Optimally combining transcranial magnetic stimulation with antidepressants in major depressive disorder: A systematic review and Meta-analysis

There is a critical knowledge gap in optimally combining transcranial magnetic stimulation (TMS) and antidepressants to treat patients with major depressive disorder (MDD). TMS is effective in treating MDD in patients who have failed at least one antidepressant trial, with accelerated protocols showing faster remission in treatment-resistant depression (TRD). Although clinicians routinely augment antidepressants with TMS, there is a knowledge gap in stopping versus continuing antidepressants or the dosing strategies when starting or tapering TMS. These considerations are important when considering maintenance TMS (delivered alone or in combination with suitable antidepressants) to maintain remission in MDD after the index course of TMS. As the first step towards filling this knowledge gap, we reviewed randomized controlled trials (RCTs) and open-label trials from 2 databases (PubMed/Medline and EMBASE) that compared active TMS combined with a pre-specified antidepressant dosed in the same manner for adults with MDD versus sham TMS combined with the same antidepressant as in the active arm. All studies were published between January 1, 2000, and December 31, 2023. We excluded case reports, case series, and clinical studies that augmented TMS with antidepressants and vice versa. We found 10 RCTs (n = 654 participants) and performed a meta-analysis. This showed active TMS combined with pre-specified antidepressants had greater efficacy for MDD treatment than sham TMS combined with the same antidepressants as in the active arm (Hedge's g = 1; 95 % CI [0.27, 1.73]). The review and meta-analysis indicate greater short-term efficacy in combining antidepressants with TMS from the get-go in MDD. Given the increasing role of accelerated TMS protocols in expediting remission in MDD and the results of our meta-analysis, we advocate for RCTs examining the short-term and long-term effects of various antidepressant classes on these TMS protocols in MDD. This can also optimize and individualize maintenance TMS protocols to prevent relapse in MDD.

Higher levels of plasma Adrenocorticotropic hormone (ACTH) are associated with lower suicidal ideation in depressed patients compared to controls and suicide attempters, independently from depression severity

Introduction

Suicidal ideation, an important risk factor for suicide attempts, has an unclear neurobiological basis and is potentially linked to the dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and immune-inflammatory systems. While inflammatory markers have been associated with suicide attempts and, to a lower extent suicidal ideation, the data on the role of a stress-response system is less robust, with most studies carried out with cortisol showing inconsistent results. The present study extends on the previous studies implicating stress-response and immune-inflammatory systems in suicidal thoughts and behaviours, focusing on the associations of several stress-response (adrenocorticotropic hormone (ACTH), cortisol, and dehydroepiandrosterone (DHEA)) and immune-inflammatory (C-reactive protein (CRP),interle ukin-6 (IL-6), and tumour necrosis factor-alpha (TNF-alpha)) with suicidal ideation severity in recent suicide attempters, patients with major depressive disorder, and non-psychiatric controls.

Methods

This observational study included 156 adults from three Vilnius hospitals, recruited into one of the three groups in equal parts: recent suicide attempters, patients with major depressive disorder in current depressive episode, and non-psychiatric controls. Measures included the Hamilton Depression Rating Scale (HDRS-17) and the Beck Scale for Suicide Ideation/Suicide Severity Index (BSS/SSI), alongside sociodemographic data, alcohol, tobacco use, and morning blood samples, measuring plasma ACTH, cortisol, DHEA, CRP, and IL-6. Data were analysed with non-parametric tests, Kendall's tau correlation, and multivariate linear regression adjusted for confounders.

Results

We found a negative correlation between the plasma ACTH levels and suicidal ideation severity (tau = -0.130, p = 0.033), which was driven by the patients with major depressive disorder (tau = -0.237, p = 0.031). Suicidal ideation severity was also negatively correlated with TNF-alpha (tau = -0.231; p < 0.001), positively correlated with IL-6 (tau = 0.154, p = 0.015), and CRP levels (tau = 0.153, p = 0.015), but no differences were observed in group-stratified analyses. The association between plasma ACTH levels and suicidal ideation severity in patients with major depressive disorder remained robust to adjustment for major confounders (adjusted for age, sex, education years, body mass index, smoking status, plasma CRP and PEth concentration (measuring chronic alcohol exposure), and antidepressant use) in the linear regression model (t = -2.71, p = 0.011), as well as additionally adjusting for depression severity (t = -2.99, p = 0.006).

