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

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

Kathleen A. Williams

Ole Numssen

Juan David Guerra

Jakub Kopal

Gesa Hartwigsen

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in dai… (voir plus)ly life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

2024-10-31

Heliyon (publié)

Genetic Interplay Between White Matter Hyperintensities and Alzheimer's Disease: A Brain-Body Perspective

Manpreet Singh

Kimia Shafighi

Flavie E. Detcheverry

Fanta Dabo

Ikrame Housni

Sridar Narayanan

Sarah A. Gagliano Taliun

AmanPreet Badhwar

MRI-detected white matter hyperintensities (WMH) are often recognized as markers of cerebrovascular abnormalities and an index of vascular b… (voir plus)rain injury, and are frequently present in individuals with Alzheimer’s disease (AD). Given the emerging bidirectional communication between the brain-body axis in both WMHs and AD, it is important to understand their genetic underpinnings across the whole body. However, literature on this is scarce. We investigated the brain-body axis by breaking down heritability estimates of these phenotypes across the whole body, – i.e., partitioning heritability. Our aims were to identify genetic underpinnings specific to WMHs, and common between WMHs and AD, by assessing (a) the partitioned heritability of WMHs and AD across the brain-body axis with tissue-specific annotations, (b) the partitioned heritability of WMHs and AD across the brain-body axis with cell-specific annotations, and (c) the genes associated with WMHs and AD, and verifying their expression levels across the whole body. Our tissue-specific analysis revealed that WMH-associated SNPs were significantly enriched in tissues beyond the brain, namely liver, cardiovascular, and kidney – with liver being a common tissue enriched for both WMHs and AD. Our cell-specific analysis showed enrichment of vascular endothelial cells across the tissue types enriched for WMHs, highlighting their central role in the development of WMHs. Additionally, our gene-level analysis highlighted overlapping patterns of tissue enrichment for both WMHs and AD, and showed interactions between WMH and AD associated genes. Our findings provide new insights into the systemic influences potentially contributing to WMH pathology, in particular, multi-system endothelial disorder. We hope that our multisystemic genetic findings will stimulate future WMH-research into specific pathways across the brain-body axis.

2024-09-27

medRxiv (prépublication)

Large language models auto-profile conscious awareness changes under psychedelic drug effects

Robin Carhart-Harris

Chloé Savignac

Gregory Bell

Steven Laureys

Abstract

Psychedelic experiences open a colorful view into drug-induced changes in conscious awareness. Small-samp… (voir plus)le studies on psychedelic drug action have gained traction in recent years. Yet, today’s means for measuring changes in subjective experience are mostly limited to legacy questionnaires of pre-assumed relevance, which could be complemented by bottom-up explorations of semantic facets that underlie experience reports. Here, we show how to harness large language models (LLMs) to i) design from scratch, ii) annotate at scale, and iii) evaluate with rigor a vast portfolio of experience dimensions during psychoactive drug influence, yielding > 2 million automatic dimension ratings that would otherwise have been done by hand. Investigator-independent LLM scoring of these drug effects on the human mind alone allowed to robustly discriminate the unique mental effects of 30 psychoactive substances. Successful knowledge integration of how psychedelics mediate shifts in subjective awareness will be an unavoidable milestone towards charting the full drug design space.

2024-08-07

Research Square (prépublication)

Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps

Mariam Zabihi

Seyed Mostafa Kia

Thomas Wolfers

Stijn de Boer

Charlotte Fraza

Richard Dinga

Alberto Llera Arenas

Christian F. Beckmann

Andre Marquand

Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapp… (voir plus)ing and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing 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’) and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.

2024-08-07

Public Library of Science ONE (publié)

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

Andrea I. Luppi

S. Parker Singleton

Justine Y. Hansen

Keith W. Jamison

Amy Kuceyeski

Richard F. Betzel

Bratislav Misic

The mechanisms linking the brain’s network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveragi… (voir plus)ng principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

2024-08-04

Nature Biomedical Engineering (publié)

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

Avram Holmes

Sarah W. Yip

Population-level analyses are inherently complex due to a myriad of latent confounding effects that underlie the interdisciplinary topics of… (voir plus) research interest. Despite the mounting demand for generative population models, the limited generalizability to underrepresented groups hinders their widespread adoption in downstream applications. Interpretability and reliability are essential for clinicians and policymakers, while accuracy and precision are prioritized from an engineering standpoint. Thus, in domains such as population neuroscience, the challenge lies in determining a suitable approach to model population data effectively. Notably, the traditional strata-agnostic nature of existing methods in this field reveals a pertinent gap in quantitative techniques that directly capture major sources of population stratification. The emergence of population-scale cohorts, like the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, provides unparalleled opportunities to explore and characterize neurobehavioral and sociodemographic relationships comprehensively. We propose diversity-aware population modeling, a framework poised to standardize systematic incorporation of diverse attributes, structured with respect to intrinsic population stratification to obtain holistic insights. Here, we leverage Bayesian multilevel regression and poststratification, to elucidate inter-individual differences in the relationships between socioeconomic status (SES) and cognitive development. We constructed 14 varying-intercepts and varying-slopes models to investigate 3 cognitive phenotypes and 5 sociodemographic variables (SDV), across 17 US states and 5 race subgroups. SDVs exhibited systemic socio-spatial effects that served as fundamental drivers of variation in cognitive outcomes. Low SES was disproportionately associated with cognitive development among Black and Hispanic children, while high SES was a robust predictor of cognitive development only among White and Asian children, consistent with the minorities’ diminished returns (MDRs) theory. Notably, adversity-susceptible subgroups demonstrated an expressive association with fluid cognition compared to crystallized cognition. Poststratification proved effective in correcting group attribution biases, particularly in Pennsylvania, highlighting sampling discrepancies in US states with the highest percentage of marginalized participants in the ABCD Study©. Our collective analyses underscore the inextricable link between race and geographic location within the US. We emphasize the importance of diversity-aware population models that consider the intersectional composition of society to derive precise and interpretable insights across applicable domains.

