Susceptibility to Treatment-Resistant Depression Within Families.

Importance: Antidepressant responses and the phenotype of treatment-resistant depression (TRD) are believed to have a genetic basis. Genetic susceptibility between the TRD phenotype and other psychiatric disorders has also been established in previous genetic studies, but population-based cohort studies have not yet provided evidence to support these outcomes. Objective: To estimate the TRD susceptibility and the susceptibility between TRD and other psychiatric disorders within families in a nationwide insurance cohort with extremely high coverage and comprehensive health care data. Design, Setting, and Participants: This cohort study assessed data from the Taiwan national health insurance database across entire population (N = 26 554 001) between January 2003 and December 2017. Data analysis was performed from August 2021 to April 2023. TRD was defined as having experienced at least 3 distinct antidepressant treatments in the current episode, each with adequate dose and duration, based on the prescribing records. Then, we identified the first-degree relatives of individuals with TRD (n = 34 467). A 1:4 comparison group (n = 137 868) of first-degree relatives of individuals without TRD was arranged for the comparison group, matched by birth year, sex, and kinship. Main Outcomes and Measures: Modified Poisson regression analyses were performed and adjusted relative risks (aRRs) and 95% CIs were calculated for the risk of TRD, the risk of other major psychiatric disorders, and different causes of mortality. Results: This study included 172 335 participants (88 330 male and 84 005 female; mean [SD] age at beginning of follow-up, 22.9 [18.1] years). First-degree relatives of individuals with TRD had lower incomes, more physical comorbidities, higher suicide mortality, and increased risk of developing TRD (aRR, 9.16; 95% CI, 7.21-11.63) and higher risk of other psychiatric disorders than matched control individuals, including schizophrenia (aRR, 2.36; 95% CI, 2.10-2.65), bipolar disorder (aRR, 3.74; 95% CI, 3.39-4.13), major depressive disorder (aRR, 3.65; 95% CI, 3.44-3.87), attention-deficit/hyperactivity disorders (aRR, 2.38; 95% CI, 2.20-2.58), autism spectrum disorder (aRR, 2.26; 95% CI, 1.86-2.74), anxiety disorder (aRR, 2.71; 95% CI, 2.59-2.84), and obsessive-compulsive disorder (aRR, 3.14; 95% CI, 2.70-3.66). Sensitivity and subgroup analyses validated the robustness of the findings. Conclusions and Relevance: To our knowledge, this study is the largest and perhaps first nationwide cohort study to demonstrate TRD phenotype transmission across families and coaggregation with other major psychiatric disorders. Patients with a family history of TRD had an increased risk of suicide mortality and tendency toward antidepressant resistance; therefore, more intensive treatments for depressive symptoms might be considered earlier, rather than antidepressant monotherapy.

Bidirectional association between eating disorder and temporomandibular joint disorder: A retrospective longitudinal nationwide population-based cohort study.

Background/purpose: An increasing body of evidence indicates correlations between the symptoms of temporomandibular disorder and those of eating disorder (ED). However, further investigation is required to elucidate the temporal and causal relationships between the aforementioned disorders. Materials and methods: This retrospective cohort study was conducted using data from the Taiwan National Health Insurance Research Database. Temporomandibular joint disorder (TMJD) was analyzed both as the cause and consequence of ED. We collected the data (from January 1, 1998, to December 31, 2011) of patients with antecedent TMJD (N = 15,059) or ED (N = 1219) and their respective controls (1:10), matched by age, sex, income level, residential location, and comorbidities. This study included patients who had received a new diagnosis of an ED or a TMJD between January 1, 1998, and December 31, 2013. Cox regression models were used to assess the risk of ED or TMJD development in patients with antecedent TMJD or ED. Results: TMJD patients had an approximately 3.70-fold (95 % confidence interval [CI]: 1.93-7.10) risk of ED development. Similarly, patients with ED had an approximately 4.78-fold (95 % CI: 2.52-9.09) risk of TMJD development. Subgroup analyses based on ED subtypes indicated antecedent TMJD and bulimia nervosa as the predictors of increased bulimia nervosa and TMJD risks (hazard ratios: 6.41 [95 % CI: 2.91 to 14.11] and 5.84 [95 % CI: 2.75 to 12.41]), respectively. Conclusion: Previous TMJD and ED are associated with increased risks of subsequent ED and TMJD; these findings suggest the presence of a bidirectional temporal association between TMJD and ED.

Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis.

BACKGROUND: Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS: Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS: Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS: Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.

Proinflammatory cytokine levels, cognitive function, and suicidal symptoms of adolescents and young adults with major depressive disorder.

Whether proinflammatory cytokine dysregulation and cognitive dysfunction are associated with suicidal symptoms in adolescents and young adults with major depressive disorder (MDD) remains uncertain. We assessed the cognitive function and proinflammatory cytokine levels of 43 and 51 patients aged 15-29 years with MDD and severe and mild suicidal symptoms, respectively, as well as those of 85 age- and sex-matched healthy controls. Specifically, we measured serum levels of C-reactive protein, tumor necrosis factor-α (TNF-α), interleukin-2, and interleukin-6 and assessed cognitive function by using working memory and go/no-go tasks. The severity of the patients' suicidal symptoms was based on Item 10 of the Montgomery-Åsberg Depression Rating Scale; scores of ≤ 2 and ≥ 4 indicated mild and severe symptoms, respectively. The patients with MDD and severe suicidal symptoms had higher levels of C-reactive protein (p = .019) and TNF-α (p = .002) than did the patients with mild symptoms or the healthy controls. The number of errors committed on the go/no-go by patients with MDD and severe suicidal symptoms (p = .001) was significantly higher than those by patients with MDD and mild symptoms or by controls. After adjusting for nonsuicidal depressive symptoms, we observed suicidal symptoms to be positively associated with TNF-α levels (p = .050) and errors on the go/no-go task (p = .021). Compared with mild suicidal symptoms, severe symptoms are associated with greater serum levels of proinflammatory cytokines and inferior cognitive function in adolescents and young adults with MDD.

Diagnostic conversion to bipolar disorder among adolescents and young adults with major depressive disorder: a nationwide longitudinal study.

Although several studies have examined a diagnostic conversion from major depressive disorder (MDD) to bipolar disorder (BD), only a few studies specifically focused on adolescents and young adults who are at the peak ages of BD onset. Data from participants (N = 130,793) aged 10-29 years who were diagnosed with MDD were extracted from the Taiwan National Health Insurance Research Database. We applied demographic analyses, survival analysis, Aalen Johansen curves, and Cox regression, investigating the diagnostic conversion rate and factors that were most or less predictive of conversion. Among the adolescents and young adults with MDD, the number of participant conversion subsample is 14,187 and the conversion rate was 13.80% (95% confidence interval: 13.54-14.06%) during the 11-year follow-up. The conversion rate was highest in the first year (4.50%; 4.39-4.61%) and decreased over time. The significant predictors were younger age of diagnosis with MDD (p < 0.001), moderate and high antidepressant resistance (p < 0.001), obesity (p < 0.001), psychiatric comorbidities (attention-deficit/hyperactivity disorder, substance use disorder, and cluster B and C personality disorder, all p < 0.001), a family history of mental disorders (schizophrenia and mood disorders, all p < 0.05), lower monthly income (p < 0.001), and more mental health visits to the clinic each year (p < 0.001). A composite of demographic characteristics, antidepressant resistance, physical and psychiatric comorbidities, and family history significantly predicted diagnostic conversion from MDD to BD (area under the curve = 0.795, p < 0.001). Compared to adult population, the adolescents and young adults had different factors that were most or less predictive of conversion, which warrants further investigation.

Exposure to psychotropic drugs and breast cancer risk in patients with bipolar disorder and major depressive disorder: a nested case-control study.

