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Link Between GM Measures and Symptom Dimensions

Schizophr Res; ePub 2019 Feb 26; Mennigen, et al

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.
Citation:

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.

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Continuous Exercise & CI in Schizophrenia, Front Psychiatry; ePub 2019 Mar 13; Herbsleb, et al

MHC Risk Variants in Cortical Thickness, Brain Behav; ePub 2019 Mar 28; Tao, et al

Disclosing the Diagnosis of Schizophrenia, Int J Soc Psychiatry; ePub 2019 Mar 27; Setti, et al