Danilo Bzdok

Why AI Models Hallucinate and How to Fix Them

A tab representation of the Pre trained CLIP Vision Transformer

October 8, 2025

Praneet Suresh

Jack Stanley

Danilo Bzdok

Sonia Joseph

Luca Scimeca

Using LLMs to better understand autism diagnosis

March 25, 2025

Jack Stanley

Danilo Bzdok

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

September 8, 2020

Danilo Bzdok

Publications

Cortical microstructural integrity predicts an exploitation bias in older adulthood

Patrick Hewan

Alfie Wearn

Jeremy Hogeveen

Kayla Williams

R Nathan Spreng

Gary R. Turner

Sylvia Villeneuve

Judes Poirier

John C S Breitner

Sylvain Baillet

Andrée-Ann Baril

Bellec Pierre

Véronique Bohbot

Mallar Chakravarty

D Louis Collins

Mahsa Dadar

Simon Ducharme

Alan Evans

Claudine Gauthier … (see 80 more)

Maiya R Geddes

Rick Hoge

Yasser Ituria‐Medina

Gerhard Multhaup

Lisa-Marie Münter

Natasha Rajah

Pedro Rosa-Neto

Taylor Schmitz

Soucy Jp

Nathan Spreng

Christine Tardif

Etienne Vachon-Presseau

Mohammadali Javanray

Meishan Ai

Philippe Amouyel

Nicholas Ashton

Gabriel Aumont‐Rodrigue

Julie Bailly

Guilia Baracchini

Kaj Blennow

Christian Bocti

Lianne Boisvert

Sophie Boutin

Ann Brinkmalm Westman

A P Dagher

Xing Dai

Samir Das

Marina Dauar‐Tedeschi

Louis De Beaumont

Christine Déry

Maxime Descoteaux

Elena Drobotea

M Elie

Alfonso Fajardo Valdez

Vladimir Fonov

David Morgan

Jonathan Gallago

Greco Cr

Louise Hudon

Gabriel Jean

Anne Labonté

Robert Laforce

Marc Lalancette

Jean-Charles Lambert

Jeannie‐Marie Leoutsakos

Danaé Lussier Dumouchel

B Misic

Béry Mohammediyan

Holly NewboldFox

Eugenia Nita Capota

Alix Noly‐Gandon

Adrian Eduardo Noriega de la Colina

Pierre Orban

Valentin Ourry

Cynthia Picard

Alexa Pichet Binette

A. L. Poirier

Nathalie Prenevost

Ting Qiu

Marc James Quesnel

Charles Ramassamy

Jean‐Michel Raoult

Jordana Remz

Safa Sanami

Frederic St‐Onge

Cherie Strikwerda‐Brown

Elisabeth Sylvain

Andràs Tikàsz

Christina Tremblay

Stefanie Tremblay

Jennifer Tremblay‐Mercier

Stéphanie Tullo

Irem Ulku

Paolo Vitali

Yara Yakoub

Robert Zatorre

Henrik Zetterberg

Pierre Bellec

Jean-Paul Soucy

Claudia Greco

OBJECTIVES: Prefrontal regions are implicated in explore-exploit decision-making during foraging. Older adults often show an exploitation bi… (see more)as, and this age period is also marked by deteriorating prefrontal myelination. To investigate whether these phenomena are linked, we examined whether lower magnetization transfer saturation (MTsat), a myelin-sensitive quantitative MRI (qMRI) measure, in these regions predicts greater exploitation bias during foraging, and whether cortical microstructure is a better predictor of bias than macrostructure (i.e., cortical thickness). METHODS: Cognitively healthy older adults with familial risk of Alzheimer's disease (AD) (N=118, 60-88 years) completed a foraging task indexing explore-exploit decision-making. qMRI was used to derive MTsat values for the frontopolar cortex (FPC), medial orbitofrontal cortex (OFC), rostral middle frontal gyrus (rMFG), dorsal anterior cingulate cortex (dACC), as well as the locus coeruleus (LC), a core subcortical region strongly implicated in explore-exploit decision-making. Secondary analyses examined associations between available AD risk markers and foraging. RESULTS: Lower MTsat in the FPC, OFC, rMFG, and LC was associated with an exploitation bias, with LC and FPC emerging as the strongest predictors. No relationship was observed for the dACC. MTsat remained a significant predictor of foraging after controlling for cortical thickness. Observed associations were largely unrelated to AD risk markers. DISCUSSION: Individual differences in cortical microstructural integrity within a well-defined explore-exploit circuit are associated with an exploitative decision-making bias in older adults. These findings highlight the value of qMRI microstructural integrity markers, beyond standard macrostructural assays, in characterizing the neural correlates of exploitation biases in later life.

