Assessing depression improvement with the remission evaluation and mood inventory tool (REMIT)

Changes in brain functional networks in remitted major depressive disorder: a six-month follow-up study

BACKGROUND

Patients with remitted major depressive disorder (rMDD) show abnormal functional connectivity of the central executive network (CEN), salience networks (SN) and default mode network (DMN). It is unclear how these change during remission, or whether changes are related to function.

METHODS

Three spatial networks in 17 patients with rMDD were compared between baseline and the six-month follow-up, and to 22 healthy controls. Correlations between these changes and psychosocial functioning were also assessed.

RESULTS

In the CEN, patients at baseline had abnormal functional connectivity in the right anterior cingulate, right dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL) compare with HCs. There were functional connection differences in the right DLPFC and left IPL at baseline during follow-up. Abnormal connectivity in the right DLPFC and medial prefrontal cortex (mPFC) were found at follow-up. In the SN, patients at baseline had abnormal functional connectivity in the insula, left anterior cingulate, left IPL, and right precuneus; compared with baseline, patients had higher connectivity in the right DLPFC at follow-up. In the DMN, patients at baseline had abnormal functional connectivity in the right mPFC. Resting-state functional connectivity of the IPL and DLPFC in the CEN correlated with psychosocial functioning.

CONCLUSIONS

At six-month follow-up, the CEN still showed abnormal functional connectivity in those with rMDD, while anomalies in the SN and DMN has disappeared. Resting-state functional connectivity of the CEN during early rMDD is associated with psychosocial function.

CLINICAL TRIALS REGISTRATION

Pharmacotherapy and Psychotherapy for MDD after Remission on Psychology and Neuroimaging. https://www.

CLINICALTRIALS

gov/ , registration number: NCT01831440 (15/4/2013).

Real-time emotion detection by quantitative facial motion analysis

BACKGROUND

Research into mood and emotion has often depended on slow and subjective self-report, highlighting a need for rapid, accurate, and objective assessment tools.

METHODS

To address this gap, we developed a method using digital image speckle correlation (DISC), which tracks subtle changes in facial expressions invisible to the naked eye, to assess emotions in real-time. We presented ten participants with visual stimuli triggering neutral, happy, and sad emotions and quantified their associated facial responses via detailed DISC analysis.

RESULTS

We identified key alterations in facial expression (facial maps) that reliably signal changes in mood state across all individuals based on these data. Furthermore, principal component analysis of these facial maps identified regions associated with happy and sad emotions. Compared with commercial deep learning solutions that use individual images to detect facial expressions and classify emotions, such as Amazon Rekognition, our DISC-based classifiers utilize frame-to-frame changes. Our data show that DISC-based classifiers deliver substantially better predictions, and they are inherently free of racial or gender bias.

LIMITATIONS

Our sample size was limited, and participants were aware their faces were recorded on video. Despite this, our results remained consistent across individuals.

CONCLUSIONS

We demonstrate that DISC-based facial analysis can be used to reliably identify an individual's emotion and may provide a robust and economic modality for real-time, noninvasive clinical monitoring in the future.

The relative importance of diagnostic specific and transdiagnostic factors in evaluating treatment outcome of depressed patients

Determinations of the efficacy of treatments for depression most commonly are based on changes in scores on symptom severity scales. This narrow symptom-focused approach towards evaluating outcome is at variance with patients' broader conceptualization of the factors deemed important in evaluating the outcome of depression treatment. In the present report we examine the factors associated with depressed patients' global ratings of improvement after a treatment intervention. Five hundred and three patients with major depressive disorder completed the Remission from Depression Questionnaire (RDQ), a self-report measure that assesses multiple constructs considered by patients to be relevant to assessing treatment outcome. The patients completed the RDQ at admission and discharge from the treatment program. At discharge, the patients made a global rating of the effectiveness of treatment. The patients significantly improved from admission to discharge on each RDQ subscale. Changes in the well-being/life satisfaction and coping subscales were the only 2 subscales that were independently associated with the patients' ratings of improvement. These results suggest that when evaluating outcome in the treatment of depression a focus on symptom improvement is too narrow. Consideration of a broader perspective in measuring outcome in treatment studies of depression is more consistent with a biopsychosocial conceptualization.

The Appalachia Mind Health Initiative (AMHI): a pragmatic randomized clinical trial of adjunctive internet-based cognitive behavior therapy for treating major depressive disorder among primary care patients

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems.

METHODS

Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE.

DISCUSSION

The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019.

Responsiveness of PROMIS and Patient Health Questionnaire (PHQ) Depression Scales in three clinical trials

BACKGROUND

The PROMIS depression scales are reliable and valid measures that have extensive normative data in general population samples. However, less is known about how responsive they are to detect change in clinical settings and how their responsiveness compares to legacy measures. The purpose of this study was to assess and compare the responsiveness of the PROMIS and Patient Health Questionnaire (PHQ) depression scales in three separate samples.

METHODS

We used data from three clinical trials (two in patients with chronic pain and one in stroke survivors) totaling 651 participants. At both baseline and follow-up, participants completed four PROMIS depression fixed-length scales as well as legacy measures: Patient Health Questionnaire 9-item and 2-item scales (PHQ-9 and PHQ-2) and the SF-36 Mental Health scale. We measured global ratings of depression change, both prospectively and retrospectively, as anchors to classify patients as improved, unchanged, or worsened. Responsiveness was assessed with standardized response means, statistical tests comparing change groups, and area-under-curve analysis.

RESULTS

The PROMIS depression and legacy scales had generally comparable responsiveness. Moreover, the four PROMIS depression scales of varying lengths were similarly responsive. In general, measures performed better in detecting depression improvement than depression worsening. For all measures, responsiveness varied based on the study sample and on whether depression improved or worsened.

CONCLUSIONS

Both PROMIS and PHQ depression scales are brief public domain measures that are responsive (i.e., sensitive to change) and thus appropriate as outcome measures in research as well as for monitoring treatment in clinical practice. Trial registration ClinicalTrials.gov ID: NCT01236521, NCT01583985, NCT01507688.