Diagnosis Of Major Depressive Disorder Research Articles

59 The Impact of Anxiety on Memory Performance in Older Adults with Depression

Objective:Late life depression (LLD) refers to a diagnosis of major depressive disorder in people older than 60, and has been linked to significant cognitive impairment and increased risk of Alzheimer's disease. Although anxiety and depression are highly comorbid, the impact of anxiety on cognition in LLD is far less researched. This is important given that over 20% of middle aged and older adults endorse clinically significant chronic worry. Generalized anxiety disorder in older adults with major depression is associated with poorer cognition and worse treatment outcomes compared with those without anxiety. Therefore, the purpose of the study is to examine the role of anxiety on memory in LLD. We hypothesized that presence of anxiety among older depressed adults would be associated with worse cognitive performance over time.Participants and Methods:Participants included 124 individuals (69.4% female, 90.3% Caucasian) aged 60 or above (M = 71.5, SD = 7.4) who met criteria for major depression, single episode or recurrent. They completed the State Trait Anxiety Inventory, Montgomery Asberg Depression Rating Scale, and a measure of verbal episodic memory (WMS-IV Logical Memory) as part of a larger neuropsychological battery. Data were collected from baseline to three years as part of a larger NIMH-supported longitudinal study. Two-level linear mixed-effect models were fitted to predict memory. State and trait anxiety were used as time-varying predictors. The between-person (level 2) and within-person (level 1) effects of anxiety on memory were assessed controlling for the time trend, age, education, gender, race, and change in depression over time.Results:Plot trajectories across variables revealed a negative correlation such that as anxiety decreased, memory improved over time. Hierarchical linear mixed-effect models revealed that average state anxiety was a marginally significant between-person (level2) predictor for memory [B=-0.041, t(128)=-1.8, p=0.083]. Individuals with greater average state anxiety were more likely to experience memory decline compared to those with lower average state anxiety. In addition, the within-person effect (level 1) of state anxiety was significant [B=-0.096, t(253)=-2.7, p=0.007]. As an individual's anxiety increased over time, their memory declined. Trait anxiety showed a significant within-person effect on memory [B=-0.087, t(254)=-2.0, p=0.048], but a non-significant between-person effect [B=-0.005, t(124)=-0.06, p=0.95].Conclusions:Anxiety appears to increase the risk of memory decline in older adults with major depression, a cohort who are already at risk of cognitive decline. Changes in anxiety increased risk of memory decline even when accounting for changes in depression over time. Although the causal link between anxiety and cognitive impairment remains unclear, it is possible that anxiety and worry may compete for cognitive resources necessary for demanding tasks and situations, detracting from abilities, such as attention and working memory. Older adults with depression may also have difficulty coping adaptively with anxiety, which may negatively affect cognition. Finally, presence of anxiety may represent a form of mild behavioral impairment, a prodrome of cognitive decline leading to dementia. Overall, the present study highlights the negative impact of anxiety on memory performance, indicating that treatment interventions targeting anxiety in older adults are essential to help prevent cognitive decline.