2026-07-22

Journals of Gerontology Series B: Psychological Sciences and Social Sciences (published)

The topology of adolescent mental health

Maria B. Jelen

Alexa Mousley

Kayson Fakhar

Estherina Trachtenberg

Yuankai He

Robert Kohler

Shambhavi Aggarwal

Varun Warrier

Sarah W. Yip

Duncan E. Astle

Abstract The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a ri… (see more)gid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p- factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.

2026-07-14

medRxiv (preprint)

The blueprint of human functional architecture shifts from cognition to anatomy during perturbations of consciousness

Andrea I. Luppi

Dragana Manasova

Justine Y. Hansen

Zhen-Qi Liu

Asa Farahani

Yonatan Sanz Perl

Jakub Vohryzek

Daniel Golkowski

Andreas Ranft

R. Ilg

Denis Jordan

Vincent Bonhomme

Audrey Vanhaudenhuyse

Athéna Demertzi

Océane Jaquet

Mohamed Ali Bahri

Naji Alnagger

Paolo Cardone

Lorina Naci

Adrian M. Owen … (see 9 more)

John Pickard

Guy Williams

Judith Allanson

Enrico Amico

Jacobo Sitt

David Menon

Emmanuel A. Stamatakis

Bratislav Misic

Consciousness and cognition arise from the ongoing interactions between brain regions. Synchronous fluctuations of fMRI signals may indicate… (see more) that two brain regions perform similar cognitive functions, but neural interactions are also constrained by anatomical connectivity and regions' molecular, cytoarchitectonic, and metabolic profiles. Here we disentangle the respective contributions of ongoing cognition and multimodal neurobiological constraints in shaping functional connectivity. We jointly contextualise haemodynamic FC against eight distinct multimodal representations of the human connectome: (i) structural connectivity from diffusion tractography; (ii) spatial embedding; (iii) similarity of transcriptional profiles from gene expression; (iv) similarity of receptor profiles from Positron Emission Tomography; (v) laminar profile similarity from histology; (vi) correlated electrophysiological activity from magnetoencephalography; (vii) correlated metabolic activity from PET glucose uptake; (viii) coordinated activation across 123 cognitive operations from the NeuroSynth meta-analytic engine. We demonstrate that cognitive co-activation is the dominant predictor of inter-regional fMRI synchrony in the awake human brain, even when quantified using intracranial electrical stimulation. Crucially, this predominance of cognitive co-activation for shaping functional connectivity is systematically obliterated across five datasets of pharmacological and pathological perturbations of consciousness (chronic disorders of consciousness; anaesthesia with sevoflurane, propofol, or ketamine) when cognition is disconnected from the environment or altogether abolished. Altogether, we show that multimodal predictors of functional architecture shift away from cognitive co-activation and toward anatomical-molecular constraints during pharmacological and pathological perturbations of consciousness.

