Identification of brain regions associated with working memory deficit in schizophrenia

Abstract

Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory. Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder

“…In several studies, including a meta‐analysis (Van Snellenberg, Torres, & Thornton, 2006), a region of interest (ROI) analysis was performed specifically for DLPFC and found stronger fMRI response (Karlsgodt et al, 2009; Manoach et al, 2000; Potkin et al, 2009; Van Snellenberg et al, 2016), weaker fMRI response (Fan et al, 2019; Kaminski et al, 2020; Menon, Anagnoson, Mathalon, Glover, & Pfefferbaum, 2001; Pu et al, 2019) or no significant difference in fMRI response (Van Snellenberg et al, 2006) in patients with SZ compared to HC. Additionally, using independent component analysis (ICA), several fMRI studies of WM tasks have revealed significant alterations of fMRI response in patients with SZ relative to HC, which are either correlated or anti‐correlated with WM performance, including stronger fMRI response in bilateral superior frontal gyrus (SFG), PCC, insula, superior temporal gyrus (STG), inferior temporal gyrus, precuneus, parahippocampal gyrus, amygdala, putamen and cerebellum, left DLPFC, cingulate gyrus and inferior parietal lobule (IPL) (Chatterjee et al, 2019; Kim et al, 2009), and weaker fMRI response in bilateral dentate gyrus and cerebellum (Kim et al, 2009). These findings suggest that the aforementioned brain regions may be implicated in the neurobiological mechanisms underlying impairment of WM in SZ (Kim et al, 2009).…”

Oxidative-Antioxidant Imbalance and Impaired Glucose Metabolism in Schizophrenia

“…Magnetic resonance imaging (MRI and fMRI) of people with the developing disease in the prodromal phase shows an enlargement of the right inferior frontal gyrus [8,22], that directly correlates with reduced neuronal activity in that region. Changes in brain activity are also observed in regions that include cerebellum, temporal and frontal gyri [23], as well as Heschl's gyrus, which is also reduced in people with schizophrenia [24,25]. There are also functional changes in the region of the insula and amygdala.…”

An era of brain-computer interface: BCI migration into space

“…Besides electrical signals, neural activity produces different signals that can still be used in BCI, such as magnetic and metabolic. The magnetic activity can be recorded with MEG, while metabolic activity can be (as shown in changes in blood flow and blood oxygenation level) monitored with fMRI, Positron Emission Tomography (PET), and optical imaging (Chatterjee et al, 2019). However, these alternative techniques require very sophisticated devices that can only be operated in special facilities, making their use impractical for prototyping and practical implementation (da Silva-Sauer et al, 2016).…”