Breast cancer is one of the most prevalent and serious types of cancer globally. Previous literature has shown that women with mental illness may have an increased risk of breast cancer, however whether this risk is associated with the use of psychotropic drugs has yet to be elucidated. This study aimed to assess such risk among women with major depressive disorder (MDD) and bipolar disorder (BD). A nested case-control study design was used with data obtained from the Taiwan National Health Insurance Research Database. Logistic regression analysis with adjustments for demographic characteristics, medical and mental comorbidities, and all-cause clinical visits was performed to estimate the risk of breast cancer according to the cumulative defined daily dose (cDDD) of psychotropic drugs. The study included 1564 women with MDD or BD who had breast cancer, and 15,540 women with MDD or BD who did not have breast cancer. After adjusting for important confounders, the long-term use of valproic acid (odds ratio, 95% confidence interval: 0.58, 0.39-0.56, cDDD ≥ 365), citalopram (0.58, 0.37-0.91, cDDD 180-365), and sertraline (0.77, 0.61-0.91, cDDD ≥ 365) was associated with a lower risk of breast cancer compared to a cDDD < 30. The short-term use of fluvoxamine (0.82, 0.69-0.96, cDDD 30-180), olanzapine (0.54, 0.33-0.89, cDDD 30-179), risperidone (0.7, 0.51-0.98, cDDD 30-179), and chlorpromazine (0.48, 0.25-0.90, cDDD 30-179) was associated with a lower risk of breast cancer. We found no evidence of an increased risk of breast cancer in patients with MDD or BD receiving psychotropic drugs.

Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia.

INTRODUCTION: Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. MATERIALS AND METHODS: A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20-85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features-global efficiency and local efficiency values-were estimated to determine their relationship with age and cognitive performance. RESULTS: The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. CONCLUSIONS: These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan.

Introduction: The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive. Methods: This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan. Results: The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect. Discussion: Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.

Suicide Attempts After a Diagnosis of Polycystic Ovary Syndrome : A Cohort Study.

BACKGROUND: Limited evidence exists about suicide risk in persons with polycystic ovary syndrome (PCOS). OBJECTIVE: To assess suicide risk in persons with PCOS, accounting for psychiatric comorbid conditions and age group. DESIGN: Cohort study. SETTING: Data from the Taiwanese nationwide database from 1997 to 2012. PATIENTS: A cohort of 18 960 patients diagnosed with PCOS, each matched with control participants in a 1:10 ratio on the basis of age, psychiatric comorbid conditions, urbanization level, and income. Suicide attempts were evaluated using Cox regression models. MEASUREMENTS: Suicide risk with hazard ratios (HRs). RESULTS: Participants with PCOS had a notable 8.47-fold increase in risk for suicide attempt compared with the control group (HR, 8.47 [95% CI, 7.54 to 9.51]), after adjustment for demographic characteristics, psychiatric comorbid conditions, Charlson Comorbidity Index scores, and frequency of all-cause clinical visits. The elevated risk was evident across the adolescent (HR, 5.38 [CI, 3.93 to 7.37]), young adult (<40 years; HR, 9.15 [CI, 8.03 to 10.42]), and older adult (HR, 3.75 [CI, 2.23 to 6.28]) groups. Sensitivity analyses involving the exclusion of data from the first year or the first 3 years of observation yielded consistent results. LIMITATION: Potential underestimation of PCOS and mental disorder prevalence due to use of administrative claims data; lack of clinical data, such as body mass index and depressive symptoms; and no assessment of a confounding effect of valproic acid exposure. CONCLUSION: This study underscores the heightened risk for suicide attempt that persons with PCOS face, even after adjustment for demographics, psychiatric comorbid conditions, physical conditions, and all-cause clinical visits. This suggests the importance of routine monitoring of mental health and suicide risk in persons diagnosed with PCOS. PRIMARY FUNDING SOURCE: Taipei Veterans General Hospital, Yen Tjing Ling Medical Foundation, and Ministry of Science and Technology of Taiwan.

Comparing the performance of ChatGPT GPT-4, Bard, and Llama-2 in the Taiwan Psychiatric Licensing Examination and in differential diagnosis with multi-center psychiatrists.

