare CNVs and phenome-wide profiling: a tale of brain-structural divergence and phenotypical convergence

J. Kopal

Kuldeep Kumar

Karin Saltoun

Claudia Modenato

Clara A. Moreau

Sandra Martin-Brevet

Guillaume Huguet

Martineau Jean-Louis

C.O. Martin

Zohra Saci

Nadine Younis

Petra Tamer

Elise Douard

Anne M. Maillard

Borja Rodriguez-Herreros

Aurélie Pain

Sonia Richetin

Leila Kushan

Ana I. Silva

Marianne B.M. van den Bree … (voir 12 de plus)

David E.J. Linden

M. J. Owen

Jeremy Hall

Sarah Lippé

Bogdan Draganski

Ida E. Sønderby

Ole A. Andreassen

David C. Glahn

Paul M. Thompson

Carrie E. Bearden

Sébastien Jacquemont

Copy number variations (CNVs) are rare genomic deletions and duplications that can exert profound effects on brain and behavior. Previous re… (voir plus)ports of pleiotropy in CNVs imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, studies to date have primarily examined single CNV loci in small clinical cohorts. It remains unknown how distinct CNVs escalate the risk for the same developmental and psychiatric disorders. Here, we quantitatively dissect the impact on brain organization and behavioral differentiation across eight key CNVs. In 534 clinical CNV carriers from multiple sites, we explored CNV-specific brain morphology patterns. We extensively annotated these CNV-associated patterns with deep phenotyping assays through the UK Biobank resource. Although the eight CNVs cause disparate brain changes, they are tied to similar phenotypic profiles across ∼1000 lifestyle indicators. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

2022-04-25

bioRxiv (prépublication)

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

Jianzhong Chen

Angela Tam

Valeria Kebets

Csaba Orban

L.Q.R. Ooi

Christopher L Asplund

Scott A. Marek

N. Dosenbach

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

2022-04-25

Nature Communications (published)

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

Jianzhong Chen

Angela Tam

Valeria Kebets

Csaba Orban

L.Q.R. Ooi

Leon Qi Rong Ooi

Christopher L. Asplund

Scott Marek

Nico Dosenbach

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

2022-04-25

Nature Communications (publié)

Multivariate, Transgenerational Associations of the COVID-19 Pandemic Across Minoritized and Marginalized Communities.

Sarah W. Yip

Ayana Jordan

Robert J. Kohler

Avram J. Holmes

Importance The experienced consequences of the COVID-19 pandemic have diverged across individuals, families, and communities, resulting in i… (voir plus)nequity within a host of factors. There is a gap of quantitative evidence about the transgenerational impacts of these experiences and factors. Objective To identify baseline predictors of COVID-19 experiences, as defined by child and parent report, using a multivariate pattern-learning framework from the Adolescent Brain and Cognitive Development (ABCD) cohort. Design, Setting, and Participants ABCD is an ongoing prospective longitudinal study of child and adolescent development in the United States including 11 875 youths, enrolled at age 9 to 10 years. Using nationally collected longitudinal profiling data from 9267 families, a multivariate pattern-learning strategy was developed to identify factor combinations associated with transgenerational costs of the ongoing COVID-19 pandemic. ABCD data (release 3.0) collected from 2016 to 2020 and released between 2019 and 2021 were analyzed in combination with ABCD COVID-19 rapid response data from the first 3 collection points (May-August 2020). Exposures Social distancing and other response measures imposed by COVID-19, including school closures and shutdown of many childhood recreational activities. Main Outcomes and Measures Mid-COVID-19 experiences as defined by the ABCD's parent and child COVID-19 assessments. Results Deep profiles from 9267 youth (5681 female [47.8%]; mean [SD] age, 119.0 [7.5] months) and their caregivers were quantitatively examined. Enabled by a pattern-learning analysis, social determinants of inequity, including family structure, socioeconomic status, and the experience of racism, were found to be primarily associated with transgenerational impacts of COVID-19, above and beyond other candidate predictors such as preexisting medical or psychiatric conditions. Pooling information across more than 17 000 baseline pre-COVID-19 family indicators and more than 280 measures of day-to-day COVID-19 experiences, non-White (ie, families who reported being Asian, Black, Hispanic, other, or a combination of those choices) and/or Spanish-speaking families were found to have decreased resources (mode 1, canonical vector weight [CVW] = 0.19; rank 5 of 281), escalated likelihoods of financial worry (mode 1, CVW = -0.20; rank 4), and food insecurity (mode 1, CVW = 0.21; rank 2), yet were more likely to have parent-child discussions regarding COVID-19-associated health and prevention issues, such as handwashing (mode 1, CVW = 0.14; rank 9), conserving food or other items (mode 1, CVW = 0.21; rank 1), protecting elderly individuals (mode 1, CVW = 0.11; rank 21), and isolating from others (mode 1, CVW = 0.11; rank 23). In contrast, White families (mode 1, CVW = -0.07; rank 3), those with higher pre-COVID-19 income (mode 1, CVW = -0.07; rank 5), and presence of a parent with a postgraduate degree (mode 1, CVW = -0.06; rank 14) experienced reduced COVID-19-associated impact. In turn, children from families experiencing reduced COVID-19 impacts reported longer nighttime sleep durations (mode 1, CVW = 0.13; rank 14), less difficulties with remote learning (mode 2, CVW = 0.14; rank 7), and decreased worry about the impact of COVID-19 on their family's financial stability (mode 1, CVW = 0.134; rank 13). Conclusions and Relevance The findings of this study indicate that community-level, transgenerational intervention strategies may be needed to combat the disproportionate burden of pandemics on minoritized and marginalized racial and ethnic populations.

