Dataset Information

Relationships between divided attention and working memory impairment in people with schizophrenia.

ABSTRACT: Recent studies suggest that people with schizophrenia (PSZ) have difficulty distributing their attention broadly. Other research suggests that PSZ have reduced working memory (WM) capacity. This study tested whether these findings reflect a common underlying deficit. We measured the ability to distribute attention by means of the Useful Field of View (UFOV) task, in which participants must distribute attention so that they can discriminate a foveal target and simultaneously localize a peripheral target. Participants included 50 PSZ and 52 healthy control subjects. We found that PSZ exhibited severe impairments in UFOV performance, that UFOV performance was highly correlated with WM capacity in PSZ (r = -.61), and that UFOV impairments could not be explained by either impaired low-level processing or a generalized deficit. These results suggest that a common mechanism explains deficits in the ability to distribute attention broadly, reduced WM capacity, and other aspects of impaired cognition in schizophrenia. We hypothesize that this mechanism may involve abnormal local circuit dynamics that cause a hyperfocusing of resources onto a small number of internal representations.

SUBMITTER: Gray BE

PROVIDER: S-EPMC4193709 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Relationships between divided attention and working memory impairment in people with schizophrenia.

Gray Bradley E BE Hahn Britta B Robinson Benjamin B Harvey Alex A Leonard Carly J CJ Luck Steven J SJ Gold James M JM

Schizophrenia bulletin 20140419 6

Recent studies suggest that people with schizophrenia (PSZ) have difficulty distributing their attention broadly. Other research suggests that PSZ have reduced working memory (WM) capacity. This study tested whether these findings reflect a common underlying deficit. We measured the ability to distribute attention by means of the Useful Field of View (UFOV) task, in which participants must distribute attention so that they can discriminate a foveal target and simultaneously localize a peripheral ...[more]

PMID: 24748559

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Project description:IntroductionPatients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm.MethodsWe tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM.ResultsThe DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs.DiscussionThese results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.

Dataset Information

Relationships between divided attention and working memory impairment in people with schizophrenia.

Publications

Relationships between divided attention and working memory impairment in people with schizophrenia.

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