Functional Brain Connectivity Studies

This paper proposes that arousal-driven transitions across critical lines reproduce human functional connectivity dynamics, showing improved fit to empirical data.

This paper uses topological data analysis to show that higher trait rumination is linked to reduced brain-state variability during self-relevant tasks.

This study investigates how brain connectivity and dynamics across temporal and spatial scales relate to intelligence test performance using fMRI and EEG data.

The paper uses functional MRI to map cortical areas in neonates, finding that their arrangement is individually specific and reliably identifiable.

IndivSTATIS integrates individualized brain parcellations into a shared component space to compare network configurations and predict individual differences.

This study identifies neuroprotective anatomical and functional connectivity patterns in older adults using multimodal neuroimaging data.

This paper introduces a data-driven method to identify functional neural networks in both resting-state and task-based fMRI data across the entire brain.

This study evaluates the feasibility and reliability of precision resting state functional connectivity (RSFC) in Parkinson's disease (PD) and finds it produces reliable, stable network measures comparable to healthy controls.

This study introduces a method to estimate the controllability Gramian matrix using perturbation inputs and network control theory to analyze brain dynamics, showing that controllable directions better differentiate brain states than overall controllability.

This paper compares molecular connectivity (MC) and covariance (mCov) methods using fPET data to assess metabolic associations between brain regions, finding that different methods perform better at varying temporal resolutions.

This study examines how macroeconomic factors like gasoline prices correlate with large-scale brain network organization in a population of healthy young adults.

This paper introduces a concept-driven disentanglement framework for interpretable GNNs to predict brain functional connectivity from structural connectivity, achieving high accuracy and providing interpretable insights.

This paper introduces Feature Similarity (FS), a method to measure brain region interactions, showing it captures functional organization and task-specific dynamics better than traditional methods.

This paper investigates the relationship between brain metastability and functional connectome fingerprints in naturalistic fMRI, finding that metastability is associated with individual and group-level connectome stability and similarity, with variations across time scales and movie content.

This study uses tFUS to modulate sgACC connectivity in healthy adults, showing increased whole-brain connectivity post-treatment and distinct network changes compared to sham.

This study shows macrovascular correction improves the relationship between EEG and rs-fMRI metrics, and sex modulates EEG-fMRI associations, with EEG complexity linked to resting-state functional activity.

The paper evaluates the reliability of small-sample task-based fMRI studies using two datasets, showing that sample size requirements vary by analysis type and effect size.

This study examines the impact of global signal regression on resting-state fMRI connectivity, finding it reduces physiological artifacts while preserving electrophysiological connectivity patterns.

The paper analyzes resting-state fMRI data to show how brain network energy redistributes with age, supporting cognitive flexibility through an allostatic mechanism.

This study shows that individual brain dynamics, not age, primarily drive cognitive training effects and brain connectivity patterns in younger and older women.

This study uses QPP and cPCA to analyze spatiotemporal network changes in AD progression, identifying early disruptions in specific brain networks and showing their potential as biomarkers.

This study uses computational modeling to simulate functional connectivity changes in Huntington's disease and assess recovery potential by perturbing striatal activity.

This paper introduces dSNCC to analyze spatial dynamics and complexity in brain networks, finding reduced dynamic flexibility and increased complexity in schizophrenia linked to cognitive impairment.

SPLASH improves task-based fMRI analysis by providing a spatially adaptive and scalable framework that enhances statistical inference and neuroscientific interpretability.

The paper shows that white-matter BOLD signals mediate time-varying cortico-cortical functional connectivity, with temporal variability in WM signals correlating with dynamic GM-GM connectivity.

The paper maps structure-function coupling in the mouse cortex, finding a unimodal-to-transmodal gradient similar to humans, influenced by cellular properties but not anatomical hierarchy.

This study uses a large language model to link naturalistic movie content to brain activity, finding alignment in association cortex but not in sensory regions, and linking explainability to cognitive abilities.

This study investigates how action video game play reshapes neural circuits to improve visuomotor decision-making through cognitive resource reallocation.

This study examines how social jetlag affects brain structure, circuit organization, and intrinsic dynamics in adolescents, finding detrimental effects on neural connectivity and development.

The paper identifies subgroups with distinct brain activation patterns during a visual short-term memory task across the adult lifespan, showing differences in brain activity-performance associations and white matter integrity.