Discussion

The present study shows an association between plasma ACTH levels and suicidal ideation severity in patients with major depressive disorder, robust to adjustment for antidepressant use and depression severity. This finding highlights the potential role of ACTH, in elucidating the effects of stress and mental health disorders. Our findings underscore the importance of the HPA axis in the diagnosis and treatment of suicidal ideation in major depressive disorder and invite further research on interventions targeting this pathway.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Abnormal energy metabolism, oxidative stress, and polyunsaturated fatty acid metabolism in depressed adolescents associated with childhood maltreatment: A targeted metabolite analysis

The purpose of this study was to explore the metabolomic differences between Major depressive disorder (MDD) and healthy individuals among adolescents and the association between childhood maltreatment (CM) and differentially abundant metabolites. The exploratory study included 40 first-episode drug-naïve adolescents with MDD and 20 healthy volunteers. We used the Beck Depression Inventory (BDI-13) to assess the severity of depression and the Childhood Trauma Questionnaire (CTQ) to assess the presence of childhood maltreatment. The plasma samples from all participants were collected for targeted metabolomics analysis using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC‒MS/MS) methods. Spearman correlation was applied to analyse the correlations between clinical variables and metabolites. We found 11 increased metabolites and 37 decreased metabolites that differed between adolescents with MDD and healthy individuals. Pathway enrichment analysis of differentially abundant metabolites showed abnormalities in energy metabolism and oxidative stress in MDD. Importantly, we found that creatine, valine, isoleucine, glutamic acid and pyroglutamic acid were negatively correlated with the BDI-13, while isocitric acid, fatty acid and acylcarnitine were negatively associated with CTQ, and 4-hydroxyproline was positively related to CTQ in adolescents with MDD. These studies provide new ideas for the pathogenesis and potential treatment of adolescents with MDD.

Subjective and objective cognitive functioning in untreated late-life depression: An exploration centered on comorbid generalized anxiety disorder

BACKGROUND

Late-Life Depression (LLD) is a prevalent mental health disorder that is often accompanied by cognitive impairments. The objective of this study is to investigate the influence of coexisting Generalized Anxiety Disorder (GAD) on both subjective and objective cognitive abilities in untreated LLD individuals.

METHODS

A total of 77 participants aged 60 years and above were recruited for this study, comprising 31 individuals with Major Depressive Disorder (LLD group), 46 with MDD and coexisting Generalized Anxiety Disorder (LLDA group), and 54 healthy controls (HC). Prior to the study, all patients had abstained from psychotropic medication for a minimum of two weeks. Comprehensive neuropsychological assessments were administered to all participants.

RESULTS

The LLDA group exhibited substantial disparities in memory, attention, processing speed,executive function,overall cognitive functioning, and subjective cognitive functioning when compared to the HC group. The LLD group displayed deficits in memory, SCWT-W in attention, SCWT-C in processing speed,overall cognitive functioning, and subjective cognitive functioning in comparison to the healthy controls. Although the LLD group achieved lower average scores in executive function, TMTA in processing speed, and DSST in attention than the HC group, no significant distinctions were identified between these groups in these domains. Linear regression analysis unveiled that anxiety symptoms had a significant impact on subjective cognitive deficits among MDD patients, but exhibited a milder influence on objective cognitive performance. After adjusting for the severity of depression, anxiety symptoms were found to affect TMTA in processing speed and subjective cognitive functioning in LLD patients.

CONCLUSION

Late-Life Depression (LLD) exhibits pervasive cognitive impairments, particularly in individuals with generalized anxiety disorder, presenting a crucial target for future therapeutic interventions. Among elderly individuals with depression, anxiety symptoms significantly impact subjective cognitive functioning, suggesting its potential utility in distinguishing between depression-associated cognitive decline and pre-dementia conditions.

Recent Advances in the Treatment of Treatment-Resistant Depression: A Narrative Review of Literature Published from 2018 to 2023

PURPOSE OF REVIEW

We review recent advances in the treatment of treatment-resistant depression (TRD), a disorder with very limited treatment options until recently. We examine advances in psychotherapeutic, psychopharmacologic, and interventional psychiatry approaches to treatment of TRD. We also highlight various definitions of TRD in recent scientific literature.

