Functional Brain Connectivity Studies

The paper investigates how connectome architecture supports dual communication modes, showing that modular networks enable within-module diffusion and between-module routing across multiple organisms.

This study evaluates the Vogt-Bailey Index using task-based fMRI across different pulse sequence protocols to assess its ability to identify task-related brain regions and the impact of spatial resolution on results.

This study uses time-resolved fMRI to analyze dynamic brain networks during emotional tasks, identifying four networks with distinct spatial and temporal features linked to different stages of emotional processing.

The paper introduces a fully automated pipeline to estimate subject-specific rs-fMRI states using constrained ICA of dFNC across 100,517 subjects.

The paper uses functional MRI and statistical modeling to show how subcortical regions receive convergence from specific cortical areas, revealing a topographic organization and the importance of subcortical contributions to brain dynamics.

This paper analyzes EEG data from epilepsy patients to study brain connectivity using cross-power spectral density, identifying hubs and networks that vary with frequency and brain state.

This study shows that brain networks with intermediate small-worldness exhibit the most dynamic behavior, with modularity strongly predicting neural dynamics.

The paper shows that synapse diversity in the mouse brain influences regional dynamics, with specific synapse types linked to distinct patterns of brain activity and connectivity.

This paper introduces VBI, a toolkit for Bayesian inference on virtual brain models, demonstrating its accuracy and reliability through in-silico testing.

This study examines how default mode network subnetworks and multiple-demand regions respond to transitions of person, place, and time using functional MRI data.

This study replicates and extends findings linking social dysfunction to reduced default mode network connectivity in schizophrenia, Alzheimer's disease, and major depressive disorder.

This study constructs structural and functional brain networks from regional brain profiles and shows that BENN provides better prediction of cognitive and emotional scores than traditional networks.

This paper introduces a conditional DDPM with attention to synthesize subject-specific 3D brain networks from rs-fMRI data, outperforming existing methods in accuracy and detail.

The paper identifies BraVe modes of brain-ventricle coupling in fMRI data and links their temporal occupancy to cognitive function across different cognitive statuses.

This study evaluates masked ICA (mICA) for identifying autonomic activation signatures in the brainstem using resting-state BOLD fMRI, showing that cerebrospinal fluid denoising significantly improves results.

The paper investigates the structural and genetic basis of functional brain networks in zebrafish using whole-brain imaging and genetic expression data, revealing similarities to mammalian networks.

The paper uses MRI similarity network analysis to study how early life stress affects cortical network development in rats, finding altered inter-areal similarity in key brain regions.

The paper examines the association between bile acid levels and functional connectivity of the executive control and default mode networks in patients with major depression.

The paper describes the neuroimaging component of the A2CPS project, detailing data collection, processing, and initial findings linking brain measures to clinical variables.

This paper introduces a method using local gradients of functional connectivity to identify infant brains with high accuracy, achieving 99% individual identification rates across ages.

The paper examines functional connectivity gradients in the posterior precuneus during altered states of consciousness, finding continuous connectivity patterns that flatten in these states.

This paper introduces CVCA, a method for contrast analysis in coordinate-based meta-analysis that avoids voxel-wise significance testing and provides effect estimates with reduced uncertainty.

This study introduces a deep learning framework for brain fingerprinting using fMRI data from adolescents, achieving high individual identification accuracy and linking fingerprints to cognitive and genetic factors.

MIND-Map is a toolbox that uses HSMM to estimate brain dynamic states, analyze their dynamics, and compare network dynamics between samples, with a method to determine the optimal number of states.

The paper identifies cell clusters associated with human brain functional connectomes and links them to neuropsychiatric disorders.

This study compares voxel-wise and region-wise denoising in functional connectivity analyses, finding region-level denoising offers equal or better performance with reduced computational cost.

This study uses Brain Entropy (BEN) to analyze fMRI data and identifies distinct neural patterns associated with rumination compared to other mental states.

This study identifies adult-like multi-scale brain networks in infants using rsfMRI, showing early emergence of functional connectivity patterns.

This study examines sex differences in the relationship between cerebral blood flow and glucose metabolism across age groups and finds that older females show stronger negative correlations, potentially indicating compensatory mechanisms.