Psychiatry and Clinical Neurosciences最新文献

Aim: This study investigated the impact of an 8-month daily-guided intensive meditation-based intervention (iMI) on persistent hallucinations/delusions and health-related quality of life (QoL) in male inpatients with schizophrenia with treatment-refractory hallucinations and delusions (TRHDs).

Methods: A randomized controlled trial assigned 64 male inpatients with schizophrenia and TRHD equally to an 8-month iMI plus general rehabilitation program (GRP) or GRP alone. Assessments were conducted at baseline and the third and eighth months using the Positive and Negative Syndrome Scale (PANSS), 36-Item Short Form-36 (SF-36), and Five Facet Mindfulness Questionnaire (FFMQ). Primary outcomes measured PANSS reduction rates for total score, positive symptoms, and hallucinations/delusions items. Secondary outcomes assessed PANSS, SF-36, and FFMQ scores for psychotic symptoms, health-related QoL, and mindfulness skills, respectively.

Results: In the primary outcome, iMI significantly improved the reduction rates of PANSS total score, positive symptoms, and hallucination/delusion items compared with GRP at both the third and eighth months. Treatment response rates (≥25% reduction) for these measures significantly increased in the iMI group at the eighth month. Concerning secondary outcomes, iMI significantly reduced PANSS total score and hallucination/delusion items, while increasing scores in physical activity and mindfulness skills at both the third and eighth months compared with GRP. These effects were more pronounced with an 8-month intervention compared with a 3-month intervention.

Conclusions: An iMI benefits patients with TRHDs by reducing persistent hallucinations/delusions and enhancing health-related QoL. Longer iMI duration yields superior treatment outcomes.

Background: Anorexia nervosa (AN) is a mental disorder characterized by dietary restriction, fear of gaining weight, and distorted body image. Recent studies indicate that the hippocampus, crucial for learning and memory, may be affected in AN, yet subfield-specific effects remain unclear. We investigated hippocampal subfield alterations in acute AN, changes following weight restoration, and their associations with leptin levels.

Methods: T1-weighted magnetic resonance imaging scans were processed using FreeSurfer. We compared 22 left and right hemispheric hippocampal subfield volumes cross-sectionally and longitudinally in females with acute AN (n = 165 at baseline, n = 110 after partial weight restoration), healthy female controls (HCs; n = 271), and females after long-term recovery from AN (n = 79) using linear models.

Results: We found that most hippocampal subfield volumes were significantly reduced in patients with AN compared with HCs (~-3.9%). Certain areas such as the subiculum exhibited no significant reduction in the acute state of AN, while other areas, such as the hippocampal tail, showed strong decreases (~-9%). Following short-term weight recovery, most subfields increased in volume. Comparisons between participants after long-term weight-recovery and HC yielded no differences. The hippocampal tail volume was positively associated with leptin levels in AN independent of body mass index.

Conclusions: Our study provides evidence of differential volumetric differences in hippocampal subfields between individuals with AN and HC and almost complete normalization after weight rehabilitation. These alterations are spatially inhomogeneous and more pronounced compared with other major mental disorders (e.g. major depressive disorder and schizophrenia). We provide novel insights linking hypoleptinemia to hippocampal subfield alterations hinting towards clinical relevance of leptin normalization in AN recovery.

Aim: This study aims to assess the association between trajectories of depressive symptoms and the risk of dementia, and to compare the predictive ability of trajectories using multiple data points with depressive symptoms at a single data point.

Methods: We included 5306 older adults from the Health and Retirement Study. We assessed depressive symptoms using the Center for Epidemiology Depression Scale (CES-D), and identified its 8- year trajectories (2002-2010) using latent class trajectory modeling. We calculated hazard ratios (HR) using Cox proportional hazards models. The concordance index (C-index) was used to compare the discriminative power of the models.

Results: We identified two trajectories of depressive symptoms, characterized by maintaining low CES-D scores, and moderate starting scores that steadily increased throughout the follow-up period. During 40,199 person-years, compared to the low trajectory, the increasing trajectory of depressive symptoms was associated with a higher risk of dementia (HR = 1.35; 95% CI: 1.09-1.67) (C-index = 0.759). For every point increase in the degree of depressive symptoms (CES-D scores) in 2010, the risk of dementia increased by 7% (95% CI: 1.03-1.12) (C-index = 0.760). The presence of depressive symptoms (CES-D scores ≥3) in 2010 was not associated with an increased risk of dementia (HR = 1.18; 95% CI: 0.98-1.43) (C-index = 0.759). The C-index values of cox models showed similar discriminative power.

Conclusions: The increasing trajectory of depressive symptoms at multiple data points and the degree of depressive symptoms at a single data point were associated with an increased risk of subsequent dementia among older adults.

The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (β = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (β = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.