2026-06-09

bioRxiv (accepted)

Danilo Bzdok

2026-05-31

Neuron (published)

Cell type transcriptomic modules reveal shared molecular mechanisms in Alzheimer’s and Parkinson’s disease

Anwesha Bhattacharya

Edward A. Fon

Alain Dagher

Yasser Iturria-Medina

Jo Anne Stratton

L. M. Hodgson

David A Bennett

Historically, Alzheimer's disease (AD) and Parkinson's disease (PD) have been investigated as two distinct disorders of the brain. However, … (see more)a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single-gene centric studies, such as differential gene expression analyses, have struggled to unravel the molecular basis for the observed pathological links between AD and PD. To address this, we tailor a latent factor framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing large, single-nucleus transcriptomics datasets in AD (70,634 nuclei) and PD (340,902 nuclei) from postmortem human brains, we systematically extract and juxtapose disease-critical molecular signatures in the brain. Our transcriptomic analysis reveals shared molecular programs between AD and PD that systematically localize to specific glial and neuronal cell types. In neurons, convergent gene groups in AD and PD relate to cytoskeletal dynamics and mitochondrial stress mechanisms. Similarly, overlapping gene groups in microglia modules implicate T cell activation mechanisms and synapse pruning pathways. In parallel, AD- and PD-associated genes in astrocytes are involved in heavy metal processing; oligodendrocytes highlight convergent dysregulation in myelin synthesis. In addition, our analysis reveals APOE, an AD GWAS gene, has disease predictive roles in PD-associated gene modules. Conversely, SNCA, a PD GWAS gene, emerges within AD associated gene modules. Our multi-module sub-cell-type approach offers unique insights into the molecular basis of shared neuropathology in AD and PD.

2026-05-20

GigaScience (published)

Widespread use of invalid statistical tests in biomedical machine learning

Tianchu Zeng

Hui Li

Shaoshi Zhang

Yan Quan Tan

Fang Tian

Csaba Orbán

Lijun An

Wanyu Che

Jingwen Cheng

Joanna Su Xian Chong

Niousha Dehestani

Zijian Dong

Xin Li

Zhizhou Li

Mervyn Jun Rui Lim

Yi Lin

Qinrui Ling

Zijie Ling

Xi Zhi Low

Sina Mansour L. … (see 24 more)

Kwun Kei Ng

Thuan Tinh Nguyen

Leon Qi Rong Ooi

Shreya Pande

Xing Qian

Jingxuan Ruan

Z WANG

Yapei Xie

Chen Zhang

Yichi Zhang

K Patil

Linden Parkes

Elvisha Dhamala

Sidhant Chopra

Andrew Zalesky

Avram Holmes

S Eickhoff

Juan Helen Zhou

Olivier Renaud

Nico Dosenbach

Konrad P. Kording

Thomas Nichols

B T Thomas Yeo

Abstract Machine learning is accelerating biomedical research. Cross-validation is widely used to compare predictive performance – not onl… (see more)y to benchmark algorithms, but also to inform scientific applications, such as ranking biomarkers. However, prediction performance estimates across cross-validation folds are not independent. Standard tests for comparing prediction performance (e.g., paired t-test) assume independence and can therefore inflate false positive rates. In a PRISMA-guided meta-analysis of 210 studies (impact factor ≥15, 1 June 2020 – 1 June 2025), we find that 97% ignored fold dependence when comparing prediction performance. This problem is ubiquitous across scientific fields and unaffected by impact factor, rigor-promoting policies, or open science practices. Simulations across 420 scenarios spanning four diverse datasets show that ignoring fold dependence leads to invalid false positive control in most settings. Repeated cross-validation further compounds this problem, with false positive rates rising toward 100% as the number of repetitions grows. Existing fold-dependence-aware tests rely on strong assumptions because the variance of fold-level statistics and the between-fold correlation cannot be disentangled under standard cross-validation. We therefore propose the SHARP (Split-HAlf RePeated) test, a simple modification to standard cross-validation that enables direct estimation of variance and correlation. Benchmarked against 12 tests, SHARP provides the best overall balance of false-positive control, statistical power, and confidence-interval calibration across simulation schemes. We conclude by providing best practices and reporting guidelines for valid model comparison inference in biomedical machine learning and beyond.

