Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

Background: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local morphometric changes and disease status for early diagnosis could be aided by machine learning algorithms trained on neuroimaging datasets.

Objective: This study intended to propose machine learning models for the accurate identification and cognitive function prediction across the AD severity spectrum based on structural magnetic resonance imaging (sMRI) of the bilateral hippocampi.

Methods: The high-resolution sMRI data of 120 AD dementia patients, 232 amnestic mild cognitive impairment (aMCI) patients, and 206 healthy controls (HCs) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The classification capacity and cognitive predict ability of hippocampal volume was evaluated by multiple pattern analysis using the support vector machine (SVM) and relevance vector regression (RVR) application of the Pattern Recognition for Neuroimaging Toolbox, separately. For validation, the analyses were performed using a biomarker-based regrouping method and another independent local dataset.

Results: The SVM application produced a total accuracy of 94.17%, 80.85%, and 70.74% and area under receiver operating characteristic curves of 0.97, 0.87, and 0.72 between HC versus AD dementia, HC versus aMCI, and aMCI versus AD dementia classification, respectively. The RVR application significantly predicted the baseline and mean cognitive function at three years of follow-up. Qualitatively consistent results were obtained using different regrouping method and the local dataset.

Conclusions: The machine learning methods based on the bilateral hippocampi distinguished across the AD severity spectrum and predicted the baseline and the longitudinal cognitive function with greater accuracy.