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1. Value-directed remembering in first-episode schizophrenia.

   https://doi.org/10.1037/neu0000840  

   Patterson, Tara K.; Nuechterlein, Keith H.; Subotnik, Kenneth L.; Castel, Alan D.; Knowlton, Barbara J. (June 2022, Neuropsychology)
2. Heart Rate Variability in Schizophrenia and Autism

   https://doi.org/10.3389/fpsyt.2021.760396  

   Haigh, Sarah M.; Walford, Tabatha P.; Brosseau, Pat (November 2021, Frontiers in Psychiatry)

   Suppressed heart rate variability (HRV) has been found in a number of psychiatric conditions, including schizophrenia and autism. HRV is a potential biomarker of altered autonomic functioning that can predict future physiological and cognitive health. Understanding the HRV profiles that are unique to each condition will assist in generating predictive models of health. In the current study, we directly compared 12 adults with schizophrenia, 25 adults with autism, and 27 neurotypical controls on their HRV profiles. HRV was measured using an electrocardiogram (ECG) channel as part of a larger electroencephalography (EEG) study. All participants also completed the UCLA Loneliness Questionnaire as a measure of social stress. We found that the adults with schizophrenia exhibited reduced variability in R-R peaks and lower low frequency power in the ECG trace compared to controls. The HRV in adults with autism was slightly suppressed compared to controls but not significantly so. Interestingly, the autism group reported feeling lonelier than the schizophrenia group, and HRV did not correlate with feelings of loneliness for any of the three groups. However, suppressed HRV was related to worse performance on neuropsychological tests of cognition in the schizophrenia group. Together, this suggests that autonomic functioning is more abnormal in schizophrenia than in autism and could be reflecting health factors that are unique to schizophrenia. 

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3. Oculomotor inhibition and location priming in schizophrenia.

   https://doi.org/10.1037/abn0000683  

   Bansal, Sonia; Gaspelin, Nicholas; Robinson, Benjamin M; Hahn, Britta; Luck, Steven J; Gold, James M (August 2021, Journal of Abnormal Psychology)
4. Computational modeling of excitatory/inhibitory balance impairments in schizophrenia

   https://doi.org/10.1016/j.schres.2020.03.027  

   Qian, Ning; Lipkin, Richard M.; Kaszowska, Aleksandra; Silipo, Gail; Dias, Elisa C.; Butler, Pamela D.; Javitt, Daniel C. (April 2020, Schizophrenia Research)

   null (Ed.)
5. Cognitive Implications of Correlated Structural Network Changes in Schizophrenia

   https://doi.org/10.3389/fnint.2021.755069  

   Jensen, Dawn M.; Zendrehrouh, Elaheh; Calhoun, Vince; Turner, Jessica A. (January 2022, Frontiers in Integrative Neuroscience)

   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. 

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