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

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.

• 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
• Journal: European journal of nuclear medicine and molecular imaging (Eur J Nucl Med Mol Imaging) Vol. 47 Issue 12 Pg. 2753-2764 (11 2020) ISSN: 1619-7089 [Electronic] Germany
• PMID: 32318784 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Fluorodeoxyglucose F18

Topics

• Alzheimer Disease (diagnostic imaging)
• Brain (diagnostic imaging)
• Cognitive Dysfunction (diagnostic imaging)
• Connectome
• Disease Progression
• Fluorodeoxyglucose F18
• Humans
• Positron-Emission Tomography
• Tomography, X-Ray Computed