AIM: Large language models (LLMs) have been suggested to play a role in medical education and medical practice. However, the potential of their application in the psychiatric domain has not been well-studied. METHOD: In the first step, we compared the performance of ChatGPT GPT-4, Bard, and Llama-2 in the 2022 Taiwan Psychiatric Licensing Examination conducted in traditional Mandarin. In the second step, we compared the scores of these three LLMs with those of 24 experienced psychiatrists in 10 advanced clinical scenario questions designed for psychiatric differential diagnosis. RESULT: Only GPT-4 passed the 2022 Taiwan Psychiatric Licensing Examination (scoring 69 and ≥ 60 being considered a passing grade), while Bard scored 36 and Llama-2 scored 25. GPT-4 outperformed Bard and Llama-2, especially in the areas of 'Pathophysiology & Epidemiology' (χ2 = 22.4, P < 0.001) and 'Psychopharmacology & Other therapies' (χ2 = 15.8, P < 0.001). In the differential diagnosis, the mean score of the 24 experienced psychiatrists (mean 6.1, standard deviation 1.9) was higher than that of GPT-4 (5), Bard (3), and Llama-2 (1). CONCLUSION: Compared to Bard and Llama-2, GPT-4 demonstrated superior abilities in identifying psychiatric symptoms and making clinical judgments. Besides, GPT-4's ability for differential diagnosis closely approached that of the experienced psychiatrists. GPT-4 revealed a promising potential as a valuable tool in psychiatric practice among the three LLMs.

Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference.

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.

Bacterial Pneumonia and Stroke Risk: A Nationwide Longitudinal Followup Study.

BACKGROUND: Pneumonia causes significant morbidity and mortality and has been associated with cardiovascular complications. Our study aimed to investigate the incidence of ischemic and hemorrhagic strokes following bacterial pneumonia. METHODS: Between 1997 and 2012, 10,931 subjects with bacterial pneumonia and 109,310 controls were enrolled from the Taiwan National Health Insurance Research Database, and were followed up to the end of 2013. The risk of stroke was estimated in Cox regression analyses with hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: When compared to the control group, subjects in the bacterial pneumonia group had a higher incidence of developing ischemic stroke (2.7% versus 0.4%, p <0.001) and hemorrhagic stroke (0.7% versus 0.1%, p <0.001). The risk of stroke increases with repeated hospitalizations due to bacterial pneumonia. Across bacterial etiologies, bacterial pneumonia was a significant risk factor among 775 subjects who developed ischemic stroke (HR, 5.72; 95% CI, 4.92-6.65) and 193 subjects who developed hemorrhagic stroke (HR, 5.33; 95% CI, 3.91-7.26). CONCLUSION: The risks of developing ischemic stroke and hemorrhagic stroke are significant following bacterial pneumonia infection. The risk factors, clinical outcomes, and the disease course should also be profiled to better inform the monitoring of stroke development and the clinical management of bacterial pneumonia patients.

Current suicide risk, but not lifetime history of attempted suicide, predicts treatment response to low-dose ketamine infusion: Post Hoc analysis of adjunctive ketamine study of Taiwanese patients with treatment-resistant depression.

Whether current suicide risk or a history of attempted suicide is related to the antidepressant effect of a low-dose ketamine infusion remains unclear. In total, 47 patients with treatment-resistant depression (TRD), including 32 with low current suicide risk and 15 with moderate or high current suicide risk, were randomized to groups receiving a low-dose ketamine infusion of either 0.2 or 0.5 mg/kg. Among the patients, 21 had a lifetime history of attempted suicide. Suicide risk was assessed based on the Suicidal scale of the Mini-International Neuropsychiatric Interview. The 17-item Hamilton Depression Rating Scale (HDRS) was used to measure depressive symptoms at baseline, at 40 and 240 min after infusion, and sequentially on Days 2-7 and 14 after ketamine infusion. Generalized estimating equation models indicated that the time effects of both 0.5 and 0.2 mg/kg ketamine infusions were significant during the study period. The models also indicated that current suicide risk (p = .037) but not lifetime history of attempted suicide (p = .184) was related to the trajectory of total HDRS scores. Patients with moderate-to-high current suicide risk benefited more from the low-dose ketamine infusion compared with those with the low current suicide risk. Patients with TRD having moderate or high current suicide risk may be prioritized to receive a low-dose ketamine infusion, which may aid suicide prevention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Focused Ultrasound Stimulates the Prefrontal Cortex and Prevents MK-801-Induced Psychiatric Symptoms of Schizophrenia in Rats.

