Techniques such as parallel independent component analysis (pICA) can reveal linked patterns of alterations across different data modalities that can help to identify biologically-informed phenotypes, which might help to improve future treatment targets in schizophrenia. This according to a study that aimed to explore the association between gray matter (GM) measures and symptom dimensions in schizophrenia. Researchers applied pICA, a higher-order statistical approach that identifies covarying patterns within ≥2 data modalities simultaneously, to link covarying brain networks of GM concentration with covarying linear combinations of the positive and negative syndrome scale (PANSS). A large sample of patients with schizophrenia (n=337) was investigated. They found:
- The pICA revealed a distinct PANSS profile characterized by increased delusional symptoms, suspiciousness, hallucinations, and anxiety, that was associated with a pattern of lower GM concentration in inferior temporal gyri and fusiform gyri and higher GM concentration in the sensorimotor cortex.
- GM alterations replicate previous findings; additionally, applying a multivariate technique, allowed for mapping a very specific symptom profile onto these GM alterations extending understanding of cortical abnormalities associated with schizophrenia.
Mennigen E, Jiang W, Calhoun VD, et al. Positive and general psychopathology associated with specific gray matter reductions in inferior temporal regions in patients with schizophrenia. [Published online ahead of print February 26, 2019]. Schizophr Res. doi:10.1016/j.schres.2019.02.010.