Danilo Bzdok

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Gregory Bell

PhD - McGill University

gregory.bell@mila.quebec

Jack Stanley

PhD - McGill University

jack.stanley@mila.quebec

Website

justin.marotta@mila.quebec

Jakub Kopal

Postdoctorate - Université de Montréal

jakub.kopal@mila.quebec

Google Scholar

Justin Marotta

Master's Research - McGill University

Karam Ghanem

Master's Research - McGill University

karam.ghanem@mila.quebec

Karin Saltoun

PhD - McGill University

karin.saltoun@mila.quebec

Le Zhou

PhD - McGill University

le.zhou@mila.quebec

Liam Hodgson

PhD - McGill University

liam.hodgson@mila.quebec

Nicole Osayande

Master's Research - McGill University

nicole.osayande@mila.quebec

Shambhavi Aggarwal

Master's Research - McGill University

shambhavi.aggarwal@mila.quebec

Zilong Wang

Master's Research - McGill University

zilong.wang@mila.quebec

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

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Blog Posts

September 8, 2020

Danilo Bzdok

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Publications

A parsimonious description of global functional brain organization in three spatiotemporal patterns

Taylor Bolt

Jason S. Nomi

Jorge A. Salas

Catie Chang

B.T. Thomas Yeo

Lucina Q. Uddin

Shella Keilholz

2022-07-28

Nature Neuroscience (published)

Global fMRI signal topography differs systematically across the lifespan

Jason S. Nomi

Jingwei Li

Taylor Bolt

Catie Chang

Salome Kornfeld

Zachary T. Goodman

B.T. Thomas Yeo

R. Nathan Spreng

Lucina Q. Uddin

2022-07-27

bioRxiv (preprint)

From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation

Laurette Dubé

Patricia P. Silveira

Daiva E. Nielsen

Spencer Moore

Catherine Paquet

J. Miguel Cisneros-Franco

Gina Kemp

Bärbel Knauper

Yu Ma

Mehmood Khan

Gillian Bartlett-Esquilant

Alan C. Evans

Lesley K. Fellows

Jorge L. Armony

R. Nathan Spreng

Jian-Yun Nie

Shawn T. Brown

Georg Northoff

Citation: Dubé L, Silveira PP, Nielsen DE, Moore S, Paquet C, Cisneros-Franco JM, Kemp G, Knauper B, Ma Y, Khan M, Bartlett-Esquilant G, Ev… (see more)ans AC, Fellows LK, Armony JL, Spreng RN, Nie J-Y, Brown ST, Northoff G and Bzdok D (2022) From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation. Front. Public Health 10:720117. doi: 10.3389/fpubh.2022.720117 From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation

2022-07-05

Frontiers in Public Health (published)

Interacting brains revisited: A cross‐brain network neuroscience perspective

Christian Gerloff

Kerstin Konrad

Christina Büsing

Vanessa Reindl

2022-06-06

Human Brain Mapping (published)

Human brain anatomy reflects separable genetic and environmental components of socioeconomic status

Hyeokmoon Kweon

Gökhan Aydogan

Alain Dagher

Christian C. Ruff

Gideon Nave

Martha J Farah

Philipp Koellinger

Recent studies report that socioeconomic status (SES) correlates with brain structure. Yet, such findings are variable and little is known a… (see more)bout underlying causes. We present a well-powered voxel-based analysis of grey matter volume (GMV) across levels of SES, finding many small SES effects widely distributed across the brain, including cortical, subcortical and cerebellar regions. We also construct a polygenic index of SES to control for the additive effects of common genetic variation related to SES, which attenuates observed SES-GMV relations, to different degrees in different areas. Remaining variance, which may be attributable to environmental factors, is substantially accounted for by body mass index, a marker for lifestyle related to SES. In sum, SES affects multiple brain regions through measurable genetic and environmental effects. One-sentence Summary Socioeconomic status is linked with brain anatomy through a varying balance of genetic and environmental influences.

2022-05-18

Science Advances (published)

Multi-tract multi-symptom relationships in pediatric concussion

Guido I Guberman

Sonja Stojanovski

Eman Nishat

Alain Ptito

Anne L Wheeler

Maxime Descoteaux

The heterogeneity of white matter damage and symptoms in concussions has been identified as a major obstacle to therapeutic innovation. In c… (see more)ontrast, the vast majority of diffusion MRI studies on concussion have traditionally employed group-comparison approaches. Such studies do not consider heterogeneity of damage and symptoms in concussion. To parse concussion heterogeneity, the present study combines diffusion MRI (dMRI) and multivariate statistics to investigate multi-tract multi-symptom relationships. Using dMRI data from a sample of 306 children ages 9 and 10 with a history of concussion from the Adolescent Brain Cognitive Development Study (ABCD study), we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first capturing a mixture of patterns suggestive of microstructural complexity, the second representing almost exclusively axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 behavioural measures that capture well-known symptoms of concussions. We found idiosyncratic symptom-specific multi-tract connectivity features, which would not be captured in traditional univariate analyses. Multivariable connectome-symptom correspondences were stronger than all single-tract/single-symptom associations. Multi-tract connectivity features were also expressed equally across different sociodemographic strata and their expression was not accounted for by injury-related variables. In a replication dataset, the expression of multi-tract connectivity features predicted adverse psychiatric outcomes after accounting for other psychopathology-related variables. By defining cross-demographic multi-tract multi-symptom relationships to parse concussion heterogeneity, the present study can pave the way for the development of improved stratification strategies that may contribute to the success of future clinical trials and the improvement of concussion management.

2022-05-17

eLife (published)

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 (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 (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

Christopher L Asplund

Scott A. Marek

N. Dosenbach

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

2022-04-25

Nature Communications (published)

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… (see more)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 (published)

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… (see more)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,… (see more) 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 (published)