The longitudinal structure of negative symptoms in treatment resistant schizophrenia

Comparison of Negative Symptom Network Structures Between Patients With Early and Chronic Schizophrenia: A Network and Exploratory Graph Analysis

BACKGROUND AND HYPOTHESIS

Despite the clinical relevance of negative symptoms in schizophrenia, our understanding of negative symptoms remains limited. Although various courses and stages of schizophrenia have been identified, variations in the negative symptom networks between distinct stages of schizophrenia remain unexplored.

STUDY DESIGN

We examined 405 patients with early schizophrenia (ES) and 330 patients with chronic schizophrenia (CS) using the Scale for the Assessment of Negative Symptoms. Network analysis and exploratory graph analysis were used to identify and compare the network structures and community memberships of negative symptoms between the two groups. Further, associations between communities and social functioning were evaluated. The potential influences of other symptom domains and confounding factors were also examined.

STUDY RESULTS

Multidimensional differences were found in the networks of negative symptoms between ES and CS. The global connectivity strength was higher in the network of ES than in the network of CS. In ES, central symptoms were mainly related to expressive deficits, whereas in CS they were distributed across negative symptom domains. A three-community structure was suggested across stages but with different memberships and associations with social functioning. Potential confounding factors and symptom domains, including mood, positive, disorganization, and excitement symptoms, did not affect the network structures.

CONCLUSION

Our findings revealed the presence of stage-specific network structures of negative symptoms in schizophrenia, with negative symptom communities having differential significance for social functioning. These findings provide implications for the future development of tailored interventions to alleviate negative symptoms and improve functionality across stages.

Network structure of psychotic symptoms and childhood trauma in first-episode versus treatment-resistant schizophrenia

OBJECTIVE

The study aims to examine the network structures of childhood trauma (CT) and psychotic symptoms in patients with first-episode schizophrenia (FES) and treatment-resistant schizophrenia (TRS). Specifically, it seeks to elucidate how different dimensions of CT influence symptoms across FES and TRS.

METHODS

289 patients with FES and 50 patients with TRS were assessed using Positive and Negative Syndrome Scale (PANSS) and Childhood Trauma Questionnaire. Partial correlation was used to elucidate the network connections between CT and symptoms in FES and TRS patients. Betweenness, closeness coefficient, and community detection were further calculated to investigate the interactions between CT and psychotic symptoms.

RESULTS

The analysis revealed three key findings: (1) Symptom-trauma networks differ between FES and TRS patients; (2) Based on network analysis, CT in TRS forms tight interlinks, as evidenced by a larger value of closeness coefficient, which influences psychotic symptoms in TRS compared to FES. Sexual abuse plays a vital role in the TRS network while emotional neglect is more important in FES; and (3) The divergent community structures suggest distinct pathways through which CT and symptoms in FES and TRS patients. Specifically, in the FES symptom-CT network, CT influences the symptoms through traditional symptom patterns, while in TRS the pathway cannot be divided by traditional divisions and it involves a complex manner.

CONCLUSION

The findings suggest that the pathways linking childhood trauma experiences and clinical symptoms differ between FES and TRS patients, providing valuable insights into how early traumatic stress may contribute to symptom evolution in schizophrenia.

Measuring the clinical dimensions of negative symptoms through the Positive and Negative Syndrome Scale

The negative symptoms of schizophrenia can determine functional outcome in patients. Despite its clinical significance, no treatment exists to date, as numerous pharmacological and non-pharmacological clinical trials have failed to demonstrate efficacy. Many of these trials evaluated negative symptoms as a single clinical construct. However, consistent evidence in the past two decades has found that negative symptoms constitute at least two independent clinical dimensions, namely deficits in motivation and pleasure (MAP) and in emotional expression (EXP). These dimensions are best evaluated using new assessment tools, such as the Brief Negative Symptom Scale (BNSS). However, older assessment tools, and particularly the Positive and Negative Syndrome Scale (PANSS), remain widely used in past and current research. Here, we sought to predict BNSS MAP and EXP dimensions from the PANSS. Using complementary modelling approaches across three heterogeneous, multi-centre, multi-culture patient samples (n = 1241 patients, 1846 observations), we show that MAP can be estimated (43-60 % variance explained) predominantly using N2 and N4. Moreover, EXP can be estimated predominantly using the two PANSS items N1 and N6 (55-81 % variance explained across models and samples). Additionally, PANSS-derived MAP shows associations with functioning similar to those measured by the BNSS MAP dimension. Together, our results suggest that while EXP can be reliably estimated from PANSS, MAP cannot be consistently estimated from PANSS across samples and cultures. This warrants caution when using the PANSS to estimate MAP and emphasises the need for using the newer assessment tools for negative symptoms.

Network analysis of clinical features in patients with treatment-resistant schizophrenia

Objective

This study compares the clinical features of Treatment-Resistant Schizophrenia (TRS) and Non-Treatment-Resistant Schizophrenia (NTRS) using network analysis.