BACKGROUND AND HYPOTHESIS: Treatment of schizophrenia remains a major challenge. Recent studies have focused on glutamatergic signaling hypoactivity through N-methyl-D-aspartate (NMDA) receptors. Low-intensity pulsed ultrasound (LIPUS) improves behavioral deficits and ameliorates neuropathology in dizocilpine (MK-801)-treated rats. The aim of this study was to investigate the efficacy of LIPUS against psychiatric symptoms and anxiety-like behaviors. STUDY DESIGN: Rats assigned to 4 groups were pretreated with or without LIPUS for 5 days. The open field and prepulse inhibition tests were performed after saline or MK-801 (0.3 mg/kg) administration. Then, the neuroprotective effects of LIPUS on the MK-801-treated rats were evaluated using western blotting and immunohistochemical staining. STUDY RESULTS: LIPUS stimulation of the prefrontal cortex (PFC) prevented deficits in locomotor activity and sensorimotor gating and improved anxiety-like behavior. MK-801 downregulated the expression of NR1, the NMDA receptor, in rat medial PFC (mPFC). NR1 expression was significantly higher in animals receiving LIPUS pretreatment compared to those receiving only MK-801. In contrast, a significant increase in c-Fos-positive cells in the mPFC and ventral tegmental area was observed in the MK-801-treated rats compared to those receiving only saline; this change was suppressed by pretreatment with LIPUS. CONCLUSIONS: This study provides new evidence for the role of LIPUS stimulation in regulating the NMDA receptor and modulating c-Fos activity, which makes it a potentially valuable antipsychotic treatment for schizophrenia.

Cause-specific mortality and comorbid neurodevelopmental disorder in 167,515 patients with bipolar disorder: An entire population longitudinal study.

OBJECTIVE: Studies addressing premature mortality in bipolar disorder (BD) patients are limited by small sample sizes. Herein, we used almost 99 % of the population of Taiwan to address this issue, and its association with comorbid neurodevelopmental disorders and severe BD. METHODS: Between 2003 and 2017, we enrolled 167,515 individuals with BD and controls matched 1:4 for sex and birth year from the National Health Insurance Database linked to the Database of National Death Registry in Taiwan. Time-dependent Cox regression models were used to examine cause-specific mortality (all-cause, natural, and unnatural causes [accidents or suicide]). RESULTS: With adjustments of sex, age, income, urbanization, and physical conditions, suicide was associated with the highest risk of mortality (reported as hazard ratio with 95 % confidence interval: 9.15; 8.53-9.81) among BD patients, followed by unnatural (4.94; 4.72-5.17), accidental (2.15; 1.99-2.32), and natural causes (1.02; 1.00-1.05). Comorbid attention-deficiency hyperactivity disorder did not contribute to the increased risk of cause-specific mortality; however, comorbid autism spectrum disorder (ASD) increased such risks, particularly for natural (3.00; 1.85-4.88) and accidental causes (7.47; 1.80-31.1). Cause-specific mortality revealed a linear trend with the frequency of psychiatric hospitalization (all, p for trend <0.001), and BD patients hospitalized twice or more each year had 34.63-fold increased risk of suicide mortality (26.03-46.07). CONCLUSIONS: BD patients with a higher frequency of psychiatric hospitalization have the highest risk of suicide mortality, and comorbid ASD was associated with an increased risk of natural and accidental causes of mortality.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated. METHODS: A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics. RESULTS: The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel). CONCLUSIONS: The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

Effects of melancholic features on positive and negative suicidal ideation in patients with treatment-resistant depression and strong suicidal ideation receiving low-dose ketamine infusion.