73 Sleep Onset Latency and Duration in rTMS Treatment in Veterans with Treatment-Resistant Major Depressive Disorder

Objective:This study builds on the work by Rehman et al (2022) who argued that transcranial magnetic stimulation (TMS) treatment not only helps treat depression but also decreases sleep problems such as difficulty falling asleep,staying asleep, and waking too early. The present study further explores differences in sleep onset latency, meaning the time it takes to fall asleep, and duration of sleep per night in the pre and post treatment phases of rTMS. The information regarding major attributes of sleep is critical because recent research shows that about 90% of patients with major depressive disorder (MDD) also struggle with sleep disorders (Li et al., 2022), and sleeping for less than seven hours may eventually lead to sleep deprivation (Hirshkowitz et al., 2015), with increased risk of physical and mental health problems (Sheehan et al, 2019). Sleep onset latency estimates vary from individual to individual but typical sleep latency is considered between 10 to 20 minutes (Jung et al, 2013). As it has been shown that overall sleep problems improve with rTMS, we hypothesized that self-reported sleep onset latency will decrease, and sleep duration will increase.Participants and Methods:All participants met inclusion criteria for MDD diagnosis and completed a full course of TMS treatment (N=470; Mean age=53.45, SD=13.73). The sample was mostly male (81%) and ethnically diverse: 77.7% non-Hispanic White, 13.3% Black Americans, 1.9% Asian, 0.2 % Asian Indian, and 1.9% other ethnicities. Sleep problems were assessed using the following questions at the pre and post treatment stages: the number of minutes it takes to fall asleep and duration of sleep each night.Results:A Wilcoxon matched-pairs signed-rank test was conducted to determine whether there was a difference in sleep onset latency and hours of sleep per night between pre and post intervention. The results indicated a significant difference in time to fall asleep between pre and post treatment (pre-treatment M = 1.19, SD = 0.99, post-treatment M = 0.93, SD = 0.91; z = -5.01, p < .001. In addition, there was a significant increase in the minutes of sleep per night in pre (M = 6.11, SD = 2.07) compared to the post treatment (M = 6.32, SD = 1.77), z = -2.56, p = .010.Conclusions:Reduced sleep is known to negatively impact mood, cognitive ability, work performance, and immune function (Besedovsky et al., 2012; Killgore, 2010; Massar et al, 2019; Vandekerckhove & Wang, 2018). Similarly, longer sleep onset latency can cause an individual to enter the first sleep stage later than expected and complete fewer sleep cycles. The results of the present study show the effectiveness of rTMS in decreasing sleep onset latency and increasing the duration of sleep. Given the comorbidity and bidirectionality between sleep disturbances and mood disorders (Fang et al., 2019; Palagini et al., 2019), further researching treatments such as rTMS to improve sleep as a means to also improve mood is crucial. We propose acquiring knowledge about sleep attributes as an essential part of clinicians’ work early on in the rTMS treatment in order to monitor an individual’s global functioning level in light of improved sleep.

Serendipitous neurotransmitter findings in a frontline defender participating in a magnetic resonance spectroscopy study of first responders and military personnel with PTSD

We report a case study of a patient with a clinical diagnosis of Post-traumatic Stress Disorder (PTSD) referred to a neurochemistry Magnetic Resonance Spectroscopy (MRS) study. The clinical evaluation prior to scanning suggested the presence of PTSD, Major Depressive Disorder (MDD) accompanied by suicidal ideation. MRS prior to and at the completion treatment and 12-weeks post treatment identified neurotransmitters consistent with a diagnosis of PTSD and MDD. The neurochemistry evaluation immediately post-treatment identified levels that compared to healthy subjects while at 12-weeks post-treatment the neurochemistry reflected a recurrence of depressive symptoms that were described by the patient. These results offer the potential for neurochemical markers to monitor the response of an individual to therapy.

TCF4 and RBFOX1 as peripheral biomarkers for the differential diagnosis and treatment of major depressive disorder

BackgroundRecent genome-wide association studies on major depressive disorder (MDD) have indicated the involvement of LRFN5 and OLFM4; however, the expression levels and roles of these molecules in MDD remain unclear. The present study aimed to determine the serum levels of TCF4 and RBFOX1 in patients with MDD and to investigate whether these molecules could be used as biomarkers for MDD diagnosis. MethodsThe study included 99 drug-naïve MDD patients, 90 drug-treated MDD patients, and 81 healthy controls (HCs). Serum TCF4 and RBFOX1 levels were measured by ELISA. Pearson's correlation analysis was conducted to determine the association between TCF4/RBFOX1 and clinical variables. Linear support vector machine classifier was used to evaluate the diagnostic capabilities of TCF4 and RBFOX1. ResultsSerum TCF4 and RBFOX1 levels were substantially higher in MDD patients than in HCs and significantly lower in drug-treated MDD patients than in drug-naïve MDD patients. Moreover, serum TCF4 and RBFOX1 levels were associated with the Hamilton Depression Scale score, duration of illness, serum lipids levels, and hepatic function. Thus, both these molecules showed potential as biomarkers for MDD. TCF4 and RBFOX1 combination exhibited a higher diagnostic performance, with the mean area under the curve values of 0.9861 and 0.9936 in the training and testing sets, respectively. LimitationsSmall sample size and investigation of only the peripheral nervous system. ConclusionsTCF4 and RBFOX1 may be involved in the pathogenesis of MDD, and their combination may serve as a diagnostic biomarker panel for MDD.

