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.