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

General anaesthesia decreases the uniqueness of brain functional connectivity across individuals and species

Andrea I. Luppi

Daniel Golkowski

Andreas Ranft

Rudiger Ilg

Denis Jordan

Adrian M. Owen

Lorina Naci

Emmanuel A. Stamatakis

Enrico Amico

Bratislav Misic

The human brain is characterized by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neura… (see more)l activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI scans acquired under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain, both with respect to the brains of other individuals and the brains of another species. Using functional connectivity, we report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organized: it co-localizes with the archetypal sensory–association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol and reversed upon recovery. Providing convergent evidence, we show that anaesthesia shifts the functional connectivity of the human brain closer to the functional connectivity of the macaque brain in a low-dimensional space. Finally, anaesthesia diminishes the match between spontaneous brain activity and cognitive brain patterns aggregated from the Neurosynth meta-analytic engine. Collectively, the present results reveal that anaesthetized human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.

2025-03-23

Nature Human Behaviour (published)

Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort

Avram Holmes

Sarah W. Yip

2025-03-19

Nature Computational Science (published)

Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort.

Avram J. Holmes

Sarah W. Yip

2025-03-19

Nature Computational Science (unknown)

Carriers of
<i>LRRK2</i>
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

LRRK2 gene variants are a major genetic risk factor for both familial and sporadic Parkinson’s disease (PD), opening an … (see more)unattended window on the disease’s 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 leveraged a large cohort of 641 participants (including 364 with a PD diagnosis) to examine MRI-detectable cortical atrophy patterns associated with the LRRK2 pathogenic variants in people with PD and non-manifesting individuals. LRRK2 PD patients exhibited 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 showed no significant cortical atrophy, indicating no structural signs of subclinical PD. We further analyzed the relationship between aggregated alpha-synuclein in cerebrospinal fluid and atrophy. We found that those with evidence of aggregated alpha-synuclein experienced pronounced neurodegeneration and increased cortical thinning, possibly defining another aggressive PD subtype. Our findings highlight avenues for distinguishing PD subtypes, which could lead to more targeted treatment approaches and a more complete understanding of Parkinson’s disease progression.

2025-03-09

medRxiv (preprint)

Large language models deconstruct the clinical intuition behind diagnosing autism

Emmett Rabot

Laurent Mottron

2025-02-28

Cell (published)

Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer’s and Parkinson’s disease

Anwesha Bhattacharya

Edward A. Fon

Alain Dagher

Yasser Iturria-Medina

Jo Anne Stratton

David A Bennett

Historically, Alzheimer’s disease (AD) and Parkinson’s disease (PD) have been investigated as two distinct disorders of the brain. Howev… (see more)er, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.

2025-02-18

bioRxiv (preprint)

Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer’s and Parkinson’s disease

Anwesha Bhattacharya

Edward A. Fon

Alain Dagher

Yasser Iturria-Medina

Jo Anne Stratton

David A Bennett

2025-02-18

bioRxiv : the preprint server for biology (published)

A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets

Da Zhi

Ladan Shahshahani

Caroline Nettekoven

Ana Lúısa Pinho

Jörn Diedrichsen

2025-01-01

Imaging Neuroscience (published)

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… (see more)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 (published)

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… (see more)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 (preprint)

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… (see more)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 (preprint)

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… (see more)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 (published)