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

ABSTRACT:

Introduction

Patients 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.

Methods

We tested a custom dense attention network (DAN) machine learning model to discrimi

SUBMITTER: Perellon-Alfonso R 

PROVIDER: S-EPMC10560761 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature