Background and purpose: Recent studies indicate disrupted functional mechanisms of salience network (SN) regions-right anterior insula, left anterior insula, and anterior cingulate cortex-in mild cognitive impairment (MCI). However, the underlying anatomical and molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multimodal-anatomical and molecular-markers could predict cognitive impairment better in MCI.
Methods: Herein we quantified anatomical volumetric markers via structural MRI and molecular amyloid markers via PET with Pittsburgh compound B in SN regions of MCI (n = 33) and healthy controls (n = 27). From these markers, we built support vector machine learning models aiming to estimate cognitive dysfunction in MCI.
Results: We found that anatomical markers are significantly reduced and molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < .05). These altered markers in MCI patients were associated with their worse cognitive performance (p < .05). Our machine learning-based modeling further suggested that the integration of multimodal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers.
Conclusions: These findings shed light on the underlying anatomical volumetric and molecular amyloid alterations in SN regions and show the significance of multimodal markers integration approach in better predicting cognitive impairment in MCI.
Keywords: PET; machine learning; neuroimaging; structural MRI.
© 2022 American Society of Neuroimaging.