Prediction of Alzheimer’s Disease in Amnestic Mild Cognitive Impairment Subtypes: Stratification Based on Imaging Biomarkers

Article type: Research Article

Authors: Ota, Kenichi^{a; b} | Oishi, Naoya^{a; c; *} | Ito, Kengo^d | Fukuyama, Hidenao^{a; b} | and SEAD-J Study Group¹ | for the Alzheimer’s Disease Neuroimaging Initiative²

Affiliations: [a] Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan | [b] Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan | [c] Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan | [d] Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan

Correspondence: [*] Correspondence to: Naoya Oishi, MD, PhD, Human Brain Research Center, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan. Tel.: +81 75 751 3695; Fax: +81 75 751 3202; E-mail: [email protected].

Note: [1] Data used in the preparation of this article were obtained from the Research group of the Studies on Diagnosis of Early Alzheimer’s Disease-Japan (SEAD-J), which comprised investigators from nine different facilities (see also online Supplementary Table 1). As such, the investigators within the Research group of SEAD-J contributed to the design and implementation of SEADJ and/or provided data, but did not participate in the analysis or writing of this report

Note: [2] 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: Background: Prediction of progression to Alzheimer’s disease (AD) in amnestic mild cognitive impairment (MCI) is challenging because of its heterogeneity. Objective: To evaluate a stratification method on different cohorts and to investigate whether stratification in amnestic MCI could improve prediction accuracy. Methods: We identified 80 and 79 patients with amnestic MCI from different cohorts, respectively. They underwent baseline magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans. We performed hierarchical clustering with three imaging biomarkers: Brain volume on MRI, left hippocampus grey matter loss on MRI, and left inferior temporal gyrus glucose hypometabolism on FDG-PET. Regions-of-interest for biomarkers were defined by the Automated Anatomical Labeling atlas. We performed voxel-wise statistical parametric mapping to explore differences between clusters in patterns of grey matter loss and glucose hypometabolism. We compared time to progression using an interval-censored parametric model. We evaluated predictive performance using logistic regression. Results: Similar clusters were found in different cohorts. MCI1 had the healthiest biomarker profile of cognitive performance and imaging biomarkers. MCI2 had cognitive performance and MRI measures intermediate between those of nonconverters and converters. MCI3 showed the severest reduction in brain volume and left hippocampal atrophy. MCI4 showed remarkable glucose hypometabolism in the left inferior temporal gyrus, and also demonstrated significant decreases in most cognitive scores, including non-memory functions. MCI4 showed the highest risk for progression. The prediction of progression of MCI2 especially benefited from the stratification. Conclusion: Stratification with imaging biomarkers in amnestic MCI can be a good approach for improving predictive performance.

Keywords: Alzheimer’s disease, clustering, ¹⁸F-FDG, heterogeneity, magnetic resonance imaging, mild cognitive impairment, positron-emission tomography

DOI: 10.3233/JAD-160145

Journal: Journal of Alzheimer's Disease, vol. 52, no. 4, pp. 1385-1401, 2016