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18F-FDG PET and a classifier algorithm reveal a characteristic glucose metabolic pattern in adult patients with moyamoya disease and vascular cognitive impairment

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Abstract

Vascular cognitive impairment (VCI) is a critical issue in moyamoya disease (MMD). However, the glucose metabolic pattern in these patients is still unknown. This study aimed to identify the metabolic signature of cognitive impairment in patients with MMD using 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) and establish a classifier to identify VCI in patients with MMD. One hundred fifty-two patients with MMD who underwent brain 18F-FDG PET scans before surgery were enrolled and classified into nonvascular cognitive impairment (non-VCI, n = 52) and vascular cognitive impairment (VCI, n = 100) groups according to neuropsychological test results. Additionally, thirty-three health controls (HCs) were also enrolled. Compared to HCs, patients in the VCI group exhibited extensive hypometabolism in the bilateral frontal and cingulate regions and hypermetabolism in the bilateral cerebellum, while patients in the non-VCI group showed hypermetabolism only in the cerebellum and slight hypometabolism in the frontal and temporal regions. In addition, we found that the patients in the VCI group showed hypometabolism mainly in the left basal ganglia compared to those in the non-VCI group. The sparse representation-based classifier algorithm taking the SUVr of 116 Anatomical Automatic Labeling (AAL) areas as features distinguished patients in the VCI and non-VCI groups with an accuracy of 82.4%. This study demonstrated a characteristic metabolic pattern that can distinguish patients with MMD without VCI from those with VCI, namely, hypometabolic lesions in the left hemisphere played a more important role in cognitive decline in patients with MMD.

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Data availability

The data and materials of this study are available from the corresponding author on reasonable request.

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Funding

This work was supported in part by grants from the National Natural Science Foundation of China (NSFC) (81771237).

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  1. Ruiyuan Weng, Shuhua Ren and Jiabin Su contributed equally.

Authors and Affiliations

  1. Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Ruiyuan Weng, Jiabin Su, Wei Ni, Chunlei Yang, Xinjie Gao, Weiping Xiao, Xin Zhang, Hanqiang Jiang & Yuxiang Gu

  2. PET Center, Huashan Hospital, Fudan University, Shanghai, China

    Shuhua Ren, Yihui Guan & Qi Huang

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  1. Ruiyuan Weng
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Contributions

All authors contributed substantially to the scientific process leading to this manuscript. Ruiyuan Weng, Shuhua Ren, and Jiabin Su contributed equally to the article. Qi Huang and Yuxiang Gu are cocorresponding authors. Ruiyuan Weng, Jiabin Su, Shuhua Ren, and Qi Huang contributed to the conception and design. Shuhua Ren, Chunlei Yang, Weiping Xiao, Xinjie Gao, Xin Zhang, and Haniang Jiang were responsible for the acquisition of clinical data and PET data. Qi Huang and Yihui Guan analyzed the data. Ruiyuan Weng, Jiabin Su, Wei Ni, and Yuxiang Gu interpreted the results and drafted the manuscript. All authors reviewed and approved the final manuscript.

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Correspondence to Yuxiang Gu.

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Weng, R., Ren, S., Su, J. et al. 18F-FDG PET and a classifier algorithm reveal a characteristic glucose metabolic pattern in adult patients with moyamoya disease and vascular cognitive impairment. Brain Imaging and Behavior 17, 185–199 (2023). https://doi.org/10.1007/s11682-022-00752-4

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