Personalized Medicine in Psychiatry最新文献

Background

We previously identified a cognitive biotype of depression characterized by dysfunction of the brain’s cognitive control circuit, comprising the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), derived from functional magnetic resonance imaging (fMRI). We evaluate these circuit metrics as personalized predictors of antidepressant remission.

Methods

We undertook a secondary analysis of data from the international Study to Predict Optimized Treatment in Depression (iSPOT-D) for 159 patients who completed fMRI during a GoNoGo task, 8 weeks treatment with one of three study antidepressants and who were assessed for remission status (Hamilton Depression Rating Scale score of ≤ 7). Circuit predictors of remission were dLPFC and dACC activity and connectivity quantified in standard deviations. Using established software implementing receiver operating analysis (ROC) we calculated the sensitivity and specificity of these predictors at every cut-point for every circuit measure. We calculated number needed to treat (NNT) metrics for the ROC model identifying optimal cut-point values.

Results

ROC models identified maximum separation of remitters (62.5%) from non-remitters (21.2%) at an initial cut-point of −0.75 standard deviations for dLPFC activity and averaged circuit metrics at secondary cutpoints. The NNT was 3.72, implying that if 4 patients (rounding of 3.72) were randomly selected, one would be likely to remit, but if circuit metrics informed treatment, two would be likely to remit.

Conclusions

Our findings contribute to identifying clinically actionable circuit measures for clinical trials and clinical practice. Future studies are needed to replicate these findings and expand the assessment of longer-term outcomes.

The Texas Child Mental Health Care Consortium (TCMHCC) includes all twelve Health-Related Institutions (HRI) in the State of Texas. At each HRI, teams of child health faculty and staff are reviewing needs of children in Texas and providing new primary care interventions for children (eg, CPAN, TCHATT, CPWE). In addition to the clinical care programs, two research registries (for Trauma and Depression) have been developed to inform future care pathways with new data. The registries collect similar kinds of descriptive, medical and psychosocial data reliably. Because advances have suggested that events and experiences in early years could be the origins of adult mental health challenges, these research registries could contribute to the development and application of treatments during youth to mitigate both youth and adult manifestations of associated mental health vulnerabilities. These registries are set up and the members are reliably gathering baseline data throughout the state, which was the initial goal. The registries now include treatments, Trauma-CBT for Trauma and Behavioral Activation for Depression, to test for efficacy and specificity. We look forward to testing and comparing additional treatments in the future studies.

Depression is regarded as one of the world's primary concerns. Recent researchers use artificial intelligence techniques like machine learning and deep learning to identify depressive symptoms automatically. This literature review focuses on using machine learning and deep learning models in depression detection on social media. Advances in deep learning have improved methods for identifying depression, which is one of the illnesses that affect the health of individuals. Some researchers employ a variety of deep-learning approaches to improve the diagnosis, detection, and prediction of depression to support expert decision-making. The researchers identified the available prediction techniques and tools used to detect, forecast, compare, and classify depression in victims systematically. Twenty-eight (28) articles relevant to machine learning and thirty-two (32) articles linked to deep learning were chosen and considered using boolean keyword searches in different publishing databases and filters. A significant number of the studies, according to the conclusions of the analysis, used machine learning techniques such as decision trees, K-nearest neighbours, naive bayes, random forests, and support vector machines. The deep learning models that are most frequently utilised include convolutional neural networks, long short-term memory, and recurrent neural networks with different datasets to detect subjects suffering from depression using social media data. The datasets used in these studies include Twitter, Facebook, Reddit, tweets from the Kaggle website, and clinic patients’ records. These datasets can include posts, comments, audio, video, images, and interviews. The results of this study revealed that, recently, several approaches have focused on using deep learning for depression detection. The paper highlighted that most research focuses on the detection and identification of depression. Prospects for cutting-edge studies in the detection of depression and other illnesses that are related to health were also suggested.

Background and Objectives

Patients with chronic mood and anxiety disorders experience many life stressors and are more reactive to these stressors. Although mindful yoga might reduce stress reactivity, little is known about the affect regulation mechanisms involved, such as repetitive negative thinking, fear of emotion, acting with awareness and body awareness.

