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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 01-11-2020 | Alzheimer's Disease | Original Article

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia

Authors: Min Wang, Jiehui Jiang, Zhuangzhi Yan, Ian Alberts, Jingjie Ge, Huiwei Zhang, Chuantao Zuo, Jintai Yu, Axel Rominger, Kuangyu Shi, Alzheimer’s Disease Neuroimaging Initiative

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 12/2020

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).

Conclusion

The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

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Title: Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia
Authors: Min Wang
Jiehui Jiang
Zhuangzhi Yan
Ian Alberts
Jingjie Ge
Huiwei Zhang
Chuantao Zuo
Jintai Yu
Axel Rominger
Kuangyu Shi
Alzheimer’s Disease Neuroimaging Initiative
Publication date: 01-11-2020
Publisher: Springer Berlin Heidelberg
Keywords: Alzheimer's Disease
Dementia
Positron Emission Tomography
Alzheimer's Disease
Dementia
Mild Cognitive Impairment
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 12/2020
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-020-04814-x

At a glance: The STEP trials

Springer Medicine

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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