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

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Xiaoxuan Yan

Ru Kong

Aihuiping Xue

Qing Yang

Csaba Orbán

Lijun An

Avram J. Holmes

Xing Qian

Jianzhong Chen

Xi-Nian Zuo

Juan Helen Zhou

Marielle V Fortier

Ai Peng Tan

Peter Gluckman

Yap Seng Chong

Michael J Meaney

Simon B. Eickhoff

B.T. Thomas Yeo

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human… (voir plus) neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

2022-12-31

NeuroImage (publié)

Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study

Jianzhong Chen

Leon Qi Rong Ooi

Trevor Wei Kiat Tan

Shaoshi Zhang

Jingwei Li

Christopher L. Asplund

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation… (voir plus) of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

2022-12-31

NeuroImage (publié)

Orientation and Context Entangled Network for Retinal Vessel Segmentation

Xinxu Wei

Kaifu Yang

Yongjie Li

Most of the existing deep learning based methods for vessel segmentation neglect two important aspects of retinal vessels, one is the orient… (voir plus)ation information of vessels, and the other is the contextual information of the whole fundus region. In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels. To achieve complex orientation aware, a Dynamic Complex Orientation Aware Convolution (DCOA Conv) is proposed to extract complex vessels with multiple orientations for improving the vessel continuity. To simultaneously capture the global context information and emphasize the important local information, a Global and Local Fusion Module (GLFM) is developed to simultaneously model the long-range dependency of vessels and focus sufficient attention on local thin vessels. A novel Orientation and Context Entangled Non-local (OCE-NL) module is proposed to entangle the orientation and context information together. In addition, an Unbalanced Attention Refining Module (UARM) is proposed to deal with the unbalanced pixel numbers of background, thick and thin vessels. Extensive experiments were performed on several commonly used datasets (DRIVE, STARE and CHASEDB1) and some more challenging datasets (AV-WIDE, UoA-DR, RFMiD and UK Biobank). The ablation study shows that the proposed method achieves promising performance on maintaining the continuity of thin vessels and the comparative experiments demonstrate that our OCE-Net can achieve state-of-the-art performance on retinal vessel segmentation.

2022-12-28

Expert Systems with Applications (inconnu)

arxiv.org

Simulation-based power analysis could improve the design of clinical trials in Alzheimer's disease

Daniel Andrews

Douglas L. Arnold

Simon Ducharme

Howard Chertkow

D. Louis Collins

Clinical trials of new treatments in different progressive diseases use power analysis to determine the sample size needed for a trial to ob… (voir plus)tain a statistically significant estimate for an anticipated treatment effect. In trials with parallel designs, the standard power analysis approach is based on a two-sample t-test. For example, the standard t-test approach was used in determining the sample size for the Phase 3 trials of aducanumab, the first drug approved by the United States Food and Drug Administration (FDA) to potentially slow cognitive decline in early-stage Alzheimer’s disease. However, t-tests contain normality assumptions, and t-test-based power analyses do not implicitly factor in the uncertainty about anticipated treatment effects that arises due to inter-subject heterogeneity in disease progression. These limitations may lead to recommended sample sizes that are too small, potentially making a trial blind to a treatment effect that is truly present if the cohort’s endpoints are not normally distributed and/or the anticipated treatment effect is overestimated. To address these issues, we present a novel power analysis method that (1) simulates clinical trials in a progressive disease using real-world data, (2) accounts for inter-subject heterogeneity in disease progression, and (3) does not depend on normality assumptions. As a showcase example, we used our method to calculate power for a range of sample sizes and treatment effects in simulated trials similar to the Phase 3 aducanumab trials EMERGE and ENGAGE. As expected, our results show that power increases with number of subjects and treatment effect (here defined as the cohort-level percent reduction in the rate of cognitive decline in treated subjects vs. controls). However, inclusion of realistic inter-subject heterogeneity in cognitive decline trajectories leads to increased sample size recommendations compared to a standard t-test power analysis. These results suggest that the sample sizes recommended by the t-test power analyses in the EMERGE and ENGAGE Statistical Analysis Plans were possibly too small to ensure a high probability of detecting the anticipated treatment effect. Insufficient sample sizes could partly explain the statistically significant effect of aducanumab being detected only in EMERGE. We also used our method to analyze power in simulated trials similar the Phase 3 lecanemab trial Clarity AD. Our results suggest that Clarity AD was adequately powered, and that power may be influenced by a trial’s number of analysis visits and the characteristics of subgroups within a cohort. By using our simulation-based power analysis approach, clinical trials of treatments in Alzheimer’s disease and potentially in other progressive diseases could obtain sample size recommendations that account for heterogeneity in disease progression and uncertainty in anticipated treatment effects. Our approach avoids the limitations of t-tests and thus could help ensure that clinical trials are more adequately powered to detect the treatment effects they seek to measure.

