title: Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers.
abstract: Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g., Alzheimer's diseases (AD). Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG-PET and CSF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM-RVR method achieves a root-mean-square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS-Cog scores, demonstrating very promising performances in AD studies.
authors.affiliation: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjin 210016, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Cheng
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
authors.forename: Songcan
authors.initials: S
authors.lastname: Chen
authors.forename: Daniel I
authors.initials: DI
authors.lastname: Kaufer
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000175:diagnosis*; D001185:Artificial Intelligence; D015415:Biomarkers / Q000134:cerebrospinal fluid; D001931:Brain Mapping; D003072:Cognition Disorders / Q000134:cerebrospinal fluid / Q000175:diagnosis*; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D009316:National Institutes of Health (U.S.); D009483:Neuropsychological Tests; D049268:Positron-Emission Tomography; D012044:Regression Analysis; D015203:Reproducibility of Results; D014481:United States
chemical_list: D015415:Biomarkers; D019788:Fluorodeoxyglucose F18