Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

• Published in: PloS one Vol. 7; no. 3; p. e33182
• Main Authors: Zhang, Daoqiang, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.03.2012; Public Library of Science (PLoS)
• Subjects: Activities of daily living; Advertising executives; Aged; Aged, 80 and over; Aging; Alzheimer Disease - pathology; Alzheimer's disease; Biology; Biomarkers; Biomedical research; Brain; Brain - diagnostic imaging; Brain - pathology; Brain research; Classification; Cognition & reasoning; Cognition Disorders - pathology; Cognitive ability; Computer Science; Conversion; Development and progression; Discriminant analysis; Disease Progression; Experiments; Feature extraction; Female; Humans; Longitudinal Studies; Magnetic Resonance Imaging; Male; Medical imaging; Medical research; Medicine; Methods; Neurobiology; Neurodegenerative diseases; Neuroimaging; Neuropsychological Tests; Neurosciences; NMR; Nuclear magnetic resonance; Patients; Performance prediction; Positron emission; Positron emission tomography; Psychiatry; Regression models; Regularization; Schizophrenia; Severity of Illness Index; Tomography; North Carolina; Los Angeles California; California; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0033182

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.