Functional Brain Connectivity Studies

Dissecting the strain and sex specific connectome signatures of unanesthetized C57BL/6J and DBA/2J mice using magnetic resonance imaging

This study identifies strain and sex-specific differences in brain connectome features of awake C57BL/6J and DBA/2J mice using MRI.

Entropy-based integration index for quantifying network integration in resting-state functional MRI

This paper introduces an entropy-based framework to quantify network integration in resting-state fMRI by analyzing the distribution of ICA-derived functional contributions across Yeo atlas networks.

Divergent changes in perturbation-induced brain reconfiguration following depression treatment with psilocybin and escitalopram

This study compares how psilocybin and escitalopram affect brain reconfiguration in MDD patients using whole-brain models and perturbation analysis.

Geometric constraints on the architecture of mammalian cortical connectomes

This paper develops a geometric model of cortical connectomes that outperforms existing approaches in reproducing topological and topographical properties across multiple species.

Unsupervised Representation Learning Reveals Individualized Neurophysiological Profiles

This paper introduces an autoencoder framework to derive individualized neurophysiological profiles from resting-state MEG data without participant labels, achieving high accuracy in differentiation and prediction.

Decomposition of task-specific responses in the multiple demand network

The paper characterizes functional subdivisions within the multiple demand network using fMRI data from 20 subjects performing 14 tasks and passive viewing.

Boosting Hyperalignment Performance with Age-specific Templates

This study shows that using age-specific templates improves hyperalignment accuracy in brain data across different age groups.

Neurodevelopment Trajectory of Electrophysiological Functional Connectivity Following Alcohol Use Initiation

This study tracks how electrophysiological functional connectivity changes during adolescence following alcohol initiation, finding divergent trajectories based on drinking severity.

Mapping Higher-Order Topology in OCD Brain Networks with Hodge Laplacian

This paper introduces a Hodge-Laplacian framework to analyze higher-order topology in OCD brain networks, identifying loop-level abnormalities not detectable by conventional functional connectivity analysis.

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease

The paper uses fMRI data to identify normative brain-state trajectories in healthy aging and shows increased deviation in Alzheimer's disease.

Adiposity in Adolescence has Detrimental Effects on Developing Brain Structures and Organization, Communication, and Controllability of Resting-State Networks

This study examines how adolescent adiposity affects brain structure and network organization, finding associations with weaker neural networks and altered brain morphology.

Co-existence of Modularity and Anti-modularity in the Functional brain connectomes

The paper analyzes functional brain connectomes to show that positive correlations are modular while negative correlations are anti-modular, with differences in topology across brain states and frequency bands.

Functional gradients dissociate between cortical layers

The paper identifies dissociated functional gradients across cortical layers using fMRI, showing superficial layers align with sensory-to-association gradients and deep layers with visual-to-somatomotor gradients.

Searchlight Optimization Using Representational Similarity Analysis for Subject-Level Voxel Selection in Emotional State Decoding

This paper introduces a subject-level searchlight optimization framework combining univariate and multivariate analyses to identify condition-sensitive voxels for emotional state decoding in fMRI.

Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration

The paper introduces a metabolism-weighted connectome framework that links brain network organization with metabolic activity, revealing that high metabolic hubs are more vulnerable to neurodegeneration.

Increasing the reliability of functional connectivity by predicting long-scan functional connectivity based on short-scan functional connectivity: model exploration, explanation, validation, and application.

This paper develops three generalized linear models to predict long-scan functional connectivity from short-scan data, improving reliability and performance in connectome-based predictive models.

The genetic architecture of cortical similarity networks

The paper investigates genetic effects on cortical similarity networks using MRI data and finds two genetically determined gradients linked to brain evolution and neuropsychiatric risks.

MIRATS framework: Normative multiscale characterization of brain regulatory systems across sex and age using multimodal MRI

The paper introduces a framework to characterize brain regulatory systems across sex and age using multimodal MRI, revealing system-specific patterns in healthy adults.

Development of the cognitive energy landscape from infancy to adolescence

This study uses network control theory and developmental MRI to show how brain structural maturation supports cognitive development, finding decreasing control energy for most cognitive tasks and varying optimal efficiency ages across cognitive functions.

Structural Connectivity Selectively Constrains Intrinsic BOLD Timescales through Graph-Smooth Neural Activity

The paper uses graph signal processing to analyze how structural brain connectivity influences intrinsic BOLD timescales, showing structure-coupled signals strongly correlate with connectivity strength.

High-Frequency Spatial Feature Fusion with 3D CNN for Early Stage Schizophrenia Classification

The paper develops a 3D CNN with high-pass spatial filtering and Laplacian initialization to classify early-stage schizophrenia using fMRI data, achieving 75.54% test accuracy.

Temporal structure of task engagement reorganizes infra-slow BOLD dynamics

This paper shows that task engagement in fMRI reorganizes infra-slow BOLD dynamics by redistributing power across frequencies rather than just adding to ongoing activity.

Human striatal population state dynamics

The paper identifies human striatal population states resembling canonical SPN states and shows their modulation by task demands, arousal, and behavior using fMRI data.

Latent Representations of Early Brain Development: A Multivariate Normative Model of Brain Structure and Behaviour

This paper uses a semi-supervised autoencoder to create multivariate normative models of brain structure and behaviour in children, showing associations between structural deviations and cognitive/behavioural differences.

From topography to connectome: Towards an integrated understanding of the resting brain

The paper develops a deep-learning model to translate spatial topography maps into individualized connectomes, confirming its accuracy and ability to retain brain-cognition associations.

Predicting pain location from resting-state brain fMRI

This study uses resting-state fMRI to stratify chronic low back pain patients into phenotypes based on brain activity and sensory abnormalities, revealing distinct connectivity patterns and predicting pain locations.

Robustness of NeuroMark-Derived Functional Networks to fMRI Spatial Normalization Across the Human Lifespan

This study evaluates the robustness of NeuroMark-derived functional networks to fMRI spatial normalization across different age groups, finding high stability regardless of normalization strategy.

A Structural Principle for Macroscopic Neural Dynamics Correlations

The paper identifies coupling correlation as a structural determinant of macroscopic neural dynamics and validates this principle across species using empirical data.

Directed Human Structural Connectome Reveals Hierarchical Organization and Shapes Large-Scale Brain Dynamics

Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion

This paper introduces mRBM-HBP, a Bayesian framework that integrates resting-state and task fMRI to improve cortical parcellation accuracy and individual specificity.