Prediction of Individual Treatment Response Based on Brain Changes at the Early Phase of Antidepressant Treatment in Major Depressive Disorder Using Machine Learning Classification Analysis

Major depressive disorder (MDD) is a highly prevalent, chronic disabling condition with substantial morbidity and mortality. Depression currently is the fourth leading cause of global burden of disease (DALYs) and disability worldwide, and is expected to be second by 20201. Around one in eight people in Canada will develop depression during their lifetime, with the total cost to the Canadian economy estimated at $51 billion per year2. The costs of treating MDD are high in part due to limitations in effectiveness of antidepressant treatment. Approximately 60% of patients fail to remit to the first antidepressant prescribed3 and the subsequent selection of antidepressants remains a matter of trial and error. Using this trial and error approach, it may take a year or more to find the successful treatment for a patient4,5. The protracted ineffective treatment results in prolonged suffering, substantial morbidity, loss of productivity and an increased burden on patient's family. Brain-based biomarkers could assist in predicting clinical response to treatment intervention and in tailoring treatment for individual patients. The results of previous neuroimaging studies that examined brain markers of treatment response were derived from group averages 6,-9 and have limited predictive value at individual level. Another limitation of these studies is that predictors derived from the pretreatment baseline brain scans could be influenced by many personal (personality, childhood trauma, genotypes) and clinical (course, duration of illness, episodes, symptom clusters, and severity of symptoms and past medication exposure) characteristics which may limit the generalizability. On the other hand, there is growing evidence that the early clinical response within 2 weeks of antidepressant treatment and EEG changes in the first week of treatment can predict later outcomes. Furthermore, early treatment changes in brain function may provide crucial information on the brain's capacity to change with treatment and on the interactive effects between personal/ clinical characteristics and pharmacological factors, which may help differential prediction of treatment responses to two antidepressants. Hence, examining the predictive value of dynamic brain changes during the first two weeks of treatment in individual patients would improve statistical reliability and predictive accuracy and minimize the confounding effect inherent to pretreatment scans.

In this study, we propose to investigate the predictive value of resting state and task related fMRI data collected at the pretreatment baseline and 2 weeks after treatment to predict remitters and non-remitters to desvenlafaxine antidepressant treatment at week 12 using machine learning classifier. Desvenlafaxine is a serotonin norepinephrine reuptake inhibitor (SNRI) with proven efficacy, and safety and is easy to administer in single daily dose. It has limited sedative and cognitive side effects such as drowsiness, lack of alertness and poor attention, which may confound early brain changes with treatment. This study will provide brain-based predictive biomarkers that can be tested prospectively in clinical trials and eventually in clinical practice for accuracy.

Machine Learning Classification (Support Vector Machine): The support vector machine (SVM) is a computer based analytical technique designed for high dimensional biological data such as fMRI data and provides the best classification of individual observations into distinct groups 38. Diagnostic classification (depression diagnosis and healthy control) and classification of treatment responsiveness (responders and non-responders) have been examined in a clinical population with fMRI data using SVM 39-41. This technique consists of two phases: training phase and testing phase. During the training phase an SVM is trained to develop a decision function or hyperplane that separates the data into two groups according to a class label. In the testing phase, this decision function can be used to predict the class label of a new subject as being a responder or non-responder. The accuracy of prediction by SVM depends on its specificity (identification of true negatives) and sensitivity (identification of true positives). In recent years a few neuroimaging studies have employed SVM to structural and functional MRI data in order to predict the MDD patients who improved with treatment and who did not. Fu et al (2008) showed that applying SVM on emotional task-related fMRI data, 62% of patients who achieved remission (sensitivity) and 75% of patients who did not achieve remission (specificity) following 8 weeks of fluoxetine treatment could be predicted. But these results were not statistically significant due to small sample sizes (remitters =8, non-remitters=10). Similarly, Costafreda et al (2009) applied SVM to pretreatment structural scans and showed prediction with a sensitivity of 88.9 % and a specificity of 88.9% and accuracy of 88.9% in a small sample comprised of 18 patients 40. In a recent study involving 61 MDD patients, SVM analysis of pretreatment white matter data predicted clinical outcome of refractory and non- refractory depression with an accuracy of 65.22%, sensitivity of 56.2% and specificity of 73.91% 41. Although the results of the later study were statistically significant, the low sensitivity and accuracy may limit its clinical use. Moreover, the structural imaging may not be useful to examine predictive value of early treatment changes in the brain function. In summary, there are no studies, to date that have applied SVM to functional data generated from a large sample for use in evaluating predictive accuracy at the individual level.

Main objective : Using machine learning method to examine the predictive value (sensitivity, specificity, accuracy) of resting state and emotional task-related fMRI data collected at the pretreatment time (week 0) and at the early phase of antidepressant treatment (week 2) in the classification of remitters and non-remitters in patients with MDD after 12 weeks of treatment. Secondary objective: To compare the predictive value of pretreatment baseline brain activity (week 0) with early treatment brain activity (week 2).

Primary hypothesis: By employing a machine learning method to pretreatment and 2 week post-treatment fMRI data, we hypothesize that it is possible to predict with significant accuracy whether an individual patient with MDD could be classified as remitter or non-remitter at the end of 12 weeks of antidepressant treatment.