静息态下低频振幅与功能连接异常可识别重性抑郁症

Identifying Patients with Major Depression Disorder With Altered Amplitude of Low Frequency Fluctuations and Functional Connectivity In Resting State

目的:考察静息状态下异常的低频振幅(Amplitude of Low Frequency Fluctuations,ALFF)与功能连接(Functional Connectivity,FC)能否识别出重性抑郁症(Major Depressive Disorder,MDD),并作为MDD可能的神经生物学标记。方法:35名MDD患者与33名对照组被试完成了结构磁共振与静息态功能磁共振的扫描,并完成流调中心用抑郁量表与状态-特质焦虑量表。比较ALFF与FC的组间差异,采用机器学习算法进行特征挑选、建模与分类,并计算支持向量机分类边际值与抑郁得分的相关程度。结果:与对照组相比,MDD组右侧脑岛的ALFF显著增加,额上回与左侧额下回的ALFF显著降低,右侧脑岛至同侧前扣带回的FC显著增加,额上回至双侧梭状回与右侧楔前叶的FC显著降低。脑岛、额上回与额下回的ALFF异常、额上回至左侧梭状回的FC异常可以有效识别MDD,且MDD组的SVM分类边际值与其抑郁症状严重程度呈显著正相关。结论:在静息状态下,MDD患者的ALFF与FC存在异常,且该异常模式可作为识别MDD的候选神经生物学标记。

Objective:This study was aimed to investigate whether the abnormal Amplitude of Low Frequency Fluctuations(ALFF)and Functional Connectivity(FC)could identify Major Depressive Disorder(MDD)and be as potential neurobiological markers of MDD.Methods:Thirty-five MDD patients and 33 Healthy Controls(HCs)completed structural Magnetic Resonance Imaging(sMRI)and resting-state functional MRI scans.All participants also completed the Center for Epidemiologic Studies Depression Scale(CESD)and the State-Trait Anxiety Inventory.The Independent-samples t test was conducted to compare the differences in ALFF between MDD group and HC group,and the group differences in FC were compared.The machine learning algorithm was conducted to select the ALFF/FC indexes that could effectively identify MDD patients from HCs,and the Pearson correlation analysis was performed to calculate the correlation between the value of classify margin and the scores of CESD.Results:(1)Compared with HCs,MDD patients showed increased ALFF in right insular and decreased ALFF in superior frontal gyrus(SFG)and left inferior frontal gyrus.(2)Compared with HCs,MDD patients showed elevated FC from right insular to right anterior cingulate cortex and reduced FC from SFG to bilateral fusiform and right precuneus.(3)The multi-features fusion of the altered ALFF and FC in resting state could identify MDD patients,and the value of classify margin was positively correlated with the scores of CESD.Conclusion:MDD patients has abnormal ALFF and FC in resting state,which may be the candidate neurobiological markers of MDD.