Functional Brain Connectivity Studies

This study examines how the number of gradients used in Procrustes alignment affects identification accuracy and differential identifiability in functional connectivity analyses across multiple fMRI datasets.

This study correlates regional brain entropy with cell-type data, finding positive and negative associations with specific cell types across different brain regions.

The paper uses deep learning to predict cognitive performance from functional connectomes during different brain states and identifies the brain-cognition gap as a dopamine-related marker of cognitive vulnerability.

The paper quantifies non-linear brain connectivity across modalities and scales, finding that non-linear interactions are more prominent in microscale recordings than in non-invasive, large-scale modalities.

This study investigates whole-brain causal connectivity during decoded neurofeedback and identifies network dynamics associated with performance differences.

This study examines the relationship between brain entropy and structural-functional coupling in the human brain, revealing complex correlations with network efficiency and the sensorimotor-association axis.

The paper predicts individual brain activity patterns during tasks using resting-state networks, showing task-specificity and relevance of cortical microstructure/morphology.

The paper uses machine learning to analyze functional connectivity in brain networks to distinguish cocaine use disorder from healthy controls, finding that SN and CEN are more critical than DMN in addiction.

This study examines morphological brain changes and network disorganizations in heroin and methamphetamine abstinent patients compared to healthy controls.

This study investigates how functional brain networks change during tasks versus rest, showing task-evoked networks differ from resting-state networks in task-relatedness and spatial topology.

This study predicts nonverbal intelligence levels from EEG data using connectome-based modeling, finding significant variability in accuracy based on processing pipelines and highlighting the importance of alpha frequency bands and frontal-parietal regions.

This study uses multilayer network analysis to identify the left insula as a central bridge between cognition, emotion, and brain structure in the Human Connectome Project-Young Adult dataset.

The paper predicts general intelligence using structural-functional brain network coupling during task performance and resting state.

The paper maps hippocampal-cerebellar functional connectivity across the adult lifespan using resting-state fMRI data and finds age-related decreases in connectivity in specific regions.

The paper examines DMN hyperconnectivity in psychosis risk individuals and links two distinct connectivity patterns to specific symptom clusters.

The paper examines how specific lifetime stressor characteristics and brain network connectivity predict anxiety and depression symptoms in adolescents.

This study uses computational whole-brain modeling to show that competitive interactions in mammalian connectomes enhance computational capacity and fit brain dynamics better than cooperative-only models.

This paper introduces a template-driven method to generate dynamic functional network connectivity features from neuroimaging data, showing improved prediction of medication response in mood disorders.

This study examines how brain network organization changes in children during go/no-go tasks with and without rewards, finding that rewards enhance sustained attention-related network patterns.

This study maps cerebellocortical functional connectivity in children from birth to 60 months, revealing hierarchical organization and age-related changes in cerebellar network gradients.

This study uses resting-state fMRI and a hidden Markov model to show that early visual deprivation alters temporal dynamics and spatial organization of brain networks in blind individuals.

This study replicates and extends previous findings on the effects of oral contraceptive pills on brain networks using a randomized crossover trial and advanced analytical methods.

This study uses fMRI and MEG to analyze information coding and exchange in the multiple demand network, revealing distinct encoding patterns and introducing Fusion-RCA for connectivity analysis.

The paper investigates how neuromodulatory tone influences brain wave propagation along the unimodal-transmodal gradient and its relation to arousal and brain integration.

This paper compares two models for mapping brain structure to function in health and psychosis, finding that deep learning outperforms analytical models in predicting functional connectivity in healthy individuals.

This study evaluates how physiological factors affect functional connectivity in brain signals, finding that multivariate methods are more robust than bivariate ones and that physiological artifacts significantly distort connectivity estimates.

This paper introduces a deep convolutional neural network method to extract nonlinear functional network connectivity from fMRI data and applies it to classify mental disorders with high accuracy.

This study uses resting-state fMRI and causal analysis to show hyperactivity and hypoconnectivity in schizophrenia patients compared to healthy controls.

This study examines brain dynamics across women's reproductive stages using fMRI and machine learning, finding that brain dynamics decline at menopause and that combining brain dynamics with hormone levels improves stage classification.

The paper uses unsupervised machine learning to analyze fMRI data from an emotional face matching task, identifying large-scale brain networks and their relation to cognition and mental health.