A Novel Individual Metabolic Brain Network for 18F-FDG PET Imaging

Sheng-Yao Huang; Jung-Lung Hsu; Kun-Ju Lin; Ing-Tsung Hsiao

doi:10.3389/fnins.2020.00344

A Novel Individual Metabolic Brain Network for 18F-FDG PET Imaging

Front Neurosci. 2020 May 12:14:344. doi: 10.3389/fnins.2020.00344. eCollection 2020.

Authors

Sheng-Yao Huang^{1

2}, Jung-Lung Hsu^{3

4

5}, Kun-Ju Lin^{1

6}, Ing-Tsung Hsiao^{1

6}

Affiliations

¹ Department of Medical Imaging and Radiological Sciences, Healthy Aging Research Center, Taoyuan, Taiwan.
² Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.
³ Department of Neurology, New Taipei Municipal TuCheng Hospital, New Taipei City, Taiwan.
⁴ Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center and College of Medicine, Neuroscience Research Center, Chang-Gung University, Taoyuan, Taiwan.
⁵ Graduate Institute of Humanities in Medicine and Research Center for Brain and Consciousness, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan.
⁶ Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.

Abstract

Introduction: Metabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer's disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities.

Method: First, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD.

Results: Our results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters.

Conclusion: We proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.

Keywords: Alzheimer’s disease; FDG-PET; individual metabolic network; progressive MCI; stable MCI.