Functional Brain Connectivity Studies

EiDA is a method for analyzing dynamic functional connectivity in resting-state fMRI data from ageing rats, revealing reduced phase-locking intensity and structural complexity with age.

This study examines how exercise affects functional connectivity in the cortico-basal ganglia network in mice, revealing changes in connectivity patterns and metabolic activity in specific brain regions.

The paper examines the relationship between alpha and beta peak frequencies from MEG and functional connectivity between the Default Mode and Dorsal Attention Networks using fMRI in two datasets.

This study compares the predictive power of resting-state fMRI features and individual characteristics for cognitive phenotypes in 20,000 UK Biobank subjects, finding individual characteristics generally perform better.

This study examines age-related changes in brain network controllability using diffusion MRI data and finds that redundancy mitigates these changes and supports cognitive function.

The paper examines prefronto-subcortical functional connectivity in schizophrenia and related disorders, finding reduced connectivity in schizophrenia but not in autism or depression.

The paper develops a multivariate residualization method for medical imaging data to better control for nuisance variables and applies it to ADNI data.

The paper investigates BOLD signal changes in both white and gray matter using model-free analysis and simple tasks, finding time-locked activations across the whole brain.

This paper develops a multivariate signal processing pipeline to build high-order brain networks from fMRI data and identifies high-order hubs associated with distinct brain systems.

Low phenotypic measurement reliability severely limits out-of-sample brain-behaviour prediction accuracy and prevents models from fully benefiting when neuroimaging sample sizes scale from hundreds to thousands of participants.

This study shows that incorporating functional connectivity variability improves machine learning classification of psychiatric conditions and prediction of psychosocial traits in resting-state fMRI data.

This study links individual differences in delay discounting to specific patterns of dorsal prefrontal cortex connectivity in youth using resting-state fMRI.

This paper introduces nTL-MDPC, a nonlinear functional connectivity method for EEG/MEG, and shows it detects nonlinear relationships better than its linear counterpart in simulations but with subtle differences in real data.

This study identifies widespread brain-body co-fluctuations across multiple physiological systems and shows they are linked to autonomic nervous system activity.

This paper evaluates manifold learning techniques for visualizing time-varying functional connectivity in fMRI data, finding that intrinsic dimension varies and methods differ in capturing subject and task information.

This study identifies the location and connectivity of the rodent equivalent of primate area 23, showing its role in integrating multimodal information and suggesting its use in modeling primate brain functions.

The paper analyzes temporal dynamics of default mode network activity using Human Connectome Project data, showing that the network's activity changes over time during tasks, challenging the traditional view of it being task-negative.

This study evaluates how different preprocessing pipelines affect static and dynamic functional connectivity metrics in healthy controls and Parkinson's disease patients, showing dynamic connectivity is highly sensitive to motion correction methods.

This study combines DTI and fMRI in healthy adults to delineate human homologues of medial and lateral entorhinal cortex subdivisions, revealing a confined border between posteromedial and anterolateral regions.

The paper investigates how different parcellation atlases affect deep learning tasks on structural connectomes for age and sex prediction, finding that atlas choice is generally not critical.

This study uses a novel connectomic measure to identify structural connectivity alterations in ASD and links network topology changes to autism severity.

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.

The paper investigates neural pathways linking psychological stress and brain health in healthy individuals and multiple sclerosis patients, finding similar functional connectivity patterns.

This study identifies and compares spontaneous brain activity patterns in marmosets and humans, finding similar functional connectivity states in visual and auditory processing.

This paper examines how parcellation methods and sample size affect canonical correlation analysis results in neuroimaging studies using UK Biobank data.

This study uses a deep learning classifier to subtype schizophrenia patients based on dynamic functional network connectivity, identifying 5 subtypes with distinct network interactions and symptom severity.

The paper identifies age-related changes in DMN connectivity patterns and their behavioral correlates using resting-state fMRI data from young and old individuals.

This paper integrates seven multimodal brain connectivity profiles to uncover universal organizational principles and maps them onto brain structure and function.

This study evaluates two methods for measuring background connectivity in task-based fMRI and finds that both preserve large-scale network organization but differ in capturing individual differences in connection strength.

This study uses resting-state fMRI to track longitudinal changes in brain network connectivity in a mouse model of Huntington's disease, revealing age-related alterations in cortical and subcortical networks.