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

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 (unknown)

Social isolation and the brain in the pandemic era

Robin I. M. Dunbar

2022-10-17

Nature Human Behaviour (published)

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity

Benjamin Clemens

Jeremy Lefort-Besnard

Christoph Ritter

Elke Smith

Mikhail Votinov

Birgit Derntl

Ute Habel

Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors. Here, we… (see more) bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. In both brain assessments, we used penalized logistic regression models and nonparametric permutation. We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.

2022-09-13

Cerebral Cortex (unknown)

Association between Anxiety and Disease Pathophysiology in Participants of Longitudinal Observational Studies in Aging during the COVID-19 Lockdown

Stijn Servaes

Firoza Lussier

Cécile Tissot

Joseph Therriault

Gleb Bezgin

Yi-Ting Wang

Jenna Stevenson

Nesrine Rahmouni

Vanessa Pallen

Guillaume Elgbeili

Jaime Fernandez Arias

Min Su Kang

Andrea Benedet

Mira Chamoun

Tharick Pascoal

Kok Pin Ng

Suzanne King

Serge Gauthier

Pedro Rosa-Neto

Abstract

The burden imposed by the COVID-19 pandemic deferentially interferes with the outcomes of clinical trials of aging… (see more) and dementia. We examined the impact of the lockdown on cognitive impairment due to Alzheimer’s Disease (AD), anxiety, and COVID-19-related stress in participants from the Translational Biomarkers In Aging and Dementia (TRIAD) cohort using neuropsychiatric assessments, tau and amyloid PET. We found that, before the lockdown, anxiety was higher in cognitively impaired individuals (CI) and positively associated with brain tau load. However, during the lockdown, anxiety increased only in the cognitively unimpaired (CU) and was positively associated with COVID-19 related stress. Interestingly, we found that in patients, tau load was anti-correlated with higher anxiety during lockdown. Our findings contribute to a framework for interpreting the effects of the pandemic on neuropsychiatric symptoms among clinical trial participants. Collectively, our results suggest that caregivers are more vulnerable to external stressors than patients.

2022-08-29

Research Square (preprint)

Bringing language to dynamic brain states: the default network dominates neural responses to evolving movie stories

Enning Yang

Filip Milisav

Jakub Kopal

Avram J. Holmes

Georgios D. Mitsis

Bratislav Misic

Emily S. Finn

Naturalistic neuroscience opened the door to new insights into neural circuits that serve real-world dynamic perception. Such studies have o… (see more)ften neglected the rich texture of the movie narrative itself, but semantic content can be used to contextualize the induced neural responses. Here, we translated natural language processing tools from machine learning to characterize brain states estimated from hidden Markov models. Our analytical strategy allowed pitting shallow unimodal against the deep associative brain network layers in explaining how semantic content of the movie links to observed neural activity. Pooling information across >53,000 brain image time points watching Forrest Gump, we could show that distinct dynamic brain states capture unique semantic facets along the unfolding movie narrative. The spatiotemporal dynamics of brain states explicitly captured subject-level responses throughout the brain network hierarchy. Across all analyses, the default network was most intimately linked to semantic information integration, and this neural system switched online for longest durations during movie watching. Further, we identified and described two mechanisms of how the default network liaises dynamically with microanatomically defined subregion partners: the amygdala and the hippocampus. Our study thus unlocks the potential of natural language processing to explore neural processes in everyday life situations that engage key aspects of conscious awareness.

2022-08-23

bioRxiv (accepted)

Bringing language to dynamic brain states: the default network dominates neural responses to evolving movie stories

Enning Yang

Filip Milisav

Jakub Kopal

Avram J. Holmes

Georgios D. Mitsis

Bratislav Misic

Emily S. Finn

2022-08-23

bioRxiv (accepted)

Endorsing Complexity Through Diversity: Computational Psychiatry Meets Big Data Analytics

Jakub Kopal

2022-08-04

Biological Psychiatry (unknown)

A Parsimonious Description of Global Functional Brain Organization in Three Spatiotemporal Patterns

Taylor Bolt

Jason S. Nomi

Jorge A. Salas

Catie Chang

B.T. Thomas Yeo

Lucina Q. Uddin

Shella D. Keilholz

Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-… (see more)scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.

2022-07-27

Nature Neuroscience (published)

Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets

Mariam Zabihi

Seyed Mostafa Kia

Thomas Wolfers

Stijn de Boer

Charlotte Fraza

Sourena Soheili-Nezhad

Richard Dinga

Alberto Llera Arenas

Christian F. Beckmann

Andre Marquand

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral ma… (see more)pping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving 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’) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

2022-07-26

bioRxiv (preprint)

Global fMRI signal topography differs systematically across the lifespan

Jason S. Nomi

Jingwei Li

Taylor Bolt

Catie Chang

Salome Kornfeld

Zachary T. Goodman

B.T. Thomas Yeo

R. Nathan Spreng

Lucina Q. Uddin

The global signal (GS) in resting-state fMRI, known to contain artifacts and non-neuronal physiological signals, also contains important neu… (see more)ral information related to individual state and trait characteristics. Here we show distinct linear and curvilinear lifespan patterns of GS topography in a cross-sectional lifespan sample, demonstrating its importance for consideration in studies of development and aging. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across the lifespan. The thalamus has stronger coupling in older-age individuals compared with younger-aged individuals, while the putamen has stronger coupling in younger individuals compared with older individuals. The subcortical nucleus basalis shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where coupling with the GS is stronger at early and old age, with weaker coupling in middle age. This differentiation in coupling strength between subcortical and cortical brain activity across the lifespan supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on the lifespan stage of individuals. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.

2022-07-26

bioRxiv (preprint)

From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation

Laurette Dubé

Patricia P. Silveira

Daiva E. Nielsen

Spencer Moore

Catherine Paquet

J. Miguel Cisneros-Franco

Gina Kemp

Bärbel Knauper

Yu Ma

Mehmood Khan

Gillian Bartlett-Esquilant

Alan C. Evans

Lesley K. Fellows

Jorge L. Armony

R. Nathan Spreng

Jian-Yun Nie

Shawn T. Brown

Georg Northoff

2022-07-04

Frontiers in Public Health (published)

A guided multiverse study of neuroimaging analyses

Jessica Dafflon

Pedro F. da Costa

František Váša

Ricardo Pio Monti

Peter J. Hellyer

Federico Turkheimer

Jonathan Smallwood

Emily Jones

Robert Leech

For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic… (see more) method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.

2022-06-28

Nature Communications (published)