Interrelationships between different negative symptom domains in schizophrenia

Summary: The use of a Brief Negative Symptom Scale (BNSS) to assess negative symptoms of schizophrenia can bridge the gap between clinical interviews and patient self-reported measurement of symptoms.

Source: chinese academy of sciences

Negative symptoms are essential features of schizophrenia and determinants of clinical and functional outcomes. Negative symptoms are a complex psychopathology and include avolition, anhedonia, asociality, alogy, and affective blunting.

However, the underlying factor structure of the negative symptoms of schizophrenia remains to be determined. It is unclear whether the previous results are “measurement invariant”, which further strengthens the validity of the reported factor structure.

In clinical practice, several measurement scales are used to assess negative symptoms, including self-reported scales and clinical interview. Moreover, the question of the “domain-specific correspondence” of the five consensus domains of negative symptoms in schizophrenia has not yet been investigated.

In order to address this unclear issue, Dr. Raymond Chan of the Institute of Psychology, Chinese Academy of Sciences (CAS) and his collaborators adopted network analysis to specifically examine the interrelationship between negative symptom domains. captured by different rating scales, and to examine domain-specific correspondence across multiple scales in schizophrenic patients.

They assessed negative symptoms using the Brief Negative Symptom Scale (BNSS) and the Negative Symptom Self-Rating (SNS) and Negative Symptom Rating Scale (SANS) in 204 patients. suffering from schizophrenia.

It is unclear whether the previous results are “measurement invariant”, which further strengthens the validity of the reported factor structure. Image is in public domain

According to the researchers, the SANS and the BNSS mixed together, while the SNS clustered together.

Additionally, the SANS attention domain was at the periphery of the network while the SANS anhedonia-asociality and the SANS affect flattening showed the highest node strength.

Specifically, the five BNSS nodes linked the SANS and SNS nodes. Blunted affect BNSS and anhedonia-asociality SANS also had the highest bridging strength.

Taken together, these findings support that the BNSS may link clinical interview assessment to self-reported measure of negative symptoms in schizophrenic patients. Their results further suggest a domain-specific correspondence in the bridge centrality network, supporting the consensus measure invariance of NIMH negative symptoms.

About this schizophrenia research news

Author: Zhang Nannan
Source: chinese academy of sciences
Contact: Zhang Nannan – Chinese Academy of Sciences
Image: Image is in public domain

Original research: Access closed.
Structure of the bridge centrality network of negative symptoms in people with schizophrenia” by Ling-ling Wang et al. European Archives of Psychiatry and Clinical Neurosciences


Summary

Structure of the bridge centrality network of negative symptoms in people with schizophrenia

See also

This shows a diagram of the study

Negative symptoms are a complex psychopathology. Although the evidence generally supports the NIMH’s five consensus domains, research has rarely examined this model’s measurement invariance and domain-specific correspondence across multiple scales.

This study aimed to examine the interrelationship between negative symptom domains captured by different rating scales and to examine domain-specific correspondence across multiple scales.

We administered the Brief Negative Symptom Scale (BNSS), Negative Symptom Self-Rating (SNS), and Negative Symptom Rating Scale (SANS) to 204 people with schizophrenia. We used network analysis to examine the interrelationship between negative symptom domains.

In addition to the regularized partial correlation network, we estimated bridge centrality indices to investigate domain-specific correspondence, while taking each scale as an independent community.

The regularized partial correlation network showed that SNS nodes clustered, while SANS and BNSS nodes blended. The SANS attention domain was at the periphery of the network according to the Fruchterman-Reingold algorithm.

Anhedonia-asociality SANS (strength=1.48; EI=1.48) and affect flattening SANS (strength=1.06; EI=1.06) had the highest knot strength and EI . In addition, the five BNSS nodes connected the SANS and SNS nodes. Blunt affect BNSS (strength = 0.76; EI = 0.76) and anhedonia-asociality SANS (strength = 0.76; EI = 0.74) showed bridge strength and bridge EI the highest.

The BNSS captures negative symptoms and links the symptom domains measured by the SANS and the SNS. All three scales showed domain-specific correspondence.

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