Highly automated computer-aided diagnosis of neurological disorders using functional brain imaging

摘要

We have implemented a highly automated analytical method for computer aided diagnosis (CAD) of neurological disorders using functional brain imaging that is based on the Scaled Subprofile Model (SSM). Accurate diagnosis of functional brain disorders such as Parkinson's disease is often difficult clinically, particularly in early stages. Using principal component analysis (PCA) in conjunction with SSM on brain images of patients and normals, we can identify characteristic abnormal network covariance patterns which provide a subject dependent scalar score that not only discriminates a particular disease but also correlates with independent measures of disease severity. These patterns represent disease-specific brain networks that have been shown to be highly reproducible in distinct groups of patients. Topographic Profile Rating (TPR) is a reverse SSM computational algorithm that can be used to determine subject scores for new patients on a prospective basis. In our implementation, reference values for a full range of patients and controls are automatically accessed for comparison. We also implemented an automated recalibration step to produce reference scores for images generated in a different imaging environment from that used in the initial network derivation. New subjects under the same setting can then be evaluated individually and a simple report is generated indicating the subject's classification. For scores near the normal limits, additional criteria are used to make a definitive diagnosis. With further refinement, automated TPR can be used to efficiently assess disease severity, monitor disease progression and evaluate treatment efficacy.