Functional Brain Connectivity Studies

This paper introduces an automated individual cortical parcellation method using a novel weakly supervised classifier, improving performance over previous models and identifying new atypical language-related brain areas.

This study performs a GWAS on functional brain network metrics and finds genetic overlap with brain structure and autism.

The paper uses simultaneous calcium imaging and fMRI in mice to identify cell-type specific signals in the brain with high spatiotemporal resolution.

This paper maps global cortical organization by integrating microstructural, connectivity, and functional data with cytoarchitectural maps, revealing a sensory-fugal gradient and functional diversity across brain areas.

The paper uses functional connectivity to parcellate the mouse prefrontal cortex and shows that perturbing specific clusters affects only their associated functional networks.

The paper develops a framework using optimal control theory to estimate brain structure-function interactions from MRI data, showing correlations between self-inhibition parameters and neural timescales across species.

The paper examines how aging, Alzheimer's pathology, and APOE4 genotype affect longitudinal functional connectivity and episodic memory in older adults.

The paper shows that estimating correlations across brain regions from separate cohorts can be misleading due to biases and errors in interpretation.

This study investigates the relationship between resting-state brain entropy (BEN), ovarian hormones, and behavioral inhibition and activation systems (BIS/BAS) using fMRI data.

The paper identifies Default Mode B (DMB) as a task-negative cognitive mode detectable in task-based fMRI, characterized by specific BOLD signal patterns and task-induced changes across multiple cognitive tasks.

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

The paper uses fALFF to compare brain activity patterns in ASD, SSD, and TDC groups, finding shared neurobiological features and variability differences.

This paper introduces a model-free method, Temporal Synchronization Analysis, to detect nonlinear brain activations in fMRI data across multiple cognitive tasks.

The paper introduces a framework using SHAP to identify FNC biomarkers distinguishing between schizophrenia patients and controls, and young vs. old adults with high accuracy.

This paper uses computational modeling to show how excitatory-inhibitory balance and cholinergic modulation affect default mode network (DMN) function and dysfunction in mice.

This study compares unsupervised feature selection with supervised methods to identify robust resting-state functional connectivity patterns associated with psychiatric disorders.

The paper uses functional connectivity networks to analyze calcium dynamics in non-neural tissues, revealing non-random structural and informational patterns post-injury.

This study links short-term brain dynamics to long-term changes in the functional connectome during postnatal development, finding a strong association between specific brain states and non-linear developmental acceleration.

This study links multi-scale brain connectivity networks to cognitive and behavioral functions in adolescents, finding significant associations with attention, working memory, and behavioral regulation.

This study evaluates the accuracy of reconstructing cortical functional maps using subject-specific geometric and connectome eigenmodes, finding that spatially smooth eigenmodes best explain functional data.

This study compares symptom networks and brain characteristics between first and recurrent Major Depressive Disorder episodes using a network approach, finding differences in symptom centrality and global network strength.

The paper introduces Brain-DiT, a deep generative model for analyzing task-fMRI data, identifying cognitive regions and subgroups linked to psychiatric conditions like depression and alcohol abuse.

BRAPH 2 provides a flexible, open-source framework for network analyses in neuroscience with advanced statistical models and deep learning capabilities.

This study compares brain organization during rest and naturalistic processing, finding greater functional gradient compression in older adults linked to worse cognitive performance.

This study shows that stroke alters intrinsic neural timescales and long-range temporal correlations, indicating a shift toward criticality and potential as biomarkers for recovery.

FREQ-NESS estimates frequency-resolved brain networks using MEG data, revealing simultaneous networks and their interactions during resting state and auditory stimulation.

NaDyNet provides a toolbox for dynamic network analysis of naturalistic stimuli, offering methods to isolate and analyze brain responses in real-time.

This study identifies altered functional connectivity and network controllability in NF1 adolescents during working memory tasks, which predict cognitive performance and distinguish NF1 from controls.

The paper identifies robust sex differences in personalized functional brain network topography in youth, linked to X-linked genes and brain regions involved in psychiatric disorders.