Functional Brain Connectivity Studies

This study identifies mesoscopic functional connectivity alterations in ASD by analyzing contrast subgraphs across brain networks of typically developed individuals and ASD patients.

The paper identifies common neural signatures of consciousness loss and recovery during sleep and anesthesia using intracranial EEG and diffusion map embedding.

The paper uses VR-based real-time imaging to study cortical functional connectivity dynamics during behavioral transitions in a mouse model of autism, finding altered FC patterns in motor areas associated with motor clumsiness.

This study uses individual-specific functional connectivity and machine learning to detect and predict Alzheimer's disease symptoms in elderly people with and without APOE ε4 allele.

The paper examines cerebellum-basal ganglia network integration in psychosis by comparing network global efficiency across different patient groups and its association with symptoms and cortical networks.

The paper identifies a task-related network, the Topological Discrimination Network (TDN), which shares regions with the default mode network (DMN) and exhibits similar temporal dynamics during resting states.

The paper uses fMRI to decode sustained emotional states during naturalistic movie viewing and identifies specific functional connectivity patterns associated with happiness and sadness.

This paper introduces a multi-scale spatially constrained ICA approach to analyze fMRI data, revealing consistent group differences and predictive patterns in schizophrenia.

The paper identifies integrated and segregated brain states during rest and tasks using graph theory and deep learning, finding integration correlates with information compression and segregation with specialization.

The paper uses functional ultrasound imaging to track functional connectivity changes in a 15q13.3 microdeletion mouse model across development, identifying a critical period of network maturation linked to genotype.

This paper introduces a Bayesian non-parametric cross-mapping method to estimate task-evoked directed functional connectivity from fMRI data, revealing new patterns of information flow in face processing.

This study uses persistent homology-based functional connectivity to analyze resting-state brain networks and finds it better explains cognitive ability variance than traditional measures.

The paper introduces MIND, a method to estimate structural similarity networks from brain MRI, showing improved consistency with known biology and genetic heritability.

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

This study examines how ayahuasca use affects functional connectomes, finding shared functional spaces and predictive relationships between connectivity patterns and drug experiences.

This study introduces a graph-matching based metric to quantify functional connectome differences between individuals and finds it correlates with age, cognitive performance, and familial relationships.

The paper evaluates how different levels of co-fluctuation in functional connectivity predict individual-level phenotypes using machine learning.

This paper introduces biologically-constrained graph neural networks to decode cognitive processes, revealing functional gradients from sensation to cognition and showing improved inter-subject alignment and behavioral prediction.

This study tracks the development of three brain networks from late gestation to the newborn period, finding slower maturation in the default mode network and environmental influences on the salience network.

This paper uses a sliding-window clustering method to analyze age-related changes in resting-state functional connectivity (FC) from fMRI data, identifying sex-specific developmental patterns.

The paper introduces a model that efficiently estimates time-dependent brain connectivity using structural connectivity constraints and sparse MAR parameters.

This study examines changes in progestogen levels and their effects on brain connectivity and mood in women starting or discontinuing oral contraceptives using longitudinal fMRI and behavioral measures.

This study identifies abnormal intrinsic neural timescales and reduced grey matter volume in Parkinson's disease patients, with stage-specific patterns observed in different brain regions.

This paper identifies two distinct sub-systems within the cingulo-opercular network using fMRI data, showing differential roles in task control and other cognitive/motor functions.

This paper introduces joint cmICA, a method that integrates structural and functional brain connectivity data to simultaneously parcellate and link cortical regions using MRI data.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

The paper develops a parcellation scheme for mouse isocortex based on connectivity gradient reversals, revealing 41 subregions with distinct connectivity patterns.

The paper introduces a new analysis method to detect dynamic adaptations in the default mode network during different mental states using fMRI data from a single subject.

This study investigates the physiological factors influencing regional homogeneity (REHO) across multiple datasets and finds that REHO correlates with motion, heart rate, and respiratory rate, suggesting caution in using REHO to infer neuronal activity differences.

This study examines the relationship between BOLD-fMRI and EEG signals in the resting human brain, finding frequency- and network-specific time-lags and correlations.