Large-scale structural covariance networks changes relate to executive function deficit in betel quid-dependent chewers.

Abstract

Previous studies demonstrate deficits in executive function for betel quid-dependent (BQD) patients. Large-scale structural covariance network (SCN) based on gray matter (GM) morphometry may be able to explore the neural mechanism of executive dysfunction in BQD individuals. This study aims to identify spatial covariance patterns of GM volume and to investigate their association with executive dysfunction in BQD individuals. Sixty-four BQD individuals and 48 sex- and age-matched healthy controls (HCs) underwent T1-weighted structural MRI examination and executive function assessments, including the Backward Digit Span (BDS) test and Stroop Color and Word (SCW) test. Seventy SCNs based on GM volume covariance patterns were defined using independent component analysis. An SCN-based classifier was constructed to differentiate between BQD and HC individuals. Receiver operating characteristic (ROC) curves were applied to evaluate the performance of the SCN-based classifier. Linear regression analyses were performed to investigate the association between SCN network indices and executive function indices. Six SCNs had higher classifications for differentiating between BQD and HC individuals. The area under the ROC curve of the SCN-based classifier was 0.812 in the training set and 0.771 in the testing set. Furthermore, linear regression analyses demonstrated that the network indices in the thalamus were associated with BDS scores adjusted for age, sex, and education. Large-scale SCNs could provide potential imaging markers for differentiating BQD and HC groups. The loss of network index in the thalamus was associated with working memory, indicating that SCNs could reveal executive dysfunction in BQD individuals.