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

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)

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)

Threading the needle: Practical considerations for merging theory-driven computational psychiatry with data-driven analytics to enhance precision health at scale

Annie Cheng

Anna Konova

Albert Powers

Philip Corlett

Ifat Levy

Xiaosi Gu

Quentin Huys

Helen Pushkarskya

Sarah Fineberg

Tobias Hauser

Ilan Harpaz-Rotem

Theresa Babuscio

Lisa Nichols

Yize Zhao

Manu Sharma

Daniella Meeker

Hua Xu

Robb B. Rutledge

Godfrey D. Pearlson … (see 2 more)

Christopher Pittenger

Sarah W. Yip

The rapidly evolving field of computational psychiatry enables quantification of specific cognitive processes, and their underlying mechanis… (see more)ms, in a translational and potentially scalable manner, using a combination of data collection via mechanistically informed behavioral tasks and theory-driven mathematical modeling. In parallel, transdiagnostic, dimensional approaches to psychiatric diagnostics, such as RDoC and HiTOP, seek to facilitate links between clinical research and real-world clinical reality, which rarely respects traditional diagnostic boundaries. These two approaches are seldom combined. In addition, while most psychiatric disorders are defined by their longitudinal course, our ability to predict symptom trajectories and tailor treatments to the individual remains limited, in part due to a dearth of longitudinal data collected using assessments sensitive to individual change over time. To address these gaps, the recently launched 'Individually Measured Phenotypes to Advance Computational Translation at Yale' (IMPACT-Y) study is collecting longitudinal data from a transdiagnostic cohort of 2400 individuals, using a combination of 'traditional' clinical research methods (e.g., health records, standardized assessments) and more novel computational approaches (e.g., behavioral tasks with demonstrated sensitivity to latent constructs and to within-person change, spoken narrative data). Here, we discuss unique challenges and opportunities in study design and analysis considerations of IMPACT-Y. Incorporating both theory- and data-driven analytics, we hope that IMPACT-Y will provide an unprecedented resource for characterizing longitudinal trajectories of core computational psychiatry constructs (e.g., reward learning) within and between individuals, for parsing heterogeneity beyond traditional diagnostic categories, and for linking inter- and intra-individual clinical variability to underlying mechanisms.

2026-01-31

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (published)

Carriers of LRRK2 pathogenic variants show a milder, anatomically distinct brain signature of Parkinson's disease

Jakub Kopal

Andrew Vo

Qin Tao

Tanya Simuni

Lana M. Chahine

Alain Dagher

Pathogenic LRRK2 gene variants are a major genetic risk factor for both familial and sporadic Pa… (see more)rkinson’s dissease (PD), opening an unattended window into disease mechanisms and potential therapies. Investigating the influence of pathogenic variants in LRRK2 gene on brain structure is a crucial step toward enabling early diagnosis and personalized treatment. Yet, despite its significance, the ways in which LRRK2 genotype affects brain structure remain largely unexplored. Work in this domain is plagued by small sample sizes and differences in cohort composition, which can obscure genuine distinctions among clinical subgroups. In this study, we overcome such important limitations by combining explicit modeling of population background variation and pattern matching. Specifically, we leverage a cohort of 603 participants (including 370 with a PD diagnosis) to examine MRI-detectable cortical atrophy patterns associated with the LRRK2 pathogenic variants in people with PD and carriers without Parkinson’s symptoms. LRRK2 PD patients exhibit milder cortical thinning compared to sporadic PD, with notable preservation in temporal and occipital regions, suggesting a distinct pattern of neurodegeneration. Non-manifesting LRRK2 carriers show no significant cortical atrophy, indicating no structural signs of subclinical PD. We further analyze the relationship between aggregated alpha-synuclein in cerebrospinal fluid and atrophy. We find that those with evidence of aggregated alpha-synuclein experienced pronounced neurodegeneration and increased cortical thinning, possibly defining another aggressive PD subtype. Our findings highlight genetic avenues for distinguishing PD subtypes, which could lead to more targeted treatment approaches and a more complete understanding of Parkinson’s disease progression.

2026-01-02

Communications Medicine (published)

Quantifying LLM Attention-Head Stability: Implications for Circuit Universality.

