Functional Brain Connectivity Studies

Neuroticism Heterogeneity Through Item-Level Associations in Resting-State Functional Connectivity

The paper identifies neuroticism-related resting-state functional connectivity patterns using UK Biobank data, revealing genetically and psychometrically defined clusters.

Diurnal dynamics of multilayer brain networks predict cognitive trajectories in aging

The paper examines diurnal changes in brain network dynamics and their association with cognitive aging and Alzheimer's disease markers.

Modeling Hierarchical Brain Dynamics Outperforms Hormonal Biomarkers in Predicting Menstrual Cycle Phases

This study uses a thermodynamics-inspired framework to model brain dynamics across menstrual cycle phases, finding that model-based biomarkers outperform hormonal and traditional connectivity measures in phase prediction.

Node persistence from topological data analysis reveals changes in brain functional connectivity

The paper uses persistent homology to analyze brain functional connectivity changes in healthy aging and ASD, identifying clinically relevant regions with node persistence.

Large Scale Functional and Effective Connectivity Alterations cross the Huntington's Disease Integrated Staging System

This study uses fMRI to assess functional and effective connectivity changes across HD-ISS stages, finding attenuated connectivity in early stages and sensitivity to motion.

Robust brain complexity measurement using T2* informed multivariate sample entropy from multi-echo BOLD fMRI

The paper introduces a T2*-informed multivariate sample entropy method for fMRI data to better measure brain complexity and shows it detects significant group differences in older vs. younger adults.

Memory network activity flow failures in temporal and frontal lobe epilepsy

This study uses activity flow mapping to show reduced task activation prediction accuracy in TLE and FLE patients, linking intrinsic network dysconnectivity to memory impairments.

Testing for Interactions in Multivariate Data

The paper introduces a Bayesian multivariate linear model to test for interactions in multivariate brain imaging data, offering improved statistical inference compared to standard decoding methods.

Prediction of individual melodic contour processing in sensory association cortices from resting state functional connectivity

The paper predicts individual differences in melodic contour processing from resting state functional connectivity, finding higher predictability in sensory association cortices.

A Functional Resting-State Network Atlas Based on 420 Older Adults with Hypertension

The paper develops a functional MRI atlas for hypertensive older adults, addressing age-related changes not captured by standard atlases.

Cellular and molecular associations with intrinsic brain organization

The paper integrates transcriptomic, molecular imaging, and neuroimaging data to show that cellular and molecular architecture aligns with intrinsic connectivity networks and functional network connectivity, mediating cognitive processes.

Improved Estimation of Correlation Accuracy for Machine Learning Brain-Phenotype Associations

The paper develops a new estimator to more accurately assess machine learning model performance in predicting brain-phenotype associations and shows it reduces bias in estimating predictive accuracy.

Learning evoked centrality dynamics in the schizophrenia brain: Entropy, heterogeneity and inflexibility of brain networks

This study introduces Centrality Dynamics (CD) to analyze task-induced changes in brain networks of schizophrenia patients and healthy controls, finding reduced adaptability and increased stochasticity in schizophrenia.

Associations Between Age, Heart Rate Variability, and BOLD fMRI Signal Variability

This study examines the relationship between age, heart rate variability (HRV), and BOLD fMRI signal variability, finding that HRV positively correlates with BOLD variability while age-related decreases in BOLD are partially explained by cardiovascular factors.

A default-control network cortical gradient differentiates the imagination of social and solitary experiences

The paper uses fMRI to show that imagined social and solitary scenarios activate different patterns of the frontoparietal and default mode networks, revealing a gradient that reflects semantic differences in internal cognitive states.

SPATIAL DISSIMILARITY TRAJECTORIES REVEAL RAPID FUNCTIONAL NETWORK REFINEMENT DURING EARLY INFANCY

This study shows that spatial brain network organization becomes more coherent with age in infants, using fMRI data and spatial dissimilarity metrics.

Salience Network Segregation and Symptom Profiles in Psychosis Prodrome Subgroups

The paper identifies neurobiologically distinct subgroups in psychosis prodrome by integrating brain connectivity and symptom data, revealing age-related differences in salience network segregation and symptom profiles.

Time-resolved functional connectivity during visuomotor graph learning

The paper investigates how neural responses differ during learning of modular and non-modular graph structures using fMRI data, finding distinct connectivity patterns associated with performance on different graph types.

Dog brain atlas generated via spatially constrained spectral clustering

The paper generates a multi-scale functional dog brain atlas using spatially constrained spectral clustering and validates its replicability and coherence across scales.

The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI.

The paper compares 10 functional coupling measures in rs-fMRI to assess their motion sensitivity and predictive utility for behavioral outcomes.

Methamphetamine modulates functional connectivity signatures of sustained attention and arousal

This study examines how a single dose of methamphetamine affects brain functional connectivity related to sustained attention and arousal in healthy adults.

Enhancing Functional Connectivity Analysis in Task-Based fMRI Using the BOLD-Filter Method: Greater Network and Activation Voxel Sensitivities

This study applies the BOLD-filter method to task-based fMRI data to improve functional connectivity analysis, resulting in increased sensitivity and clearer task-related patterns.

Electrophysiological resting-state signatures link polygenic scores to general intelligence

This study links polygenic scores for intelligence to general intelligence using resting-state EEG graph metrics, showing parieto-frontal brain connectivity mediates the genetic association.

Microstate in rats EEG: a proof of concept study

This study detects and characterizes microstates in rat EEG, showing they can be captured using human-like metrics and identifying their frequency bands and sources.

Topologically Optimized Intrinsic Brain Networks

This paper introduces TOIBN, a method that uses topological information from group-level brain networks to improve subject-level network estimation while preserving individual variability.

Functional network collapse in neurodegenerative disease

This study identifies functional network changes in neurodegenerative diseases using MRI data, linking brain atrophy to specific connectivity patterns and cognitive deficits.

Independent Filter Analysis for Group Discrimination in fMRI

This paper introduces Independent Filter Analysis (IFA), a supervised method for group discrimination in fMRI that outperforms traditional ICA in detecting group differences.

High extraversion enhances attentional control through dynamic network reorganization

This study uses fMRI to show that high extraversion is associated with better attentional control under distraction due to distinct functional connectivity patterns.

Spatiotemporal Complexity in the Psychotic Brain

This study identifies spatiotemporal complexity disruptions in the psychotic brain using resting-state fMRI, revealing altered information integration and network instability.

Precision Functional Neuroimaging Reveals Individually Specific Auditory Responses in Infants

The paper demonstrates the feasibility of precision functional imaging in infants to identify individual-specific auditory responses and their relation to brain functional networks.