073_ Differences in the centrality of cognitive domains and polygenic scores in affective and psychotic disorders: a network analysis
Research Question and Aims
Cognitive deficits are a core feature of both schizophrenia (SCZ) and bipolar disorder (BD). These deficits impact attention, memory, processing speed, and executive functioning. The severity of cognitive deficits is associated with the severity of other symptoms, such as depressive and psychotic symptoms, in both disorders. Additionally, cognitive deficits can worsen during psychotic episodes, further impairing the individual's ability to function. The interplay between cognitive deficits and other symptoms is complex and bidirectional. Therefore, understanding the relationship between cognitive deficits and other symptoms in SCZ and BD is essential for developing effective treatments that target both domains. The network approach, in this perspective, may be particularly useful for analyzing and visualizing complex relationships among psychopathology symptoms in specific populations. In network analysis, nodes reflect symptoms, and edges between nodes reflect relationships between symptoms. Central symptoms reveal how different symptoms are interconnected within a disorder or symptom network, while bridge symptoms are particularly relevant in explaining comorbidity. Although risk factors and genetic liability are also expected to influence symptom interactions, only a few studies have integrated them into network models thus far. This study aims to identify the relationship between cognitive impairment, affective symptoms, psychotic symptoms, and functioning in a large sample of patients with affective or psychotic disorders using a network approach.
Additionally, the study aims to evaluate the influence of polygenic and environmental risk factors, such as trauma, on these symptoms.
1. PMID: 32663999
2. PMID: 23450289
3. PMID: 23537483
The network structure and central and bridge symptoms are different among people with affective disorders compared with people with psychosis spectrum disorders. Patients with BD have a network model with higher centrality for BD-PRS, MDD-PRS and affective symptoms compared with SCZ or HC. Patients with SCZ have a network model with higher centrality for cognitive deficits, psychotic symptoms and SCZ-PRS compared with BD or HC.
For this purpose, we would use cross-sectional data derived from two different cohorts:
1. The Bipogent Cohort: composed of outpatients of the Bipolar and Depressive Disorders Unit of the Hospital Clinic of Barcelona, diagnosed with BDI or BDII, recruited from February 2017 to July 2019. In the cohort, we have a total of 531 individuals phenotyped and genotyped (251 patients and 280 matched healthy controls).
2. The PsyCourse Cohort: 1200 participants of the PsyCourse study with bipolar-spectrum diagnoses (baseline evaluations)
Baseline evaluations from the Bipogent and PsyCouse cohorts will be considered:
- Psychiatric and medical history
- Medication data and treatment response
- Family history of psychiatric disease
- Clinical evaluations: mood assessment (HDRS, IDS, YMRS), functioning (FAST, GAF), early life stress (CTQ, CTS), illness severity (CGI), suicide (attempt, behavior)
- Neurocognitive assessment: Processing Speed (TMT-A total time); attention and working memory (WAIS-III digit span); executive functions (TMT-B); verbal memory (CVLT/VLMT), IQ (WAIS-III, MWT-B).
Polygenic Risk Scores:
Polygenic risk scores (PRS) for major psychiatric disorders (BD, schizophrenia, major depressive disorder) will be constructed based on the summary statistics of the latest genome-wide association studies (GWAS) for these disorders. PRS-CS tool will be used to infer posterior SNP effect sizes under continuous shrinkage priors and estimate the global shrinkage parameter (φ) using a fully Bayesian approach. PRS will be then calculated in PLINK 1.9 using dosage data independently in the two cohorts, and data will be meta-analyzed.
Network analysis will be performed to explore the relationships between psychiatric symptoms (i.e., depressive, manic, positive, and negative symptoms), cognitive outcomes, functioning, trauma, clinical variables (substance use, suicide, predominant polarity, seasonality, duration of illness), BD-PRS, MD-PRS, SCZ-PRS, in individuals diagnosed BD, SCZ and HC. In addition, network results for different populations will be compared.
Network structures were estimated using Gaussian Markov random field (Costantini et al., 2015; Lauritzen, 1996) with the EBICglasso model. A nonparanormal transformation of the data will be applied before the network estimation as data did not follow a normal distribution. To control for spurious connections in the network estimation, an optimal regularization parameter will be selected by using graphical LASSO and extended Bayesian information criterion (EBIC). A threshold will be used to remove edges not surviving p-value <0.05.
In the network, nodes represent the studied variables and edges the bidirectional and undirected correlation between each pair of nodes. Thicker and more saturated edges represented stronger correlations; blue and red edges indicated positive and negative partial correlations, respectively.
Network centrality measures of expected influence, betweenness, and closeness of different nodes will be explored. The accuracy of edge weights will be measured by the 95% confidence intervals (CIs) computed through bootstrapping. The centrality indices' stability will be quantified using a case-dropping bootstrap procedure, and the correlation stability coefficient (CS-coefficient) between centrality indices for the full sample was calculated.
To examine whether network structure changes among patients with different diagnosis, we separately assessed differences in network structure, global strength, and significant edges in the three groups. Statistical significance was evaluated by a p-value <0.05. Network estimation and accuracy will be conducted by the “bootnet” R package and “qgraph” R package. Network comparison will be conducted by “Network Comparison Test” R package.
Diagnosis (personal and familiar):
Genetic data (imputed).