2026-05-19

bioRxiv (preprint)

Profiling the Cell-Type Specific Effects of Psilocybin in Medial Prefrontal Cortexh

Heike Schuler

Delong Zhou

Chloé Savignac

Vedrana Cvetkovska

Yiu-Chung Tse

Juliet Meccia

Rosemary C. Bagot

2026-04-30

Biological Psychiatry (published)

Neurovascular Coupling as Early, High-Sensitive Biomarker for Cognitive Decline and Vascular Pathology: Protocol for Systematic Review and Meta-Analysis

V. D. Abramova

Veronika Egovtseva

Ksenya Pronyaeva

Shamsa H. Alshamsi

Marta Estrada

Rustam Talybov

Taleb~M. Almansoori

Bassem Sadek

Mohammed Khogali

Mohammad~I.K. Hamad

Milos Ljubisavljevic

Yauhen Statsenko

2026-04-14

Research Square (accepted)

An international mega-analysis of psychedelic drug effects on brain circuit function

Manesh Girn

Manoj K. Doss

Leor Roseman

Katrin H. Preller

Fernanda Palhano-Fontes

Lorenzo Pasquini

Frederick S. Barrett

Pablo Mallaroni

Natasha L. Mason

Christopher Timmermann

Drummond E. McCulloch

Patrick M. Fisher

Brian S. Winston

Flora Moujaes

Felix Muller

Matthias E. Liechti

Franz X. Vollenweider

Johannes G. Ramaekers

Kim Kuypers

Draulio B. Araujo … (see 7 more)

Olaf Sporns

Joshua Siegel

Nico Dosenbach

David J. Nutt

Robin L. Carhart-Harris

Emmanuel A. Stamatakis

Psychedelic drugs are re-emerging as promising scientific and clinical tools. However, despite a rapidly expanding literature on their thera… (see more)peutic value, the neural mechanisms underlying psychedelic effects remain unclear. Resting-state functional magnetic resonance imaging studies of acute psychedelic effects, conducted independently by several research groups, have so far yielded fragmented and sometimes inconsistent findings. Here, to help facilitate greater convergence, we conducted a 'mega-analysis' integrating 11 independent resting-state functional magnetic resonance imaging datasets across five psychedelic drugs (psilocybin, lysergic acid diethylamide, mescaline, N,N-dimethyltryptamine and ayahuasca) from research groups spanning three continents and five countries. By applying a uniform preprocessing pipeline and a Bayesian hierarchical modeling framework, we discovered several common features in the induced alterations to brain function across drugs and sites. Most prominently, we identified a core signature of increased functional connectivity between transmodal (default, frontoparietal and limbic) and unimodal networks (visual and somatomotor), with subnetwork specificity. Furthermore, key subcortical regions (thalamus, caudate and putamen) and the cerebellum exhibited altered coupling with sensorimotor networks. In contrast to several single-site reports, Bayesian modeling revealed weak-to-moderate and selective reductions in within-network functional connectivity, with substantial variability across drugs and networks. Together, these findings extend past work by demonstrating that psychedelics reconfigure large-scale cortical organization while selectively engaging subcortical circuitry. This study provides the most comprehensive synthesis of psychedelic brain action to date, helping resolve inconsistencies and offering a probabilistic map of how psychedelics alter large-scale brain organization. We hereby provide a cornerstone to benchmark and shepherd future psychedelic neuroimaging research.