RECENT FINDINGS

Recent evidence suggests some forms of psychotherapy can be effective as adjunctive treatments for TRD, but not as monotherapies alone. Little recent evidence supports the use of adjunctive non-antidepressant pharmacotherapies such as buprenorphine and antipsychotics for the treatment of TRD; side effects and increased medication discontinuation rates may outweigh the benefits of these adjunctive pharmacotherapies. Finally, a wealth of recent evidence supports the use of interventional approaches such as electroconvulsive therapy, ketamine/esketamine, and transcranial magnetic stimulation for TRD. Recent advances in our understanding of how to treat TRD have largely expanded our knowledge of best practices in, and efficacy of, interventional psychiatric approaches. Recent research has used a variety of TRD definitions for study inclusion criteria; research on TRD should adhere to inclusion criteria based on internationally defined guidelines for more meaningfully generalizable results.

Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis

Evidence of whether the intrinsic functional connectivity of anterior cingulate cortex (ACC) and its subregions is altered in major depressive disorder (MDD) remains inconclusive. A systematic review and meta-analysis were therefore performed on the whole-brain resting-state functional connectivity (rsFC) studies using the ACC and its subregions as seed regions in MDD, in order to draw more reliable conclusions. Forty-four ACC-based rsFC studies were included, comprising 25 subgenual ACC-based studies, 11 pregenual ACC-based studies, and 17 dorsal ACC-based studies. Specific alterations of rsFC were identified for each ACC subregion in patients with MDD, with altered rsFC of subgenual ACC in emotion-related brain regions, of pregenual ACC in sensorimotor-related regions, and of dorsal ACC in cognition-related regions. Furthermore, meta-regression analysis revealed a significant negative correlation between the pgACC-caudate hypoconnectivity and percentage of female patients in the study cohort. This meta-analysis provides robust evidence of altered intrinsic functional connectivity of the ACC subregions in MDD, which may hold relevance to understanding the origin of, and treating, the emotional, sensorimotor and cognitive dysfunctions that are often observed in these patients.

Association Between Taurine Level in the Hippocampus and Major Depressive Disorder in Young Women: A Proton Magnetic Resonance Spectroscopy Study at 7T

BACKGROUND

Major depressive disorder (MDD) is characterized by depressed mood or loss of interest or pleasure. Generally, women are twice as likely as men to have depression. Taurine, a type of amino acid, plays critical roles in neuronal generation, differentiation, arborization, and formation of synaptic connections. Importantly, it enhances proliferation and synaptogenesis in the hippocampus. When injected into animals, taurine has an antidepressant effect. However, there is no in vivo evidence to show an association between taurine concentration in the human brain and the development of MDD.

METHODS

Forty-one unmedicated young women with MDD (ages 18-29) and 43 healthy control participants matched for gender and age were recruited in South Korea. Taurine concentration was measured in the hippocampus, anterior cingulate cortex, and occipital cortex of the MDD and healthy control groups using proton magnetic resonance spectroscopy at 7T. Analysis of covariance was used to examine differences in taurine concentration, adjusting for age as a covariate.

RESULTS

Taurine concentration in the hippocampus was lower (F1,75 = 5.729, p = .019, Δη2 = 0.073) for the MDD group (mean [SEM] = 0.91 [0.06] mM) than for the healthy control group (1.13 [0.06] mM). There was no significant difference in taurine concentration in the anterior cingulate cortex or occipital cortex between the two groups.

CONCLUSIONS

This study demonstrates that a lower level of taurine concentration in the hippocampus may be a novel characteristic of MDD.

Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.

