Functional Brain Connectivity Studies

This paper identifies vulnerable brain networks associated with Alzheimer's disease risk using connectome analysis and composite risk factors, finding age and genotype as significant predictors.

The paper shows that omitting spatial smoothing in neuroimaging can improve regional specificity without reducing sensitivity, particularly for small-scale brain structures.

This paper uses computational modeling to show that early adversity increases stochasticity in the formation of structural brain networks in mice.

The paper uses generative network models to show that socio-economic disadvantage in children is linked to altered brain network development, specifically slower modularity emergence.

This paper proposes a deep learning framework to subtype major depression using brain functional connectivity, achieving 75% generalization accuracy across multiple sites.

This study uses optogenetic stimulation to show that anterior insular cortex neurons suppress default mode network activity and reduce its connectivity with the salience network in rats.

The paper identifies four transdiagnostic functional domains across psychiatric disorders and links them to striatal connectivity patterns.

This study evaluates how EEG channel density, inverse solutions, and connectivity measures affect resting-state network reconstruction accuracy using simulations.

This study identifies genes associated with structural and functional connectivity in resting-state networks and finds genetic overlap within functional domains.

This study uses REACT to examine how propofol affects neurotransmission-related functional connectivity during rest and naturalistic stimulation, revealing changes in GABAergic, noradrenergic, and cholinergic networks.

The paper identifies increased connectivity between sensorimotor cortex and anterior cingulate cortex as a marker of physical resilience in older adults with high white matter hyperintensity volumes.

The paper introduces a deep learning approach to estimate parameters in connectome-based neural mass models using fMRI data and demonstrates its effectiveness on synthetic and real resting-state fMRI data.

The paper compares information transmission patterns in human, macaque, and mouse brains, finding that humans use parallel pathways while other mammals use single pathways.

The paper introduces a multi-policy communication framework for the human connectome that outperforms existing models and explains variance in time-varying functional connectivity.

The paper assesses reproducibility and variability in multi-modal, longitudinal brain networks across different modalities and tasks, finding structural networks more reliable than functional ones for individual identification.

StandardRat improves functional connectivity specificity in rat brains through a standardized MRI protocol tested across 20 centers.

This study tracks spatiotemporal changes in brain network integration and segregation during task and rest using fMRI data, revealing quasicyclic patterns linked to experimental timing.

The paper introduces FastDMF, a computationally efficient implementation of the Dynamic Mean Field model, enabling simulations of whole-brain dynamics with significantly faster performance and reduced memory usage.

The paper links hubness in brain networks to embryonic neurogenesis, showing that earlier-born nodes become stronger hubs and are associated with genes related to neurological diseases.

The paper investigates how affective memory enhancement relates to intrinsic brain connectivity patterns across the adult lifespan using fMRI data.

The paper examines functional connectivity in sensorimotor networks using EEG in stroke and MS patients with fatigue, finding increased small-worldness in sensory networks and decreased in motor networks in specific frequency bands.

This study shows that neuronal communication speed increases with age, reaching peak efficiency in middle age, across both long- and short-range brain connections.

The paper introduces iSparse kmeans, a two-step clustering method for large dFNC datasets, which is significantly faster than traditional kmeans while producing comparable results.

The paper identifies three subgroups of eating behavior traits independent of obesity using functional connectivity and autoencoder-based feature representation in healthy adults.

The paper simulates individual functional connectivity using individual structural connectivity data and neural mass modeling, finding that individual simulations better match empirical data than group-averaged simulations.

This paper develops a causal inference framework to link structural connectivity and brain function in aging, showing increased structural modulation in poor cognitive performers.

The paper identifies distinct misconfigurations of striatal connectivity profiles in smokers, revealing trait and state-specific alterations linked to nicotine dependence severity.

The paper uses principled clustering to reveal functional gradients in the hippocampus, showing non-linear changes in information processing along its axis.

This paper introduces a new effective connectivity measure called directional absolute coherence (DAC) and demonstrates its reliability in estimating neural communication direction and magnitude.

The paper maps auditory cortical networks using resting state intracranial electrophysiology, revealing hierarchical organization across spatial scales and frequency-dependent hub identities.