School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.
Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China.
J Magn Reson Imaging. 2021 Aug;54(2):551-559. doi: 10.1002/jmri.27577. Epub 2021 Feb 26.
Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy.
To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment.
Prospective.
A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site.
FIELD STRENGTH/SEQUENCE: A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence.
Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site.
Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run.
Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%).
These findings support future research on clinical antidepressant selection for MDD.
1 TECHNICAL EFFICACY: Stage 2.
由于生物学的异质性,60%-70%的重度抑郁症(MDD)患者对一线抗抑郁药无反应或未达到缓解。预测神经影像学生物标志物可用于早期抗抑郁治疗,以指导初始抗抑郁治疗。
评估早期抗抑郁治疗中抗抑郁药物选择的神经影像学生物标志物。
前瞻性。
来自主要研究地点的 85 名 MDD 患者和来自样本外测试地点的 33 名 MDD 患者。
场强/序列:使用磁化准备快速获取梯度回波序列的 3.0T T1 加权成像和使用回波平面序列的扩散张量成像(DTI)。
对接受 2 周抗抑郁治疗(选择性 5-羟色胺再摄取抑制剂 [SSRIs] 或 5-羟色胺去甲肾上腺素再摄取抑制剂 [SNRIs])后早期改善的患者进行基线 DTI 数据分析。使用主要研究地点的数据构建集成模型,然后应用于评估样本外测试地点患者的早期反应。
支持向量机结合留一法交叉验证用于从不同脑区的个体基础模型构建整体模型。通过计算从整体模型中移除单个基础模型时获得的灵敏度和特异性的变化来评估判别性生物标志物,在每次运行中移除的基础模型都在变化。
在主要研究地点对 MDD 患者的训练性能达到 75%的准确率,而在样本外测试地点的准确率为 70%。评估从突出模型中移除单个基础模型后灵敏度和特异性的变化,突出了两个神经回路的功能:与 SSRIs 相关的情绪调节回路,以海马体(灵敏度变化:10%)和杏仁核(灵敏度变化:11%)为中心;与 SNRIs 相关的情绪和奖励回路,以壳核(特异性变化:8%)和额上回眶部(特异性变化:12%)为中心。
这些发现支持未来关于 MDD 临床抗抑郁药物选择的研究。
1 技术效果:2 期。