抑郁症的客观判别：基于光学脑成像的静息态功能性连接检测和分析

Objective Discrimination of Depression: Detection and Analysis of Resting State Functional Connectivity Based on Optical Brain Imaging

近年来,基于功能性近红外光谱的静息态功能性连接逐渐用于精神疾病的研究。然而,由功能性近红外光谱得到的静息态功能性连接是否可以用于抑郁症的客观判别仍然是一个未知数。采用42通道的功能性近红外光谱技术,测量28个抑郁症患者和30个健康对照组的8 min前额皮层的自发血液动力活动。在独立成分分析和0.008~0.09 Hz的带通滤波器滤除不相关的成分后,计算前额皮层3个区域(额下回、额中回和额上回)左右半球连接性。然后,选择其中两个有显著性差异的参数作为样本的两个特征维度,并采用线性判别分析和支持向量机对随机抽取的75%样本进行训练,并对剩余的25%样本进行预测。最终均获得73%~74%的预测正确率和83%~87%的辨别率。这个结果支持由功能性近红外光谱技术得到的大脑静息态功能性连接在客观辨别抑郁症患者的可行性和有效性。

Recently,resting-state functional connectivity（ RSFC） has gradually been studied in patients with mental disorders by functional near-infrared spectroscopy（ f NIRS）. However,it is still unknown whether RSFC derived from f NIRS is predictable for depressive disorders. In this work,we employed f NIRS（ 42 channels） to measure 8-minute spontaneous hemodynamic activity in the prefrontal cortex（ PFC） of 28 patients having depressive disorders and 30 healthy controls. After filtering irrelative components by independent component and band-pass filter（ 0. 008-0. 09 Hz）,we calculated left-right correlations in the prefrontal cortex which included inferior prefrontal cortex（ IFG）,middle prefrontal cortex（ MFG） and superior prefrontal cortex（ SFG）. Then we selected two significant parameters（ left-right correlations in the IFG and MFG as a participant＇s two features for further classification（ 75% of the participants） and prediction（ 25% of the participants） using linear discriminant analysis（ LDA） and support vector machine（ SVM）. Finally,asensitivity of 73-74% and specificity of 83-87% was yielded. These results supported that RSFC derived from f NIRS is a feasible and effective technique to identify whether someone is suffered from depressive disorders.