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

Diagnosing as autistic people increasingly distant from prototypes lead neither to clinical benefit nor to the advancement of knowledge

Laurent Mottron

2021-10-11

Molecular Psychiatry (publié)

Population modeling with machine learning can enhance measures of mental health

Kamalaker Dadi

Gael Varoquaux

Josselin Houenou

Bertrand Thirion

Denis Engemann

We applied machine learning on more than 10.000 individuals from the general population to define empirical approximations of health-related… (voir plus) psychological measures that do not require human judgment.We found that machine-learning enriched the given psychological measures via approximation from brain and sociodemographic data: Resulting proxy measures related as well or better to real-world health behavior than the original measures.Model comparisons showed that sociodemographic information contributed most to characterizing psychological traits beyond aging.

2021-09-30

GigaScience (inconnu)

Connectivity alterations in autism reflect functional idiosyncrasy

Oualid Benkarim

Casey Paquola

Bo-yong Park

Seok-Jun Hong

Jessica Royer

Reinder Vos de Wael

Sara Larivière

Sofie Valk

Laurent Mottron

Boris C. Bernhardt

Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-develo… (voir plus)ping controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism. Benkarim et al devise an approach to profile inter-individual variability in functional network organization and test whether such idiosyncrasy contributes to the connectivity alterations found in Autism Spectrum Disorder. Their approach provides potential biomarkers to study atypical brain development and may be used to consolidate prior research findings on the variable nature of connectome level anomalies in autism.

2021-09-14

Communications Biology (publié)

Decision Models and Technology Can Help Psychiatry Develop Biomarkers

Daniel S. Barron

Justin T. Baker

Kristin S. Budde

Simon B. Eickhoff

Karl J. Friston

Peter T. Fox

Paul Geha

Stephen Heisig

Avram J. Holmes

Jukka-Pekka Onnela

Albert Powers

David Silbersweig

John H. Krystal

2021-09-08

Frontiers in Psychiatry (publié)

The default mode network in cognition: a topographical perspective

Jonathan Smallwood

Boris C Bernhardt

Robert Leech

Elizabeth Jefferies

Daniel S. Margulies

2021-07-04

Nature Reviews Neuroscience (publié)

Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods

Taylor Bolt

Jason S. Nomi

Lucina Q. Uddin

Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is incr… (voir plus)easing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the “Methods” and “Results” sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.

2021-06-30

PLoS Biology (publié)

Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns

Taylor Bolt

Jason S. Nomi

Catie Chang

B.T. Yeo

Lucina Q. Uddin

Shella Keilholz

2021-06-19

(publié)

The Cost of Untracked Diversity in Brain-Imaging Prediction

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 Bernhardt

Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success … (voir plus)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 stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two 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 to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

2021-06-16

bioRxiv (prépublication)

Publisher Correction: The default network of the human brain is associated with perceived social isolation

R. Nathan Spreng

Emile Dimas

Laetitia Mwilambwe-Tshilobo

Alain Dagher

Philipp Koellinger

Gideon Nave

Anthony Ong

Julius M. Kernbach

Thomas V. Wiecki

Tian Ge

Yue Li

Avram J. Holmes

B. T. Thomas Yeo

Gary R. Turner

Robin I. M. Dunbar

2021-05-20

Nature Communications (publié)

Variability in Brain Structure and Function Reflects Lack of Peer Support

Matthias Schurz

Lucina Q. Uddin

Philipp Kanske

Claus Lamm

Jérôme Sallet

Boris C. Bernhardt

Rogier B. Mars

Humans are a highly social species. Complex interactions for mutual support range from helping neighbors to building social welfare institut… (voir plus)ions. During times of distress or crisis, sharing life experiences within one’s social circle is critical for well-being. By translating pattern-learning algorithms to the UK Biobank imaging-genetics cohort (n = ~40 000 participants), we have delineated manifestations of regular social support in multimodal whole-brain measurements. In structural brain variation, we identified characteristic volumetric signatures in the salience and limbic networks for high- versus low-social support individuals. In patterns derived from functional coupling, we also located interindividual differences in social support in action–perception circuits related to binding sensory cues and initiating behavioral responses. In line with our demographic profiling analysis, the uncovered neural substrates have potential implications for loneliness, substance misuse, and resilience to stress.

2021-05-12

Cerebral Cortex (New York, NY) (publié)

How does hemispheric specialization contribute to human-defining cognition?

Gesa Hartwigsen

Yoshua Bengio

2021-05-10

Neuron (publié)

Phenotypical predictors of pregnancy-related restless legs syndrome and their association with basal ganglia and the limbic circuits

Natalia Chechko

Jeremy Lefort-Besnard

Tamme W. Goecke

Markus Frensch

Patricia Schnakenberg

Susanne Stickel

Restless legs syndrome (RLS) in pregnancy is a common disorder with a multifactorial etiology. A neurological and obstetrical cohort of 308 … (voir plus)postpartum women was screened for RLS within 1 to 6 days of childbirth and 12 weeks postpartum. Of the 308 young mothers, 57 (prevalence rate 19%) were identified as having been affected by RLS symptoms in the recently completed pregnancy. Structural and functional MRI was obtained from 25 of these 57 participants. A multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors of RLS. A decreased volume of the parietal, orbitofrontal and frontal areas shortly after delivery was found to be linked to persistent RLS symptoms up to 12 weeks postpartum, the symptoms' severity and intensity in the most recent pregnancy, and a history of RLS in previous pregnancies. The same negative relationship was observed between brain volume and not being married, not receiving any iron supplement and higher numbers of stressful life events. High cortisol levels, being married and receiving iron supplements, on the other hand, were found to be associated with increased volumes in the bilateral striatum. Investigating RLS symptoms in pregnancy within a brain-phenotype framework may help shed light on the heterogeneity of the condition.

2021-05-10

Scientific Reports (publié)