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Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission.
Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm.
Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638–0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001).
We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies.
Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.
Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.
Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.
Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.
The upsurge in the number of people affected by the COVID-19 is likely to lead to increased rates of emotional trauma and mental illnesses. This article systematically reviewed the available data on the benefits of interventions to reduce adverse mental health sequelae of infectious disease outbreaks, and to offer guidance for mental health service responses to infectious disease pandemic. PubMed, Web of Science, Embase, PsycINFO, WHO Global Research Database on infectious disease, and the preprint server medRxiv were searched. Of 4278 reports identified, 32 were included in this review. Most articles of psychological interventions were implemented to address the impact of COVID-19 pandemic, followed by Ebola, SARS, and MERS for multiple vulnerable populations. Increasing mental health literacy of the public is vital to prevent the mental health crisis under the COVID-19 pandemic. Group-based cognitive behavioral therapy, psychological first aid, community-based psychosocial arts program, and other culturally adapted interventions were reported as being effective against the mental health impacts of COVID-19, Ebola, and SARS. Culturally-adapted, cost-effective, and accessible strategies integrated into the public health emergency response and established medical systems at the local and national levels are likely to be an effective option to enhance mental health response capacity for the current and for future infectious disease outbreaks. Tele-mental healthcare services were key central components of stepped care for both infectious disease outbreak management and routine support; however, the usefulness and limitations of remote health delivery should also be recognized.
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