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Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia [Elektronski vir]Perellón Alfonso, Ruben ...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.Source: Frontiers in psychiatry. - ISSN 1664-0640 (Vol. 14, [article no.] 1205119, 2023, str. 1-12)Type of material - e-article ; adult, seriousPublish date - 2023Language - englishCOBISS.SI-ID - 166182915
Author
Perellón Alfonso, Ruben |
Oblak, Aleš |
Kuclar, Matija |
Škrlj, Blaž |
Škodlar, Borut |
Pregelj, Peter |
Repovš, Grega |
Bon, Jurij
Topics
shizofrenija |
delovni spomin |
elektroencefalografija EEG |
kontralateralna negativnost |
gosto pozornostno omrežje |
gosto pozornostno omrežje |
schizophrenia |
working memory |
electroencephalography EEG |
contralateral delay negativity |
dense attention network DAN |
dense attention network DAN
Author | Perellón Alfonso, Ruben ... |
Title | Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia [Elektronski vir] |
Publication date | 2023-09-25 |
COBISS.SI-ID | 166182915 |
Publication version in repository | Publisher's version |
Publication licence | Creative Commons Attribution 4.0 International |
Embargo | Immediate publication for public |
Project(s) from which the publication was funded
Title | Acronym | Project ID | Funder |
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“la Caixa” Foundation | 100010434, LCF/BQ/DI19/11730050 |
"la Caixa” Foundation |
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Juan de la Cierva-Formacion research grant | FJC2021-047380- I |
Spanish Ministry of Science and Innovation |
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“la Caixa” Foundation | 100010434, LCF/BQ/DI18/11660026 |
"la Caixa” Foundation |
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Marie Skłodowska-Curie grant | 713673 |
European Union’s Horizon 2020 |
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Psihološki in nevroznanstveni vidiki kognicije | P5-0110-2019 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
|
Fiziološki mehanizmi nevroloških motenj in bolezni | P3-0338-2020 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
|
Vpliv individualizacije stimulacijske frekvence v realnem času na učinkovitost zdravljenja depresije s transkranialno magnetno stimulacijo | J3-1763-2019 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
|
Razstavljanje kognicije: Mehanizmi in reprezentacije delovnega spomina | J3-9264-2018 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
Files that belong to the publication
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https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1205119/full |
https://repozitorij.uni-lj.si/IzpisGradiva.php?id=153575 |
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Perellón Alfonso, Ruben | |
Oblak, Aleš | 55036 |
Kuclar, Matija | 39116 |
Škrlj, Blaž | 52066 |
Škodlar, Borut | 22235 |
Pregelj, Peter | 18323 |
Repovš, Grega | 17893 |
Bon, Jurij | 33621 |
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