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Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases

机译:使用Hurst指数识别当前和缓解的重性抑郁症:两种自动解剖标记图谱的比较研究

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摘要

Major depressive disorder (MDD) is a leading world-wide psychiatric disorder with high recurrence rate, therefore, it is desirable to identify current MDD (cMDD) and remitted MDD (rMDD) for their appropriate therapeutic interventions. In the study, 19 cMDD, 19 rMDD and 19 well-matched healthy controls (HC) were enrolled and scanned with the resting-state functional magnetic resonance imaging (rs-fMRI). The Hurst exponent (HE) of rs-fMRI in AAL-90 and AAL-1024 atlases were calculated and compared between groups. Then, a radial basis function (RBF) based support vector machine was proposed to identify every pair of the cMDD, rMDD and HC groups using the abnormal HE features, and a leave-one-out cross-validation was used to evaluate the classification performance. Applying the proposed method with AAL-1024 and AAL-90 atlas respectively, 87% and 84% subjects were correctly identified between cMDD and HC, 84% and 71% between rMDD and HC, and 89% and 74% between cMDD and rMDD. Our results indicated that the HE was an effective feature to distinguish cMDD and rMDD from HC, and the recognition performances with AAL-1024 parcellation were better than that with the conventional AAL-90 parcellation.
机译:重度抑郁症(MDD)是一种全球领先的精神疾病,复发率高,因此,有必要鉴定当前的MDD(cMDD)和缓解的MDD(rMDD)以进行适当的治疗。在这项研究中,纳入了19个cMDD,19个rMDD和19个匹配良好的健康对照(HC),并用静止状态功能磁共振成像(rs-fMRI)进行了扫描。计算AAL-90和AAL-1024地图集中rs-fMRI的Hurst指数(HE),并在各组之间进行比较。然后,提出了一种基于径向基函数(RBF)的支持向量机,利用异常的HE特征来识别每对cMDD,rMDD和HC组,并使用留一法交叉验证来评估分类性能。 。分别对AAL-1024和AAL-90地图集应用该方法,在cMDD和HC之间正确识别出87%和84%的受试者,在rMDD和HC之间正确识别出84%和71%的受试者,而在cMDD和rMDD之间识别出89%和74%的受试者。我们的结果表明,HE是区分HC的cMDD和rMDD的有效特征,并且AAL-1024碎片的识别性能优于常规AAL-90碎片。

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