Diagnosis Of Psychiatric Disorders Research Articles

Impact of Culture, Race, Social Determinants Reflected Throughout New DSM-5-TR

Impact of Culture, Race, Social Determinants Reflected Throughout New <i>DSM-5-TR</i>

Functional Neuroimaging of Auditory Linguistic and Vocal Affective Processing for Diagnosis and Treatment of Psychiatric Disorders

Functional Neuroimaging of Auditory Linguistic and Vocal Affective Processing for Diagnosis and Treatment of Psychiatric Disorders

Characteristics of frontal activity relevant to cognitive function in bipolar depression: an fNIRS study.

Memory shortness, verbal influence, and disturbed attention are a few of the cognitive dysfunctions reported by individuals of bipolar disorder in depression phase (BD-D). As neuroimaging modalities can investigate such responses, therefore neuroimaging methods can be used to assist the diagnosis of bipolar disorder (BD). Functional near-infrared spectroscopy (fNIRS) is a neural imaging method that is proved to be prominent in the diagnosis of psychiatric disorders. It is the desired method because of its feasible setup, high resolution in time, and its partial resistance to head movements. This study aims to investigate the brain activity in subjects of BD-D during cognitive tasks compared to the healthy controls. A decreased activation level is expected in individuals of BD-D as compared to the healthy controls. This study aims to find new methods and experimental paradigms to assist in the diagnosis of bipolar depression. Participants of BD-D and healthy controls (HC) performed four cognitive tasks including verbal fluency task (VFT), symbol working memory task (symbol check), attention task (spotter) and multiple cognitive task (code break). fNIRS was used to measure levels of oxy-hemoglobin (HbO) representing the brain activity. The generalized linear model (GLM) method was used to estimate the hemodynamic response related to the task. The wavelet transform coherence (WTC) method was used to calculate the intra-hemispheric functional connectivity. We also analyzed the correlation between hemodynamic response and scores of psychiatric disorders. Results showed decreased levels of HbO in BD-D groups compared to the HC, indicating lower activity, during the tasks except for spotter. The difference between BD-D and HC was significant during VFT, symbol check and code break. Group difference during symbol working memory was significant both in brain activity and connectivity. Meanwhile, the individual brain activity during working memory is more related to the illness degree. Lower activity in BD-D reflects unspecific dysfunctions. Compared with other cognitive tasks, the single-trial symbol-check task may be more suitable to help the diagnosis of bipolar depression.

Systemic inflammatory regulators and 7 major psychiatric disorders: A two-sample Mendelian randomization study

Systemic inflammation has been thought to play a considerable part in psychiatric disorders. However, the causal relationships between systemic inflammation and psychiatric disorders and the directions of the causal effects remain elusive and need further investigation. By leveraging the summary statistics of genome-wide association studies, the standard inverse variance weighted method was applied to assess the causal associations among 41 systemic inflammatory regulators and 7 major psychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), autism spectrum disorder (ASD), bipolar disorder (BIP), major depression disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia (SCZ), within a two-sample bidirectional Mendelian randomization analysis. Additionally, the weighted median test and the Mendelian randomization pleiotropy residual sum and outlier test were conducted for sensitivity analyses. The results suggested a total of 15 unique systemic inflammatory regulators might be causally associated with disease risk, including 2 for ADHD, 4 for AN, 2 for ASD, 2 for MDD, 2 for OCD, and 5 for SCZ. Among them, the genetically predicted concentration of basic fibroblast growth factor was significantly related to AN at the Bonferroni-corrected threshold (Odds ratio = 0.403, 95% confidence interval = (0.261, 0.622), P = 4.03 × 10−5). Furthermore, the concentrations of 9 systemic inflammatory regulators might be influenced by neuropsychiatric disorders, including 2 by ADHD, 2 by BIP, 3 by MDD, and 2 by SCZ, and the causal effects of ASD, AN, and OCD need to be further assessed when more significant genetic variants are identified in the future. Overall, this study provides additional insights into the relationships between systemic inflammation and psychiatric disorders and may provide new clues regarding the aetiology, diagnosis and treatment of psychiatric disorders.

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders.

Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. This staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. We propose a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 individuals, we identified clusters of imaging features with distinct developmental trajectories and built machine learning models to obtain brain-age predictions from each of the clusters. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age. Importantly, brain-ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to distinguish disorder groups (e.g., depression and oppositional defiant disorder) from healthy controls. In summary, we show that MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns that may serve as biomarkers that contribute to our understanding of healthy and pathological brain development and the characterization and diagnosis of psychiatric disorders.

