20.500.12556/RUL-153575 Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia 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 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. Results: The 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. Discussion: These results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities. schizophrenia working memory contralateral delay negativity electroencephalography EEG dense attention network DAN shizofrenija delovni spomin elektroencefalografija EEG kontralateralna negativnost gosto pozornostno omrežje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-01-16 13:17:32 2024-01-16 13:17:40 2024-01-17 03:43:22 0000-00-00 00:00:00 2023 0 0 12 str. art. 1205119 Vol. 14 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 159.91:616.895.8 1664-0640 10.3389/fpsyt.2023.1205119 166182915 Repovs_Dense_attention_network_identifies_EEG_abnormalities_during_working_memory_performance_of_patients_with_schizophrenia.pdf Repovs_Dense_attention_network_identifies_EEG_abnormalities_during_working_memory_performance_of_patients_with_schizophrenia.pdf 1 4C376F9C3E79B8D8AD14DDD6F5CE1994 11b9f81c1a8e892f34a47936dbfa53fb434b5f48afc35553a0b7223c67c04c15 29eeee14-b46a-11ee-9139-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=179655 https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1205119/full 1 3fadace2-b469-11ee-9139-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=179654 Filozofska fakulteta Medicinska fakulteta 0 0 0