The role of melancholic features on the antisuicidal effect of 0.5 mg/kg ketamine infusion has remained unclear in patients with treatment-resistant depression (TRD) and strong suicidal ideation (SI). Whether ketamine diminishes suicidal ideation in patients with TRD-SI was also unknown. We enrolled 84 patients with TRD-SI, including 27 with melancholic features and 57 without, and then randomly administered a single infusion of 0.5 mg/kg ketamine or 0.045 mg/kg midazolam. The clinician-rated Montgomery-Åsberg Depression Rating Scale (MADRS) item 10, Columbia Suicide Severity Rating Scale-Ideation Severity Subscale (CSSRS-ISS), and self-reported Positive and Negative Suicide Ideation Inventory (PANSI) were used to assess suicidal symptoms from baseline to day 7. Generalized estimating equation models showed that only patients without melancholic features (MADRS item 10: infusion group effect, p = 0.017; CSSRS-ISS: infusion group × time effect, p = 0.008; PANSI-negative suicidal ideation: infusion group effect, p = 0.028) benefited from the antisuicidal effect of low-dose ketamine. The PANSI-positive ideation scores were higher in the ketamine group than in the midazolam group (p = 0.038) for patients with melancholic features. Additional studies are necessary to clarify the neuromechanisms underlying the ketamine-related positive effect against SI and antisuicidal effects among patients with TRD-SI. Additional studies are necessary to clarify the neuromechanisms underlying the ketamine-related positive effect against SI and antisuicidal effects among patients with TRD-SI.

Risk of developing avascular necrosis of the femoral head and neck among patients with bipolar disorder: A nationwide cohort study

Background and objectives The association between bipolar disorder (BD) and avascular necrosis of the femoral head and neck (AVNHNF) remains unclear. We aimed to investigate the risk of AVNHNF among different polarity of BD. Methods Between 2001 and 2010, patients with BD were selected from the Taiwan National Health Research Database. The controls were individuals without severe mental disorder who were matched for demographic, medical and psychiatric comorbidities. Cox regression analysis was used to estimate the risk of AVNHNF, with adjustments for demographics, comorbidities, exposure to corticosteroids, and all-cause clinical visits. Results A total of 84,721 patients with BD and 169,442 controls were included. Patients with BD demonstrated a 1.92-fold (95% of confidence interval: 1.21–3.04) higher risk of AVNHNF compared with the controls. The risk was increased to 7.91-fold (4.32–14.49) in patients with severe BD compared with the controls. Importantly, patients with severe bipolar depression were associated with a 14.23-fold higher risk of AVNHNF compared with the controls, while those with sever bipolar mania were associated with a 3.55-fold higher risk. Compared with the controls with alcohol use disorder (AUD), patients with BD and comorbid AUD were associated with a 2.0-fold higher risk of AVNHNF. Finally, long-term use of atypical antipsychotics was associated with a decreased risk of AVNHNF). Conclusion Clinicians should be aware of the increased risk of AVNHNF among patients with BD. This increased risk was associated with disorder severity, polarity, and comorbidity with AUD, and attenuated by long-term atypical antipsychotic treatment. (AU)

Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study.

BACKGROUND: ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers. OBJECTIVE: We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. METHODS: We selected 30 basic research papers from Nature, Genome Biology, and Biological Psychiatry. Excluding abstracts, we inputted the full text into ChatPDF, an application of a language model based on ChatGPT, and we prompted it to generate abstracts with the same style as used in the original papers. A total of 8 experts were invited to evaluate the quality of these abstracts (based on a Likert scale of 0-10) and identify which abstracts were generated by ChatPDF, using a blind approach. These abstracts were also evaluated for their similarity to the original abstracts and the accuracy of the AI content. RESULTS: The quality of ChatGPT-generated abstracts was lower than that of the actual abstracts (10-point Likert scale: mean 4.72, SD 2.09 vs mean 8.09, SD 1.03; P<.001). The difference in quality was significant in the unstructured format (mean difference -4.33; 95% CI -4.79 to -3.86; P<.001) but minimal in the 4-subheading structured format (mean difference -2.33; 95% CI -2.79 to -1.86). Among the 30 ChatGPT-generated abstracts, 3 showed wrong conclusions, and 10 were identified as AI content. The mean percentage of similarity between the original and the generated abstracts was not high (2.10%-4.40%). The blinded reviewers achieved a 93% (224/240) accuracy rate in guessing which abstracts were written using ChatGPT. CONCLUSIONS: Using ChatGPT to generate a scientific abstract may not lead to issues of similarity when using real full texts written by humans. However, the quality of the ChatGPT-generated abstracts was suboptimal, and their accuracy was not 100%.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.