Design and Methods

Using experience sampling methodology, 12 patients with chronic mood and anxiety disorders completed five daily assessments for 15 days before and after a 9-week mindful yoga intervention. Interrupted time-series analyses were used to assess mean-level change from pre-to-post intervention and vector autoregressive models to assess change in the temporal associations.

Results

Most individuals experienced positive changes in affect and the proposed affect regulation processes. Fear of emotion showed changes from pre-to-post intervention for most individuals (67%), followed by acting with awareness (58%), body awareness (58%) and repetitive negative thinking (50%). In the dynamic relationships between stressors, the four affect regulation processes and affect, there were individual differences in which pathways changed and how they changed.

Conclusions

After a mindful yoga intervention, affect and several affect regulation processes improve in most individuals. Achieving this in the context of daily life stress, seems to be more complicated.

Trial registration

ISRCTN register (study ID ISRCTN13612864).

Both adverse childhood experiences (ACEs) and substance abuse have shown individual associations with treatment-resistant depression (TRD). Yet, limited research has explored the joint association of ACEs and substance abuse in TRD patients. This study aimed to evaluate how the amount and type of ACE exposure increase the risk of lifetime substance abuse in a clinical sample of 725 outpatients diagnosed with major depressive disorder (MDD) or persistent depressive disorder.

Logistic regression analyses were used to assess whether heightened ACE exposure increased the risk of substance abuse, and to examine the relationship between five ACE subtypes (e.g., sexual abuse (CSA), physical violence, injury/illness, childhood grief, and parental upheaval) and history of substance abuse.

Greater ACE exposure increased risk of substance abuse. CSA independently predicted a history of substance abuse. This provides further evidence for the additive effects of ACEs and that CSA may have a unique link to substance abuse.

The family brought a 48-year-old woman with major depressive disorder to the emergency room due to unresponsiveness and bizarre behavior. Catatonia was suspected and confirmed by a positive lorazepam challenge test. The patient’s home regimen included bupropion, though the family reported non-adherence. Intravenous lorazepam was scheduled, and the patient’s catatonic symptoms gradually improved. Gradual tapering of lorazepam caused the re-appearance of catatonic symptoms. Each attempt at tapering lorazepam resulted in catatonic symptoms resurfacing and requiring higher scheduled doses of lorazepam. Electroconvulsive treatment was not an option as (1) it was not easily accessible, and (2) the patient did not consent. Multiple anti-depressants were tried and discontinued due to intolerable side effects, and the patient eventually partially improved with mirtazapine, zolpidem, memantine, and lorazepam. Catatonia is a complex neuropsychiatric syndrome that requires prompt recognition, diagnosis, and treatment. Such cases pose a challenge to physicians. Our case emphasizes considering the uniqueness of each individual’s presentation of chronic catatonia during treatment.

Background

Ketamine is recognized as an effective treatment for major depressive disorder but there remains a need to identify predictors of response to ketamine therapy.

Methods

This naturalistic study was a retrospective analysis of data from 48 patients with treatment-resistant depression who received up to four doses of intranasal ketamine in an acute psychiatric hospital setting. Baseline demographic and clinical variables were examined as potential predictors of response along with treatment-emergent dissociation and blood pressure changes. Response was defined as a depression score decrease of 50% or more from baseline measured with the Hamilton Depression Rating Scale or the Montgomery–Åsberg Depression Rating Scale.

Results

The response rate was significantly lower in patients diagnosed with a personality disorder, the most common of which was borderline personality disorder. In contrast, baseline scores on patient-reported questionnaires assessing maladaptive personality traits were not associated with lower response rates. Adverse childhood experiences and dissociation during treatment were associated with higher response rates.

Conclusions

Patients with significant childhood trauma appeared to benefit more from ketamine treatment, consistent with previous research. More research to determine if personality disorders influence ketamine response is needed and should distinguish clinical diagnoses from those derived solely from patient-reported questionnaires. Treatment-emergent dissociation could be a predictor of response to intranasal ketamine but requires further study.

