School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
College of Integrated Traditional Chinese and Western Medicine, Jining Medical University, Jining, China.
J Affect Disord. 2022 Jul 1;308:1-9. doi: 10.1016/j.jad.2022.03.079. Epub 2022 Apr 6.
Major depressive disorder (MDD) is a common mood disorder. However, it still remains challenging to select sensitive biomarkers and establish reliable diagnosis methods currently. This study aimed to investigate the abnormalities of the spontaneous brain activity in the MDD and explore the clinical diagnostic value of three amplitude metrics in altered regions by applying the support vector machine (SVM) method.
A total of fifty-two HCs and forty-eight MDD patients were recruited in the study. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF) and percent amplitude of fluctuation (PerAF) metrics were calculated to assess local spontaneous brain activity. Then we performed correlation analysis to examine the association between cerebral abnormalities and clinical characteristics. Finally, SVM analysis was applied to conduct the classification model for evaluating the diagnostic value.
Two-sample t-test exhibited that MDD patients had increased ALFF value in the right caudate and corpus callosum, increased fALFF value in the same regions and increased PerAF value in the inferior parietal lobule and right caudate compared to HCs. Moreover, PerAF value in the inferior parietal lobule was negatively correlated with the slow factor scores. The SVM results showed that a combination of mean ALFF and fALFF in the right caudate and corpus callosum selected as features achieved a highest area under curve (AUC) value (0.89), accuracy (79.79%), sensitivity (65.12%) and specificity (92.16%).
Collectively, we found increased mean ALFF and fALFF may serve as a potential neuroimaging marker to discriminate MDD and HCs.
重度抑郁症（MDD）是一种常见的情绪障碍。然而，目前仍然难以选择敏感的生物标志物并建立可靠的诊断方法。本研究旨在通过支持向量机（SVM）方法，研究 MDD 患者自发性脑活动的异常，并探讨改变区域中三个幅度指标的临床诊断价值。
本研究共纳入 52 名健康对照者（HCs）和 48 名 MDD 患者。采用低频振幅（ALFF）、分数低频振幅（fALFF）和波动幅度百分比（PerAF）三种幅度指标来评估局部自发性脑活动。然后进行相关分析，以检验脑异常与临床特征之间的相关性。最后，采用 SVM 分析构建分类模型，评估诊断价值。
两样本 t 检验显示，与 HCs 相比，MDD 患者右侧尾状核和胼胝体的 ALFF 值增加，相同区域的 fALFF 值增加，以及顶下小叶和右侧尾状核的 PerAF 值增加。此外，顶下小叶的 PerAF 值与慢因子得分呈负相关。SVM 结果表明，右侧尾状核和胼胝体的平均 ALFF 和 fALFF 组合作为特征可获得最高的曲线下面积（AUC）值（0.89）、准确率（79.79%）、敏感度（65.12%）和特异性（92.16%）。
综上所述，我们发现增加的平均 ALFF 和 fALFF 可能成为鉴别 MDD 和 HCs 的潜在神经影像学标志物。
