Functional Brain Connectivity Studies

This study evaluates how trial averaging and resampling affect decoding accuracy in MVPA, finding that modest averaging improves results and provides code for parameter optimization.

This study compares aperiodic brain activity between eyes open rest and Inscapes viewing using MEG, finding distinct spectral profiles in different brain regions.

The paper introduces a method to identify sub-networks of fMRI searchlights based on temporal information changes, revealing brain networks associated with different stimuli.

The paper uses retrograde tract-tracing in macaques to compare afferent inputs to subregions of the ACC, revealing distinct connectivity patterns between sgACC and pgACC.

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

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

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

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.

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

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.

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.

This paper introduces a feedback approach to connectome-based predictive modeling to identify which brain systems standardized tests and clinical symptoms reflect, enabling better-targeted test development.

The paper uses fMRI data to estimate hierarchical networks in the human cerebral cortex, revealing a global organization with distinct functional properties across different network orders.

This study evaluates deep learning models for predicting task activation from resting-state fMRI, finding that architectural changes improve efficiency without sacrificing accuracy.

This paper introduces a structural co-modulation measure to quantify individualized interactions between brain components using source-based morphometry, finding reduced co-modulation in schizophrenia patients.

This study uses graph signal processing to analyze structural and functional brain connectivity in healthy aging, revealing distinct age-related patterns in cognitive systems.

The paper links socioeconomic status (SES) to brain-wide associations, showing SES has stronger associations with brain structure and function than IQ or psychopathology.

The paper uses connectome-based brain fingerprinting to identify early cognitive decline markers in Parkinson's patients with minor hallucinations using resting-state fMRI.

This study shows that TSPO knockout prevents hippocampal network dysfunctions in 3xTgAD mice without affecting amyloid or tau levels.

The paper assesses spatial entropy in brain network landscapes of older adults and finds higher entropy in specific regions correlates with worse physical function and higher BMI.

The paper introduces NSI, an objective measure to assess data quality for precision functional MRI, aligning with expert evaluations and enabling principled decisions on data sufficiency.

This paper identifies altered dynamic brain states in Post-COVID-19 Syndrome using resting-state fMRI, showing reduced attentional states and increased limbic-DMN states linked to cognitive impairment and inflammation.

The paper evaluates a Kuramoto model to simulate functional connectivity in swine brains, showing good agreement with empirical data but limited changes post-traumatic brain injury.

The paper demonstrates that edge time series, a method for tracking connectivity changes, are equivalent to interaction terms in a linear model and show connectivity has unique explanatory power beyond brain activity.

The paper predicts age and MMSE scores from brain connectivity graphs using a graph-based deep learning model, showing improved performance over existing methods.

The paper uses precision fMRI to reveal individual-specific, fragmented, and interdigitated network patches in the lateral prefrontal cortex, showing conserved motifs and distinct functional boundaries not visible in group-averaged data.

This study identifies cortical atrophy patterns in cocaine use disorder by integrating structural connectome data with gene expression and behavioral maps, revealing key regions and biological processes involved.