Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia

Frontiers in Psychiatry

Psychiatry

Indre Pileckyte¹  Matija Kuclar²  Grega Repovš³  David Bartrés-Faz⁴  Ruben Perellón-Alfonso⁴  Kilian Abellaneda-Pérez⁵  Blaž Škrlj⁶  Aleš Oblak⁷  Peter Pregelj⁸  Borut Škodlar⁸  Jurij Bon⁸ 

[1] Center for Brain and Cognition, Pompeu Fabra University, Barcelona, Spain;Department of Psychiatry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia;Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia;Faculty of Medicine and Health Sciences, and Institute of Neurosciences, University of Barcelona, Barcelona, Spain;Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain;Faculty of Medicine and Health Sciences, and Institute of Neurosciences, University of Barcelona, Barcelona, Spain;Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain;Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la UAB, Barcelona, Spain;Jožef Stefan Institute, Ljubljana, Slovenia;University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia;University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia;Department of Psychiatry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia;

关键词: schizophrenia;    working memory (WM);    contralateral delay activity (CDA);    electroencephalography (EEG);    dense attention network (DAN);   

DOI  :  10.3389/fpsyt.2023.1205119

 received in 2023-04-13, accepted in 2023-09-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

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Copyright © 2023 Perellón-Alfonso, Oblak, Kuclar, Škrlj, Pileckyte, Škodlar, Pregelj, Abellaneda-Pérez, Bartrés-Faz, Repovš and Bon.