Metacognition and Mentalization in Patients with Major Depressive Disorder

Social cognition is necessary to comprehend and interpret the behavioural intentions of oneself and others in social contexts. Despite an increasing understanding of social cognition deficits in mental illness, the relationship between Major Depressive Disorder (MDD) and high-order social cognition deficits (i.e., mentalization and metacognition) remains under-investigated. This paper aims to provide a holistic review of the existing empirical evidence on mentalization and metacognition in patients with MDD and suggest areas in need of further research. While a close relationship between MDD and metacognition has been consistently reported, some disagreement was raised regarding the association between mentalization capacity and MDD. Besides, the direction of the association between MDD, mentalization and metacognition was also underlined by studies examining the outcomes of high-order social cognition therapies. Mentalization and metacognition were both found to be predictors or at least moderators, of MDD diagnosis and symptom severity. Future evidence from developmental and neuroimaging studies may provide valuable insights into the field of study by uncovering the varying degrees of the impact caused by social cognition deficits and their associated neural mechanisms.

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools. To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information. This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023. Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview. Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001). This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration.

Major depression disorder (MDD) is a devastating neuropsychiatric disease, and one of the leading causes of suicide. Ferroptosis, an iron-dependent form of regulated cell death, plays a pivotal role in numerous diseases. The study aimed to construct and validate a gene signature for diagnosing MDD based on ferroptosis-related genes (FRGs) and further explore the biological functions of these genes in MDD. The datasets were downloaded from the Gene Expression Omnibus (GEO) database and FRGs were obtained from the FerrDb database and other literatures. Least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were performed to develop a gene signature. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic power of the signature. Gene ontology (GO) enrichment analysis was used to explore the biological roles of these diagnostic genes, and single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune infiltration in MDD. Animal model of depression was constructed to validate the expression of the key genes. Eleven differentially expressed FRGs were identified in MDD patients compared with healthy controls. A signature of three FRGs (ALOX15B, RPLP0, and HP) was constructed for diagnosis of MDD. Afterwards, ROC analysis confirmed the signature's discriminative capacity (AUC = 0.783, 95% CI = 0.719-0.848). GO enrichment analysis revealed that the differentially expressed genes (DEGs) related to these three FRGs were mainly involved in immune response. Furthermore, spearman correlation analysis demonstrated that these three FRGs were associated with infiltrating immune cells. ALOX15B and HP were significantly upregulated and RPLP0 was significantly downregulated in peripheral blood of the lipopolysaccharide (LPS)-induced depressive model. Our results suggest that the novel FRG signature had a good diagnostic performance for MDD, and these three FRGs correlated with immune infiltration in MDD.

Enhancing Provider Mental Health Screening in Primary Care: A Quality Improvement Project.

Background: Depression and anxiety occur in 7.8% and 19.1% of the US population, respectively. About half of those patients are diagnosed in primary care. Objective: The purpose of this quality improvement project was to improve the screening and diagnosing of anxiety and/or depression among adult patients at a primary care clinic by implementing an evidence-based mental health screening interview technique. Methods: The team implemented a mental health screening interview technique that incorporates background, affect, trouble, handling, and empathy and motivational interviewing techniques in addition to self-report surveys. Pre- and post-intervention surveys were conducted to assess providers' perceptions of the new interview technique. ICD-10 code data were gathered to assess the effectiveness of the new mental health screening interview technique. Results: The number of documented diagnoses of single-episode major depressive disorder decreased by 18%, recurrent major depressive disorder increased by 34%, and anxiety disorders increased by 3%. There were more favorable provider perceptions of the new screening interview technique versus the traditional screening method. Conclusions: The results show an improvement in provider comfort and providers preferred the new mental health screening when using an evidence-based mental health screening interview technique. Implications for nursing: The results show the potential benefits of using a structured mental health interview with self-report screening tools when diagnosing anxiety and depression in primary care.

