Functional Brain Connectivity Studies

This study uses ROI-constrained CPM to predict 18-month cognitive, language, and motor outcomes from neonatal brain connectivity, finding that early sensory hubs are strong predictors.

The paper investigates the neural origins of quasi-periodic patterns in resting-state fMRI using combined calcium imaging and fMRI, finding a time-locked correlation between neural activity and BOLD signals.

The paper introduces channel capacity as a measure of directed information transfer between brain regions and validates it across human and rodent neuroimaging datasets to assess its sensitivity, specificity, and temporal variability.

This study tracks how brain connectivity patterns change after stroke, showing early stabilization of functional fingerprints and long-term circuit remodeling that predicts recovery outcomes.

This paper introduces AMAge-Net, a multimodal brain age prediction model combining fMRI and sMRI, achieving high accuracy and interpretability in estimating brain aging.

The paper analyzes functional network topology in mouse motor cortex using two-photon calcium imaging across behavioral states, revealing state-dependent changes in network structure.

This paper introduces a framework for constructing functional brain networks using multiscale entropy profiles instead of direct temporal synchrony, showing improved modular organization and sensitivity to sex differences.

This paper introduces BrainMetaGCN, a memory-augmented graph learning framework for AMDD diagnosis using rs-fMRI, achieving better performance than baseline models.

The paper compares group and personalized brain functional connectivity approaches for depressive constructs, finding intersection estimates more valid and predictive than group or personalized estimates alone.

The paper analyzes resting-state fMRI data to determine its statistical properties, finding approximate Gaussianity and non-stationarity, which help distinguish real data from surrogates.

This paper investigates how play behavior in children is linked to brain network integration, finding stronger connections among the default mode, executive control, and salience networks with more frequent play.

This study identifies a brain network associated with self-transcendence using lesion network mapping in patients with brain lesions.

This paper develops a method to test hypotheses about the correlation between brain activation patterns in fMRI studies, showing that a subject-wise bootstrap on the maximum-likelihood group estimate performs best.

This study shows how white matter myelin and tract caliber influence large-scale brain communication by shaping distinct functional connectivity patterns.

This study uses functional connectome harmonics to analyze neonatal brain gradients and finds that power and energy metrics differ between term and preterm neonates, predicting postmenstrual age.

This paper introduces a shallow neural network with structural connectivity message-passing for brain decoding, achieving 83.0% classification accuracy using fMRI data from 30 subjects.

The paper identifies a nonlinear developmental trajectory of the language network through multimodal neuroimaging and behavioral measures, showing stages of localization, distributed state, and return to localization.

The paper identifies multiscale principles of brain reorganization under electrical perturbation by integrating neuroimaging, connectomics, and transcriptomics data from 148 experiments.

This study examines how thalamocortical innervation timing and spatial distribution influence cortical network formation during gestation, finding that hubs emerge from spatiotemporal constraints rather than preferential targeting.

This study uses brain digital twins to compare network changes in congenital and slowly progressive cerebellar ataxias, finding distinct patterns in network topology and dynamics that correlate with clinical symptoms.

This paper introduces multi-task batteries for functional brain mapping, showing they improve localization and parcellation compared to single-contrast approaches.

The paper tests whether spatial bias in lesion network mapping is driven by connectome features, finding no consistent bias across datasets.

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

This paper introduces a method to detect time-resolved brain networks from fMRI data using instantaneous phase, revealing how brain regions synchronize and reorganize during tasks.

This study reviews and meta-analyzes neuroimaging studies on self-referential processing in psychiatric disorders, finding shared neural aberrancies but limited evidence for a transdiagnostic feature.

The paper shows that cortical excitability inversely affects fMRI connectivity, with low-frequency neuronal coupling being the key driver of these changes.

The paper identifies a reproducible functional connectivity signature in temporal lobe epilepsy through a large-scale connectome-wide analysis.

The paper uses connectopic mapping on UK Biobank data to analyze variation in language-related brain connectivity and identifies genetic loci associated with these patterns.

The paper reveals a conserved network backbone and flexible hubs in the brain during movie watching, linking network organization to stimulus features.

CREB improves fMRI data harmonization by learning site effects from training data, enabling safe application to new unseen data while preserving biological variance.