Background

Sublingual buprenorphine-naloxone combination is effective and safe as maintenance therapy in opioid dependence but there is little data to choose the right candidates based on clinical and sociodemographic profiles, often leading to poorer outcomes and relapse. Our study aims to aid clinicians in selecting appropriate candidates for such therapy.

Methods

We conducted a prospective observational study on 106 Opioid-dependent subjects over 5 years where each subject was followed up for 2 years. Sociodemographic profiles, opioid dependence severity, buprenorphine-naloxone dose needed, age at treatment onset, duration of opioid dependence, presence of comorbid Axis I, Axis II disorders, other substance use, and other relevant factors were measured.

Results

Most of the subjects were young adult males with severe dependence. Only 17% remained non-compliant while 18.87% relapsed completely. Buprenorphine-naloxone dose needed was significantly correlated with the severity of dependence. Good compliance with buprenorphine-naloxone predicted less relapse to opioids. Higher buprenorphine-naloxone dose, financial stability, employment and not having problems with primary support group predicted more compliance and abstinence. Being graduate, coexisting mood or anxiety disorders predicted better compliance. Comorbid Axis II disorders, benzodiazepine, or cannabis dependence predicted higher relapse. Ironically, having high perceived criticism was found to be protective against relapse.

Conclusion

Buprenorphine-naloxone therapy has a high rate of long-term compliance even with severe opioid dependence with a relapse rate of less than one in five subjects. Compliance is the biggest predictor of abstinence offering excellent protection against relapse, particularly with optimum dosing of Buprenorphine-naloxone, employment, financial stability, and supportive family.

Objective

Young Researchers are a strategic sector in the research community because of the future academic scholars. Recently, growing evidence suggests mental health of young generation of researchers has a significant impact on their wellness, as well the related research output and their future career development. This study aimed to contribute and exploit a conceptual framework self-determination theory tailored to the academic context considering the specifics and challenges of academia.

Methods

Participants were 134 young Early Career Academics (mean age = 30.6; SD = 4.38; range = 25–40 years) enrolled via institutional e-mail. Psychological assessment was conducted applying standardized tests evaluating depression, anxiety, stress, emotional regulation difficulties and grit trait.

Results

Our findings highlighted a general trend toward negative psychological dimensions in young researchers: PhD students and Research contracts showed signs of depression, anxiety and stress. More, they highlighted reduced positive outcomes in psychological dimensions, as well as lower ability to manage emotional experiences and then to be perseverant for long-term goals and motivation. The balancing among examined psychological dimensions represents the perspective for mental health actions towards to personal growth related to successful academia experience.

Conclusions

Policy changes, communication activities and health-promotion activities through the circulation of experience, sharing actions, and strategies could be the pillar for fostering healthy academics by raising awareness, implementing interventions, or engaging professionals concerning mental health in academia.

The iatrogenic risks of long-term psychotropic treatment can be minimized by reducing the amount of medication prescribed, which may be a factor in the promising outcomes of need-adapted treatment strategies such as the Open Dialogue (OD) approach. However, there is no comparison between long-term psychotropic treatment patterns in adolescents under OD-based and standard psychiatric care. This longitudinal register-based cohort study study aimed to describe the long-term medication patterns in Finland for all adolescents aged 13–20 receiving first-time psychiatric treatment from 2003 to 2008 (N = 44,685). Adolescents treated in other parts of Finland (n = 44,088) were compared to those in the Western Lapland catchment area (n = 780), where OD covered the entire mental healthcare service. Data on medication dispensation from the start of treatment until the 10-year follow-up or death were analyzed.

Adolescents who started treatment outside OD were more likely to be dispensed antipsychotics (aOR: 2.4, 95 %CI: 1.9–2.9), antidepressants (aOR: 1.8, 95 %CI: 1.5–2), and benzodiazepines (aOR: 2.4, 95 %CI: 1.9–3). The proportion of those receiving antipsychotics and antidepressants remained stable, with a slight increase in the OD group. Both groups showed an increasing percentage of benzodiazepine and stimulant use in the final follow-up years. Most drug treatment periods lasted less than two years. Under OD, adolescents had lower cumulative medication exposure, suggesting lower dosages than standard care.

Organizing mental health services based on OD may reduce psychotropic treatment in young people, but further research is needed to assess its clinical significance.