Comorbidity between eating disorders and psychiatric disorders.

Previous literature has established an increased risk of eating disorders among individuals with other psychiatric disorders and vice versa. However, often studies have focused on eating disorders as a single diagnostic entity and/or investigated selected psychiatric comorbidities. We conducted a comprehensive study, exploring bidirectional associations between different types of eating disorders and broad groups of all other psychiatric disorders, to identify patterns of comorbidity. We included all people born in Denmark 1963-2010. We collected information on eating disorders and considered the risk of subsequent psychiatric disorders using Cox-proportional hazards regression. Absolute risks were calculated using competing risks survival analyses. We also considered prior psychiatric disorders and subsequent eating disorders. An increased risk was seen for almost all disorder pairs of diagnoses evaluated. Following an anorexia nervosa (AN) diagnosis, the median hazard ratio for the different subsequent psychiatric disorders was 3.80 (range 2.48-6.15); following an other eating disorder (OED) diagnosis, it was 3.16 (range 2.05-5.14). After different psychiatric disorder diagnoses, the median hazard ratio was 2.66 for later AN (range 1.21-5.31), and 2.51 for later OED (range 1.25-4.10). Absolute risk of eating disorders was also higher among those with other psychiatric disorders than those without. In this broad examination, we identified bidirectional increases in risk of comorbidity for those with both eating disorder diagnoses and psychiatric disorder diagnoses. Although our findings indicate different patterns of comorbidity between eating disorders, these variations were generally small.

A new perspective on individual reliability beyond group effect for event-related potentials: A multisensory investigation and computational modeling

The dominant approach in investigating the individual reliability for event-related potentials (ERPs) is to extract peak-related features at electrodes showing the strongest group effects. Such a peak-based approach implicitly assumes ERP components showing a stronger group effect are also more reliable, but this assumption has not been substantially validated and few studies have investigated the reliability of ERPs beyond peaks. In this study, we performed a rigorous evaluation of the test-retest reliability of ERPs collected in a multisensory and cognitive experiment from 82 healthy adolescents, each having two sessions. By comparing group effects and individual reliability, we found that a stronger group-level response in ERPs did not guarantee higher reliability. A perspective of neural oscillation should be adopted for the analysis of reliability. Further, by simulating ERPs with an oscillation-based computational model, we found that the consistency between group-level ERP responses and individual reliability was modulated by inter-subject latency jitter and inter-trial variability. The current findings suggest that the conventional peak-based approach may underestimate the individual reliability in ERPs and a neural oscillation perspective on ERP reliability should be considered. Hence, a comprehensive evaluation of the reliability of ERP measurements should be considered in individual-level neurophysiological trait evaluation and psychiatric disorder diagnosis.

Trajectories of Healthcare Utilization Among Children and Adolescents With Autism Spectrum Disorder and/or Attention-Deficit/Hyperactivity Disorder in Japan.

BackgroundEarly intervention and prevention of psychiatric comorbidities of children with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are urgent issues. However, the differences in the diagnoses of ASD and ADHD and psychiatric comorbidities associated with age, long-term healthcare utilization trajectories, and its associated diagnostic features have not been fully elucidated in Japan.MethodWe conducted a retrospective observational study using the medical records. Member hospitals of three major consortiums of hospitals providing child and adolescent psychiatric services in Japan were recruited for the study. Children who accessed the psychiatry services of the participating hospitals in April 2015 were followed up for 5 years, and data on their clinical diagnoses, consultation numbers, and hospitalizations were collected. Non-hierarchical clustering was performed using two 10-timepoint longitudinal variables: consultation numbers and hospitalization. Among the major clusters, the differences in the prevalence of ASD, ADHD, comorbid intellectual disability, neurotic disorders, and other psychiatric disorders were assessed.ResultsA total of 44 facilities participated in the study (59.5%), and 1,003 participants were enrolled. Among them, 591 diagnosed with ASD and/or ADHD (58.9%) and 589 without missing data were assessed. The mean age was 10.1 years, and 363 (70.9%) were boys. Compared with the pre-schoolers, the school-aged children and adolescents had fewer ASD, more ADHD, and fewer comorbid intellectual disability diagnoses, as well as more diagnoses of other psychiatric disorders. A total of 309 participants (54.7%) continued consultation for 2 years, and 207 (35.1%) continued for 5 years. Clustering analysis identified three, two, and three major clusters among pre-schoolers, school-aged children, and adolescents, respectively. The largest cluster was characterized by early termination of the consultation and accounted for 55.4, 70.6, and 73.4% of pre-schoolers, school-aged children, and adolescents, respectively. Among the school-aged children, the diagnosis of ADHD was associated with a cluster that required longer periods of consultations. Among the adolescents, comorbid psychiatric disorders other than intellectual disability and neurotic disorders were associated with clusters that required hospitalization.ConclusionContinuous healthcare needs were common and psychiatric comorbidities were associated with complex trajectory among adolescents. The promotion of early intervention and prevention of comorbidities are important.

