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精神分裂症中神经代谢物水平的治疗效果:质子磁共振波谱的荟萃分析数据集

Treatment effects on neurometabolite levels in schizophrenia: A meta-analysis dataset of proton magnetic resonance spectroscopy.

作者信息

Kubota Manabu, Moriguchi Sho, Takahata Keisuke, Nakajima Shinichiro, Horita Nobuyuki

机构信息

Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan.

Department of Psychiatry, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan.

出版信息

Data Brief. 2020 Jun 16;31:105862. doi: 10.1016/j.dib.2020.105862. eCollection 2020 Aug.

Abstract

This article describes a dataset for a meta-analysis that aimed to investigate the effects of treatment on the neurometabolite status in patients with schizophrenia (DOI of original article: https://doi.org/10.1016/j.schres.2020.03.069[1]). The data search was performed with MEDLINE, Embase, and PsycINFO. The neurometabolites investigated include glutamate, glutamine, glutamate + glutamine, gamma-aminobutyric acid, -acetylaspartate, and myo-inositol, and the regions of interest (ROIs) include the frontal cortex, temporal cortex, parieto-occipital cortex, thalamus, basal ganglia, and hippocampus. The meta-analysis was conducted with a random-effects model, and the use of the standardized mean difference method between pre- and post-treatment of subjects for neurometabolites in each ROI of three patient groups or more. The dataset covers raw data of 39 patient groups (773 patients with schizophrenia at follow-up) with neurometabolite levels measured by magnetic resonance spectroscopy both before and after treatment. Furthermore, it contains details of clinical characteristics and treatment types for each group. Therefore, the data would be useful for a reinvestigation of treatment effects on the neurometabolite status from diverse points of view, as well as for the development of future treatment strategies for psychiatric diseases.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd5/7327820/17378fbe35c2/gr1a.jpg

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