Richard F. Betzel

This study uses multilayer modeling to analyze brain-behavior correlations across seven social support measures, finding that subcortical and control systems show varying community affiliations.

The paper investigates how brain network structure, neuromodulation, and disease influence transitions between cognitive activation patterns using multimodal neuroimaging data.

This study identifies hierarchical co-fluctuation patterns in brain activity using fMRI data from two dense-sampling studies, revealing multi-scale organization of brain systems.

The paper benchmarks ifNN methods to improve test-retest reliability of individual differences in spontaneous brain activity using the Human Connectome Project.

The paper examines how hormonal fluctuations during the menstrual cycle affect brain network states using edge time series analysis in a single individual.

This paper introduces a method to weight anatomical brain connectivity data using multi-modal MRI data and evaluates its performance on the Human Connectome Project dataset.

This paper investigates the relationship between anatomical and functional modular organization of the human brain using a novel multi-layer framework across multiple subjects.

This study compares edge-centric and sliding window methods for analyzing time-varying functional brain networks in ASD and healthy controls, finding distinct dynamic patterns in co-fluctuations.

The paper investigates local brain system events and their contributions to global events, revealing distinct co-fluctuation patterns and their spatial distribution during resting-state and movie-watching scans.

This study shows that structure-function coupling in the brain is dynamic and regionally heterogeneous, revealing organized patterns linked to cortical hierarchies and connection lengths.

The paper introduces a multi-policy communication framework for the human connectome that outperforms existing models and explains variance in time-varying functional connectivity.

This study investigates structure-function relationships in human brain networks across the lifespan, finding that local functional coupling is better predicted by structural features than global patterns, with age-related changes observed in sensorimotor regions.

The paper decomposes whole-brain functional connectivity into framewise contributions, showing that individualized brain network 'events' drive connectivity patterns, which are distinct from group-averaged patterns.

The paper investigates cortico-subcortical interactions using edge communities and finds distinct coupling patterns between subcortical regions and cortical systems.

This study examines how functional brain network modularity during rest and task conditions relates to academic learning and ability in high school students.

The paper compares task-related network profiles and activation patterns in two executive control tasks, finding greater differences in functional correlations than in evoked activity.

The paper benchmarks functional brain connectivity by analyzing its relationship to structural and geometric brain organization, finding strong functional connectivity within intrinsic networks and at the apex of the unimodal-transmodal hierarchy.

The paper shows that modular structural connectivity shapes high-amplitude co-fluctuation events in simulated brain dynamics, mirroring empirical findings.

The paper analyzes edge communities in brain networks using the Midnight Scan Club dataset, showing they transcend traditional brain systems and reveal personalized patterns.

The paper develops an edge-centric functional connectivity model to identify subject-specific features across different spatial scales and shows improved identifiability over traditional nodal FC.

This paper introduces an edge-centric model to harmonize multi-relational network datasets by integrating connectivity estimates into a unified representation, revealing mesoscale architecture and node-level properties.

The paper introduces a method to analyze temporal fluctuations in functional connectivity by decomposing time series into binary patterns that reveal transient expression of brain functional systems.

The paper introduces an edge-centric network model to analyze brain connectivity, showing that edge functional connectivity is consistent across datasets and modulated by sensory input.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

The paper decomposes resting-state functional connectivity to show that high-amplitude activity frames drive most of the variance in connectivity patterns and modular structure.

The paper analyzes temporal changes in brain network architecture during movie-watching, finding that inter-subject similarity correlates with changes in network modularity and connectivity.

The paper identifies hub and community structures in brain networks driven by long-distance functional connections, showing primary sensory cortices as more hub-like and community structures spanning multiple levels.

This study identifies organizing principles of whole-brain functional connectivity in zebrafish larvae using calcium imaging data, revealing distance-dependent connections, hierarchical modularity, and consistent network structure across individuals.

This study examines how structure-function coupling in brain networks develops during youth, finding it aligns with cortical hierarchies and supports cognitive improvements.