Bilateral rTMS Shows No Advantage in Depression nor in Comorbid Depression and Anxiety: A Naturalistic Study

The objective was to determine if adding low-frequency right-sided rTMS treatment to the standard high-frequency left-sided treatment (LUL), referred to as sequential bilateral treatment (SBT), confers additional benefit for depression or anxiety outcomes. A retrospective chart review from January 2015 through December 2018 yielded 275 patients, all of whom were treated with a figure-8 coil for a major depressive episode. Their protocol was either LUL or SBL. Outcome measures were the Generalized Anxiety Disorder 7-item scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). There was no significant difference in GAD-7 change scores between patients who had LUL or SBL (4.2 vs 4.8). This was also true when the sample was restricted to only patients who started with high GAD-7 scores. There was likewise no significant difference in PHQ-9 change scores between patients who had LUL or SBL (6.8 vs 5.1). Patients switching from LUL to SBL mid-course had poorer overall outcomes as compared to patients who stayed with the same protocol throughout treatment. This large naturalistic study shows no advantage for SBL treatment any group or condition examined. The results of this study have clinical applicability and sound a cautionary note regarding the use of combination rTMS protocols.

Repetitive transcranial magnetic stimulation (rTMS) as a tool for cognitive enhancement in healthy adults: a review study

As human beings, we have always sought to expand on our abilities, including our cognitive and motor skills. One of the still-underrated tools employed to this end is repetitive transcranial magnetic stimulation (rTMS). Until recently, rTMS was almost exclusively used in studies with rehabilitation purposes. Only a small strand of literature has focused on the application of rTMS on healthy people with the aim of enhancing cognitive abilities such as decision-making, working memory, attention, source memory, cognitive control, learning, computational speed, risk-taking, and impulsive behaviors. It, therefore, seems that the findings in this particular field are the indirect results of rehabilitation research. In this review paper, we have set to investigate such studies and evaluate the rTMS effectuality in terms of how it improves the cognitive skills in healthy subjects. Furthermore, since the most common brain site used for rTMS protocols is the dorsolateral prefrontal cortex (DLPFC), we have added theta burst stimulation (TBS) wave patterns that are similar to brain patterns to increase the effectiveness of this method. The results of this study can help people who have high-risk jobs including firefighters, surgeons, and military officers with their job performance.

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.

Structural and biochemical alterations in dendritic spines as key mechanisms for severe mental illnesses

Severe mental illnesses (SMI) collectively affect approximately 20% of the global population, as estimated by the World Health Organization (WHO). Despite having diverse etiologies, clinical symptoms, and pharmacotherapies, these diseases share a common pathophysiological characteristic: the misconnection of brain areas involved in reality perception, executive control, and cognition, including the corticolimbic system. Dendritic spines play a crucial role in excitatory neurotransmission within the central nervous system. These small structures exhibit remarkable plasticity, regulated by factors such as neurotransmitter tone, neurotrophic factors, and innate immunity-related molecules, and other mechanisms - all of which are associated with the pathophysiology of SMI. However, studying dendritic spine mechanisms in both healthy and pathological conditions in patients is fraught with technical limitations. This is where animal models related to these diseases become indispensable. They have played a pivotal role in elucidating the significance of dendritic spines in SMI. In this review, the information regarding the potential role of dendritic spines in SMI was summarized, drawing from clinical and animal model reports. Also, the implications of targeting dendritic spine-related molecules for SMI treatment were explored. Specifically, our focus is on major depressive disorder and the neurodevelopmental disorders schizophrenia and autism spectrum disorder. Abundant clinical and basic research has studied the functional and structural plasticity of dendritic spines in these diseases, along with potential pharmacological targets that modulate the dynamics of these structures. These targets may be associated with the clinical efficacy of the pharmacotherapy.

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

BACKGROUND

Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, depressed patients tend to conceal their symptoms from others based on shame and stigma, thus hesitate to visit psychiatrists especially during early phase. We hypothesize that application of humanoid robots would be a novel solution. Depressed patients may feel more comfortable talking with such robots than humans.

METHODS

We recruited 13 patients with major depressive disorder (MDD) and 27 healthy volunteers as controls. Participants took both tele-operated humanoid robot and human interviews to evaluate severity of depression using the Hamilton Depression Rating Scale (HDRS). In addition, participants completed a self-administered questionnaire asking about their impressions of the robot interview.

RESULTS

Confidence interval and t-test analysis have revealed that the HDRS scores are equally reliable between robot and human interviews. No significant differences were observed between the two interviews regarding "nervousness about the interview" and "hesitancy to talk about depressed moods and suicidal ideation." Compared to human interviews, robot interviews yielded significantly lower scores on shame-related factors especially among patients with MDD.

