Functional Brain Connectivity Studies

The paper identifies a generalizable arousal representation from dynamic functional connectivity across different individuals and movie-watching contexts, while finding no evidence of a generalizable valence representation.

This paper develops a neonatal-specific cortical parcellation based on functional connectivity, validated across multiple datasets.

This study shows that general anaesthesia reduces the uniqueness of human brain connectivity both between individuals and across species.

This paper improves fMRI-based intelligence prediction by using semi-blind machine learning with educational level as supplementary data, achieving better accuracy even with small sample sizes.

This paper identifies a dual long-axis organization in the human hippocampus during youth, showing its role in cortical hierarchy maturation and functional reorganization.

This paper introduces fcANNs, a model of brain dynamics based on functional connectivity, showing it can reconstruct and predict brain activity in rest, tasks, and disease states.

This paper introduces BrainGAT, a graph neural network for gender classification using multimodal brain connectomes, achieving 83.13% accuracy on the Human Connectome Project dataset.

This study identifies distinct brain module organizations in FTD subtypes using resting-state fMRI and machine learning, finding altered modular properties and classification accuracy of 85%.

This study combines rsfMRI and immunohistochemistry to show how whole-brain functional connectivity and EGR1 expression change during operant learning in mice.

The paper uses multilayer network analysis to explore associations between environmental exposures and brain development in children, finding dense connections within environmental and brain layers but sparse connections between them.

This study investigates the variability of fMRI BOLD signals in the human brain, showing it reflects multi-scale brain organization rather than noise.

The paper uses REACT and normative modelling to analyze functional neurocircuits in neuropsychiatric patients, revealing transdiagnostic deviations in neurotransmitter systems and symptom overlaps.

The paper identifies three distinct subnetworks within the Cingulo-Opercular network linked to decision-making, action execution, and feedback processing using functional connectivity data.

The paper addresses concerns about eigenmode reconstructions in a previous study, demonstrating how specificity is achieved through appropriate benchmarks.

This paper shows that sliding window analysis on fMRI data can reintroduce non-neuronal signal, questioning the neuronal origin of brain states identified through this method.

This commentary critiques the use of geometric eigenmodes as a model for brain function, arguing that they may overfit and fail to explain functional mechanisms beyond geometry.

This study investigates how APOE, immune factors, sex, and diet interact to shape brain networks in mouse models of aging, identifying shared vulnerabilities in key circuits.

This study examines the reliability of dynamic functional connectivity in MEG data related to language processing and identifies properties associated with reliable connections.

This study shows that higher baseline somatic arousal levels are linked to increased neural synchrony during naturalistic fMRI tasks.

This paper introduces a polyconnectomic score (PCS) to quantify brain connectivity signatures in neuropsychiatric disorders, showing higher PCS in patients compared to controls across multiple disorders.

The paper shows that resampling large neuroimaging datasets with replacement introduces bias in error estimates for brain-behavior associations, suggesting that meta-analyses should resample only up to 10% of the full sample size to avoid these biases.

The paper introduces subnet communicability to model diffusive communication in the brain through a backbone subnetwork, showing it better couples structural and functional connectomes than previous models.

The paper analyzes neural state-trait variations in healthy young adults using resting-state fMRI to identify reproducible co-activation patterns linked to behavioral and cognitive features.

The paper examines brain connectivity delays during movie watching, finding sensory regions have the longest delays and reveal new spatial organisation patterns.

This paper introduces a metric learning framework for fMRI data to extract low-dimensional latent structures that reduce intra-group variability and maximize inter-group variability, achieving better performance than PCA on benchmark datasets.

The paper investigates the interaction between spontaneous and task-evoked brain activities using fMRI, finding no significant interaction in two distinct brain networks.

A masked-prediction model pretrained on 6,700 hours of fMRI that fine-tunes to predict clinical variables, forecasts future brain states, and recovers functional networks without supervision.

The paper identifies a systematic inflation of functional connectivity in fMRI scans over time and proposes a solution to mitigate this artifact.

The paper shows that a sensorimotor-to-visual connectome gradient present at birth predicts cognitive outcomes at 2 years of age in preterm and term babies.

The paper shows that the frontoparietal control network's topographic organization supports flexible behavior by switching between unified and segregated functional states based on memory needs.