Methods

We recruited 511 patients, dividing them into TRS (N = 269) and NTRS (N = 242) groups. Eight scales were used: Positive and Negative Syndrome Scale (PANSS), Positive Symptom Assessment Scale (SAPS), Scale for Assessment of Negative Symptoms (SANS), Simpson-Angus Scale (SAS), Abnormal Involuntary Movements Scale (AIMS), Barnes Akathisia Rating Scale (BARS), Calgary Schizophrenia Depression Scale (CDSS), and Global Assessment of Functioning Scale (GAF). Demographic and clinical data were analyzed using T-tests and Chi-square tests. Network analysis was then applied to compare clinical features.

Results

Significant differences were found in the overall architectures (S = 1.396, p < 0.002) and edge weights (M = 0.289, p < 0.009) of TRS and NTRS networks. Nine edges (p < 0.05) and five nodes (p < 0.01) differed, indicating a correlation between clinical symptoms of the two groups. TRS core symptoms were linked to social functions through both positive (SAPS) and negative symptoms (SANS), while NTRS core symptoms were related to general psychopathological symptoms (PANSS-G).

Conclusion

For TRS, it is essential to address both negative and positive symptoms, focusing on the impact of negative symptoms on functioning. Additionally, managing medication side effects is crucial to avoid worsening negative symptoms.

Manic Residual Symptoms Also Deserve Attention: A Symptom Network Analysis of Residual Symptoms in Bipolar Disorder

Background

Lots of patients with bipolar disorder (BD) continue to have residual symptoms after treatment in their remission, BD exhibits intricate characteristics and transformation patterns in its residual symptoms, residual symptoms of different polarities and degrees can mix with and transform to each other. There is a need for further investigation of BD as a comprehensive multivariate disease system. The current research lacks network analyses focusing on BD's residual and subsyndromal symptoms.

Methods

242 patients were included with bipolar disorder in remission. We compared demographic data and differences in symptoms between populations with and without residual symptoms using t-tests and chi-square tests, with FDR applied for multiple comparison correction. Logistic regression was used to identify influencing factors for residual symptoms. Symptom networks were compared by network analysis to analyze the relationships between different types of residual symptoms.

Results

Depressive residual symptoms (N=111) were more common than manic residual symptoms (n=29) in the patients included. The comparison between two groups with and without residual symptoms shows no difference in demographic data and medical history information. The main influencing factors related to residual symptoms were time from diagnosis to first treatment (OR=0.88), the first(OR=1.51) and second (OR=17.1)factors of the Mood Disorder Questionnaire (MDQ), the Quick Inventory of Depressive Symptomatology Self-Report (QIDS)(OR=5.28), the psychological(OR=0.68) and environment (OR=1.53) subscale of the World Health Organization Quality of Life Short Form (WHOQOL-BREF). There was a significant difference in network structure between the groups with and without residual symptoms (network invariance difference=0.4, p =0.025). At the same time, there was no significant difference between the groups with and without depressive residual symptoms. However, the symptom network in patients with depressive residual symptoms is more loosely structured than in those without, with symptoms exhibiting weaker interconnections. When there is no depressive or manic residual symptom, it can still form a symptom network and cause an impact on social function.

Conclusion

This study underscores the complexity of bipolar disorder's residual symptoms. Although it primarily manifests as loosely structured depressive residual symptoms, manic residual symptoms should not be ignored. Future research should explore network-based interventions targeting specific symptom clusters or connections to improve residual symptom management and patient outcomes.

The Cellular Dysfunction of the Brain-Blood Barrier from Endothelial Cells to Astrocytes: The Pathway towards Neurotransmitter Impairment in Schizophrenia

Schizophrenia (SCZ) is an articulated psychiatric syndrome characterized by a combination of genetic, epigenetic, and environmental factors. Our intention is to present a pathogenetic model combining SCZ alterations and the main cellular actors of the blood-brain barrier (BBB): endothelial cells (ECs), pericytes, and astrocytes. The homeostasis of the BBB is preserved by the neurovascular unit which is constituted by ECs, astrocytes and microglia, neurons, and the extracellular matrix. The role of the BBB is strictly linked to its ability to preserve the biochemical integrity of brain parenchyma integrity. In SCZ, there is an increased BBB permeability, demonstrated by elevated levels of albumin and immunoglobulins in the cerebrospinal fluid, and this is the result of an intrinsic endothelial impairment. Increased BBB permeability would lead to enhanced concentrations of neurotoxic and neuroactive molecules in the brain. The pathogenetic involvement of astrocytes in SCZ reverberates its consequences on BBB, together with the impact on its permeability and selectivity represented by the EC and pericyte damage occurring in the psychotic picture. Understanding the strict interaction between ECs and astrocytes, and its consequent impact on cognition, is diriment not only for comprehension of neurotransmitter dyshomeostasis in SCZ, but also for focusing on other potential therapeutic targets.