2022-04-01

JAMA Psychiatry (publié)

Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging

O. Benkarim

Casey Paquola

Bo-yong Park

Valeria Kebets

Seokjun Hong

Reinder Vos de Wael

Shaoshi Zhang

B.T. Thomas Yeo

Michael Eickenberg

Tian Ge

Jean-Baptiste Poline

B. Bernhardt

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the suc… (voir plus)cess of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

2022-04-01

PLoS Biology (published)

Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Jingwei Li

Jianzhong Chen

Angela Tam

Leon Qi

Leon Qi Rong Ooi

Avram J. Holmes

Tian Ge

Kaustubh R. Patil

Mbemba Jabbi

Simon B. Eickhoff

B.T. Thomas Yeo

Sarah Genon

Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here,… (voir plus) we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.

2022-03-16

Science Advances (publié)

APOE ɛ2 vs APOE ɛ4 dosage shows sex-specific links to hippocampus-default network subregion co-variation

Chloé Savignac

Sylvia Villeneuve

AmanPreet Badhwar

Karin Saltoun

Kimia Shafighi

Chris Zajner

Vaibhav Sharma

Sarah A Gagliano Taliun

Sali Farhan

Judes Poirier

Alzheimer’s disease and related dementias (ADRD) are marked by intracellular tau aggregates in the medial-temporal lobe (MTL) and extracel… (voir plus)lular amyloid aggregates in the default network (DN). Here, we sought to clarify ADRD-related co-dependencies between the MTL’s most vulnerable structure, the hippocampus (HC), and the highly associative DN at a subregion resolution. We confronted the effects of APOE ɛ2 and ɛ4, rarely investigated together, with their impact on HC-DN co-variation regimes at the population level. In a two-pronged decomposition of structural brain scans from ∼40,000 UK Biobank participants, we located co-deviating structural patterns in HC and DN subregions as a function of ADRD family risk. Across the disclosed HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix’s fimbria, and their cortical partners related to ADRD risk. Phenome-wide profiling of HC-DN co- variation expressions from these population signatures revealed male-specific associations with air-pollution, and female-specific associations with cardiovascular traits. We highlighted three main factors associated with brain-APOE associations across the different gene variants: happiness, and satisfaction with friendships, and with family. We further showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our findings reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex, which we have linked to fine-grained structural divergences indicative of ADRD susceptibility.

2022-03-15

bioRxiv (prépublication)

Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Jingwei Li

Jianzhong Chen

Angela Tam

Leon Qi

Rong Ooi

Avram J. Holmes

Tian Ge

K. Patil

M. Jabbi

Simon B. Eickhoff

B.T. Thomas Yeo

Sarah Genon

2022-03-01

Science Advances (published)

More Than Meets the Eye: Art Engages the Social Brain

Janneke E. P. van Leeuwen

Jeroen Boomgaard

S. Crutch

J. Warren

Here we present the viewpoint that art essentially engages the social brain, by demonstrating how art processing maps onto the social brain … (voir plus)connectome—the most comprehensive diagram of the neural dynamics that regulate human social cognition to date. We start with a brief history of the rise of neuroaesthetics as the scientific study of art perception and appreciation, in relation to developments in contemporary art practice and theory during the same period. Building further on a growing awareness of the importance of social context in art production and appreciation, we then set out how art engages the social brain and outline candidate components of the “artistic brain connectome.” We explain how our functional model for art as a social brain phenomenon may operate when engaging with artworks. We call for closer collaborations between the burgeoning field of neuroaesthetics and arts professionals, cultural institutions and diverse audiences in order to fully delineate and contextualize this model. Complementary to the unquestionable value of art for art’s sake, we argue that its neural grounding in the social brain raises important practical implications for mental health, and the care of people living with dementia and other neurological conditions.

2022-02-25

Frontiers in Neuroscience (published)