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

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)

From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning

Nahiyan Malik

2022-06-17

Neural Networks (accepted)

Interacting brains revisited: A cross‐brain network neuroscience perspective

Christian Gerloff

Kerstin Konrad

Christina Büsing

Vanessa Reindl

Elucidating the neural basis of social behavior is a long‐standing challenge in neuroscience. Such endeavors are driven by attempts to ext… (see more)end the isolated perspective on the human brain by considering interacting persons' brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current hyperscanning analyses mostly rely on global metrics, we encode the regions' roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

2022-06-05

Human Brain Mapping (unknown)

Human brain anatomy reflects separable genetic and environmental components of socioeconomic status

Hyeokmoon Kweon

Gökhan Aydogan

Alain Dagher

Christian C. Ruff

Gideon Nave

Martha J. Farah

Philipp D. Koellinger

Socioeconomic status (SES) correlates with brain structure, a relation of interest given the long-observed relations of SES to cognitive abi… (see more)lities and health. Yet, major questions remain open, in particular, the pattern of causality that underlies this relation. In an unprecedently large study, here, we assess genetic and environmental contributions to SES differences in neuroanatomy. We first establish robust SES–gray matter relations across a number of brain regions, cortical and subcortical. These regional correlates are parsed into predominantly genetic factors and those potentially due to the environment. We show that genetic effects are stronger in some areas (prefrontal cortex, insula) than others. In areas showing less genetic effect (cerebellum, lateral temporal), environmental factors are likely to be influential. Our results imply a complex interplay of genetic and environmental factors that influence the SES-brain relation and may eventually provide insights relevant to policy.

2022-05-17

Science Advances (published)

Population variation in social brain morphology: Links to socioeconomic status and health disparity

Nathania Suryoputri

Hannah Kiesow

ABSTRACT Health disparity across layers of society involves reasons beyond the healthcare system. Socioeconomic status (SES) shapes people… (see more)s daily interaction with their social environment and is known to impact various health outcomes. Using generative probabilistic modeling, we investigate health satisfaction and complementary indicators of socioeconomic lifestyle in the human social brain. In a population cohort of ~10,000 UK Biobank participants, our first analysis probed the relationship between health status and subjective social standing (i.e., financial satisfaction). We identified volume effects in participants unhappy with their health in regions of the higher associative cortex, especially the dorsomedial prefrontal cortex (dmPFC) and bilateral temporo-parietal junction (TPJ). Specifically, participants in poor subjective health showed deviations in dmPFC and TPJ volume as a function of financial satisfaction. The second analysis on health status and objective social standing (i.e., household income) revealed volume deviations in regions of the limbic system for individuals feeling unhealthy. In particular, low-SES participants dissatisfied with their health showed deviations in volume distributions in the amygdala and hippocampus bilaterally. Thus, our population-level evidence speaks to the possibility that health status and socioeconomic position have characteristic imprints in social brain differentiation.

2022-05-03

Social Neuroscience (unknown)

Using Population Datasets to Identify the Brain Basis of Social Isolation

2022-04-30

Biological Psychiatry (published)

Multi-tract multi-symptom relationships in pediatric concussion

Guido I. Guberman

Sonja Stojanovski

Eman Nishat

Alain Ptito

Anne Wheeler

Maxime Descoteaux

The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In co… (see more)ntrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships. Using cross-sectional data from 306 previously concussed children aged 9–10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures. Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results. Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity. Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).

2022-04-26

eLife (published)

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

Tong He

Lijun An

Pansheng Chen

Jianzhong Chen

Jiashi Feng

Avram J. Holmes

Simon B. Eickhoff

B. T. Thomas Yeo

We propose a simple framework—meta-matching—to translate predictive models from large-scale datasets to new unseen non-brain-imaging phe… (see more)notypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = −0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching. Individual-level prediction is critical for precision medicine, but many neuroimaging prediction studies are underpowered. Here the authors present a simple yet powerful approach that effectively translates predictive models from big to small data.

2022-04-24

Nature Neuroscience (published)