Neuro-anatomical and neuro-molecular markers in salience network and their integration in machine learning-based prediction of cognitive dysfunction in mild cognitive impairment

Abstract

Recent studies indicate disrupted functional mechanisms of salience network regions, especially right anterior insula (RAI), left AI (LAI), and anterior cingulate cortex (ACC), in mild cognitive impairment (MCI). However, the underlying neuro-anatomical and neuro-molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multi-modal neuro-anatomical and neuro-molecular markers could predict cognitive impairment better in MCI. Herein we quantified neuro-anatomical volumetric markers via structural magnetic resonance imaging (sMRI) and neuro-molecular amyloid markers via positron emission tomography with Pittsburgh compound B (PET PiB) in SN regions of MCI (n = 33) and healthy controls (n = 27). We found that neuro-anatomical markers are significantly reduced while neuro-molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < 0.05). These altered markers in MCI patients were associated with their worse cognitive performance (p < 0.05). Our machine learning-based modeling further suggested that the integration of multi-modal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers. Overall, these findings shed light on the underlying neuro-anatomical volumetric and neuro-molecular amyloid alterations in SN regions and show the significance of multi-modal markers integration approach in better predicting cognitive impairment in MCI.