LIMITATION

Small sample size, and the evaluator is male only.

CONCLUSIONS

This is the first report to show the reliability of tele-operated humanoid robot interviews for assessment of depression. Robot interviews are potentially equally reliable as human interviews. Robot interviews are suggested to be more appropriate in assessing shame-related suppressed emotions and hidden thoughts of depressed patients in clinical practice, which may reduce the stigma associated with depression.

Converged avenues: depression and Alzheimer's disease- shared pathophysiology and novel therapeutics

Depression, a highly prevalent disorder affecting over 280 million people worldwide, is comorbid with many neurological disorders, particularly Alzheimer's disease (AD). Depression and AD share overlapping pathophysiology, and the search for accountable biological substrates made it an essential and intriguing field of research. The paper outlines the neurobiological pathways coinciding with depression and AD, including neurotrophin signalling, the hypothalamic-pituitary-adrenal axis (HPA), cellular apoptosis, neuroinflammation, and other aetiological factors. Understanding overlapping pathways is crucial in identifying common pathophysiological substrates that can be targeted for effective management of disease state. Antidepressants, particularly monoaminergic drugs (first-line therapy), are shown to have modest or no clinical benefits. Regardless of the ineffectiveness of conventional antidepressants, these drugs remain the mainstay for treating depressive symptoms in AD. To overcome the ineffectiveness of traditional pharmacological agents in treating comorbid conditions, a novel therapeutic class has been discussed in the paper. This includes neurotransmitter modulators, glutamatergic system modulators, mitochondrial modulators, antioxidant agents, HPA axis targeted therapy, inflammatory system targeted therapy, neurogenesis targeted therapy, repurposed anti-diabetic agents, and others. The primary clinical challenge is the development of therapeutic agents and the effective diagnosis of the comorbid condition for which no specific diagnosable scale is present. Hence, introducing Artificial Intelligence (AI) into the healthcare system is revolutionary. AI implemented with interdisciplinary strategies (neuroimaging, EEG, molecular biomarkers) bound to have accurate clinical interpretation of symptoms. Moreover, AI has the potential to forecast neurodegenerative and psychiatric illness much in advance before visible/observable clinical symptoms get precipitated.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Prevalence and risk factors for subclinical hypothyroidism in older patients with major depressive disorder

BACKGROUND

Subclinical hypothyroidism (SCH) is highly correlated with major depressive disorder (MDD). However, the prevalence and risk factors for SCH in older patients with MDD have rarely been reported in China.

METHODS

This cross-sectional study included 266 older MDD patients with SCH was performed. Clinical and anthropometric, biochemical, and thyroid function data were collected. Depression, anxiety, and psychotic symptoms were assessed using the Hamilton Depression Scale, the Hamilton Anxiety Scale, and the Positive and Negative Syndrome Scale positive subscale, respectively.

RESULTS

Among older patients with MDD, the prevalence of SCH was 64.7% (172/266). Compared to patients without SCH, older MDD patients with SCH had a longer disease course and higher TSH, A-TG, A-TPO, HDL-C, LDL-C, TC, FPG, and systolic pressure levels (all P ≤ 0.002). Furthermore, disease progression (OR 1.082, 95% CI 1.020-1.147, P = 0.009), A-TG (OR 1.005, 95% CI 1.001-1.009, P = 0.017), TC (OR 2.024, 95% CI 1.213-3.377, P = 0.007), FPG (OR 2.916, 95% CI 1.637-5.194, P < 0.001), systolic pressure (OR 1.053, 95% CI 1.008-1.100, P = 0.022) were independently associated with SCH, in older patients with MDD.

CONCLUSIONS

Our findings suggest a high prevalence of SCH in older patients with MDD. Several demographic and clinical variables were independently associated with SCH in older patients with MDD.

Adjunctive duration-doubled transcranial direct current stimulation for the treatment of depressive patients with suicidal ideation: study protocol for a double-blind, randomized, sham-controlled trial

BACKGROUND

The problem of suicide has become increasingly common in individuals with major depressive disorder (MDD). Transcranial direct current stimulation (tDCS) is an effective treatment for MDD with 2 milliamperes (mA) for at least 30 min per day for 2 weeks. This study aims to investigate the efficacy of daily duration-doubled tDCS as an adjunctive intervention for rapidly reducing suicidal ideation and improving depression in MDD patients.

