Psychiatry and Clinical Neurosciences最新文献

Objective: This study aimed to explore the utility of these biomarkers for evaluating glymphatic dysfunction in patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis and establish their effectiveness in differentiating patients from healthy controls (HCs).

Method: In this study, we enrolled 20 patients with anti-NMDAR encephalitis and 17 HCs. Glymphatic function was assessed using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index, free water in white matter (FW-WM), and perivascular space volume fraction (PVSVF). Among the patient cohort, 13 completed follow-up MRI examinations at 3 months post-discharge. We performed correlation analyses between these glymphatic MRI indices and clinical parameters, and compared glymphatic function between patients with normal structural MRI findings and HCs.

Results: Patients exhibited impaired glymphatic function compared to HCs, evidenced by a lower DTI-ALPS (1.44 ± 0.16 vs. 1.62 ± 0.10, q < 0.001) and elevated FW-WM (0.21 ± 0.02 vs. 0.18 ± 0.01, q < 0.001) and PVSVF (all q < 0.05). Notably, these differences persisted even in patients with normal structural MRI (DTI-ALPS: 1.43 ± 0.22 vs. 1.62 ± 0.10, q = 0.014; FW-WM: 0.20 ± 0.02 vs. 0.18 ± 0.01, q = 0.005). Glymphatic dysfunction correlated with disease severity, showing a negative association between DTI-ALPS and CASE scores. Longitudinal analysis of the participants available for follow-up revealed improved glymphatic function (DTI-ALPS index: from 1.41 ± 0.17 to 1.50 ± 0.12; q = 0.042), alongside clinical recovery (CASE score: from 16 to 2, q = 0.005; mRS: from 5 to 1, q = 0.039).

Conclusion: The glymphatic system could be impaired in patients with anti-NMDAR encephalitis. MRI indices of the glymphatic system may serve as biomarkers to differentiate these patients from HCs and evaluate disease severity.

Objective: Cognitive biases contribute to symptoms of depression in both its primary diagnosis and comorbid expressions. Exploring the module-specific effects of Metacognitive Training for Depression (D-MCT) in patients with comorbid depression offers valuable insights into how the various D-MCT modules address shared mechanisms, paving the way for a better understanding of how it works for these patients.

Method: Participants with primarily comorbid depression were recruited on a specialized ward for OCD and anxiety-related disorders (N = 108). The study investigated the session-specific effects of each module of the D-MCT by linear mixed effect models (i) over the course of the intervention, (ii) within each session, and (iii) between sessions. Secondly, a Lasso regression analysis was performed to examine whether changes in cognitive biases in specific modules would predict overall change in depressive symptoms of the D-MCT.

Results: The results indicated small effects for the change in beliefs about rumination (d = 0.49) and mind reading (d = 0.24) over the time of the intervention. Within-session analyses revealed several session-specific effects, including a change in maladaptive beliefs about rumination within the module on this topic, with a medium effect size (d = 0.67). The Lasso regression indicated the change in negative filter as a predictor for the change in depressive symptoms, but this was not confirmed by a subsequent linear regression analysis.

Conclusions: The results suggest that patients with comorbid depression might benefit from special emphasis on themes such as rumination that bridge the primary and comorbid diagnoses in groups with a high prevalence of OCD and an anxiety-related disorder.

Circadian rhythm disruption has been frequently reported in stroke patients. However, its impact on prognosis remains unclear. This systematic review aimed to: (i) comprehensively characterize circadian rhythm disruption following stroke, and (ii) evaluate its relationship with various outcomes among stroke patients. We performed a systematic literature search on 4th May 2024 across Embase, Medline, CINAHL, and the Cochrane Register of Controlled Trials, with additional articles identified through related reviews. A total of 50 studies were included in the qualitative synthesis with 28 characterizing circadian rhythms in stroke patients, and 30 evaluating the relationship with stroke prognosis. Most studies were rated as moderate (55%) or high quality (28%). Across all studies characterizing circadian rhythm disruption, 26 studies (93%) found evidence of circadian rhythm disruption in stroke patients measured using hormonal, autonomic, behavioral, and genetic circadian markers. Most included studies (27 studies; 90%) also found a positive association between circadian rhythm disruption and at least one indicator of poor prognosis among stroke patients including poorer functional outcomes, cognitive impairment, mood disorder, impaired consciousness, sleep disturbances, increased in-hospital mortality, as well as other stroke-related complications. These findings highlight the commonality of circadian rhythm disruption and the importance of circadian rhythm assessments among stroke patients. Our results showing the adverse impact of circadian rhythm disruption also support further investigation into novel circadian-based therapeutic interventions to optimize stroke recovery. This systematic review was registered with PROSPERO (CRD42024539223).