2024-07-29

Research Square (publié)

Estimating Unknown Population Sizes Using the Hypergeometric Distribution

Liam Hodgson

The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into mult… (voir plus)iple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the sizes of its constituent categories are unknown. Here, we propose a novel solution using the hypergeometric likelihood to solve this estimation challenge, even in the presence of severe under-sampling. We develop our approach to account for a data generating process where the ground-truth is a mixture of distributions conditional on a continuous latent variable, such as with collaborative filtering, using the variational autoencoder framework. Empirical data simulation demonstrates that our method outperforms other likelihood functions used to model count data, both in terms of accuracy of population size estimate and in its ability to learn an informative latent space. We demonstrate our method's versatility through applications in NLP, by inferring and estimating the complexity of latent vocabularies in text excerpts, and in biology, by accurately recovering the true number of gene transcripts from sparse single-cell genomics data.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images

Chen Liu

Ke Xu

Liangbo L. Shen

Jay Stewart

Jay C. Wang

Lucian V. Del Priore

Smita Krishnaswamy

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same pat… (voir plus)ients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose ImageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. ImageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate ImageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of ImageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets. The official implementation is available at https://github.com/KrishnaswamyLab/ImageFlowNet.

2024-06-19

ArXiv (prépublication)

arxiv.org

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B.T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging genetics field has st… (voir plus)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, environment, human health, daily living skills and outcomes. Hence, we interrogated the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and corresponding implications on cognitive, behavioral, psychosocial, and socioeconomic traits. Specifically, we designed a holistic pattern-learning algorithm that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 962 behavioral variables spanning 20 categories in 7,657 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks, and specific parameters of the family well-being (increased parental and child stress, anxiety and depression) or neighborhood dynamics (decreased safety); effects extending beyond the impairment of cognitive ability or language capacity, dominantly reported in the CNV literature. Our investigation thus spotlights a far-reaching interplay between genetic variation and subjective life quality in adolescents and their families.

2024-05-21

medRxiv : the preprint server for health sciences (publié)

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B.T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

2024-05-21

medRxiv (publié)

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

Liam Hodgson

Yue Li

Yasser Iturria-Medina

Jo Anne Stratton

Guy Wolf

Smita Krishnaswamy

David A. Bennett

Late onset Alzheimer’s disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface… (voir plus). AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain’s major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject’s progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci. Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.

2024-05-16

Communications Biology (publié)

Longitudinal microstructural changes in 18 amygdala nuclei resonate with cortical circuits and phenomics

Karam Ghanem

Karin Saltoun

Aparna Suvrathan

Bogdan Draganski

The amygdala nuclei modulate distributed neural circuits that most likely evolved to respond to environmental threats and opportunities. So … (voir plus)far, the specific role of unique amygdala nuclei in the context processing of salient environmental cues lacks adequate characterization across neural systems and over time. Here, we present amygdala nuclei morphometry and behavioral findings from longitudinal population data (>1400 subjects, age range 40-69 years, sampled 2-3 years apart): the UK Biobank offers exceptionally rich phenotyping along with brain morphology scans. This allows us to quantify how 18 microanatomical amygdala subregions undergo plastic changes in tandem with coupled neural systems and delineating their associated phenome-wide profiles. In the context of population change, the basal, lateral, accessory basal, and paralaminar nuclei change in lockstep with the prefrontal cortex, a region that subserves planning and decision-making. The central, medial and cortical nuclei are structurally coupled with the insular and anterior-cingulate nodes of the salience network, in addition to the MT/V5, basal ganglia, and putamen, areas proposed to represent internal bodily states and mediate attention to environmental cues. The central nucleus and anterior amygdaloid area are longitudinally tied with the inferior parietal lobule, known for a role in bodily awareness and social attention. These population-level amygdala-brain plasticity regimes in turn are linked with unique collections of phenotypes, ranging from social status and employment to sleep habits and risk taking. The obtained structural plasticity findings motivate hypotheses about the specific functions of distinct amygdala nuclei in humans.

2024-04-17

Communications Biology (publié)