Functional Brain Connectivity Studies

This paper introduces brain age intervals (BAIs) as a method to quantify uncertainty in brain age predictions using conformal prediction, showing stable coverage and highlighting the need for larger samples.

This study uses individualized network mapping in fMRI data from children and adults to show reduced network assignment confidence and weaker within-network connectivity in children, with individualized approaches improving sensitivity to age effects.

The paper links gray matter microarchitecture to functional brain network connectivity in preadolescents using diffusion and functional MRI data from the ABCD Study.

The paper identifies sex-dominant neurosubtypes in psychosis using fMRI data and shows their unique dysconnectivity patterns and cognitive implications.

The paper examines network-specific metabolic costs of functional connectivity in the human brain using PET/MR data, revealing differences in metabolic demand across brain networks and changes in Alzheimer's disease.

The paper examines frontolimbic functional connectivity development from birth to emerging adulthood, finding that fronto-hippocampus and fronto-amygdala connectivity differentiate with distinct developmental timing and sensitivity to adversity and cognitive ability.

The paper uses fPET and Granger causality to analyze directed metabolic connectivity in brain circuits, finding that metabolic connections predict cognition and affect, and vary in efficiency and functional benefit.

The paper introduces netneurotools, a Python toolkit developed by trainees to streamline brain imaging workflows by integrating diverse data types and addressing interoperability challenges.

This study investigates how different doses of ketamine affect functional connectivity in mice brains using fMRI and finds dose-dependent changes in connectivity patterns.

This study evaluates how thresholding structural connectomes affects whole-brain network models, finding that optimal sparsity depends on resolution and that tailored thresholding improves model performance.

The paper uses energy metabolism to infer directed communication patterns in the human cortex, showing sensory systems drive higher-order networks and default mode networks act as receivers.

The paper provides methodological guidance for using MIND, a structural MRI similarity metric, and demonstrates how input properties affect its accuracy using simulated and observational data.

This paper introduces a deep learning framework, FC-CNN, to predict brain states from frequency-resolved MEG functional connectivity and shows it outperforms conventional methods in age prediction.

This study identifies neural activity patterns predicting transitions between internal and external thought using fMRI data.

The paper maps intrinsic neural timescales across rest and tasks, showing task-induced changes while preserving temporal hierarchy and structural constraints.

This study integrates molecular-enriched functional connectivity with structural MRI to improve brain-age prediction, showing enhanced accuracy and biological interpretability.

The paper examines neural individuality in human brain regions with evolutionary signatures, finding stronger individuality in more evolutionarily novel areas.

This paper evaluates specificity testing in Lesion Network Mapping to address concerns about the biological interpretation and disease-specificity of derived brain circuits.

This study shows that schizophrenia-like neurodevelopmental pathology alters how adolescent alcohol exposure affects brain network connectivity in rats.

The paper investigates how early blindness affects functional connectivity in different neuronal populations in mice, showing coordinated yet identity-dependent network plasticity.

This study introduces a neuroimaging framework to discover state-dependent signal flow hierarchy in the human cerebral cortex using integrated effective connectivity and signal flow mapping.

The paper analyzes resting-state fMRI-electrogastrogram data to reveal the time-averaged and time-varying structure of the gastric network, showing it is dynamically reconfiguring and coordinated by multisensory integration.

This study identifies and characterizes fifteen subnetworks within subcortical brain regions, revealing their functional organization and potential applications in understanding neuropsychiatric disorders.

This paper introduces the intrinsic network flow framework to explain cognitive states through phase dynamics, showing that task classification accuracy is higher using phase information than activation patterns.

The paper identifies five distinct depression phenotypes using MEG-oscillation functional connectivity and low-dimensional brain-symptom associations.

The paper uses a longitudinal subject-pairing framework on cross-sectional neonatal data to reveal asynchronous structural and functional brain maturation, finding a functional maturation lag of about five days relative to structural development.

This study maps individualized default mode network subnetworks across development, finding increased functional segregation and spatial distinctiveness with age.

The paper uses gene gradients to infer directed structural connectivity across species and applies it to human data to reveal source-sink network organization.

The paper introduces a Python toolkit that transforms fNIRS hyperscanning data into functional connectivity graphs for network-level analysis of brain organization.

This paper develops an interpretable AI framework to map brain dynamics onto a latent space, identifying a cerebello-prefrontal signature associated with schizophrenia symptoms.