Aim: This study compared the diffusion tensor image along the perivascular space (DTI-ALPS) index among individuals with schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), and normal controls. We also examined associations between the ALPS index, gray matter volume (GMV), cognitive performance, and clinical symptom severity to evaluate its potential as a biomarker of neurobiological alterations in psychiatric disorders.

Methods: Participants were recruited from the Taiwan Aging and Mental Illness cohort. Group differences in ALPS indices were tested with analysis of covariance (ANCOVA) controlling for age and sex. General linear regression assessed correlations between ALPS indices and GMV. We performed partial correlations to evaluate the associations between ALPS indices and cognitive tests, as well as clinical symptoms.

Results: All psychiatric groups showed significantly lower ALPS indices compared with normal controls, with schizophrenia exhibiting the greatest reduction. Higher ALPS indices in schizophrenia were associated with larger GMV in the bilateral cerebellum and fusiform gyri and with better cognitive performance. In the BD group, the ALPS index was positively associated with left hippocampal volume and cognitive test and negatively associated with depression and anxiety severity. The MDD group showed a reduced ALPS index but did not exhibit significant associations with GMV or cognitive tests.

Conclusion: The ALPS index may reflect glymphatic dysfunction across psychiatric disorders and relate to brain structure and clinical characteristics. These findings support its potential as an indirect biomarker of neurobiological alterations. Further research using direct glymphatic imaging is needed to clarify the mechanistic role of glymphatic function in psychiatric disorders.

Neuropsychiatric disorders pose profound challenges to both research and treatment, largely due to their clinical heterogeneity and the limited understanding of their underlying biological mechanisms. While bulk RNA sequencing (bulk RNA-seq) has been widely used to study gene expression, it cannot resolve cell-type-specific signals or detect rare cellular subpopulations. In contrast, single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have emerged as transformative technologies, enabling transcriptomic profiling at single-cell resolution. These approaches have revealed immunological alterations across a wide range of disorders. This review introduces recent findings from sc/snRNA-seq studies of immune-related mechanisms in psychiatric disorders-including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder-as well as in neurological conditions such as Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, multiple sclerosis, and anti-NMDA receptor encephalitis. While sc/snRNA-seq overcome averaging effects of bulk RNA-seq by resolving cell types, these methods still face challenges. We outline a roadmap that integrates bulk RNA-seq and sc/snRNA-seq to mitigate the remaining gaps.

Aims: Psychiatric diagnostic categories are challenged by comorbidity, symptom heterogeneity, and blurred nosological boundaries, sparking interest in trans-diagnostic dimensions such as the general psychopathology factor (p-factor). Yet it remains unclear whether p-factor represents a valid psychopathological construct or merely echoes semantic overlap among questionnaire items. Leveraging recent advances in large language models (LLMs), we conduct an empirical investigation that aims to disentangle linguistic similarity from syndromic co-occurrence in the p-factor.

Methods: We analyzed 145 symptom items spanning eight diagnostic categories with two similarity metrics: (1) Semantic-cosine similarity of LLM embeddings; and (2) Syndromic-polychoric correlations of responses from 985 community participants. Exploratory factor analyses extracted latent structures, their alignment was quantified, and factor scores were tested against lifetime psychiatric diagnoses.

Results: Both similarity matrices converged on a dominant p-factor with high cross-diagnostic loadings. Loading patterns were strongly congruent, indicating that LLM-derived semantics alone recapitulate much of the latent structure in human data. Crucially, syndromic p-factor scores predicted individual diagnostic histories more accurately than semantic p-factor scores, revealing clinically informative variance beyond language.

Conclusions: LLM embeddings reveal that the linguistic fabric of symptom items contains a shadow of the p-factor, yet real-world co-occurrence embeds additional, diagnosis-relevant information. Our findings position LLM-driven text analytics as a new lens for probing psychiatric constructs while underscoring the complex interplay between linguistic semantics and symptom co-occurrence in shaping our understanding of psychiatric constructs.

Neurodevelopmental disorders (NDDs), such as schizophrenia (SCZ), Attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), learning disabilities, and intellectual disabilities (ID), are highly prevalent. One significant genetic factor associated with NDDs is copy number variations (CNVs), which are structural changes in the genome that involve deletions or duplications of DNA segments. CNVs are known to significantly elevate the risk of developing NDDs and are increasingly being studied for their role in these conditions. While CNVs encompass a wide range of genetic alterations, emerging evidence suggests they may disrupt key biological processes, such as synaptic development and function in the brain, which are critical for learning and behavior. This review synthesizes findings from genetics, molecular biology, and related fields to explore the link between CNVs and synaptic pathology with therapeutic investigations. By understanding how CNVs compromise synaptic function, we identify paths to more targeted and effective therapies for neurodevelopmental disorders associated with CNVs.