2026-04-05

Nature Medicine (published)

Multiscale reorganization of brain and behavior under large-scale electrical perturbation

Sarah Kreuzer

Juergen Dukart

Justine Y. Hansen

Hoang K. Nguyen

Michael Bentsch

Sophia Zieger

Katrin Sakreida

Thomas C. Baghai

Caroline Nothdurfter

Michael Groezinger

Bogdan Draganski

Bratislav Misic

Simon B. Eickhoff

Timm B. Poeppl

2026-03-24

bioRxiv (preprint)

Sex Differences in P-Tau217, Tau Aggregation, and Cognitive Decline

Gillian Coughlan

Valentin Ourry

Diana Townsend

Hannah M Klinger

Jane A. Brown

Madison Cuppels

Tobey Betthauser

Rebecca Langhough

Karly Alex Cody

Mabel Seto

Colin Birkenbihl

Annie Li

Michelle Farrell

Emma G. Thibault

Pia Kivisäkk Webb

S. R. Arnold

Robert A. Rissman

Michael Properzi

Aaron Schultz

Keith Johnson … (see 81 more)

Oliver Langford

Michael C. Donohue

Sylvia Villeneuve

Sterling C. Johnson

Hyun-Sik Yang

JoAnn E. Manson

Reisa Sperling

Rachel F. Buckley

Orest Hurko

Sanra E Black

Rachelle Doody

Murali Doraiswamy

Anthony Gamst

Jeffrey Kaye

Thomas Obisesan

Henry Rusinek

Doug Scharre

Reisa Sperling

Michael W Weiner

R. Green

Paul Aisen

Keith Johnson

Jason Karlawish

Kenneth Marek

Karen Holdridge

REMA RAMAN

R. Yaari

Cheryl Brown

John R. Sims

Robert A. Rissman

Michael C. Donohue

Maria Arampatizdou

Jeremy Pizzola

Mike Rafii

Clifford Jack Jr.

