Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data

• Published in: NeuroImage (Orlando, Fla.) Vol. 59; no. 3; pp. 2217 - 2230
• Main Authors: Cho, Youngsang, Seong, Joon-Kyung, Jeong, Yong, Shin, Sung Yong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2012; Elsevier Limited
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - classification; Alzheimer Disease - pathology; Alzheimer's disease; Artificial Intelligence; Atrophy; Brain - pathology; Brain research; Cerebral Cortex - pathology; Classification; Cognitive Dysfunction - pathology; Colleges & universities; Cortical thickness; Data compression; Databases, Factual; Disease Progression; Entorhinal Cortex - pathology; False Negative Reactions; False Positive Reactions; Female; Frequency representation; Humans; Image Processing, Computer-Assisted - methods; Incremental learning; Individual subject classification; Longitudinal Studies; Magnetic Resonance Imaging; Male; Memory - physiology; Methods; Middle Aged; Neuropsychological Tests; Noise; Pharmaceutical industry; Positron-Emission Tomography; Principal components analysis; Reproducibility of Results; Frequency representation; Individual subject classification; Incremental learning; Cortical thickness; Alzheimer's disease

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classifica...