Danilo Bzdok

Pourquoi les modèles d'IA hallucinent et comment y remédier

A tab representation of the Pre trained CLIP Vision Transformer

8 octobre 2025

par

Praneet Suresh

Jack Stanley

Danilo Bzdok

Sonia Joseph

Luca Scimeca

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

25 mars 2025

par

Jack Stanley

Danilo Bzdok

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

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

8 octobre 2020

par

Danilo Bzdok

Publications

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity

Benjamin Clemens

Jeremy Lefort-Besnard

Christoph Ritter

Elke Smith

Mikhail Votinov

Birgit Derntl

Ute Habel

Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors. Here, we… (voir plus) bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. In both brain assessments, we used penalized logistic regression models and nonparametric permutation. We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.

2022-09-13

Cerebral Cortex (inconnu)

Association between Anxiety and Disease Pathophysiology in Participants of Longitudinal Observational Studies in Aging during the COVID-19 Lockdown

Stijn Servaes

Firoza Lussier

Cécile Tissot

Joseph Therriault

Gleb Bezgin

Yi-Ting Wang

Jenna Stevenson

Nesrine Rahmouni

Vanessa Pallen

Guillaume Elgbeili

Jaime Fernandez Arias

Min Su Kang

Andrea Benedet

Mira Chamoun

Tharick Pascoal

Kok Pin Ng

Suzanne King

Serge Gauthier

Pedro Rosa-Neto

Abstract

The burden imposed by the COVID-19 pandemic deferentially interferes with the outcomes of clinical trials of aging… (voir plus) and dementia. We examined the impact of the lockdown on cognitive impairment due to Alzheimer’s Disease (AD), anxiety, and COVID-19-related stress in participants from the Translational Biomarkers In Aging and Dementia (TRIAD) cohort using neuropsychiatric assessments, tau and amyloid PET. We found that, before the lockdown, anxiety was higher in cognitively impaired individuals (CI) and positively associated with brain tau load. However, during the lockdown, anxiety increased only in the cognitively unimpaired (CU) and was positively associated with COVID-19 related stress. Interestingly, we found that in patients, tau load was anti-correlated with higher anxiety during lockdown. Our findings contribute to a framework for interpreting the effects of the pandemic on neuropsychiatric symptoms among clinical trial participants. Collectively, our results suggest that caregivers are more vulnerable to external stressors than patients.

2022-08-29

Research Square (prépublication)

Endorsing Complexity Through Diversity: Computational Psychiatry Meets Big Data Analytics

Jakub Kopal

2022-08-04

Biological Psychiatry (inconnu)

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 D. Keilholz

Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-… (voir plus)scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.

2022-07-27

Nature Neuroscience (publié)

Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets

Mariam Zabihi

Seyed Mostafa Kia

Thomas Wolfers

Stijn de Boer

Charlotte Fraza

Sourena Soheili-Nezhad

Richard Dinga

Alberto Llera Arenas

Christian F. Beckmann

Andre Marquand

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral ma… (voir plus)pping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

2022-07-26

bioRxiv (prépublication)

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

2022-07-04

Frontiers in Public Health (publié)

A guided multiverse study of neuroimaging analyses

Jessica Dafflon

Pedro F. da Costa

František Váša

Ricardo Pio Monti

Peter J. Hellyer

Federico Turkheimer

Jonathan Smallwood

Emily Jones

Robert Leech

For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic… (voir plus) method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.

2022-06-28

Nature Communications (publié)

Reproducible between-person brain-behavior associations do not always require thousands of individuals

Colin G. DeYoung

Tyler A. Sassenberg

Rany Abend

Timothy A. Allen

Roger E. Beaty

Mark A. Bellgrove

Scott D. Blain

Robert S. Chavez

Stephen A. Engel

Ma Feilong

Alex Fornito

Erhan Genç

Vina M. Goghari

Rachael Grazioplene

Jamie L. Hanson

James V. Haxby

Kirsten Hilger

Philipp Homan

Keanan J. Joyner … (voir 12 de plus)

Antonia N. Kaczkurkin

Robert D. Latzman

Elizabeth A. Martin

Luca Passamonti

Alan D. Pickering

Adam Safron

Michelle N. Servaas

Luke D. Smillie

R. Nathan Spreng

Jeggan Tiego

Essi Viding

Jan Wacker

Marek et al. analyzed three very large magnetic resonance imaging (MRI) datasets and concluded that thousands of participants are necessary … (voir plus)to ensure replicable results in “brain-wide associations studies,” which they defined as “studies of the associations between common inter-individual variability in human brain structure/function and cognition or psychiatric symptomatology.” This conclusion overgeneralizes the implications of their findings and is likely to have an unwarranted chilling effect on neuroimaging research focused on individual differences, preventing good research with samples in the hundreds from being funded and conducted. To fend off these negative consequences, we explain why their conclusion is not fully justified, discuss methods that can yield larger effects, and suggest practical guidelines for sample size, recognizing the potential utility of samples in the hundreds.

2022-06-28

PsyArXiv Preprints (prépublication)

From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning

Nahiyan Malik

2022-06-17

Neural Networks (accepté)

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 ext… (voir plus)end 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 hyperscanning 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.

2022-06-05

Human Brain Mapping (inconnu)

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 D. Koellinger

Socioeconomic status (SES) correlates with brain structure, a relation of interest given the long-observed relations of SES to cognitive abi… (voir plus)lities and health. Yet, major questions remain open, in particular, the pattern of causality that underlies this relation. In an unprecedently large study, here, we assess genetic and environmental contributions to SES differences in neuroanatomy. We first establish robust SES–gray matter relations across a number of brain regions, cortical and subcortical. These regional correlates are parsed into predominantly genetic factors and those potentially due to the environment. We show that genetic effects are stronger in some areas (prefrontal cortex, insula) than others. In areas showing less genetic effect (cerebellum, lateral temporal), environmental factors are likely to be influential. Our results imply a complex interplay of genetic and environmental factors that influence the SES-brain relation and may eventually provide insights relevant to policy.

2022-05-17

Science Advances (publié)

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

Nathania Suryoputri

Hannah Kiesow

ABSTRACT Health disparity across layers of society involves reasons beyond the healthcare system. Socioeconomic status (SES) shapes people… (voir plus)s daily interaction with their social environment and is known to impact various health outcomes. Using generative probabilistic modeling, we investigate health satisfaction and complementary indicators of socioeconomic lifestyle in the human social brain. In a population cohort of ~10,000 UK Biobank participants, our first analysis probed the relationship between health status and subjective social standing (i.e., financial satisfaction). We identified volume effects in participants unhappy with their health in regions of the higher associative cortex, especially the dorsomedial prefrontal cortex (dmPFC) and bilateral temporo-parietal junction (TPJ). Specifically, participants in poor subjective health showed deviations in dmPFC and TPJ volume as a function of financial satisfaction. The second analysis on health status and objective social standing (i.e., household income) revealed volume deviations in regions of the limbic system for individuals feeling unhealthy. In particular, low-SES participants dissatisfied with their health showed deviations in volume distributions in the amygdala and hippocampus bilaterally. Thus, our population-level evidence speaks to the possibility that health status and socioeconomic position have characteristic imprints in social brain differentiation.

2022-05-03

Social Neuroscience (inconnu)