2022-12-26

medRxiv (prépublication)

Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival

Julius M. Kernbach

Daniel Delev

Georg Neuloh

Hans Clusmann

Simon B. Eickhoff

Victor E. Staartjes

Flavio Vasella

Michael Weller

Luca Regli

Carlo Serra

Niklaus Krayenbühl

Kevin Akeret

The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study … (voir plus)was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology’s unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical–clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.

2022-12-19

Brain Communications (publié)

APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia

Chloé Savignac

Sylvia Villeneuve

AmanPreet Badhwar

Karin Saltoun

Kimia Shafighi

Chris Zajner

Vaibhav Sharma

Sarah A. Gagliano Taliun

Sali Farhan

Judes Poirier

Alzheimer’s disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in th… (voir plus)e default network (DN). Here, we examined codependent structural variations between the MTL’s most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer’s disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer’s disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix’s fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.

2022-11-30

PLoS Biology (publié)

Age differences in functional brain networks associated with loneliness and empathy

Laetitia Mwilambwe-Tshilobo

Roni Setton

Gary R. Turner

R. Nathan Spreng

Network Neuroscience

Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- … (voir plus)and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality—loneliness and empathic responding—and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.

2022-11-22

Network Neuroscience (publié)

Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles

Karin Saltoun

Ralph Adolphs

Lynn K. Paul

Vaibhav Sharma

Joern Diedrichsen

B. T. Thomas Yeo

2022-11-06

Nature Human Behaviour (inconnu)

Representational ethical model calibration

Robert Carruthers

Isabel Straw

James K. Ruffle

Daniel Herron

Amy Nelson

Delmiro Fernandez-Reyes

Geraint Rees

Parashkev Nachev

2022-11-03

NPJ Digital Medicine (publié)

arxiv.org

Social isolation and the brain in the pandemic era

Robin I. M. Dunbar

2022-10-17

Nature Human Behaviour (publié)

Lesions in putative language and attention regions are linked to more severe strokes in patients with higher white matter hyperintensity burden

Anna K. Bonkhoff

Sungmin Hong

Martin Bretzner

Markus D. Schirmer

Robert W. Regenhardt

E. Murat Arsava

Kathleen L. Donahue

Marco J. Nardin

Adrian V. Dalca

Anne-Katrin Giese

Mark R. Etherton

Brandon L. Hancock

Steven J. T. Mocking

Elissa C. McIntosh

John Attia

Oscar R. Benavente

John W. Cole

Amanda Donatti

Christoph J. Griessenauer

Laura Heitsch … (voir 37 de plus)

Lukas Holmegaard

Katarina Jood

Jordi Jimenez-Conde

Steven J. Kittner

Robin Lemmens

Christopher R. Levi

Caitrin W. McDonough

James F. Meschia

Chia-Ling Phuah

Arndt Rolfs

Stefan Ropele

Jonathan Rosand

Jaume Roquer

Tatjana Rundek

Ralph L. Sacco

Reinhold Schmidt

Pankaj Sharma

Agnieszka Slowik

Martin Söderholm

Alessandro Sousa

Tara M. Stanne

Daniel Strbian

Turgut Tatlisumak

Vincent Thijs

Achala Vagal

Johan Wasselius

Daniel Woo

Ramin Zand

Patrick F. McArdle

Bradford B. Worrall

Christina Jern

Arne G. Lindgren

Jane Maguire

Polina Golland

Ona Wu

Natalia S. Rost

To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in… (voir plus) lesion pattern–specific ways. MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3–6 months after stroke were obtained from the MRI–Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning–based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location–specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location. Higher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.

2022-09-26

Neurology (publié)

Social belonging: brain structure and function is linked to membership in sports teams, religious groups, and social clubs

Carolin Kieckhaefer

Leonhard Schilbach

Human behavior across the life span is driven by the psychological need to belong, right from kindergarten to bingo nights. Being part of so… (voir plus)cial groups constitutes a backbone for communal life and confers many benefits for the physical and mental health. Capitalizing on the neuroimaging and behavioral data from ∼40,000 participants from the UK Biobank population cohort, we used structural and functional analyses to explore how social participation is reflected in the human brain. Across 3 different types of social groups, structural analyses point toward the variance in ventromedial prefrontal cortex, fusiform gyrus, and anterior cingulate cortex as structural substrates tightly linked to social participation. Functional connectivity analyses not only emphasized the importance of default mode and limbic network but also showed differences for sports teams and religious groups as compared to social clubs. Taken together, our findings establish the structural and functional integrity of the default mode network as a neural signature of social belonging.

2022-09-25

Cerebral Cortex (inconnu)