Treatment Of Major Depressive Disorder Research Articles

Cognitive Behavioral Therapy and Hypnosis in the Treatment of Major Depressive Disorder: A Randomized Control Trial

ABSTRACT We investigated whether adding hypnosis to CBT (CBTH) improved treatment outcomes for MDD with a two-armed, parallel-treated, randomized-controlled trial using anonymous self-report and clinician-blinded assessments. Expectancy, credibility, and attitude to hypnosis were also examined. Participants (n = 66) were randomly allocated to 10-weekly sessions of group-based CBT or CBTH. LMM analyses of ITT and Completer data at post-treatment, six-month and 12-month follow-up showed that both treatments were probably efficacious but we did not find significant differences between them. Analyses of remission and response to treatment data revealed that the CBTH Completer group significantly outperformed CBT at 12-month follow-up (p = .011). CBTH also displayed significantly higher associations between credibility, expectancy and mood outcomes up to 12-month follow-up (all p < .05 or better), while attitude to hypnosis showed one significant association (r = -0.57, p < .05). These results suggest that hypnosis shows promise as an adjunct in the treatment of MDD but a larger sample size is required to fully test its merits.

Differential effects of allopregnanolone and diazepam on social behavior through modulation of neural oscillation dynamics in basolateral amygdala and medial prefrontal cortex.

Effective treatments for major depressive disorder (MDD) have long been needed. One hypothesis for the mechanism of depression involves a decrease in neuroactive steroids such as allopregnanolone, an endogenous positive allosteric modulator of the γ-aminobutyric acid-gated chloride channel (GABAA) receptor. In our previous study, we discovered that allopregnanolone, not diazepam, exhibited antidepressant-like effects in the social interaction test (SIT) of social defeat stress (SDS) model mice. However, the dynamics of neuronal activity underlying the antidepressant-like effect remain unknown. In the current study, we conducted local field potentials (LFPs) recordings from the basolateral amygdala (BLA) and the medial prefrontal cortex (mPFC) during the SIT to elucidate the relationship between the antidepressant-like effect and neuronal oscillation. We discovered that allopregnanolone has antidepressant-like effects in the SIT of SDS model mice by decreasing intervals of repetitive social interaction (inter-event intervals), resulting in increase of total social interaction time. We also found that theta and beta oscillation increased in BLA at the onset of social interaction following administration of allopregnanolone, which differed from the effects of diazepam. Theta and beta power in BLA within the social interaction zone exhibited a positive correlation with interaction time. This increase of theta and beta power was negatively correlated with inter-event intervals. Regarding theta-band coordinated activity between the BLA and mPFC, theta power correlation decreased at the onset of social interaction with the administration of allopregnanolone. These findings suggest that theta activity in BLA following social interaction and the reduced theta-band coordinated activity between the BLA and mPFC are implicated in social interaction, which is one of the antidepressant behaviors. These differences in neural activity could elucidate the distinctive mechanism underlying antidepressant-like effects of neuroactive steroids, as opposed to benzodiazepines.

Beyond the serotonin deficit hypothesis: communicating a neuroplasticity framework of major depressive disorder.

The serotonin deficit hypothesis explanation for major depressive disorder (MDD) has persisted among clinicians and the general public alike despite insufficient supporting evidence. To combat rising mental health crises and eroding public trust in science and medicine, researchers and clinicians must be able to communicate to patients and the public an updated framework of MDD: one that is (1) accessible to a general audience, (2) accurately integrates current evidence about the efficacy of conventional serotonergic antidepressants with broader and deeper understandings of pathophysiology and treatment, and (3) capable of accommodating new evidence. In this article, we summarize a framework for the pathophysiology and treatment of MDD that is informed by clinical and preclinical research in psychiatry and neuroscience. First, we discuss how MDD can be understood as inflexibility in cognitive and emotional brain circuits that involves a persistent negativity bias. Second, we discuss how effective treatments for MDD enhance mechanisms of neuroplasticity-including via serotonergic interventions-to restore synaptic, network, and behavioral function in ways that facilitate adaptive cognitive and emotional processing. These treatments include typical monoaminergic antidepressants, novel antidepressants like ketamine and psychedelics, and psychotherapy and neuromodulation techniques. At the end of the article, we discuss this framework from the perspective of effective science communication and provide useful language and metaphors for researchers, clinicians, and other professionals discussing MDD with a general or patient audience.

The Use of Text Messaging as an Adjunct to Internet-Based Cognitive Behavioral Therapy for Major Depressive Disorder in Youth: Secondary Analysis.