A preliminary composite of blood-based biomarkers to distinguish major depressive disorder and bipolar disorder in adolescents and adults

BackgroundSince diagnosis of mood disorder heavily depends on signs and symptoms, emerging researches have been studying biomarkers with the attempt to improve diagnostic accuracy, but none of the findings have been broadly accepted. The purpose of the present study was to construct a preliminary diagnostic model to distinguish major depressive disorder (MDD) and bipolar disorder (BD) using potential commonly tested blood biomarkers.MethodsInformation of 721 inpatients with an ICD-10 diagnosis of MDD or BD were collected from the electronic medical record system. Variables in the nomogram were selected by best subset selection method after a prior univariable screening, and then constructed using logistic regression with inclusion of the psychotropic medication use. The discrimination, calibration and internal validation of the nomogram were evaluated by the receiver operating characteristic curve (ROC), the calibration curve, cross validation and subset validation method.ResultsThe nomogram consisted of five variables, including age, eosinophil count, plasma concentrations of prolactin, total cholesterol, and low-density lipoprotein cholesterol. The model could discriminate between MDD and BD with an area under the ROC curve (AUC) of 0.858, with a sensitivity of 0.716 and a specificity of 0.890.ConclusionThe comprehensive nomogram constructed by the present study can be convenient to distinguish MDD and BD since the incorporating variables were common indicators in clinical practice. It could help avoid misdiagnoses and improve prognosis of the patients.

Associations between serum estradiol and IL-6/sIL-6R/sgp130 complex in female patients with major depressive disorder

BackgroundIt has been hypothesized that the IL-6/sIL-6R/sgp130 complex, an inflammatory complex, plays a critical role in the pathogenesis of major depressive disorder (MDD). Estradiol (E2) is a sex steroid hormone involved in emotional regulation and MDD. This study aimed to investigate the relationship between E2 and IL-6/sIL-6R/sgp130 complex in patients with MDD.MethodsUsing enzyme-linked immunosorbent assay, the levels of IL-6, sIL-6Rα, and sgp130 were compared between 117 female patients with MDD and 122 healthy controls.The serum concentrations of E2 and other biomarkers were also measured.Results(1) The serum levels of IL-6 and sIL-6Rα in patients with MDD were significantly higher than those in the control group, while the serum levels of sgp130 and E2 were significantly lower (all P < 0.05). (2) Low levels of E2 were associated with high levels of IL-6 and low levels of sgp130 (all P < 0.01). (3) HAMD-24 score was positively correlated with the serum level of IL-6, but negatively correlated with the serum levels of sgp130 and E2(all P < 0.05). (4) IL-6 and sgp130 had certain prognostic values in MDD, and the combination of various indicators showed a significantly superior prognostic value.ConclusionsThe IL6/sIL-6R/sgp130 complex in female patients with MDD was closely related to E2 level. In addition, IL-6 and sgp130 may be valuable serum biomarkers for the diagnosis and prognosis of MDD in women.

Use of Lysergic Acid Diethylamide by Major Depression Status

Renewed interest in the clinical potential of hallucinogens may lead people with depression to a generally more positive view of the use of lysergic acid diethylamide (LSD). Therefore, past-year LSD use among people with depression may be increasing in prevalence. To assess time trends in the prevalence of past-year nonmedical LSD use by past-year major depression status and the variation in this association by sociodemographic characteristics. This survey study used pooled publicly available data from 478 492 adults aged 18 years or older who were administered the National Survey on Drug Use and Health from 2008 through 2019. Statistical analysis was conducted from December 2022 to June 2023. Past-year major depression diagnoses per criteria from the DSM-IV were analyzed. Logistic regression models examined whether time trends in past-year nonmedical LSD use differed between adults with vs without past-year depression, adjusting for sociodemographic characteristics. Secondary analyses examined whether the trends in LSD use by depression status differed between sociodemographic subgroups. The analytic sample included 478 492 adults, of whom 51.8% were female, 56.1% were younger than 50 years, 11.7% were Black, 15.1% were Hispanic, 65.8% were White, and 7.5% were another race. Weighted interview response rates ranged from 64.9% to 75.6% during the study time frame. From 2008 to 2019, past-year use of LSD increased significantly more among adults with major depression (2008 prevalence, 0.5%; 2019 prevalence, 1.8%; prevalence difference [PD], 1.3% [95% CI, 1.0%-1.6%]) compared with adults without major depression (2008 prevalence, 0.2%; 2019 prevalence, 0.8%; PD, 0.6% [95% CI, 0.5%-0.7%]) (difference in difference, 0.8% [95% CI, 0.5%-1.1%]). This difference was particularly pronounced among young adults aged 34 years or younger (PD among those aged 18-25 years with depression, 3.3% [95% CI, 2.5%-4.2%]; PD among those aged 26-34 years with depression, 2.7% [95% CI, 1.6%-3.8%]) and individuals with incomes less than $75 000 per year (PD among those with income <$20 000, 1.9% [95% CI, 1.3%-2.6%]; PD among those with income $20 000-$49 999, 1.5% [95% CI, 1.0%-2.1%]; PD among those with income $50 000-$74 999, 1.3% [95% CI, 0.7%-2.0%]). This study suggests that, from 2008 to 2019, there was a disproportionate increase in the prevalence of past-year LSD use among US adults with past-year depression. Among those with depression, this increase was particularly strong among younger adults and those with lower household incomes. Among individuals with depression who also report LSD use, clinicians should discuss potential strategies for mitigating harm and maximizing benefits in medically unsupervised settings.

