Insufficient Oligodendrocyte Turnover in Optic Nerve Contributes to Age-Related Axon Loss and Visual Deficits.

Age-related decline in visual functions is a prevalent health problem among elderly people, and no effective therapies are available up-to-date. Axon degeneration and myelin loss in optic nerves (ONs) are age-dependent and become evident in middle-aged (13-18 months) and old (20-22 months) mice of either sex compared with adult mice (3-8 months), accompanied by functional deficits. Oligodendrocyte (OL) turnover is actively going on in adult ONs. However, the longitudinal change and functional significance of OL turnover in aging ONs remain largely unknown. Here, using cell-lineage labeling and tracing, we reported that oligodendrogenesis displayed an age-dependent decrease in aging ONs. To understand whether active OL turnover is required for maintaining axons and visual function, we conditionally deleted the transcription factor Olig2 in the oligodendrocyte precursor cells of young mice. Genetically dampening OL turnover by Olig2 ablation resulted in accelerated axon loss and retinal degeneration, and subsequently impaired ON signal transmission, suggesting that OL turnover is an important mechanism to sustain axon survival and visual function. To test whether enhancing oligodendrogenesis can prevent age-related visual deficits, 12-month-old mice were treated with clemastine, a pro-myelination drug, or induced deletion of the muscarinic receptor 1 in oligodendrocyte precursor cells. The clemastine treatment or muscarinic receptor 1 deletion significantly increased new OL generation in the aged ONs and consequently preserved visual function and retinal integrity. Together, our data indicate that dynamic OL turnover in ONs is required for axon survival and visual function, and enhancing new OL generation represents a potential approach to reversing age-related declines of visual function.SIGNIFICANCE STATEMENT Oligodendrocyte (OL) turnover has been reported in adult optic nerves (ONs), but the longitudinal change and functional significance of OL turnover during aging remain largely unknown. Using cell-lineage tracing and oligodendroglia-specific manipulation, this study reported that OL generation was active in adult ONs and the efficiency decreased in an age-dependent manner. Genetically dampening OL generation by Olig2 ablation resulted in significant axon loss and retinal degeneration, along with delayed visual signal transmission. Conversely, pro-myelination approaches significantly increased new myelin generation in aging ONs, and consequently preserved retinal integrity and visual function. Our findings indicate that promoting OL generation might be a promising strategy to preserve visual function from age-related decline.

Deficient deposition of new myelin impairs adult optic nerve function in a murine model of diabetes.

Visual impairment in diabetes is a growing public health concern. Apart from the well-defined diabetic retinopathy, disturbed optic nerve function, which is dependent on the myelin sheath, has recently been recognized as an early feature of visual impairment in diabetes. However, the underlying cellular mechanisms remain unclear. Using a streptozotocin-induced diabetic mouse model, we observed a myelin deficiency along with a disturbed composition of oligodendroglial lineage cells in diabetic optic nerve. We found that new myelin deposition, a continuous process that lasts throughout adulthood, was diminished during pathogenesis. Genetically dampening newly generated myelin by conditionally deleting olig2 in oligodendrocyte precursor cells within this short time window extensively delayed the signal transmission of the adult optic nerve. In addition, clemastine, an antimuscarinic compound that enhances myelination, significantly restored oligodendroglia, and promoted the functional recovery of the optic nerve in diabetic mice. Together, our results point to the role of new myelin deposition in optic neuropathy under diabetic insult and provide a promising therapeutic target for restoring visual function.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

The Relation between Neuroticism and Non-Suicidal Self-Injury Behavior among College Students: Multiple Mediating Effects of Emotion Regulation and Depression.

BACKGROUND: Non-suicidal self-injury (NSSI) behavior among college students is a focus of attention in current society. In the information era, the Internet serves as a public health concern and as an effective pathway for prevention. In order to reduce NSSI behavior, we explore its influence factors, especially the relations between neuroticism, emotion regulation (ER), depression, and NSSI behavior. METHODS: A total of 450 college students were surveyed with the Big Five Inventory-2, Emotion Regulation Questionnaire, Self-Rating Depression Scale, and Adolescent Non-Suicidal Self-Injury Assessment Questionnaire. RESULTS: Regression analysis showed that neuroticism significantly negatively predicted emotion regulation, while it positively predicted depression and NSSI. Multiple mediation modeling demonstrated that neuroticism and emotion regulation had no significant direct effects on NSSI. However, neuroticism could indirectly affect NSSI through four pathways of multiple mediating effects, including depression, cognitive reappraisal-depression, expressive suppression-depression, and cognitive reappraisal-expressive suppression-depression. CONCLUSIONS: Neuroticism positively predicts depression and NSSI behavior, and affects NSSI through the mediating effect of ER and depression. Therefore, amelioration of neuroticism from the perspectives of emotion regulation and depression is recommended, so as to reduce NSSI behavior among college students with highly neurotic personalities.

Development and validation of police mental health ability scale.

OBJECTIVES: Police officers are generally under long-term occupational stress. Good mental health ability enables them to better deal with emergencies and enhance their combat effectiveness. We aimed to develop the Police Mental Health Ability Scale (PMHAS) to provide a reference for police selection and ability training. METHODS: Through literature analysis, individual interviews, half-open and half-closed questionnaire surveys, and expert consultations, the components of police mental health ability (PMHA) were theoretically constructed. Then, we enrolled 824 in-service police officers who participated in the training in Chongqing City and Sichuan Province from November 2018 to January 2019 and recovered 767 valid questionnaires (recovery rate, 93.08%). RESULTS: Exploratory factor analysis generated five factors for PMHAS, including cognitive intelligence, emotional catharsis, swift decisiveness, behavioral drive, and reward pursuit, accounting for 58.904% of the variance. Confirmatory factor analysis demonstrated that the model fit well (χ2/df = 1.117, RMSEA = 0.020, GFI = 0.948, CFI = 0.990, IFI = 0.990, TLI = 0.987). The correlation coefficients of factors (r = -0.023 ~ 0.580) were lower than that of each factor and total score (r = 0.477 ~ 0.819). The Cronbach's α coefficients of PMHAS and its factors were 0.606-0.863, and the test-retest reliabilities were 0.602-0.732. CONCLUSION: These results suggest that PMHAS is reliable and valid enough for measuring PMHA, which shows that it is a potentially valuable tool for assessing the mental health ability of police officers.