Prevalence and correlates of aggressive behavior in psychiatric inpatient populations.

Aggressive behavior in patients with psychiatric disorders is attracting increasing research interest. One reason for this is that psychiatric patients are generally considered more likely to be aggressive, which raises a related question of whether diagnoses of psychiatric disorders predict the prevalence of aggressive behavior. Predicting aggression in psychiatric wards is crucial, because aggressive behavior not only endangers the safety of both patients and staff, but it also extends the hospitalization times. Predictions of aggressive behavior also need careful attention to ensure effective treatment planning. This literature review explores the relationship between aggressive behavior and psychiatric disorders and syndromes (dementia, psychoactive substance use, acute psychotic disorder, schizophrenia, bipolar affective disorder, major depressive disorder, obsessive-compulsive disorder, personality disorders and intellectual disability). The prevalence of aggressive behavior and its underlying risk factors, such as sex, age, comorbid psychiatric disorders, socioeconomic status, and history of aggressive behavior are discussed as these are the components that mostly contribute to the increased risk of aggressive behavior. Measurement tools commonly used to predict and detect aggressive behavior and to differentiate between different forms of aggressive behavior in both research and clinical practice are also reviewed. Successful aggression prevention programs can be developed based on the current findings of the correlates of aggressive behavior in psychiatric patients.

Prevalence Rates and Evolution of Psychiatric Disorders Among Incarcerated Youths in Comparison With Non-incarcerated Youths.

Background: Our main objective was to provide estimates of the prevalence rates of psychiatric disorders and comorbidities among youths in a juvenile detention center in Geneva, Switzerland. We also aimed to investigate potential positive effects of intensive psychotherapeutic and educational services this center provides. Finally, we examined psychiatric care prior to and after custody as well as the evolution of the youths' mental health during detention.Methods: We conducted a longitudinal study including a group of incarcerated (n = 86) and a group of non-incarcerated (n = 169) youths (12–18 years old). Measures included diagnoses of psychiatric disorders, cognitive functions, trauma, psychopathic traits and the Youth Self-Report (aggressive behavior, attentional disorders, criminal behavior, social withdrawal, anxiety, depression and somatic complaints) collected at baseline and at discharge for the incarcerated group. Data were analyzed using mixed-effect models.Results: Psychiatric disorders were prevalent in the incarcerated group (82.6, 95% CI: 71.6–90.7%), but young people also often suffered from several disorders simultaneously. Two-thirds of the incarcerated participants had a diagnosis of two or more psychiatric disorders. Regarding health care, most incarcerated participants (79.1%) had psychiatric care prior to detention. The planned care after detention was associated with psychiatric comorbidities, care being more likely planned for those with comorbidities (p = 0.030). Compared to the non-incarcerated group, the incarcerated group had lower scores on cognitive functioning (p < 0.001) and higher scores on trauma (p < 0.021) and psychopathic traits (p < 0.034). The youths' stay in the detention center was associated with a positive change of mental health, with externalized problems being significantly reduced at the end of their stay (p = 0.017).Conclusion: Our findings showed that youths in conflict with the law are characterized by (1) their internal vulnerabilities: a high prevalence of psychiatric disorders and psychiatric comorbidities, lower cognitive functions, externalized problems and psychopathic traits; (2) environmental factors: victims of violence and sexual abuse; and (3) their psychiatric history. Besides, the evolution of the most prevalent issues was favorable over time, which puts into question the usual perspective about the deleterious effect of detention.

Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France

BackgroundPrior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19 or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders has yet to be evaluated. MethodsWe performed an observational, multicenter, retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals. ResultsOf 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up period of 14.6 days (SD = 17.9), 326 of 857 (38.0%) patients with a diagnosis of psychiatric disorder died compared with 1276 of 14,311 (8.9%) patients without such a diagnosis (odds ratio 6.27, 95% CI 5.40–7.28, p < .01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (adjusted odds ratio 3.27, 95% CI 2.78–3.85, p < .01). However, additional adjustments for obesity and number of medical conditions resulted in a nonsignificant association (adjusted odds ratio 1.02, 95% CI 0.84–1.23, p = .86). Exploratory analyses after the same adjustments suggested that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants. ConclusionsThese findings suggest that the increased risk of COVID-19–related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population and not by the underlying psychiatric disease.

Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts.

Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart. To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability. We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores. Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including 'minimally affected', 'inactive restless', active restless', 'focused creative' and 'extensively affected' individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment. Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Automated Rest EEG-Based Diagnosis of Depression and Schizophrenia Using a Deep Convolutional Neural Network

Depression (DP) and schizophrenia (SCZ) are both highly prevalent psychiatric disorders, and their diagnosis depends on the examination of symptoms and clinical tests, which can be subjective. As a measure of real-time neural activity, Electroencephalographic (EEG) has shown its usability to classify people either as normal or as having DP or SCZ, but automatic classification between the three categories (DP, SCZ and the normal) was rarely reported. Here, we propose an automatic diagnostic framework based on a convolutional neural network called the Multi-Channel Frequency Network (MUCHf-Net), which automatically learns feature representations of EEGs that characterize them as normal, DP, or SCZ. Two EEG databases were used in this study, the first one contains EEGs from 300 individuals (DP: 100, SCZ: 100, normal: 100) collecting from our hospital, and the second contains EEGs from 30 individuals (DP: 10, SCZ: 10, normal: 10) from public available datasets, and the spectrum matrices from these multi-channel EEGs were feed into MUCHf-Net. The results showed that: (1) MUCHf-Net accurately distinguished normal EEGs from DP or SCZ EEGs (accuracy: 91.12%; F1 score: 0.8947); (2) low-frequency bands (delta, theta, alpha) contributed the most important information to the classification model; (3) features located in the frontal and parietal lobes contributed more than other regions did; (4) MUCHf-Net fine-tuned on public datasets also had high classification accuracy: 87.71% (triple: normal, SCZ or DP) and 79.27% (binary: psychiatric disorders (DP or SCZ) or normal). Our study shows that deep leaning has the potential to become an important tool for assisting in the diagnosis of psychiatric disorders.

COVID 19 and the Opioid Epidemic: An Analysis of Clinical Outcomes During COVID 19.

Background and Objectives:Here we aimed to characterize clinical outcomes in those receiving treatment at a Veterans Health Administration (VHA) methadone maintenance treatment program (MMT) during the COVID 19 pandemic in which SAMSHA regulations for MMTs were changed to provide a greater number of methadone allotments and decreased clinic-visit frequency.Methods:We report results of a single-site, pre-post cohort study of urine drug screen data 3 months before and after an increase in allotments of take-home medication from the methadone clinic. One hundred twenty-nine patients met inclusion criteria for this study. The study was reviewed by the NYHHS IRB committee and granted final approval by the Research and Development Committee.Results:The sample was predominately male, average age 66years and average years in most recent treatment is 4.1 years. No statistical significance was found between period 1 and period 2 in the positive test detection for nonprescribed opiates, methadone and illicit substances (P > .05), number of new medical illnesses or overdoses. We controlled for participant age, substance use disorder diagnosis, psychiatric disorder diagnosis, and number of years in treatment.Discussion/Conclusions:The results of the study illustrate the relative safety of the changes made at this particular MMT during the pandemic. Additionally, there was continued adherence to methadone treatment with minimal change in illicit substance use during period 1 and period 2.Scientific Significance:To these authors’ knowledge this paper is one of the first to examine clinical outcomes in those with opioid addiction prescribed methadone from MMTs during the COVID 19 pandemic.

Eye Movement Patterns Can Distinguish Schizophrenia From the Major Affective Disorders and Healthy Control Subjects.