Sleep spindles in adolescents with major depressive disorder

Sleep spindle differences in adolescents with major depressive disorder (MDD) compared to healthy adolescents is an ongoing debate. Results mostly indicate decreased sleep spindle activity in adolescents with MDD. Given that sleep spindles predominate NREM and that acutely delaying the sleep period via a “sleep delay challenge” (SDC) increases non-rapid eye movement (NREM) sleep duration, it may be possible to increase spindle density in adolescents with MDD, which may provide a therapeutic benefit to depression symptoms. Here, we examined the impact of a SDC on spindle density and depression symptomology in adolescents with MDD (n = 66) and healthy controls (n = 62) tested across three nights: adaptation, normal sleep, and a SDC night which delayed bedtime by three hours. The results showed that; (1) there was no difference in spindle density between groups on the normal sleep night, (2) following the SDC, both males and females with MDD had a decrease in the frequency of slow spindles, while only females with MDD had an increase in the frequency of fast spindles, (3) acute SDC reduced depression symptoms in both groups, and (4) light sleep on the normal sleep night and slow spindle frequency at SDC predicted an 8 % improvement in depression symptoms, regardless of sex or MDD diagnosis. Taken together, these results suggest that; (a) spindles may be a useful biological marker of depression symptomatology regardless of clinical MDD diagnosis, and (b) that acute SDC may help alleviate depression symptoms in adolescents with MDD.

B - 95 A Case Study of Suspected Klein-Levin Syndrome and Associated Anxiety.

Neuropsychological assessment of ethnoracially diverse patients can be challenging for various reasons, including limited normative data, differing worldviews, stigma, and mental health service utilization and access. This case study describes the use of a comprehensive neuropsychological assessment to assist with differential diagnosis and treatment recommendations. The patient was a 25-year-old Asian and White female who was referred to rule out attention deficit hyperactivity disorder (ADHD). Her history included homeschooling through 8th grade, uncommon sleep disturbances, seasonal allergies, and a diagnosis of major depressive disorder. Regarding sleep disturbance, she endorsed monthly episodes of sleeping for up to 12hours at a time, sometimes sleeping and waking on the floor or elsewhere not in her bedroom. She underwent a comprehensive neuropsychological evaluation. The patient's cognitive profile was generally intact, with relative cognitive inefficiencies in visuospatial/visuoconstructional abilities, working memory, and processing speed domains. She also endorsed clinically significant levels of anxiety. These findings, in conjunction with family history and sleep disturbance, raised concern for Klein-Levin Syndrome (KLS) and the impact of co-occurring anxiety. A sleep study, educational/vocational accommodations, psychotherapy, and follow up evaluation in one year were recommended. While sleep-wake disorders and ADHD can co-occur, sleep-wake disorders can be overlooked by mental health professionals, thus increasing the likelihood of misdiagnosis and potential for harm. Adding additional complexity, expressions of psychological distress vary cross-culturally. Taken together, neuropsychological training must include frameworks that attend to cultural and contextual factors. These cases may frequently be seen in the context of educational or vocational concern referrals.

