Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts prog
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ORIGINAL ARTICLE
Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia Min Wang 1 & Jiehui Jiang 1,2 & Zhuangzhi Yan 1 & Ian Alberts 3 & Jingjie Ge 4 & Huiwei Zhang 4 & Chuantao Zuo 4,5 Jintai Yu 6 & Axel Rominger 3 & Kuangyu Shi 3,7 & Alzheimer’s Disease Neuroimaging Initiative
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Received: 19 November 2019 / Accepted: 6 April 2020 # The Author(s) 2020
Abstract Purpose Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD. Methods FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics. Results The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model). This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence). Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00259-020-04814-x) contains supplementary material, which is available to authorized users. * Jiehui Jiang [email protected]
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Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
* Chuantao Zuo [email protected]
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Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai 201103, China
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Institute of Functional
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