Li Hanxiaoran, Song Sutao, Wang Donglin, Zhang Danning, Tan Zhonglin, Lian Zhenzhen, Wang Yan, Zhou Xin, Pan Chenyuan, Wu Yue
Institutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China.
Front Comput Neurosci. 2022 Jun 1;16:837093. doi: 10.3389/fncom.2022.837093. eCollection 2022.
Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suitable to serve as predictive aids for treatment responses at the individual level. Here, we tested the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) of the thalamus using multivariate pattern analysis (MVPA). A total of 74 MDD patients and 44 healthy control (HC) subjects were recruited. Thirty-nine MDD patients and 35 HC subjects underwent scanning twice. Between the two scanning sessions, patients in the MDD group received selective serotonin reuptake inhibitor (SSRI) treatment for 3-month, and HC group did not receive any treatment. Gaussian process regression (GPR) was trained to predict the percentage decrease in the Hamilton Depression Scale (HAMD) score after treatment. The percentage decrease in HAMD score after SSRI treatment was predicted by building GPRs trained with baseline thalamic data. The results showed significant correlations between the true percentage of HAMD score decreases and predictions ( < 0.01, = 0.11) in GPRs trained with GMD. We did not find significant correlations between the true percentage of HAMD score decreases and predictions in GMV ( = 0.16, = 0.00), ALFF ( = 0.125, = 0.00), and fALFF ( = 0.485, = 0.10). Our results suggest that GMD of the thalamus has good potential as an aid in individualized treatment response predictions of MDD patients.
抗抑郁治疗作为临床实践中的一种重要方法，并不适用于所有重度抑郁症（MDD）患者。尽管磁共振成像（MRI）研究发现MDD患者存在丘脑异常，但尚不清楚丘脑的特征是否适合作为个体水平治疗反应的预测辅助指标。在此，我们使用多变量模式分析（MVPA）测试了丘脑灰质密度（GMD）、灰质体积（GMV）、低频波动幅度（ALFF）和分数ALFF（fALFF）的预测价值。共招募了74例MDD患者和44名健康对照（HC）受试者。39例MDD患者和35名HC受试者接受了两次扫描。在两次扫描期间，MDD组患者接受了3个月的选择性5-羟色胺再摄取抑制剂（SSRI）治疗，而HC组未接受任何治疗。训练高斯过程回归（GPR）以预测治疗后汉密尔顿抑郁量表（HAMD）评分的降低百分比。通过构建以基线丘脑数据训练的GPR来预测SSRI治疗后HAMD评分的降低百分比。结果显示，在以GMD训练的GPR中，HAMD评分降低的真实百分比与预测值之间存在显著相关性（<0.01，=0.11）。我们未发现GMV（=0.16，=0.00）、ALFF（=0.125，=0.00）和fALFF（=0.485，=0.10）中HAMD评分降低的真实百分比与预测值之间存在显著相关性。我们的结果表明，丘脑的GMD在MDD患者个体化治疗反应预测方面具有良好的潜力。
