Introduction:
No objective markers to predict remission (absence of depression following treatment) early in antidepressant treatment for patients with major depressive disorder (MDD) have been validated for clinical practice. Structural magnetic resonance imaging (sMRI) can provide valuable information before treatment that may relate to the final antidepressant response; however, the related research findings have been inconsistent.
Hypothesis:
Pretreatment neuroimaging data can be used to predict remission after eight weeks of antidepressant treatment.
Methods:
This study used pretreatment sMRI from a multi-site clinical trial on participants with depression (n=177) who were initiating treatment. The imaging measures were entered into a tree boosting classifier called XGBoost to predict remission after eight weeks, while controlling for age, sex, treatment (antidepressant/placebo), handedness and scan site.
Results:
Our predictive model showed >70% accuracy with 83% specificity for predicting remission, with the 10 most predictive neuroimaging features selected by XGBoost.
Conclusion:
This study was the first effort to predict remission in antidepressant treatment using a tree boosting classifier for neuroimaging data, and provides guidelines to effectively process high dimensional, small sample neuroimaging data for developing predictive algorithms.
Impact:
This study pioneers the application of tree boosting classifier for developing a predictive algorithm using neuroimaging data for antidepressant treatment response.

**_Introduction:_**
No objective markers to predict remission (absence of depression following treatment) early in antidepressant treatment for patients with major depressive disorder (MDD) have been validated for clinical practice. Structural magnetic resonance imaging (sMRI) can provide valuable information before treatment that may relate to the final antidepressant response; however, the related research findings have been inconsistent.
**_Hypothesis:_**
Pretreatment neuroimaging data can be used to predict remission after eight weeks of antidepressant treatment.
**_Methods:_**
This study used pretreatment sMRI from a multi-site clinical trial on participants with depression (n=177) who were initiating treatment. The imaging measures were entered into a tree boosting classifier called XGBoost to predict remission after eight weeks, while controlling for age, sex, treatment (antidepressant/placebo), handedness and scan site.
**_Results:_**
Our predictive model showed >70% accuracy with 83% specificity for predicting remission, with the 10 most predictive neuroimaging features selected by XGBoost.
**_Conclusion:_**
This study was the first effort to predict remission in antidepressant treatment using a tree boosting classifier for neuroimaging data, and provides guidelines to effectively process high dimensional, small sample neuroimaging data for developing predictive algorithms.
**_Impact:_**
This study pioneers the application of tree boosting classifier for developing a predictive algorithm using neuroimaging data for antidepressant treatment response.

Early Prediction of Antidepressant Treatment Response Using Machine Learning For Neuroimaging

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)