Qing Chen1, Yanmeng Bi2, Xiaohua Zhao3, Yuqi Lai4, Weixin Yan5, Lingpeng Xie6, Tingting Gao7, Shuwen Xie1, Ting Zeng1, Jun Li1, Shanshan Kuang1, Lei Gao8, Zhiping Lv9. 1. School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China. 2. College of Integrated Traditional Chinese and Western Medicine, Jining Medical University, Jining, China. 3. School of Beauty, Yichun University, Yichun, China. 4. Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China. 5. The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. 6. Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China. 7. Department of General medicine, The first affiliated hospital of Guangdong Pharmaceutical University, Guangzhou, China. 8. School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China. Electronic address: raygaolei@smu.edu.cn. 9. School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China. Electronic address: Lzping@smu.edu.cn.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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%). CONCLUSION: Collectively, we found increased mean ALFF and fALFF may serve as a potential neuroimaging marker to discriminate MDD and HCs.
PURPOSE: 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. METHODS: 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. RESULTS: 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%). CONCLUSION: Collectively, we found increased mean ALFF and fALFF may serve as a potential neuroimaging marker to discriminate MDD and HCs.
Keywords:
Amplitude of low-frequency fluctuation; Fractional amplitude of low-frequency fluctuation; Major depressive disorder; Percent amplitude of fluctuation; Support vector machine; rs-fMRI