Cognitive Implications of Correlated Structural Network Changes in Schizophrenia

• Authors: Dawn M. Jensen, Georgia State University; Elaheh Zendrehrouh, Georgia State University; Vince Calhoun, Georgia State University; Jessica A. Turner, Georgia State University
• Journal Title: Frontiers in Integrative Neuroscience
• Volume: Volume 15
• Publisher: Frontiera Media S.A. | 2022-01-20
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fnint.2021.755069
• Subjects: Biology, Neuroscience; Psychology, Cognitive; Computer Science
• Keywords: Science & Technology; Life Sciences & Biomedicine; Behavioral Sciences; Neurosciences; Neurosciences & Neurology; multimodal imaging; cognition; gray matter density; fractional anisotropy; schizophrenia; Matter abnormalities; Negative symptoms; Brain; Impairment; Connecticvity; Gray

Abstract

Background Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance. Methods Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score. Results Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem. Conclusion The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ.