Classification and Diagnosis of Alzheimer's Disease Based on Functional and Structural MRI

Alzheimer's disease (AD) is a common neurodegenerative disease, and early diagnosis of AD is crucial for timely intervention and treatment. This study combined clinical neuropsychological examinations, functional Magnetic Resonance Imaging local brain network properties, structural Magnetic Resonance Imaging gray matter, white matter and cerebrospinal fluid volume values to analyze the features with significant differences among the AD group, mild cognitive impairment group, and normal controls. Using the support vector machine model, a three-class classification was performed on all significantly different features, achieving an accuracy of 85.29%. The feature selection method of multimodal data in this study provides valuable assistance for classification and diagnosis.