Predicting Treatment Response in Depression: The Role of Anterior Cingulate Cortex

Beata R Godlewska, Michael Browning, Ray Norbury, Artemis Igoumenou, Philip J Cowen, Catherine J Harmer, Beata R Godlewska, Michael Browning, Ray Norbury, Artemis Igoumenou, Philip J Cowen, Catherine J Harmer

Abstract

Background: Identification of biomarkers predicting therapeutic outcome of antidepressant treatment is one of the most important tasks in current research because it may transform the lengthy process of finding the right treatment for a given individual with depression. In the current study, we explored the potential of pretreatment pregenual anterior cingulate cortex activity as a putative biomarker of treatment response.

Methods: Thirty-two medication-free patients with depression were treated for 6 weeks with a selective serotonin reuptake inhibitor, escitalopram. Before treatment began, patients underwent an fMRI scan testing response to brief, masked, presentations of facial expression depicting sadness and happiness.

Results: After 6 weeks of treatment, there were 20 selective serotonin reuptake inhibitor responders and 12 nonresponders. Increased pretreatment pregenual anterior cingulate cortex activity to sad vs happy faces was observed in responders relative to nonresponders. A leave-one-out analysis suggested that activity in the anterior cingulate cortex was able to predict response status at the level of the individual participant.

Conclusions: The study supports the notion of pregenual anterior cingulate cortex as a promising predictor of antidepressant response.

Figures

Figure 1.
Figure 1.
Baseline differences in neural response (percent signal change) in the pregenual anterior cingulate cortex (pgACC) region of interest (ROI) in response to sad vs happy facial expressions differentiated between responders and nonresponders to 6 weeks of treatment with escitalopram. The figure represents (a) results of small volume correction (SVC) analysis in the anterior cingulate cortex (ACC) using a parametric approach (Gaussian Random Field Theory); (b) extracted signal change in the identified cluster (mean and standard error); (c) results of SVC-corrected analysis in the anterior cingulate cortex using a nonparametric approach (Threshold-Free Cluster Enhancement). Analysis was thresholded at Z=2.3 and cluster-corrected with a family wise error (FWE) P<.05. Baseline 17-item Hamilton Depression Rating Scale (HAM-D) scores were entered as a covariate.
Figure 2.
Figure 2.
Baseline differences in neural response (percent signal change) at the whole-brain level in response to sad vs happy facial expressions differentiated between responders and nonresponders to 6 weeks of treatment with escitalopram. The figure represents (a) results of the exploratory analysis at the whole-brain level using a parametric approach (Gaussian Random Field Theory); (b) extracted signal change in the identified clusters (mean and SE); (c) results of the exploratory analysis at the whole-brain level using a nonparametric approach (Threshold-Free Cluster Enhancement). Details of the clusters can be found in Table 2. Analysis was thresholded at Z=2.3 and cluster-corrected with a FWE P<.05. ACC, anterior cingulate cortex; FOC fronto-orbital cortex; FWE, family wise error. Baseline 17-item Hamilton Depression Rating Scale (HAM-D) scores were entered as a covariate.
Figure 3.
Figure 3.
Confusion plot. Green squares represent correctly classified cases: TP, true positives; TN, true negatives, the number of correct classifications by the trained network, percentage of all cases they represent. Red squares represent incorrectly classified cases: FP, false positives, FN, false negatives, the number of correct classifications by the trained network, percentage of all cases they represent. The blue square represents the percentage of correct and incorrect classifications. The first row represents predicted nonresponders, of whom 61.5% were classified correctly and 38.5% incorrectly. The second row represents predicted responders, of whom 78.9% were classified correctly and 21.1% incorrectly. Of 12 nonresponders, 66.7% were correctly predicted as nonresponders and 33.3% were predicted as responders. Of 20 responders, 75% were correctly classified as responders and 25% were classified as nonresponders. Overall, 71.9% of the predictions were correct and 28.1% cases were classified incorrectly.
Figure 4.
Figure 4.
Histogram of the cut-off scores used in the classifier.

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