Danilo Bzdok

Google Scholar

Badr Ait Hammou

Postdoctorate - McGill University

Karan Bali Bali

PhD - McGill University

Gregory Bell

PhD - McGill University

Anwesha Bhattacharya

PhD - McGill University

Pedro Carneiro Carneiro

PhD - McGill University

ryan.mcphedrain@mail.mcgill.ca

Liam Hodgson

PhD - McGill University

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

Google Scholar

Jack Stanley

PhD - McGill University

Website

Using LLMs to better understand autism diagnosis

Praneet Suresh

PhD - McGill University

Le Zhou

PhD - McGill University

Blog Posts

March 25, 2025

Jack Stanley

Danilo Bzdok

Read the article

September 8, 2020

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

Danilo Bzdok

Read the article

Publications

Inferring disease subtypes from clusters in explanation space

Marc-Andre Schulz

Matt Chapman-Rounds

Manisha Verma

Konstantinos Georgatzis

2020-07-30

Scientific Reports (published)

Molecular signatures of cognition and affect

Justine Y. Hansen

Ross D Markello

Jacob W. Vogel

Jakob Seidlitz

Bratislav Mišić

2020-07-16

bioRxiv (preprint)

Hemispheric specialization within the inferior parietal lobe across cognitive domains

Ole Numssen

Gesa Hartwigsen

The inferior parietal lobe (IPL) is a key neural substrate underlying diverse mental processes, from basic attention to language and social … (see more)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 (preprint)

Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment.

Anna K. Bonkhoff

Thomas Hope

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… (see more)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 (published)

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

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… (see more) 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 (preprint)

Dark control: The default mode network as a reinforcement learning agent

Elvis Dohmatob

Guillaume Dumas

2020-06-05

Human Brain Mapping (published)

The Neurobiology of Social Distance

Robin I. M. Dunbar

2020-06-03

Trends in Cognitive Sciences (published)

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

2020-06-01

Social Cognitive and Affective Neuroscience (published)

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

Sarah Lippé

Mallar Chakravarty

Bogdan Draganski

Sébastien Jacquemont

2020-05-01

Biological Psychiatry (published)

What Can Machine Learning Do for Psychiatry?

Daniel S. Barron

John H. Krystal

R. Todd Constable

2020-05-01

Biological Psychiatry (published)

Autism spectrum heterogeneity: fact or artifact?

Laurent Mottron

2020-04-30

Molecular Psychiatry (published)

CNN to detect differences in cerebral cortical anatomy of left- and right- handers

Lisa Meyer-Baese

Erik Roecher

Lucas Moesch

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… (see more) 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 (published)