As an established treatment for major depressive disorder (MDD), cognitive behavioral therapy (CBT) is now implemented and assessed in internet-based formats that, when combined with smartphone apps, enable secure text messaging. As an adjunct to such internet-based CBT (ICBT) approaches, text messaging has been associated with increased adherence and therapeutic alliance. This study analyzed data from the intervention arm of a randomized control trial evaluating 24-week ICBT for MDD (intervention arm) against standard-care psychiatry (waitlist control). The aim of this secondary analysis was to assess MDD symptom improvement in relation to the frequency and content of text messages sent by ICBT participants to Navigator-Coaches during randomized control trial participation. Higher text frequency in general and in 3 conceptual categories (appreciating alliance, alliance building disclosures, and agreement confirmation) was hypothesized to predict larger MDD symptom improvement. Participants were young adults (18-30 years) from the Centre for Addiction and Mental Health. The frequencies of categorized texts from 20 ICBT completers were analyzed with respect to MDD symptom improvement using linear regression models. Texts were coded by 2 independent coders and categorized using content analysis. MDD symptoms were measured using the Beck Depression Inventory-II (BDI-II). Participants sent an average of 136 text messages. Analyses indicated that BDI-II improvement was negatively associated with text messaging frequency in general (β=-0.029, 95% CI -0.11 to 0.048) and in each of the 3 categories: appreciating alliance (β=-0.096, 95% CI -0.80 to 0.61), alliance building disclosures (β=-0.098, 95% CI -0.28 to 0.084), and agreement confirmation (β=-0.076, 95% CI -0.40 to 0.25). Altogether, the effect of text messaging on BDI-II improvement was uniformly negative across statistical models. More text messaging appeared associated with less MDD symptom improvement. The hypothesized positive associations between conceptually categorized text messages and MDD symptom improvement were not supported in this study. Instead, more text messaging appeared to indicate less treatment benefit. Future studies with larger samples are needed to discern the optimal use of text messaging in ICBT approaches using adjunctive modes of communication. Clinical Trials.gov NCT03406052; https://www.clinicaltrials.gov/ct2/show/NCT03406052.

The impact of antidepressant treatment on the network structure of neurocognition and core emotional depressive symptoms among depressed individuals with a history of suicide attempt: An 8-week clinical study

BackgroundA more in-depth understanding of the relationship between depressive symptoms, neurocognition and suicidal behavior could provide insights into the prognosis and treatment of major depressive disorder (MDD) and suicide. We conducted a network analysis among depressed patients examining associations between history of suicide attempt (HSA), core emotional major depression disorder, and key neurocognitive domains. MethodDepressed patients (n = 120) aged 18–65 years were recruited from a larger randomized clinical trial conducted at the Douglas Institute in Montreal, Canada. They were randomly assigned to receive one of two antidepressant treatments (i.e., escitalopram or desvenlafaxine) for 8 weeks. Core emotional MDD and key neurocognitive domains were assessed pre-post treatment. ResultsAt baseline, an association between history of suicide attempt (HSA) and phonemic verbal fluency (PVF) suggested that HSA patients reported lower levels of the latter. After 8 weeks of antidepressant treatment, HSA became conditionally independent from PVF. Similar results were found for both the HAM-D and the QIDS-SR core emotional MDD/neurocognitive networks. ConclusionNetwork analysis revealed a pre-treatment relationship between a HSA and decreased phonemic VF among depressed patients, which was no longer present after 8 weeks of antidepressant treatment.

Modulation of brain function and antidepressant effects by transcranial alternating current stimulation in patients with major depressive disorder: Evidence from ERP

Transcranial alternating current stimulation (tACS) is an emerging non-invasive neuromodulation treatment for major depressive disorder (MDD), but its mechanism remains unclear. Therefore, we evaluated the effects of tACS on event-related potentials (ERP) based on a randomized controlled study. All patients were divided into two groups to receive either 20 sessions 77.5Hz-tACS or 20 sessions of sham stimulation during 4 weeks. The Hamilton Depression Rating Scale for Depression -17 item (HAMD-17) and ERP during face-word Stroop task were recorded before and after the treatment (the fourth weekend). Our findings indicate a significant alleviation of depressive symptoms after tACS. For the behavioral performance, sham group showed a significant decrease in reaction time to the sad incongruent condition and an increase in accuracy to the happy condition. The active group showed an increase in accuracy to the incongruent condition. ERP analysis revealed that tACS significantly shortened the latency of P2 to incongruent condition, decreased the amplitude and prolonged the latency of N2 to negative condition. These ERP alterations suggest a potential rectification of negative bias and enhancement of cognitive functioning in patients with MDD, offering insights into the antidepressant mechanisms of tACS.

