Functional Brain Connectivity Studies

This study examines how meningiomas and gliomas affect brain dynamics using resting-state fMRI, finding distinct disruptions in dynamical complexity metrics between the two tumor types.

The paper identifies dynamic resting-state brain network markers associated with disruptive behavior problems in children using fMRI data from the ABCD Study.

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

This study examines how spatiotemporal brain activity propagations change from childhood to early adulthood, finding that certain propagation patterns become more prevalent with age and correlate with cognitive performance.

This study uses movie-driven fMRI to compare functional brain networks between humans and marmosets, finding conserved sensory networks but species-specific higher-order cognition networks.

This study shows that incorporating sample size into fMRI cluster threshold calculations reduces type II error and improves accuracy.

This paper introduces a novel oscillatory network model to simulate simultaneous EEG-fMRI data, showing improved replication of functional connectivity and dynamics across spatiotemporal scales.

The paper characterizes the auditory cortex's function through dynamic coactivation patterns identified in fMRI data, showing consistency between resting-state and task conditions.

The paper examines how systematic fMRI signal differences across cohorts affect lifespan trajectories of functional brain networks and shows how harmonization can mitigate these effects.

This paper evaluates hierarchical shallow piecewise-linear RNNs for modeling individual resting-state fMRI, finding they capture functional connectivity but have limited predictive power for phenotypic variables.

This paper introduces FOCA, a framework to analyze spatial coordination of functional brain networks, revealing convergent and divergent patterns and their developmental origins.

The paper shows that pre-stimulus brain connectivity patterns predict perceptual outcomes across different tasks, with task-specific connections dominating predictions.

The paper investigates ethnicity/race-related differences in brain functional connectivity, linking them to lifestyle factors and transcriptional patterns.

The paper examines age-related changes in functional connectivity between the dLPFC and DMN during memory tasks and links it to cognitive control decline in aging.

This paper improves brain connectome analysis by incorporating global and local features, enhancing individual identification and phenotype prediction.

This study identifies an interoception network in the rat brain that is dynamically regulated by visceral inputs and depends on vagus nerve signaling.

This study shows that individualized functional brain network mapping in older adults reveals more heterogeneity and improves network fidelity compared to group-averaged approaches.

This study examines how hippocampal dysfunction in temporal lobe epilepsy affects single voxel autocorrelation, finding longer intrinsic timescales in left TLE patients and a link between cluster preservation and memory performance.

This study evaluates denoising strategies for resting-state fMRI, finding that multi-echo pipelines generally outperform single-echo ones but no single method is optimal for all tasks.

This paper introduces a constrained Laplacian method to analyze function-structure coupling at the edge level in brain networks, demonstrating its application across multiple datasets.

The paper applies quality control to the Allen Mouse Brain Connectivity Atlas, rebuilding connectomes and showing changes in connectivity patterns and organizational structure.

The paper introduces a framework to analyze dynamic functional connectivity in epilepsy patients, identifying distinct network flow styles linked to seizure onset and postictal aphasia.

This paper introduces two methods to estimate timescales in resting-state fMRI, showing the time-domain method is more accurate and provides valid statistical inference with Python implementations.

This paper introduces a whole-brain model of turbulent vortices to understand cognition by analyzing neuroimaging data and showing its ability to distinguish cognitive states and tasks.

This study identifies recurring transient cortical co-activation patterns from resting-state EEG that spatially resemble classical resting-state networks, suggesting they capture fast neuronal dynamics underlying spontaneous brain activity.

This paper introduces DIP-DCM, a method that improves parameter estimation in neural mass models by using dynamics-informed priors, showing better performance in predicting neuroimaging data and capturing disease mechanisms.

This study estimates the dimension of structural connectomes to understand their influence on brain dynamics in health and disease, finding it correlates with cognitive performance and lesion effects.

The paper develops a whole-brain modeling approach to mitigate activity mixing in neuroimaging data, improving brain-symptom correlation estimates and localization.

The paper examines the relationship between loneliness, age, and brain network organization using graph theory on neuroimaging data, finding an age-by-loneliness interaction in specific networks.

This study identifies distributed cortical networks involved in binocular convergent eye movements using resting-state functional connectivity and task-evoked activations.