Cognitive and structural neuroimaging characteristics of schizophrenia patients with large, rare copy number deletions

White matter microstructural deficits in 22q11.2 deletion syndrome

Diffusion tensor imaging (DTI) studies in 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with psychosis, report brain white matter (WM) microstructure aberrations. Several studies report that WM disruptions in 22q11DS are similar to deficits in idiopathic schizophrenia. Yet, DTI results in 22q11DS are inconsistent. We used DTI to compare WM structure in 22q11DS individuals to healthy controls (HC) and explored WM differences in 22q11DS with (+) and without (-) psychosis spectrum symptoms. We examined 39 22q11DS individuals and 39 age, sex and race equivalent HC. DTI was performed at 3T using a 64-direction protocol. Fractional anisotropy (FA) was lower, while radial diffusivity was higher in 22q11DS within the cingulum bundle. Mean diffusivity was lower in the inferior longitudinal fasciculus, while axial diffusivity (AD) was lower in the cingulum bundle, forceps major, and several posterior to anterior fasciculi. 22q11DS+ had lower FA in the cingulum bundle and lower AD in the uncinate fasciculus compared to 22q11DS-. Overall, we found aberrant WM microstructure in individuals with 22q11DS compared to age and sex matched HC and exploratory analysis indicated subtle WM deficits associated with psychosis. The findings highlight the dysfunction of WM microstructure in 22q11DS and its potential importance in elucidating WM abnormalities in psychosis.

Increased rare duplication burden genomewide in patients with treatment-resistant schizophrenia

BACKGROUND

A significant number of patients with schizophrenia fail to respond to antipsychotic medication. Although several studies have investigated associated patient characteristics, the emerging findings from genetic studies offer further scope for study.

METHOD

In 612 schizophrenia patients with detailed clinical information, common genetic variants indexed by polygenic risk scores, and rare variants indexed by deletion and duplication burden genomewide, we explored potential genetic predictors alongside other established risk factors for treatment resistance. Clinical outcomes of treatment resistance were also calculated using lifetime measures of positive, negative/disorganized and mood symptoms as well as number of hospitalizations and suicide attempts.

RESULTS

Logistic regression models identified a significant relationship between treatment resistance and total duplication burden genomewide, years of formal schooling and age at onset. Clinically, treatment-resistant patients were characterized by greater negative/disorganized and positive symptoms and greater number of hospitalizations.

CONCLUSIONS

Taken together, these findings suggest genetic information, specifically the genomewide burden of rare copy number variants, may increase our understanding and clinical management of patients with treatment-resistant schizophrenia.

Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics

OBJECTIVES

One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia.

METHODS

Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging.

RESULTS

The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity--connections among high degree "rich club" nodes, "feeder" connections to these rich club nodes, and "local" connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length.

CONCLUSIONS

Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.