Discovery and in vitro Evaluation of Novel Serotonin 5-HT2A Receptor Ligands Identified Through Virtual Screening.

The 5-HT2A receptor is a molecular target of high pharmacological importance. Ligands of this protein, particularly atypical antipsychotics, are useful in the treatment of numerous mental disorders, including schizophrenia and major depressive disorder. Structure-based virtual screening using a 5-HT2A receptor complex was performed to identify novel ligands for the 5-HT2A receptor, serving as potential antidepressants. From the Enamine screening library, containing over 4 million compounds, 48 molecules were selected for subsequent experimental validation. These compounds were tested against the 5-HT2A receptor in radioligand binding assays. From the tested batch, six molecules were identified as ligands of the main molecular target and were forwarded to a more detailed in vitro profiling. This included radioligand binding assays at 5-HT1A, 5-HT7, and D2 receptors and functional studies at 5-HT2A receptors. These compounds were confirmed to show a binding affinity for at least one of the targets tested in vitro. The success rate for the inactive template-based screening reached 17 %, while it was 9 % for the active template-based screening. Similarity and fragment analysis indicated the structural novelty of the identified compounds. Pharmacokinetics for these molecules was determined using in silico approaches.

A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis

BackgroundIn order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment. MethodsA total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks’ taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients. ResultsThis study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856. ConclusionThe study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

Use of transcranial magnetic stimulation (TMS) for studying cognitive control in depressed patients: A systematic review.

Major depressive disorder (MDD) is a debilitating mental disorder and the leading cause of disease burden. Major depressive disorder is associated with emotional impairment and cognitive deficit. Cognitive control, which is the ability to use perceptions, knowledge, and information about goals and motivations to shape the selection of goal-directed actions or thoughts, is a primary function of the prefrontal cortex (PFC). Psychotropic medications are one of the main treatments for MDD, but they are not effective for all patients. An alternative treatment is transcranial magnetic stimulation (TMS). Previous studies have provided mixed results on the cognitive-enhancing effects of TMS treatment in patients with MDD. Some studies have found significant improvement, while others have not. There is a lack of understanding of the specific effects of different TMS protocols and stimulation parameters on cognitive control in MDD. Thus, this review aims to synthesize the effectiveness of the TMS methods and a qualitative assessment of their potential benefits in improving cognitive functioning in patients with MDD. We reviewed 21 studies in which participants underwent a treatment of any transcranial magnetic stimulation protocol, such as repetitive TMS or theta-burst stimulation. One of the primary outcome measures was any change in the cognitive control process. Overall, the findings indicate that transcranial magnetic stimulation (TMS) may enhance cognitive function in patients with MDD. Most of the reviewed studies supported the notion of cognitive improvement following TMS treatment. Notably, improvements were predominantly observed in inhibition, attention, set shifting/flexibility, and memory domains. However, fewer significant improvements were detected in evaluations of visuospatial function and recognition, executive function, phonemic fluency, and speed of information processing. This review found evidence supporting the use of TMS as a treatment for cognitive deficits in patients with MDD. The results are promising, but further research is needed to clarify the specific TMS protocol and stimulation locations that are most effective.

Basmisanil, an α5-GABAAR negative allosteric modulator, produces rapid and sustained antidepressant-like responses in male mice

There is a critical need for safer and better-tolerated alternatives to address the current limitations of antidepressant treatments for major depressive disorder. Recently, drugs targeting the GABA system via α5-containing GABAA receptors (α5-GABAAR) as negative allosteric modulators (α5-NAMs) have shown promise in alleviating stress-related behaviors in preclinical studies, suggesting that α5-NAMs may have translational relevance as novel antidepressant medications. Here, we evaluated the efficacy of Basmisanil, an α5-NAM that has been evaluated in Phase 2 clinical studies as a cognitive enhancer, in a battery of behavioral tests relevant to coping strategies, motivation, and aversion in male mice, along with plasma and brain pharmacokinetic measurements. Our findings reveal that Basmisanil induces dose-dependent rapid antidepressant-like responses in the forced swim test and sucrose splash test without promoting locomotor stimulating effects. Furthermore, Basmisanil elicits sustained behavioral responses in the female urine sniffing test and sucrose splash test, observed 24 h and 48 h post-treatment, respectively. Bioanalysis of plasma and brain samples confirms effective blood–brain barrier penetration by Basmisanil and extrapolation to previously published data suggest that effects were observed at doses (10 and 30 mg/kg i.p.) corresponding to relatively modest levels of α5-GABAAR occupancy (40–65 %). These results suggest that Basmisanil exhibits a combination of rapid and sustained antidepressant-like effects highlighting the potential of α5-NAMs as a novel therapeutic strategy for depression.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Accelerated Theta Burst Transcranial Magnetic Stimulation for Refractory Depression in Autism Spectrum Disorder.