Karan Bali

Jack Stanley

Praneet Suresh

In mechanistic interpretability, recent work scrutinizes transformer"circuits"- sparse, mono or multi layer sub computations, that may refle… (see more)ct human understandable functions. Yet, these network circuits are rarely acid-tested for their stability across different instances of the same deep learning architecture. Without this, it remains unclear whether reported circuits emerge universally across labs or turn out to be idiosyncratic to a particular estimation instance, potentially limiting confidence in safety-critical settings. Here, we systematically study stability across-refits in increasingly complex transformer language models of various sizes. We quantify, layer by layer, how similarly attention heads learn representations across independently initialized training runs. Our rigorous experiments show that (1) middle-layer heads are the least stable yet the most representationally distinct; (2) deeper models exhibit stronger mid-depth divergence; (3) unstable heads in deeper layers become more functionally important than their peers from the same layer; (4) applying weight decay optimization substantially improves attention-head stability across random model initializations; and (5) the residual stream is comparatively stable. Our findings establish the cross-instance robustness of circuits as an essential yet underappreciated prerequisite for scalable oversight, drawing contours around possible white-box monitorability of AI systems.

2025-12-31

arXiv (preprint)

arxiv.org

Causally informed, multifactorial pathways linking cognition and personality to adolescent mental health

Jiadong Yan

Bin Wan

Paule Joanne Toussaint

Judy Chen

Gleb Bezgin

Yasser Iturria-Medina

Alan Evans

Sherif Karama

Adolescence is a sensitive period for the emergence of psychopathology. During this time, physiological changes and environmental exposures … (see more)jointly shape brain development and influence cognitive and personality maturation, collectively heightening vulnerability to mental disorders. However, the complexity of interactions between these factors has hindered a systems-level understanding of mental health and the causal roles of cognition and personality in psychopathology. In this study, we proposed a multifactorial causal framework integrating brain, pubertal, environmental, and behavioral factors to characterize heterogeneity in adolescent mental health trajectories at the individual level. We then investigated latent causal pathways linking cognition and personality to mental health outcomes and identified potential personalized intervention targets. Leveraging the Adolescent Brain Cognitive Development (ABCD) dataset ( N = 4,501), we analyzed 165 behavioral pairs connecting cognition and personality traits to mental health symptoms. Using cross-sectional multivariate mediation and longitudinal interaction-inclusive analyses, we identified 68 behavioral pairs showing significant causal relationships, with brain and environmental exposures contributing to most pathways, while pubertal factors exhibited limited involvement. Individualized interpretive analyses further revealed 23 pairs suggesting potential interventions with response rates exceeding 50%. Among these, behavioral inhibition, negative urgency, and processing speed emerged as the most common intervention targets, whereas psychosis symptoms and attention problems were the most likely issues to improve. Overall, our study advances a comprehensive framework capturing the multifactorial and heterogeneous nature of adolescent mental health, delineates specific causal pathways from cognitive and personality traits to psychopathology, and provides a principled basis for potential individualized intervention strategies.

2025-12-25

bioRxiv (published)

Latent brain subtypes of chronotype reveal unique behavioral and health profiles across population cohorts

Julie Carrier

Kai-Florian Storch

Robin I. M. Dunbar

Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This time-enduring trait ties into societal behaviors an… (see more)d is linked to psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints. To uncover hidden subgroups, we develop a supervised pattern-learning framework integrating three complementary brain-imaging modalities with deep behavioral and health profiling from 27,030 UK Biobank participants. We identify five distinct, biologically valid chronotype subtypes. Each demonstrates unique patterns across brain, behavioral and health profiles. External validation in 10,550 US children from the ABCD Study cohort reveals reversed age distributions and replicates sex-associated brain-behavioral patterns, suggesting that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.