The Prevalence of Cognitive Impairment and Dementia in Incarcerated Older Adults.

In view of the growing number of older incarcerated persons in the United States, cognitive impairment represents one of the most challenging and costly health care issues facing the U.S. correctional system. This study examined the prevalence and correlates of this growing public health issue in the nation's largest prison system. In this study of a random sample of 143 older (≥55 years) adults incarcerated in the Texas prison system, we assessed-using the Montreal Cognitive Assessment (MoCA)-the percentage of inmates who met the MoCA thresholds for mild cognitive impairment (MCI; <23) and dementia (<18). Due to sample size limitations, our multivariable analysis assessed the binary outcome, MoCA <23. Overall, 35.0% of our random sample of incarcerated older adults in Texas met the threshold for MCI and 9.1% met the threshold for dementia. After adjusting for covariates, study participants who were Black (odds ratio [OR] = 4.12, 95% confidence interval [CI] = 1.57-10.82), Hispanic (OR = 4.34, 95% CI = 1.46-12.93), and those with a diagnosis of major depressive disorder (8.56, 95% CI = 1.21-60.72) all had higher prevalence of a positive screen for MCI or dementia. Dementia was underdiagnosed in our study sample of incarcerated adults, with 15.4% of MoCA-diagnosed dementia patients having a dementia diagnosis documented in their medical records. Future studies of cognitive impairment in prisons and jails can inform health care planning and resource allocation, such as expansion of access to palliative care, advance care planning, and targeted cognitive screening in older age groups.

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder.

Major depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls. We first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model. The reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully. The systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research.

Hierarchical Multifeature Fusion via Audio-Response-Level Modeling for Depression Detection

The clinical diagnosis of major depressive disorder (MDD) relying heavily on the subjective judgment assisted by questionnaires, could result in a low detection rate of MDD. Automatic depression detection (ADD) technology based on physiological and psychological information provides an objective and quantitative way for MDD detection. As a useful data modality, audio signals have attracted increasing interest in mental disorder detection. However, most recent audio-based depression detection methods underestimate the importance of subtly organizing audio data. They either simply use equally split audio segments, or directly build the model upon the entire data sequence, which pose challenges to learning task-specific features. To address this issue, we propose to reorganize the audio data at response level. Based on that, we construct a novel end-to-end model that hierarchically learns discriminative features for accurate depression detection. The stages of intraresponse fusion and interresponse fusion facilitate the extraction and aggregation of ADD-specific information from multiple kinds of acoustic features. Experimental results show that our proposed method significantly outperforms other state-of-the-art audio-based methods. In addition, the flexibility and the robustness of our model are also validated.

Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

Meningkatkan Kemampuan Regulasi Emosi melalui Pendekatan Dialectical Behavior Therapy (DBT) secara Daring

Adverse childhood experiences, insecure attachment relationships with parents, the formation of negative self-perceptions, and maladaptive emotional regulation can form strong risk factors for difficulties in emotional regulation and the diagnosis of Major Depressive Disorder (MDD). Several interventions can be used, including the Dialectical Behaviour Therapy (DBT) approach. This study aims to determine the effectiveness of online DBT intervention in adolescents who experience difficulties in emotion regulation and already have a tendency to depressive symptoms. The participant is a teenage girl aged 17 years and 7 months. The research design is a single-case experimental design. The intervention consisted of 1 pre-intervention session, 8 DBT intervention sessions, 1 post-intervention session, and 1 follow-up session. Quantitative analysis was carried out by comparing scores from the Difficulties in Emotion Regulation Scale (DERS), Strength and Difficulties Questionnaire (SDQ), Beck Depression Inventory (BDI), and Subjective Units of Distress (SUD) given before and after the intervention. The results showed that quantitatively there was a decrease in scores of difficulties in emotion regulation, depressive symptoms, and distress faced by the participant compared to before intervention. Qualitatively, emotion regulation skills change in a more positive and adaptive direction.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response. In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Multi-view graph network learning framework for identification of major depressive disorder

Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures’ high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.