METHODS

In this double-blind, randomized, sham-controlled study, 76 MDD patients with suicidal ideation are randomly assigned to either active (n=38) or sham (n=38) tDCS group. The anode and cathode are placed over the scalp areas corresponding to left and right dorsolateral prefrontal cortex (DLPFC), respectively, and each stimulation lasts for 60 min. The primary outcome is defined as change of Beck Scale for Suicide Ideation (BSI) after 5 and 10 sessions. The change of other clinical assessments, blood biomarkers related to suicidal ideation and depressive sumptoms are defined as secondary outcomes. Blood biomarkers related to suicidal ideation are collected at baseline and after 10 sessions.

DISCUSSION

This study suggests the adjunctive duration-doubled tDCS might be a novel method to rapidly reduce suicidal ideation and improve depressive symptom. The variation of biomarkers could be potential predictive models of suicide risk.

TRIAL REGISTRATION

The trial protocol is registered with ClinicalTrials.gov under protocol registration number NCT05555927. Registered on September 25, 2022.

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

BACKGROUND

Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).

METHODS

Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.

RESULTS

We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM.

CONCLUSIONS

The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.

Whole blood mitochondrial copy number in clinical populations with mood disorders: A meta-analysis: Blood mitochondrial copy number and mood disorders

Major depressive disorder (MDD) and bipolar disorder (BD), are globally prevalent, contributing to significant disease burden and adverse health outcomes. These mood disorders are associated with changes in many aspects of brain reward pathways, yet cellular and molecular changes in the brain are not readily available in clinical populations. Therefore, the use of biomarkers as proxies for changes in the brain are necessary. The proliferation of mitochondria in blood has emerged as a potentially useful biomarker, yet a clear consensus on how these mood disorders impact mitochondrial DNA copy number (mtDNAcn) has not been reached. To determine the current available consensus on the relationship of mood disorder diagnosis and blood mtDNcn, we performed a meta-analysis of available literature measuring this biomarker. Following PRISMA guidelines for a systematic search, 22 papers met inclusion criteria for meta-analysis (10 MDD, 10 BD, 2 both MDD and BD). We extracted demographic, disorder, and methodological information with mtDNAcn. Using the metafor package for R, calculated effect sizes were used in random effects or meta regression models for MDD and BD. Overall, our data suggest blood mtDNAcn may be a useful biomarker for mood disorders, with MDD and BD Type II associated with higher mtDNAcn, and BD Type I associated with lower mtDNAcn. Initially, we observed a trending increase in mtDNAcn in patients with MDD, which reached significance when one study with outlying demographic characteristics was excluded. Subgroup and meta-regression analysis indicated the relationship between mtDNAcn and diagnosis in patients with BD is dependent on BD type, while no relationship is detectable when BD types are mixed. Further study of blood mtDNAcn could predict downstream health outcomes or treatment responsivity in individuals with mood disorders.

Atrophy, hypometabolism and implication regarding pathology in late-life major depression with suspected non-alzheimer pathophysiology (SNAP)

BACKGROUND

A substantial proportion of individuals with late-life major depression could be classified as having a suspected non-Alzheimer disease pathophysiology (SNAP), as indicated by a negative test for the biomarker β-amyloid (Aβ-) but a positive test for neurodegeneration (ND+). This study investigated the clinical features, characteristic patterns of brain atrophy and hypometabolism, and implications regarding pathology in this population.

METHODS

Forty-six amyloid-negative patients with late-life major depressive disorder (MDD) patients, including 23 SNAP (Aβ-/ND+) and 23 Aβ-/ND- MDD subjects, and 22 Aβ-/ND-healthy control subjects were included in this study. Voxel-wise group comparisons between the SNAP MDD, Aβ-/ND- MDD and control subjects were performed, adjusting for age, gender and level of education. For exploratory comparisons, 8 Aβ+/ND- and 4 Aβ+/ND + MDD patients were included in the Supplementary Material.

