Journal: Psychiatry research

Article Title: DNA methylation biomarkers prospectively predict both antenatal and postpartum depression

doi: 10.1016/j.psychres.2019.112711

Figure Lengend Snippet: a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the originally published LDA model in women without a psychiatric history. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women without a psychiatric history. c.) Plot of EPDS threshold values (x axis) as a function of the mean model output (predicted probability) for women above the EPDS threshold minus that for women below the threshold (y axis) for the originally published LDA model in women without a psychiatric history. d.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women with a previous history of PPD. e.) Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women with a previous history of PPD. f.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women without a psychiatric history. g.) Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. h.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD.

Article Snippet: From a practical standpoint, in order to generate a model with the potential to be efficacious in a clinical environment, we trained a new SVM model on the Johns Hopkins Prospective PPD cohort data incorporating antenatal depression status as an interaction covariate in the model. We applied this to the combined UC Irvine cohort, inputting previous history of PPD as the interacting covariate and observed significant correlations of predictive accuracy with increasing depression severity (T1: Rho= 0.94, p = 0, T2: Rho= 0.91, p = 0, T3: Rho= 0.57, p = 0.013) with T3 samples achieving an AUC of 0.77 (95% CI: 0.64–0.77) to detect an EPDS ≥ 13 ( ).

Techniques:

Journal: Psychiatry research

Article Title: DNA methylation biomarkers prospectively predict both antenatal and postpartum depression

doi: 10.1016/j.psychres.2019.112711

Figure Lengend Snippet: a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model accounting for antenatal depression status in women from the UC Irvine cohort where antenatal depression status is determined with T1 time point biomarker output. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model accounting for antenatal depression status in the JHU Prospective Neuroimaging cohort. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD. c.) A plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for an SVM model to detect PPD status trained on a model incorporating variation in both TTC9B and HP1BP3 across three technical replicates in a subset of N = 20 women from the JHU Prospective Neuroimaging cohort. Horizontal dashed red lines denote an AUC of 80%.

Article Snippet: From a practical standpoint, in order to generate a model with the potential to be efficacious in a clinical environment, we trained a new SVM model on the Johns Hopkins Prospective PPD cohort data incorporating antenatal depression status as an interaction covariate in the model. We applied this to the combined UC Irvine cohort, inputting previous history of PPD as the interacting covariate and observed significant correlations of predictive accuracy with increasing depression severity (T1: Rho= 0.94, p = 0, T2: Rho= 0.91, p = 0, T3: Rho= 0.57, p = 0.013) with T3 samples achieving an AUC of 0.77 (95% CI: 0.64–0.77) to detect an EPDS ≥ 13 ( ).

Techniques: Biomarker Discovery

Journal: Journal of psychiatric research

Article Title: Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: A pattern classification approach

doi: 10.1016/j.jpsychires.2015.01.015

Figure Lengend Snippet: Flow diagram illustrating SVM model training (using 2 nd order polynomial kernel), feature subset selection and model testing process. A) Multiple neuromorphometric measurements were extracted using Freesurfer and combined through concatenation. B) Relevant features were identified using a univariate t-test filter on training data only using a nested leave-one-out cross-validation process. In a single feature selection iteration (dashed line in Figure 1a) – the model selected most optimal t-test p-value = 0.006) as shown in Figure 1b.

Article Snippet: The SVM model trained without feature reduction performed poorly (accuracy= 52.9%, chi-squre p=0.69).

Techniques: Selection, Biomarker Discovery