B.T. Thomas Yeo

Predicting alcohol consumption from resting-state functional connectivity: Feature relevance and the potential of meta-matching.

This study uses machine learning to predict alcohol consumption from resting-state fMRI connectivity, finding weak but reproducible signals with limited predictive power.

Spurious model comparisons are widespread in biomedical artificial intelligence

97% of 184 high-impact biomedical AI studies compare cross-validated models with tests assuming fold independence; in 69% of re-analyzable studies a significant comparison does not survive correction.

Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function

The paper uses fMRI data to estimate hierarchical networks in the human cerebral cortex, revealing a global organization with distinct functional properties across different network orders.

Developing a multi-modal neuroimaging-based BrainAge model across childhood

This paper develops and benchmarks BrainAge models using multi-modal neuroimaging data from childhood cohorts to distinguish healthy from symptomatic developmental trajectories.

Neuroanatomy Reflects Individual Variability In Impulsivity in Youth

The paper investigates how neuroanatomical variability relates to impulsivity in youth using data from the ABCD Study, finding sex-specific and dynamic associations across multiple brain features.

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

This study links lower motor performance with poor sleep quality, depressive symptoms, and grey matter volume changes across different age groups.

Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets

The paper evaluates how different censoring strategies affect cortical parcellations and TMS targets in rs-fMRI, finding that lenient censoring yields better results than strict censoring.

Network-based Near-Scalp Personalized Brain Stimulation Targets

This paper develops a tree-based algorithm to identify personalized TMS targets for depression and anxiety by optimizing functional connectivity and scalp proximity.

Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?

This study compares brain age models and direct models for predicting Alzheimer's Disease outcomes, finding that direct models perform better despite brain age models having larger training data.

Associations between epilepsy-related polygenic risk and brain morphology in childhood

The paper examines associations between epilepsy-related polygenic risk scores and brain morphology in children, finding PRS-related cortical thinning linked to structural patterns in TLE-HS patients.

Longer scans boost prediction and cut costs in brain-wide association studies

The paper shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

Rate of brain aging associates with future executive function in Asian children and older adults

This study tests a brain age model on Singaporean children and elderly, finding that brain age gap change relates to future executive function in both groups.

Convergent and Divergent Brain-Cognition Development

The paper examines how stable and changing brain network features predict cognitive development in adolescents using rs-fMRI and cognitive data from the ABCD Study.

Benchmarking methods for mapping functional connectivity in the brain

This study benchmarks 239 pairwise interaction statistics for functional connectivity mapping, finding substantial variation in their topological, neurobiological, and behavioral properties.

Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks

This paper introduces DELSSOME, a deep learning framework that accelerates biophysical brain circuit model optimization, enabling faster parameter estimation and lifespan trajectory analysis of cortical E/I ratios.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

Protective role of parenthood on age-related brain function in mid- to late-life

This study examines the association between parenthood and brain function in mid- to late-life, finding that higher parity is linked to increased functional connectivity and counteracts age-related declines.

Geometric influences on the regional organization of the mammalian brain

This paper develops a geometrically constrained framework to map brain regions across species and shows geometry influences regional organization through patterning molecules.

Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity

The paper defines the parietal memory network (PMN) using high-resolution fMRI and finds it extends beyond traditional regions to include multiple cortical areas.

Multimodal neural correlates of childhood psychopathology

The paper identifies multimodal brain network patterns associated with dimensions of psychopathology using the Adolescent Brain Cognitive Development dataset.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

DeepResBat improves MRI data harmonization by addressing covariate distribution differences, outperforming existing methods while avoiding false associations.

Ventral attention network connectivity is linked to cortical maturation and cognitive ability in childhood

The paper links ventral attention network connectivity to cortical maturation and cognitive ability in children using independent datasets.

Intrinsic structural covariation links cerebellum subregions to the cerebral cortex

The paper identifies structural covariation patterns between cerebellum subregions and cerebral cortex in a large cohort, revealing dissociations and novel associations with phenotypes.

Functional brain networks are associated with both sex and gender in children

This study shows that sex and gender are associated with distinct functional brain networks in children, with sex linked to unimodal networks and gender to heteromodal networks.

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology

The paper investigates the impact of rare high-effect genetic variants on brain morphology and related behavioral traits in adolescents using the ABCD cohort.

Sex differences in the functional network underpinnings of psychotic-like experiences in children

The paper examines sex differences in functional brain network connectivity associated with psychotic-like experiences in children using the ABCD Study dataset.

Eigenmodes of the brain: revisiting connectomics and geometry

The paper re-evaluates the comparative explanatory power of geometric and connectome eigenmodes in predicting brain activity, finding that connectome eigenmodes can achieve comparable accuracy when improved connectome mapping is used.

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures

This study identifies sleep-biopsychosocial profiles in healthy adults by linking sleep patterns to health, cognition, and lifestyle factors, revealing distinct brain network organization patterns.

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

This paper adapts meta-matching frameworks to translate phenotypic prediction models from large to small MRI datasets, showing significant performance improvements over classical methods.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

The paper introduces multilayer meta-matching to improve phenotypic prediction in small datasets by translating models from multiple large datasets, outperforming classical methods and transfer learning.