Marybeth Howlett

John Seibyl

Isabella Velon

Paula Cohen

Gustavo Jimenez-Maggiora

Marianne Manire

James B. Brewer

Paul Maruff

Mark Mintun

Alison Belsha

Jennifer Salazar

Cecily Jenkins

Vedeline Torreon

Renarda Jones

Sylvia Villeneuve

Judes Poirier

John C.S. Breitner

Mohamed Badawy

Sylvain Baillet

Andrée‐Ann Baril

Bellec Pierre

Véronique D. Bohbot

Mallar Chakravarty

D. Louis Collins

Mahsa Dadar

Simon Ducharme

Alan C. Evans

Claudine Gauthier

Maiya Geddes

Rick Hoge

Yasser Ituria-Medina

Maxime Montembeault

Gerhard Multhaup

Lisa-Marie Münter

Alexa Pichet Binette

Natasha Rajah

Pedro Rosa-Neto

Taylor W. Schmitz

Jean-Paul Soucy

Nathan R Spreng

Christine Tardif

Etienne Vachon-Presseau

Christian Bocti

Maxime Descoteaux

Pierre Bellec

Importance: Among individuals with high levels of amyloid-β (Aβ), women exhibit higher insoluble tau burden and accumulation than age-matc… (see more)hed men. It remains unclear whether this sex difference is influenced by soluble phosphorylated tau (p-tau), a biomarker that changes early in Alzheimer disease. Objective: To investigate whether sex and aggregated Aβ synergistically predict plasma phosphorylated tau 217 (p-tau217) levels and whether levels of p-tau217 predict cross-sectional and longitudinal tau aggregation in a sex-specific manner (as measured by positron emission tomography [PET]). Design, Setting, and Participants: This longitudinal study analyzed data between September 7, 2024, and October 29, 2025, from 1 clinical trial cohort and 4 observational study cohorts including men and women without cognitive impairment who had undergone multiple assessments via tau PET (18F-flortaucipir or 18F-MK-6240) and plasma p-tau217 assay at baseline. Cognitive performance was measured with the Preclinical Alzheimer Cognitive Composite. Data on cognitive performance were available from 3 of the 5 cohorts for a mean of 4.6 years (SD, 3.1 years). Across the 5 cohorts, the mean follow-up for tau PET was 3.6 years (SD, 1.7 years). Exposures: Self-reported sex (male or female), tau PET, and p-tau217 assay. Main Outcomes and Measures: The primary analyses used linear and mixed-effects models to assess baseline and longitudinal sex × p-tau217 interactions for 9 tau PET regions. The secondary analyses assessed sex × p-tau217 interactions for cognitive change using the Preclinical Alzheimer Cognitive Composite. Results: Across the 5 cohorts, there were a total of 1292 participants (63.6% women; mean age, 70.6 [SD, 6.4] years) with tau PET assessments. Compared with men, women had significantly higher baseline p-tau217 levels at higher aggregated Aβ Centiloid levels (β, -0.21 [95% CI, -0.37 to -0.05], P = .009; highest interaction was found in the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration [A4/LEARN] cohort). The sex × p-tau217 interactions at baseline were significant for 1 tau PET region in the Harvard Aging Brain Study (HABS) cohort, for 2 tau PET regions in the A4/LEARN cohort, for 6 tau PET regions in the Wisconsin Registry of Alzheimer's Prevention (WRAP) cohort, and for 4 tau PET regions in the Presymptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD) cohort. Longitudinal interactions were significant for 4 tau PET regions in the A4/LEARN cohort, for 5 tau PET regions in both the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and the WRAP cohort, and for 2 PET regions in both the HABS cohort and the PREVENT-AD cohort. Compared with men, women displayed greater tau deposition and accumulation at higher p-tau217 levels. Use of a secondary model showed women with higher p-tau217 levels also exhibited faster rates of cognitive decline relative to men in the both the WRAP cohort and the ADNI cohort. Conclusion and Relevance: These findings add to growing evidence that women have a differential tau response to Aβ that may emerge at the point of p-tau secretion. These findings have implications for the therapeutics and diagnostics of preclinical Alzheimer disease.

2026-02-15

JAMA Neurology (published)

Morphometric dissimilarity in association cortices linked to autism subtype with more severe symptoms

Hongxiu Jiang

Raul Rodriguez-Cruces

Ke Xie

Valeria Kebets

Yezhou Wang

Clara F. Weber

Ying He

Jonah Kember

Hilary Sweatman

Zeus Gracia Tabuenca

Jean-Baptiste Poline

Seok-Jun Hong

Boris Bernhardt

Xiaoqian Chai

Autism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental condition marked by atypical brain connectivity. Understa… (see more)nding ASD neural subtypes at the network level is critical for clarifying its neuroanatomical heterogeneity. Morphometric similarity networks (MSNs), derived from region-to-region similarity across multiple anatomical features, offer a powerful approach for capturing individual-level neural architecture. In this study, MSNs were estimated from seven anatomical features in 348 individuals with ASD and 452 typically developing (TD) controls. Across all ASD participants, the first principal component of MSN values was negatively correlated with social and communication severity. Three ASD subtypes with distinct MSN patterns were identified. Subtype-1, characterized by weaker morphometric similarity values in frontotemporal association regions compared to TD individuals, exhibited the most severe symptoms in social, communication and repetitive behaviors, and displayed hyperconnectivity between the salience and visual networks, and between language and visual networks. Subtype-2 showed greater values of morphometric similarities than TD and less severe social symptoms compared to subtype-1, along with hyperconnectivity between default and salience networks relative to TD. Subtype-3 displayed morphometric similarity values largely comparable to TD and the least severe symptoms out of the three subtypes. Transcriptomic analysis revealed that GABAergic parvalbumin and glutamatergic intratelencephalic-projecting neurons were key cell types differentiating subtypes. These findings suggest the existence of distinct ASD neuroanatomical subtypes defined by regional morphometric similarity, each linked to unique behavioral, functional, and transcriptomic profiles. Morphometric dissimilarity in association regions may serve as a neural signature for ASD subtypes characterized by more severe clinical manifestations.

2026-01-31

NeuroImage (published)