静息状态功能磁共振成像评价脑功能抑郁变化的分析

IF 0.3 4区 材料科学 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY
Hao Yu, Ye Yuan, Ashutosh Sharma, Abolfazl Mehbodniya, Mohammad Shabaz
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引用次数: 0

摘要

长期的悲伤情绪通常被认为是重度抑郁症(MDD),与其他精神疾病有相似的症状。这些平行的指示性特征往往会导致抑郁症和其他心理状况,因此需要专家预测这些症状并及时治疗重度抑郁症,以避免不良影响。磁共振成像(MRI)在推断与MDD相关的病理方面起着至关重要的作用。本文利用静息状态功能磁共振成像(fMRI)对抑郁症患者基本状态下的脑自发活动特征进行数据采集,并探讨抑郁期脑功能的变化。本文对16例抑郁症患者进行了5分12秒的脑功能MRI扫描,并采用汉密尔顿抑郁量表评估病情的严重程度。使用ReHo软件检查图像数据上的局部大脑区域。结果显示,静息脑fMRI-ReHo方法发现抑郁症患者的脑功能异常区域包括:左丘脑、左颞叶、左小脑、枕叶,且这些区域患者自发活动一致性降低。使用AUC值为0.885的SVM方法进行预测,在脑异常预测中,其最大改进率为22.24%,最小改进率为13.75%,优于目前最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of resting state functional magnetic resonance images for evaluating the changes in brain function depression
Prolonged emotions of sadness are habitually considered as major depressive disorder (MDD) that has parallel signs like other mental illnesses. These parallel indicative features can frequently lead to suffering of depression and other psychological conditions and therefore involve experts to predict such symptoms and use the timely treatment of MDD in order to evade the adverse effects. Magnetic resonance imaging (MRI) is involved as a vital role in deducing the pathologies related to MDD. This paper deals with the application of data collection for the characteristics of spontaneous brain activity in the basic state of depression patients using resting state functional magnetic resonance images (fMRI), and discusses the changes in the brain function during a depression stage. In this paper, 16 patients with depression underwent 5 minutes and 12 seconds of brain functional MRI scan, and the Hamilton Depression Scale was used to evaluate the severity of the condition. The ReHo software was used to examine local brain regions on the image data. It is revealed that the resting brain fMRI-ReHo method found that the abnormal brain function area of patients with depression included: left thalamus, left temporal lobe, left cerebellum, occipital lobe, and the spontaneous activity consistency of patients in these areas was reduced. This work is done by SVM approach that utilises AUC value of 0.885 for prediction, and it outperforms the state-of-the-art methods in a brain abnormality prediction by a maximum improvement of 22.24% and minimum improvement of 13.75%.
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来源期刊
International Journal of Nanotechnology
International Journal of Nanotechnology 工程技术-材料科学:综合
CiteScore
0.60
自引率
20.00%
发文量
45
审稿时长
6-12 weeks
期刊介绍: IJNT offers a multidisciplinary source of information in all subjects and topics related to Nanotechnology, with fundamental, technological, as well as societal and educational perspectives. Special issues are regularly devoted to research and development of nanotechnology in individual countries and on specific topics.
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