Background and hypothesisNo objective tests are currently available to help diagnosis of major psychiatric disorders. This study evaluates the potential of eye movement behavior patterns to predict schizophrenia subjects compared to those with major affective disorders and control groups.Study designEye movements were recorded from a training set of UK subjects with schizophrenia (SCZ; n = 120), bipolar affective disorder (BPAD; n = 141), major depressive disorder (MDD; n = 136), and healthy controls (CON; n = 142), and from a hold-out set of 133 individuals with proportional group sizes. A German cohort of SCZ (n = 60) and a Scottish cohort of CON subjects (n = 184) acted as a second semi-independent test set. All patients met DSMIV and ICD10 criteria for SCZ, BPAD, and MDD. Data from 98 eye movement features were extracted. We employed a gradient boosted (GB) decision tree multiclass classifier to develop a predictive model. We calculated the area under the curve (AUC) as the primary performance metric.Study resultsEstimates of AUC in one-versus-all comparisons were: SCZ (0.85), BPAD (0.78), MDD (0.76), and CON (0.85). Estimates on part-external validation were SCZ (0.89) and CON (0.65). In all cases, there was good specificity but only moderate sensitivity. The best individual discriminators included free viewing, fixation duration, and smooth pursuit tasks. The findings appear robust to potential confounders such as age, sex, medication, or mental state at the time of testing.ConclusionsEye movement patterns can discriminate schizophrenia from major mood disorders and control subjects with around 80% predictive accuracy.

Management of Psychiatric Disorders in Patients with Stroke and Traumatic Brain Injury.

Management of Psychiatric Disorders in Patients with Stroke and Traumatic Brain Injury.

Relation of Depression and Anxiety Disorders in Choosing Obesity Management in Obese Patients.

Obesity is a chronic medical illness with a higher risk of physical and mental cascade. People who seek obesity treatment were reported to have some psychiatric disorders affecting their disease and selection of management. This study aims to estimate the prevalence of depressive and anxiety disorders in obese patients seeking obesity management and explore the relationship between common psychiatric disorders (depression and anxiety disorders) and selection of the type of obesity management (surgical or non-surgical). Patients were recruited from Alazhar Universityhospitals, Egypt, and the total number completing the study was 1115 patients. All subjects underwent psychiatric interview through Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID-5 for DSM-5) for diagnosis of psychiatric disorders and completed two questionnaires, Hamilton Rating Scale for Depression (HRSD) and Hamilton Rating Scale for Anxiety (HRSA). The prevalences of depressive and anxiety disorders were 29.23% and 25.56%, respectively, in all subjects. The most prevalent diagnoses were dysthymic disorder (20.7%), general anxiety disorder (16.95%), major depressive disorder (13.04%), and social phobia (12.4%). Our sample was divided into two groups (surgical and non-surgical). Dysthymia was more common in the surgical group (21.4% versus 19.8% P = 0.560), whereas major depressive disorder was more common in the non-surgical group (7.4% versus 5.4 P = 0.593); also, the non-surgical group was more likely to have "anxiety disorders" (29.23% versus 22.4%, P = 0.840), but severity of anxiety was higher in the surgical group according to HRSA score with a highly significant difference. A high prevalence of depression and anxiety disorders was found among patients who sought obesity treatment. Severity of anxiety was higher in the surgical group according to HRSA score with a highly significant difference, which may affect selection of treatment, so psychiatric evaluation and management are needed before and after obesity management to improve the outcome.

Accelerating the impact of artificial intelligence in mental healthcare through implementation science.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

노인 아임상 우울군에서 편도체 세부영역 부피의 특징

Objectives The purpose of this study was to identify the volumetric alterations of the amygdala and their association with geriatric subclinical depression. Method Eighteen and 19 subjects aged 65 and above were recruited for the subclinical depression and the control group, respectively. Subclinical depression was defined by a cut-off value of 8 points on the Short Geriatric Depression Scale and no diagnosis of clinical psychiatric disorders. Participants of both groups must have normal cognitive functions assessed using Mini-Mental Status Examination. T1-weighted magnetic resonance images were acquired using 3T magnetic resonance imaging scanner. Volumes of bilateral amygdala and their nuclei, and lateralization indexes were determined using FreeSurfer(version 7.1.1). Statistical methods of Mann-Whitney U test, and Analysis of Covariance were used. Results Volumes of the left and right amygdala were smaller in the geriatric subclinical depression group compared to the control group(p≤0.048). There were significant volume reductions in left lateral nucleus, left anterior amygdaloid area, right lateral nucleus, right basal nucleus, right accessory basal nucleus, right anterior amygdaloid area, right cortical nucleus and right paralaminar nucleus(p≤0.034). Conclusion Various regions of amygdala nuclei which are known to be associated with emotional processing were smaller in size in the group of geriatric subclinical depression. Further studies are warranted to identify the neurophysiological factors that provoke the amygdala volume reduction in geriatric subclinical depression.

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients.

The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients’ susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.