RESULTS

The SNAP MDD patients had atrophy extending to regions outside the hippocampus, predominately in the medial temporal, dorsomedial and ventromedial prefrontal cortex; hypometabolism involving a large portion of the lateral and medial prefrontal cortex in addition to the bilateral temporal, parietal and precuneus cortex within typical Alzheimer disease regions were observed. Metabolism ratios of the inferior to the medial temporal lobe were significantly elevated in the SNAP MDD patients. We further discussed the implications with regards to underlying pathologies.

CONCLUSION

The present study demonstrated characteristic patterns of atrophy and hypometabolism in patients with late-life major depression with SNAP. Identifying individuals with SNAP MDD may provide insights into currently unspecified neurodegenerative processes. Future refinement of neurodegeneration biomarkers is essential in order to identify potential pathological correlates while in vivo reliable pathological biomarkers are not forthcoming.

Association between rare earth elements and depression: Evidence from pilot mice model of chronic unpredictable mild stress-induced depression and human studies of major depressive disorder

The etiology of Major Depressive Disorder (MDD) has been associated with levels of trace elements in the human body. The source of trace elements in the human body may be rare earth elements (REEs). Our study aimed to identify the potential relationship between t REEs in blood and brain samples and depression from two paths: animal experiments and population studies. In the animal experiments, 35 adult Sprague-Dawley rats were randomly allocated to the control group (n = 14) and treatment group (n = 21), which received the chronic unpredictable mild stress (CUMS) procedure for four weeks and further categorized into the sensitive group (n = 9) and resilient group (n = 12) by sucrose water preference test. Then, all rats were executed to obtain serum and brain tissue samples. We also recruited 197 participants and divided them into the major depressive disorder (MDD) group (n = 100) and the control group (n = 97) then serum samples were collected for REEs detection. Our finding reported that significant differences were found in the levels of La and Ce in blood samples from different groups in the CUMS rat model (sensitive group < resilient group < control group) (all p < 0.05), with similar patterns for other elements (Pr, Nd, and Y) (but p > 0.5). No significant inter-group difference was reported in rat brain tissue samples. After adjusting for demographic variables, we found that the concentrations of all five REEs (La, Ce, Pr, Nd, Y) were lower in depression group than in control group (all p < 0.01). The current conjoint animal and human data supported appropriate levels of REEs have a certain protective effect on body health. These results may be attributed to Hormesis effects. Whether the possible favorable effects of REEs on improving symptoms of depression or can be applied to drug development remains to be further investigated.

Comparison of 60-minute vs 30-minute transcranial direct current stimulation (tDCS) in major depressive disorder: Effects on depression suicidal ideation and anxiety

We investigated whether changes through doubling the duration of each tDCS session would increase efficacy of tDCS for depression. tDCS was applied for 10 sessions, followed by two additional weekly sessions. 63 patients with MDD underwent randomization, with 22 being assigned to 60-min/d group, 25 to 30 min/d group, and 16 to sham group. HAMD-17 reductive ratios at week 2 and 4 were of no significant differences among treatment groups. 60 min group had a greater decrease in anxiety compared to 30 min group and sham group based on HAMA at 4 weeks but only in the completer analysis, not in ITT analysis.

Daily Affective Dynamics in Major Depressive Disorder: The Role of Daily Stressors and Positive Events

This study examined daily affective dynamic indices among individuals with a major depressive disorder (MDD) diagnosis in the past one year at the time of the interview, focusing on affective variability and change in affect in response to daily events (affective reactivity). Data were from the main survey and daily diary project of the Midlife in the United States (MIDUS) study. Participants (N = 1,970; nMDD = 202; nnon-MDD = 1,768) completed structured clinical interviews on mental health and telephone interviews about their daily experiences spanning eight consecutive days. Multilevel models revealed that the MDD group experienced greater positive (PA) and negative affect (NA) variability than the non-MDD group. On days that at least one stressful event was reported, the MDD group experienced a greater decrease in PA and a greater increase in NA. On days that at least one positive event was reported, the MDD group experienced a greater increase in PA and a greater decrease in NA. Changes in affect to daily events, particularly the mood brightening effect, may be indicators of depression and potential targets for intervention. Limitations of the study include a community sample, reliance on self-reported measures of daily stressors and positive events, inclusion of remitted and current MDD participants, and the DSM-III-R based criteria for MDD diagnosis.