Danilo Bzdok

Badr Ait Hammou

Postdoctorate - McGill University

Gregory Bell

PhD - McGill University

Anwesha Bhattacharya

PhD - McGill University

Liam Hodgson

PhD - McGill University

Jakub Kopal

Postdoctorate - Université de Montréal

ryan.mcphedrain@mail.mcgill.ca

Justin Marotta

Master's Research - McGill University

Anny Maza

Collaborating researcher - Universitat Politècnica

Ryan McPhedrain McPhedrain

Postdoctorate - McGill University

David Payette

Master's Research - McGill University

Sepehr Radmannia

Independent visiting researcher - McGill University

sradmannia@gmail.com

Karin Saltoun

PhD - McGill University

Chloé Savignac

PhD - McGill University

Jack Stanley

PhD - McGill University

Website

Praneet Suresh

PhD - McGill University

Zilong Wang

Master's Research - McGill University

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

Le Zhou

PhD - McGill University

Blog Posts

September 8, 2020

Danilo Bzdok

Read the article

Publications

Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns

Chris Zajner

Nathan Spreng

Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominatin… (see more)g factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40,000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3, and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC-DN co-variation followed a clear segregation divide into the left and right brain hemispheres. Separable regimes of structural HC-DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.

2021-08-19

bioRxiv (preprint)

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

H. Kweon

Gökhan Aydogan

Alain Dagher

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.

2021-07-29

bioRxiv (preprint)

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

S. Keilholz

2021-06-20

Nature Neuroscience (published)

Multi-tract multi-symptom relationships in pediatric concussion

Guido Ivan Guberman

Sonja Stojanovski

Eman Nishat

Alain Ptito

A. 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.

2021-04-07

medRxiv (preprint)

Interacting brains revisited: A cross‐brain network neuroscience perspective

Christian Gerloff

Kerstin Konrad

Christina Büsing

Vanessa Reindl

Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to exten… (see more)d the isolated perspective on the human brain by considering interacting persons’ brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions’ roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

2021-02-20

bioRxiv (preprint)

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