Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression
Stéphane Potvin,1,2 Charles-Édouard Giguère,1 Adrianna Mendrek3 1Centre de recherche de l’Institut Universitaire en Santé Mentale de Montréal, Montreal, Quebec, Canada; 2Department of Psychiatry and Addiction, Faculty of Medicine, Univ...
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2021
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| Acceso en línea: | https://doaj.org/article/44aa5c5709434a1195fc3370f8888826 |
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| Sumario: | Stéphane Potvin,1,2 Charles-Édouard Giguère,1 Adrianna Mendrek3 1Centre de recherche de l’Institut Universitaire en Santé Mentale de Montréal, Montreal, Quebec, Canada; 2Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada; 3Department of Psychology, Bishop’s University, Lennoxville, Quebec, CanadaCorrespondence: Stéphane PotvinCentre de recherche de l’Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, Quebec, H1N 3V2, CanadaEmail stephane.potvin@umontreal.caBackground: Robust evidence shows that schizophrenia is associated with significant cognitive impairments, including deficits in visuospatial abilities. While other cognitive domains have sparked several functional neuroimaging studies in schizophrenia, only a few brain activation studies have examined the neural correlates of visuospatial abilities in schizophrenia.Purpose: Here, we propose to perform a functional connectivity study on visuospatial processing in schizophrenia, and to determine the classification accuracy of the observed alterations.Methods: Thirty-nine schizophrenia patients and 42 healthy controls were scanned using functional magnetic resonance imaging while performing a mental rotation task. Task-based functional connectivity was examined using a region-of-interest (ROI) to ROI approach, as implemented in the CONN Toolbox. ROIs were selected from a previous meta-analysis on mental rotation. Logistic regression with Lasso regularization was performed, using train-test cross-validation.Results: Schizophrenia was associated with a complex pattern of dysconnectivity between the superior, middle and inferior frontal gyrus, the precentral gyrus, the superior parietal lobule (SPL) and the inferior lateral occipital cortex. The classification accuracy was 86.1%. Mental rotation performance was predicted by the dysconnectivity between the right and left superior frontal gyrus (SFG), as well as between the left SFG and left SPL.Conclusion: The results of the current study highlight that visuospatial processing is useful for examining the widespread dysconnectivity between executive, motor and visual brain regions in schizophrenia. We also demonstrate that very good classification accuracy can be achieved using visuospatial-related functional connectivity data.Keywords: schizophrenia, mental rotation, functional connectivity, machine learning |
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