Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers

Article type: Research Article

Authors: Xu, Lele^a | Wu, Xia^{a; b; *} | Li, Rui^c | Chen, Kewei^d | Long, Zhiying^b | Zhang, Jiacai^a | Guo, Xiaojuan^a | Yao, Li^{a; b} | the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] College of Information Science and Technology, Beijing Normal University, Beijing, China | [b] State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China | [c] Center on Aging Psychology, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China | [d] Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA

Correspondence: [*] Correspondence to: Xia Wu, College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing 100875, China. Tel./Fax: +86 010 58800441; E-mail: wuxia@bnu.edu.cn.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer’s disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).

Keywords: Florbetapir positron emission tomography, fluorodeoxyglucose positron emission tomography, magnetic resonance imaging, mild cognitive impairment, multi-modality, prediction, progressive mild cognitive impairment

DOI: 10.3233/JAD-151010

Journal: Journal of Alzheimer's Disease, vol. 51, no. 4, pp. 1045-1056, 2016