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Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective.

ABSTRACT: The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are "easily" reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

SUBMITTER: Dimulescu C

PROVIDER: S-EPMC8273511 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background To compare estimated 10-year and 30-year cardiovascular risk in primary care patients with and without serious mental illness (SMI; bipolar disorder, schizophrenia, or schizoaffective disorder). Methods and Results All patients aged 18 to 75 years with a primary care visit in January 2016 to September 2018 were included and were grouped into those with and without SMI using diagnosis codes. Ten-year cardiovascular risk was estimated using atherosclerotic cardiovascular disease scores for patients aged 40 to 75 years without cardiovascular disease; 30-year cardiovascular risk was estimated using Framingham risk scores for patients aged 18 to 59 years without cardiovascular disease. Demographic, vital sign, medication, diagnosis, and health insurance data were collected from the electronic health record by a clinical decision support system. Descriptive statistics examined unadjusted differences, while general linear models examined differences for continuous variables and logistic regression models for categorical variables. Models were then adjusted for age, sex, race, ethnicity, and insurance type. A total of 11 333 patients with SMI and 579 924 patients without SMI were included. After covariate adjustment, 10-year cardiovascular risk was significantly higher in patients with SMI (mean, 9.44%; 95% CI, 9.29%-9.60%) compared with patients without SMI (mean, 7.99%; 95% CI, 7.97-8.02). Similarly, 30-year cardiovascular risk was significantly higher in those with SMI (25% of patients with SMI in the highest-risk group compared with 11% of patients without SMI; P<0.001). The individual cardiovascular risk factors contributing most to increased risk for those with SMI were elevated body mass index and smoking. Among SMI subtypes, patients with bipolar disorder had the highest 10-year cardiovascular risk, while patients with schizoaffective disorder had the highest 30-year cardiovascular risk. Conclusions The significantly increased cardiovascular risk associated with SMI is evident even in young adults. This suggests the importance of addressing uncontrolled major cardiovascular risk factors in those with SMI at as early an age as possible. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02451670.

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Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective.

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