Major depressive disorder (MDD) disproportionately affects those living with autism spectrum disorder (ASD) and is associated with significant impairment and treatment recidivism. We studied the use of accelerated theta burst stimulation (ATBS) for the treatment of refractory MDD in ASD (3 treatments daily x 10 days). This prospective open-label 12-week trial included 10 subjects with a mean age of 21.5 years, randomized to receive unilateral or bilateral stimulation of the dorsolateral prefrontal cortex. One participant dropped out of the study due to intolerability. In both treatment arms, depressive symptoms, scored on the Hamilton Depression Rating Scale scores, diminished substantially. At 12 weeks post-treatment, full remission was sustained in 5 subjects and partial remission in 3 subjects. Treatment with ATBS, regardless of the site of stimulation, was associated with a significant, substantial, and sustained improvement in depressive symptomatology via the primary outcome measure, the Hamilton Depression Rating Scale. Additional secondary measures, including self-report depression scales, fluid cognition, and sleep quality, also showed significant improvement. No serious adverse events occurred during the study. Mild transient headaches were infrequently reported, which are expected side effects of ATBS. Overall, ATBS treatment was highly effective and well-tolerated in individuals with ASD and co-occurring MDD. The findings support the need for a larger, sham-controlled randomized controlled trial to further evaluate efficacy of ATBS in this population.

Emergency department risk model: timely identification of patients for outpatient care coordination.

Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) EDvisits. This retrospective cohort study utilized electronic health records from Mayo Clinic's primary care system to develop and validate a machine learning-based risk identification model. The model predicts the likelihood of frequent ED visits among patients with MDD within a 12-month period. Data were collected from Mayo Clinic's primary care system between May 1, 2006, and December 19, 2018. Risk identification models were developed and validated using machine learning classifiers to estimate frequent ED visit risks over 12 months. The Shapley Additive Explanations model identified variables driving frequent EDvisits. The patient population had a mean (SD) age of 39.78 (16.66) years, with 30.3% being male and 6.1%experiencing frequent ED visits. The best-performing algorithm (elastic-net logistic regression) achieved an area under the curve of 0.79 (95% CI, 0.74-0.84), a sensitivity of 0.71 (95% CI, 0.57-0.82), and a specificity of 0.76 (95% CI, 0.64-0.85) in the development data set. In the validation data set, the best-performing algorithm (random forest) achieved an area under the curve of 0.79, a sensitivity of 0.83, and a specificity of 0.61. Significant variables included male gender, prior frequent ED visits, high Patient Health Questionnaire-9 score, low education level, unemployment, and use of multiple medications. The risk identification model has potential for clinical application in triaging primary care patients with MDD in CoCM, aiming to reduce future ED utilization.

Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder.

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), but substantial heterogeneity in outcomes remains. We examined a potential mechanism of action of rTMS to normalize individual variability in resting-state functional connectivity (rs-fc) before and after a course of treatment. Variability in rs-fc was examined in healthy controls (baseline) and individuals with MDD (baseline and after 4-6 weeks of rTMS). Seed-based connectivity was calculated to 4 regions associated with MDD: left dorsolateral prefrontal cortex (DLPFC), right subgenual anterior cingulate cortex (sgACC), bilateral insula, and bilateral precuneus. Individual variability was quantified for each region by calculating the mean correlational distance of connectivity maps relative to the healthy controls; a higher variability score indicated a more atypical/idiosyncratic connectivity pattern. We included data from 66 healthy controls and 252 individuals with MDD in our analyses. Patients with MDD did not show significant differences in baseline variability of rs-fc compared with controls. Treatment with rTMS increased rs-fc variability from the right sgACC and precuneus, but the increased variability was not associated with clinical outcomes. Interestingly, higher baseline variability of the right sgACC was significantly associated with less clinical improvement (p = 0.037, uncorrected; did not survive false discovery rate correction).Limitations: The linear model was constructed separately for each region of interest. This was, to our knowledge, the first study to examine individual variability of rs-fc related to rTMS in individuals with MDD. In contrast to our hypotheses, we found that rTMS increased the individual variability of rs-fc. Our results suggest that individual variability of the right sgACC and bilateral precuneus connectivity may be a potential mechanism of rTMS.

Early-treatment cerebral blood flow change as a predictive biomarker of antidepressant treatment response: evidence from the EMBARC clinical trial.

