Classifying Post-COVID "Brain Fog" Patients and Identifying Key ROIs via Graph Neural Network Model.
Authors
Abstract
Brain fog has raised significant public health concerns as a common neurocognitive impairment in the post-COVID-19 condition, involving memory loss, poor concentration, and language difficulties. However, their neural mechanisms remain unclear, and objective resting-state fMRI-based diagnostic tools are still lacking. To address these challenges, we first recruited 72 patients who experienced persistent brain fog symptoms following COVID-19 infection, along with 68 post-COVID participants without brain fog (PC-noBF), and collected resting-state functional magnetic resonance imaging (rs-fMRI) data from all participants. The interpretable graph neural network model BrainGNN was employed to model and classify individual brain networks, utilizing functional connectivity graphs constructed from the Automated Anatomical Labeling (AAL) atlas. Using out-of-fold predictions from 5-fold cross-validation, BrainGNN achieved an accuracy of 75.71% (Bootstrap 95% CI: 68.57%-82.86%) and an area under the ROC curve (AUC) of 76.07% (Bootstrap 95% CI: 73.93%-88.48%). Furthermore, on an independent test set, BrainGNN outperformed traditional machine learning methods and other GNN models, achieving an accuracy of 82.14% and an AUC of 82.82% (classification threshold: 0.5). Moreover, the model identified several key brain regions-bilateral insula, bilateral Heschl's gyri, and the left superior temporal gyrus-as potential neurobiological markers. Notably, in post-hoc analyses, the ALFF and ReHo values of the left insula were significantly associated with scores related to language and memory symptoms. These findings collectively underscore the effectiveness and interpretability of the proposed approach in identifying functional markers of brain fog. This study not only demonstrates the potential of individual-level identification of brain fog using resting-state fMRI empowered by interpretable GNN, but also reveals its capacity to provide novel insights into the neurobiological mechanisms underlying COVID-19-related cognitive impairment.