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Background: Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder patients never achieve remission. Transcranial Direct Current Stimulation is a non-invasive intervention used to treat individuals diagnosed with major depressive disorder and bipolar disorder. Since the last transcranial direct current stimulation review specifically focusing on cognitive symptoms in major depressive disorder, twice as many papers have been published. Methods: A systematic review was conducted with 5 electronic databases from database inception until March 21, 2022. Randomized controlled trials with at least 1 arm evaluating transcranial direct current stimulation in adults (diagnosed with major depressive disorder or bipolar disorder using the Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases criteria) aged 18 or older were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were adopted. Results: : A total of 972 participants were included across 14 studies (60.5% female; mean age of 47.0 years [SD = 16.8]). Nine studies focused on participants with major depressive disorder and all studies used the Diagnostic and Statistical Manual of Mental Disorders to diagnose the participants. Seven out of the 14 studies showed significant improvements in at least 1 cognitive outcome measure in the active transcranial direct current stimulation group compared to the sham group. Several cognitive measures were used across studies, and 12 of the 14 studies reported mild-to-moderate side effects from treatment. Conclusion: : Current transcranial direct current stimulation literature has shown limited evidence for the treatment of cognitive impairments in major depressive disorder and bipolar disorder. Future research that applies machine learning algorithms may enable us to distinguish responders from non-responders, increasing clinical benefits of transcranial direct current stimulation.
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Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.
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BACKGROUND: The ß2 subunit of the voltage-gated l-type calcium channel gene(CACNB2) rs11013860 polymorphism is a putative genetic susceptibility marker for bipolar disorder (BD). However, the neural effects of CACNB2 rs11013860 in BD are largely unknown. METHODS: Forty-six bipolar patients with first-episode mania and eighty-three healthy controls (HC) were genotyped for CACNB2 rs11013860 and were scanned with a 3.0 Tesla structural magnetic resonance imaging system to measure cortical thickness of prefrontal cortex (PFC) components (superior frontal cortex, orbitofrontal cortex, middle and inferior frontal gyri). RESULTS: Cortical thickness was thinner in patients on all PFC measurements compared to HC (p < 0.050). Moreover, we found a significant interaction between CACNB2 genotype and diagnosis for the right superior frontal cortical thickness (F = 8.190, p = 0.040). Bonferroni corrected post-hoc tests revealed that, in CACNB2 A-allele carriers, patients displayed thinner superior frontal thickness compared to HC (p < 0.001). In patients, CACNB2 A-allele carriers also exhibited reduced superior frontal thickness compared to CACNB2 CC-allele carriers (p = 0.016). LIMITATIONS: Lithium treatment may influence our results, and the sample size in our study is relatively small. CONCLUSIONS: Our results suggest that the CACNB2 rs11013860 might impact PFC thickness in patients with first-episode mania. These findings provide evidence to support CACNB2 rs11013860 involvement in the emotion-processing neural circuitry abnormality in the early stage of BD, which will ultimately contribute to revealing the link between the variation in calcium channel genes and the neuropathological mechanism of BD.
