Karen S. Y. Hung

Validation of the Chinese version of the Clinical Assessment Interview for Negative Symptoms (CAINS): a preliminary report

The present study aimed to examine the psychometric properties of the Chinese version of the Clinical Assessment Interview for Negative Symptoms (CAINS). We recruited 68 patients with schizophrenia from the Chinese setting. The findings showed a generally consistent two-factor structure with the original version, namely “expression” and “motivation–pleasure.” There is a minor cultural variation in perceiving these items in the Chinese culture. However, the present study demonstrated that the Chinese version of the CAINS appears to be a valid and reliable clinical tool for the assessment of negative symptoms in the Chinese setting.

Course of neurological soft signs in first-episode schizophrenia: Relationship with negative symptoms and cognitive performances

This prospective study examined the course of neurological soft signs (NSS) in patients with first-episode schizophrenia and its relationship with negative symptoms and cognitive functions. One hundred and forty-five patients with first-episode schizophrenia were recruited, 29 were classified as having prominent negative symptoms. NSS and neuropsychological measures were administered to all patients and 62 healthy controls at baseline. Patients were then followed-up prospectively at six-month intervals for up to a year. Patients with prominent negative symptoms exhibited significantly more motor coordination signs and total NSS than patients without prominent negative symptoms. Patients with prominent negative symptoms performed worse than patients without negative symptoms in working memory functions but not other fronto-parietal or fronto-temporal functions. Linear growth model for binary data showed that the prominent negative symptoms were stable over time. Despite general improvement in NSS and neuropsychological functions, the prominent negative symptoms group still exhibited poorer motor coordination and higher levels of NSS, as well as poorer working memory than patients without prominent negative symptoms. Two distinct subtypes of first-episode patients could be distinguished by NSS and prominent negative symptoms.

In vivo gamma-aminobutyric acid and glutamate levels in people with first-episode schizophrenia: A proton magnetic resonance spectroscopy study

BACKGROUND Gamma-aminobutyric acid (GABA) dysfunction and its consequent imbalance are implicated in the pathophysiology of schizophrenia. Reduced GABA production would lead to a disinhibition of glutamatergic neurons and subsequently cause a disruption of the modulation between GABAergic interneurons and glutamatergic neurons. In this study, levels of GABA, Glx (summation of glutamate and glutamine), and other metabolites in the anterior cingulate cortex were measured and compared between first-episode schizophrenia subjects and healthy controls (HC). Diagnostic potential of GABA and Glx as upstream biomarkers for schizophrenia was explored. METHODS Nineteen first-episode schizophrenia subjects and fourteen HC participated in this study. Severity of clinical symptoms of patients was measured with Positive and Negative Syndrome Scale (PANSS). Metabolites were measured using proton magnetic resonance spectroscopy, and quantified using internal water as reference. RESULTS First-episode schizophrenia subjects revealed reduced GABA and myo-inositol (mI), and increased Glx and choline (Cho), compared to HC. No significant correlation was found between metabolite levels and PANSS scores. Receiver operator characteristics analyses showed Glx had higher sensitivity and specificity (84.2%, 92.9%) compared to GABA (73.7%, 64.3%) for differentiating schizophrenia patients from HC. Combined model of both GABA and Glx revealed the best sensitivity and specificity (89.5%, 100%). CONCLUSION This study simultaneously showed reduction in GABA and elevation in Glx in first-episode schizophrenia subjects, and this might provide insights on explaining the disruption of modulation between GABAergic interneurons and glutamatergic neurons. Elevated Cho might indicate increased membrane turnover; whereas reduced mI might reflect dysfunction of the signal transduction pathway. In vivo Glx and GABA revealed their diagnostic potential for schizophrenia.

Re-visiting the nature and relationships between neurological signs and neurocognitive functions in first-episode schizophrenia: An invariance model across time

The present study examined different types of neurological signs in patients with first-episode schizophrenia and their relationships with neurocognitive functions. Both cross-sectional and longitudinal designs were adopted with the use of the abridged Cambridge Neurological Inventory which comprises items capturing motor coordination, sensory integration and disinhibition. A total of 157 patients with first-episode schizophrenia were assessed at baseline and 101 of them were re-assessed at six-month interval. A structural equation model (SEM) with invariance model across time was used for data analysis. The model fitted well with the data at baseline assessment, X^2(21) = 21.78, p = 0.413, NFI = 0.95, NNFI = 1.00, CFI = 1.00, IFI = 1.00, RMSEA = 0.015. Subsequent SEM analysis with invariance model at six-month interval also demonstrated the same stable pattern across time and showed strong measurement invariance and structure invariance across time. Our findings suggest that neurological signs capture more or less the same construct captured by conventional neurocognitive tests in patients with schizophrenia. The measurement and structure of these relationships appear to be stable over time.

Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals

Background Schizophrenia has been characterized as a neurodevelopmental disorder of brain disconnectivity. However, whether disrupted integrity of white matter tracts in schizophrenia can potentially serve as individual discriminative biomarkers remains unclear. Methods A random forest algorithm was applied to tractography‐based diffusion properties obtained from a cohort of 65 patients with first‐episode schizophrenia (FES) and 60 healthy individuals to investigate the machine‐learning discriminative power of white matter disconnectivity. Recursive feature elimination was used to select the ultimate white matter features in the classification. Relationships between algorithm‐predicted probabilities and clinical characteristics were also examined in the FES group. Results The classifier was trained by 80% of the sample. Patients were distinguished from healthy individuals with an overall accuracy of 71.0% (95% confident interval: 61.1%, 79.6%), a sensitivity of 67.3%, a specificity of 75.0%, and the area under receiver operating characteristic curve (AUC) was 79.3% (χ2 p < 0.001). In validation using the held‐up 20% of the sample, patients were distinguished from healthy individuals with an overall accuracy of 76.0% (95% confident interval: 54.9%, 90.6%), a sensitivity of 76.9%, a specificity of 75.0%, and an AUC of 73.1% (χ2 p = 0.012). Diffusion properties of inter‐hemispheric fibres, the cerebello‐thalamo‐cortical circuits and the long association fibres were identified to be the most discriminative in the classification. Higher predicted probability scores were found in younger patients. Conclusions Our findings suggest that the widespread connectivity disruption observed in FES patients, especially in younger patients, might be considered potential individual discriminating biomarkers. HighlightsThis MRI study attempted to identify distinctive white‐matter tract‐based features in first‐episode schizophrenia patients using machine‐learning.The discriminatory ability of putative tractography‐based biomarkers to distinguish schizophrenia patients from healthy individuals was examined.Widespread connectivity disruption is observed in first‐episode schizophrenia patients.Diffusion properties of inter‐hemispheric fibres, cerebello‐thalamo‐cortical circuits and the long association fibres are the most discriminative.

Clustering of Schizotypal Features in Unaffected First-Degree Relatives of Schizophrenia Patients

Meehl conceptualized schizotypy as the phenotypic manifestations of a neural integrative defect resulting from a schizophrenia diathesis. The majority of schizotypy studies recruited subjects from the general population and revealed a multidimensional construct. This 2-phase investigation first examined the clustering of schizotypy in 194 unaffected relatives of schizophrenia patients using the Chapman Psychosis Proneness scales and then directly compared the cognitive profiles of negative schizotypal individuals and positive schizotypal individuals with schizophrenia patients and controls. In the first phase, cluster analysis categorized 194 unaffected relatives of schizophrenia patients into positive schizotypy (n = 33), negative schizotypy (n = 66), mixed schizotypy (n = 27), and low schizotypy (n = 64). Positive schizotypal participants showed more self-report pleasure experiences than negative schizotypal participants, replicating earlier cluster analytic findings. In the second phase, 27 negative schizotypal individuals, 18 positive schizotypal individuals, 19 schizophrenia patients, and 29 controls were recruited. Although the groups were matched in terms of age, gender, and IQ, they differed significantly in cognitive profiles. While schizophrenia patients exhibited the broadest cognitive impairments, negative schizotypal participants exhibited visual memory, working memory, and verbal fluency impairments, and positive schizotypal participants exhibited logical memory, visual memory, working memory, and theory-of-mind impairments. Among people with familial risk of schizophrenia, individuals exhibiting positive rather than negative schizotypal features resembled schizophrenia patients in cognitive profiles. Using the psychometric-familial method to identify schizotypy, our findings support the heterogeneity of schizotypy as well as the potential utility of the positive schizotypy dimension in genetically high-risk individuals to predict the risk of developing schizophrenia.