Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study

Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer’s disease (AD). For this preliminary study, the authors recruited one hundred ten SCD individuals and well-matched healthy controls (HCs) in order to find if machine learning based on the multimodal connectome could predict the preclinical stage of AD. The study found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), which achieved an accuracy of 88.73% based on the integration of the three modalities. The study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and also provide insight into understanding the pathophysiological mechanisms underlying SCD.

Key points

• Multimodal brain networks improve the detection accuracy of SCD.
• Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.

Article: Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study

Authors: Haifeng Chen, Weikai Li, Xiaoning Sheng, Qing Ye, Hui Zhao, Yun Xu & Feng Bai for the Alzheimer’s Disease Neuroimaging Initiative