2025-12-21

Nature Communications (published)

Latent brain subtypes of chronotype reveal unique behavioral and health profiles across population cohorts

Julie Carrier

Kai-Florian Storch

Robin I. M. Dunbar

2025-12-21

Nature Communications (published)

Cell-type specific transcriptional modulation by psilocybin induces sustained plasticity in mouse medial prefrontal cortex

Delong Zhou

Heike Schuler

Vedrana Cvetkovska

Juliet Meccia

Ashot S. Harutyunyan

Jiannis Ragoussis

Rosemary C. Bagot

Ashot S. Harutyunyan

Jiannis Ragoussis

Rosemary C. Bagot

Despite enormous interest in psychedelics for psychiatric interventions, potential underlying biological mechanisms remain unclear. Here, we… (see more) confirm that a single dose of psilocybin increases synaptic transmission in mouse medial prefrontal cortex. Using scRNA-sequencing, we identify cell-type specific mechanisms of sustained neuroplastic effects. We show that, 24h post-psilocybin, expression of plasticity-related genes is increased in excitatory neurons and that transcription in a type of deep layer near projecting neuron, L5/6 NP, is robustly altered. Analyzing receptor expression patterns reveals that this cell-type specificity does not align with 5-HT 2A expression but aligns with 5-HT 2C expression patterns. Further, multivariate analyses identify psilocybin-induced gene expression patterns in L5/6 NP neurons predict 5-HT 2C , but not 5-HT 2A , transcript levels. Pharmacologic manipulation with a 5-HT 2C antagonist attenuates the post-acute sustained effect of psilocybin on synaptic transmission, highlighting 5-HT 2C signaling and L5/6 NP neurons as key mediators of psychedelic drug action’s sustained neuroplastic effects in mPFC.

2025-12-07

bioRxiv (preprint)

Cognitive cartography of mammalian brains using meta-analysis of AI experts

Andrea I. Luppi

Hana Ali

Zhen-Qi Liu

Filip Milisav

Alessandro Gozzi

Bratislav Misic

The complexity of the brain is increasingly mirrored by the complexity of the neuroscientific literature, yet no individual mind can fully g… (see more)rasp the diversity of scales, methodologies and model organisms. Where human experts flag, the latest AI models excel: large language models can seamlessly integrate knowledge across scientific domains. Here we show how large language models can systematically and quantitatively synthesise literature-wide neuroscientific knowledge about the cognitive operations and dysfunctions associated with each brain region. Meta-analysis of AI experts reveals structure-function mappings to which existing meta-analytic frameworks are blind, demonstrated by lesions and direct intracranial stimulation. It also unlocks the possibility of extending quantitative literature meta-analysis and decoding of brain maps to other model organisms beyond human. As proof of concept, we integrate LLM meta-analysis with species-specific transcriptomics in human, macaque, and mouse, to discover an evolutionarily conserved molecular circuit for cognition. Altogether, meta-analysis of AI experts can fundamentally catalyze neuroscientific discovery by overcoming the barrier of data aggregation from heterogeneous studies, finally bringing together a scattered literature to identify emergent patterns and latent insights across disparate subfields, modalities, and species.

2025-12-02

bioRxiv (preprint)

From Noise to Narrative: Tracing the Origins of Hallucinations in Transformers

As generative AI systems become competent and democratized in science, business, and government, deeper insight into their failure modes now… (see more) poses an acute need. The occasional volatility in their behavior, such as the propensity of transformer models to hallucinate, impedes trust and adoption of emerging AI solutions in high-stakes areas. In the present work, we establish how and when hallucinations arise in pre-trained transformer models through concept representations captured by sparse autoencoders, under scenarios with experimentally controlled uncertainty in the input space. Our systematic experiments reveal that the number of semantic concepts used by the transformer model grows as the input information becomes increasingly unstructured. In the face of growing uncertainty in the input space, the transformer model becomes prone to activate coherent yet input-insensitive semantic features, leading to hallucinated output. At its extreme, for pure-noise inputs, we identify a wide variety of robustly triggered and meaningful concepts in the intermediate activations of pre-trained transformer models, whose functional integrity we confirm through targeted steering. We also show that hallucinations in the output of a transformer model can be reliably predicted from the concept patterns embedded in transformer layer activations. This collection of insights on transformer internal processing mechanics has immediate consequences for aligning AI models with human values, AI safety, opening the attack surface for potential adversarial attacks, and providing a basis for automatic quantification of a model's hallucination risk.

2025-12-02

Conference on Neural Information Processing Systems (Accept (poster))