Major depressive disorder (MDD) is one of the most prevalent and disabling illnesses worldwide. Treatment of MDD typically relies on trial-and-error to find an effective approach. Identifying early response-related biomarkers that predict response to antidepressants would help clinicians to decide, as early as possible, whether a particular treatment might be suitable for a given patient. Data were from the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial. A whole-brain, voxel-wise, mixed-effects model was applied to identify early-treatment cerebral blood flow (CBF) changes as biomarkers of treatment response. We examined changes in CBF measured with arterial spin labeling 1-week after initiating double-masked sertraline/placebo. We tested whether these early 1-week scans could be used to predict response observed after 8-weeks of treatment. Response to 8-week placebo treatment was associated with increased cerebral perfusion in temporal cortex and reduced cerebral perfusion in postcentral region captured at 1-week of treatment. Additionally, CBF response in these brain regions was significantly correlated with improvement in Hamilton Depression Rating Scale score in the placebo group. No significant associations were found for selective serotonin reuptake inhibitor treatment. We conclude that early CBF responses to placebo administration in multiple brain regions represent candidate neural biomarkers of longer-term antidepressant effects.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Technical Report: Efficacy and Safety of Low-Intensity Transcranial Magnetic Stimulation in the Remission of Depressive Symptoms in Patients With Treatment-Resistant Depression in Mexico.

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that induces action potentials in the stimulated cortical area and has been approved by the Food and Drug Administration (FDA)for the treatment of major depressive disorder (MDD). The prevalence of MDD in Mexico almost tripled after the COVID-19 pandemic. In this study, we evaluated the safety and therapeutic effects of low-intensity TMS (Li-TMS) - characterized by inducing electric currents below the action potential threshold on the cerebral cortex - in 41 subjects diagnosed with treatment-resistant depression (TRD). A Li-TMS device dispensed repetitive magnetic pulses at 30 mT for 60 minutes during 20 sessions (once daily from Monday to Saturday) with the theta burst pattern. Our results suggest that Li-TMS is a safe therapy with antidepressant effects, demonstrated by the decrease in Beck Depression Inventory (BDI) scores and lessening of depressive symptoms.

At the Forefront: Social Workers' Role in Psilocybin Treatment for Depression and Substance Misuse.

This article underscores the critical role of social workers in harnessing the potential therapeutic benefits of psilocybin for treating major depressive disorder (MDD) and substance use disorder (SUD). Contemporary treatments for MDD often have side effects, and the success rate for SUD treatments remains low. The pervasiveness of MDD, combined with the challenges in treating SUD, highlights a need for innovative treatments. This article provides an overview of the resurgence of literature over the past two decades that illuminates the therapeutic promise of psilocybin for mental health treatment; clinical trials elucidate the efficacy of psilocybin-assisted therapy in mitigating MDD and demonstrate great promise in reducing SUD symptoms. The long-lasting posttreatment effect emphasizes its potential as a novel treatment modality. Furthermore, psilocybin's recognition as a "breakthrough therapy" by the U.S. Food and Drug Administration (FDA) and the accelerating pace of psychedelic reform bills indicate growing acceptance and interest in its therapeutic capacities. Psilocybin-assisted therapy emerges as a potent treatment option, showcasing remarkable effectiveness even after a single dose. Recommendations and pathways for social workers to be involved in psilocybin-assisted therapy investigation, advocacy, and implementation are provided.

Subject-Specific Resting-State Functional Connectivity to Personalize rTMS Interventions In Major Depressive Disorder

Subject-Specific Resting-State Functional Connectivity to Personalize rTMS Interventions In Major Depressive Disorder

Alzheimer Disease-Link With Major Depressive Disorder and Efficacy of Antidepressants in Modifying its Trajectory.

Alzheimer disease (AD) is a devastating neurodegenerative disorder that affects millions of individuals worldwide, with no effective cure. The main symptoms include learning and memory loss, and the inability to carry out the simplest tasks, significantly affecting patients' quality of life. Over the past few years, tremendous progress has been made in research demonstrating a link between AD and major depressive disorder (MDD). Evidence suggests that MDD is commonly associated with AD and that it can serve as a precipitating factor for this disease. Antidepressants such as selective serotonin reuptake inhibitors, which are the first line of treatment for MDD, have shown great promise in the treatment of depression in AD, although their effectiveness remains controversial. The goal of this review is to summarize current knowledge regarding the association between AD, MDD, and antidepressant treatment. It first provides an overview of the interaction between AD and MDD at the level of genes, brain regions, neurotransmitter systems, and neuroinflammatory markers. The review then presents current evidence regarding the effectiveness of various antidepressants for AD-related pathophysiology and then finally discusses current limitations, challenges, and future directions.