Rao, H.-Y. | Search bioRxiv

bioRxiv2026

EEG Functional Connectivity Reveals Accelerated Brain Aging in Young Adults with Cognitive Deficits and Mental Health Conditions

The discrepancy between chronological age and predicted brain age, derived from neuroimaging data, serves as a biomarker for neurological health. However, the exact electroencephalography (EEG) mechanisms contributing to the variation of brain age are yet to be understood. This paper comprehensively investigated the relationship between brain age estimated from EEG-based functional connectivity and cognitive or mental manifestations of healthy adults. A mean absolute error (MAE) of 5.97 and R-squared (R2) of 0.85 were reported for training data, while an MAE of 10.73 and R2 of 0.56 were reported for the prediction data. Increased brain age was found to be negatively related to the scores of memory and attention tasks. Most young adults (19 of 26) suffering from mental symptoms such as addictions, depression, phobia, and anorexia nervosa demonstrated higher brain age than their chronological age. Alcohol consumption was also significantly correlated to the difference between brain and chronological ages. The functional connectivity of frontal and central motor regions was found to be most distinguishing between individuals with high and low brain ages. These brain regions were significantly correlated with brain age but not chronological age, suggesting a relatively stronger relationship with biological age. Ablation studies further confirmed the importance of the frontal brain and all bands EEG in the prediction of brain age. This work advances the understanding of EEG mechanisms contributing to brain age and could provide an interpretation for physiological or psychological conditions associated with brain age.

neuroscience↗

bioRxiv2023

Collagen co-localised with macrovesicular steatosis for fibrosis progression in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a commonly occurring liver disease; however, its exact pathogenesis is not fully understood. The purpose of this study was to quantitatively evaluate the progression of steatosis and fibrosis by examining their distribution, morphology, and co-localisation in NAFLD animal models. qSteatosis showed a good correlation with steatosis grade (R: 0.823-0.953, P<0.05) and demonstrated high performance (area under the curve [AUC]: 0.617-1) in all six mouse models. Based on their high correlation with histological scoring, qFibrosis containing four shared parameters were selected to create a linear model that could accurately identify differences among fibrosis stages (AUC: 0.725-1). qFibrosis co-localised with macrosteatosis generally correlated better with histological scoring and had a higher AUC in all six animal models (AUC: 0.846-1). Quantitative assessment using second-harmonic generation/two-photon excitation fluorescence imaging technology can be used to monitor different types of steatoses and fibrosis progression in NAFLD models. The collagen co-localised with macrosteatosis could better differentiate fibrosis progression and might aid in developing a more reliable and translatable fibrosis evaluation tool for animal models of NAFLD.

pathology↗