Predicting ECT Response with Baseline Neuroimaging Data
文献类型：期刊论文
| 作者 | Christopher Abbott; Sui Jing(隋婧)
|
| 刊名 | Biological Psychiatry
 |
| 出版日期 | 2017-05-15 |
| 卷号 | 81期号:10页码:S128 |
| 关键词 | Depression Structural Mri Electroconvulsive Therapy Prediction Of Treatment Outcome Machine Learning |
| 英文摘要 | BackgroundBiological markers may be informative in identifying depressed patients who respond to a specific antidepressant treatment. Due to its rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment biomarkers of eventual response. MethodsAdvanced pattern matching and data mining techniques identified structural magnetic resonance imaging (sMRI) networks predictive of recovery from depression from three independent data sets (UNM, n = 38; LIJ, n = 7; and UCLA, n = 10). ResultsFor the UNM data set, six grey matter (GM) regions were repeatedly identified as predictive of future response (change in depression ratings) at r=0.90 and classified eventual remitters with high precision (sensitivity 88.9%, specificity 90.9%). We further tested these potential biomarkers using pre-ECT GM data from two independent, demographically-matched data sets from UCLA and LIJ; high estimation accuracy of eventual change in depression severity and predictive accuracy of remitter were also achieved (UCLA: r = 0.71, sensitivity 100%, specificity 87.5%; LIJ: r = 0.77, sensitivity 66.7%, specificity 100%). Two of the six extracted predictive regions (right supplementary motor/superior frontal and right post-central gyrus) showed GM volume changes over the four-week assessment interval; the remaining predictive regions (left hippocampal/parahippocampal, left inferior temporal, left middle frontal and right angular gyrus) did not vary significantly with treatment. ConclusionsThese results suggest a particular network of GM features can serve as a prognostic sMRI biomarker to guide personalized treatment decisions. Findings also suggest that antidepressant response involve interactions between treatment predictive and treatment responsive networks. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20319]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 推荐引用方式 GB/T 7714 | Christopher Abbott,Sui Jing. Predicting ECT Response with Baseline Neuroimaging Data[J]. Biological Psychiatry,2017,81(10):S128. |
| APA | Christopher Abbott,&Sui Jing.(2017).Predicting ECT Response with Baseline Neuroimaging Data.Biological Psychiatry,81(10),S128. |
| MLA | Christopher Abbott,et al."Predicting ECT Response with Baseline Neuroimaging Data".Biological Psychiatry 81.10(2017):S128. |
入库方式： OAI收割
来源：自动化研究所
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