Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data

• Published in: NeuroImage (Orlando, Fla.) Vol. 61; no. 3; pp. 622 - 632
• Main Authors: Yuan, Lei, Wang, Yalin, Thompson, Paul M., Narayan, Vaibhav A., Ye, Jieping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 02.07.2012; Elsevier Limited
• Subjects: Accuracy; Aged; Algorithms; Alzheimer Disease - cerebrospinal fluid; Alzheimer Disease - pathology; Alzheimer's disease; Artificial Intelligence; Biomarkers; Brain research; Classification; Cognitive Dysfunction - cerebrospinal fluid; Cognitive Dysfunction - pathology; Databases, Factual; Ensemble; Female; Fluorodeoxyglucose F18; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Incomplete data; Magnetic Resonance Imaging; Male; Medical imaging; Methods; Middle Aged; Multi-source feature learning; Multi-task learning; Neuroimaging - instrumentation; Neuroimaging - methods; NMR; Nuclear magnetic resonance; Pathology; Positron-Emission Tomography; Proteomics; Radiopharmaceuticals; Studies; Multi-source feature learning; Incomplete data; Multi-task learning; Ensemble
• DOI: 10.1016/j.neuroimage.2012.03.059
• ISSN: 1053-8119; 1095-9572

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.