- AutorIn
- Amirali Vahid Technische Universität Dresden, Faculty of Medicine, Department of Child and Adolescent Psychiatry, Cognitive Neurophysiology
- Moritz MückschelTechnische Universität Dresden, Faculty of Medicine, Department of Child and Adolescent Psychiatry, Cognitive Neurophysiology
- Sebastian StoberOtto von Guericke University Magdeburg, Faculty of Computer Science, Institute for Intelligent Cooperating Systems, Artificial Intelligence Lab
- Ann-Kathrin Stock
- Christian Beste
- Titel
- Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-902488
- Quellenangabe
- Communications biology
Erscheinungsjahr: 2022
Jahrgang: 5
E-ISSN: 2399-3642
Artikelnummer: 148 - Erstveröffentlichung
- 2022
- Abstract (EN)
- Goal-directed actions frequently require a balance between antagonistic processes (e.g., executing and inhibiting a response), often showing an interdependency concerning what constitutes goal-directed behavior. While an inter-dependency of antagonistic actions is well described at a behavioral level, a possible inter-dependency of underlying processes at a neuronal level is still enigmatic. However, if there is an interdependency, it should be possible to predict the neurophysiological processes underlying inhibitory control based on the neural processes underlying speeded automatic responses. Based on that rationale, we applied artificial intelligence and source localization methods to human EEG recordings from N = 255 participants undergoing a response inhibition experiment (Go/Nogo task). We show that the amplitude and timing of scalp potentials and their functional neuroanatomical sources during inhibitory control can be inferred by conditional generative adversarial networks (cGANs) using neurophysiological data recorded during response execution. We provide insights into possible limitations in the use of cGANs to delineate the interdependency of antagonistic actions on a neurophysiological level. Nevertheless, artificial intelligence methods can provide information about interdependencies between opposing cognitive processes on a neurophysiological level with relevance for cognitive theory.
- Andere Ausgabe
- Link zum Artikel der zuerst in der Zeitschrift „Communications biology” bei Springer Link erschienen ist.
DOI: 10.1038/s42003-022-03091-8 - Freie Schlagwörter (DE)
- Kognitive Neurowissenschaften, Menschliches Verhalten, zielgerichtetes Verhalten
- Freie Schlagwörter (EN)
- Cognitive neuroscience, Human behaviour, goal-directed behavior
- Klassifikation (DDC)
- 570
- Verlag
- Springer Nature, London
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
SFB 940: Volition und kognitive Kontrolle: Mechanismen, Modulatoren, Dysfunktionen
B08 - Funktionell-neuroanatomische und neurobiologische Modulatoren der Interaktion von kognitiver Kon-trolle und automatischen Prozessen
ID: 178833530 - Deutsche Forschungsgemeinschaft (DFG)
TRR 265: Verlust und Wiedererlangung der Kontrolle über den Drogenkonsum: Von Trajektorien über Mechanismen bis hin zu Interventionen
B07 - Modulation suchtbedingter Veränderungen zur Verbesserung der kognitiven Kontrolle bei AUD durch nicht-invasive Hirnstimulation
ID: 402170461 - Volkswagen Stiftung Experiment
ID: FOR 2698 - Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-902488
- Veröffentlichungsdatum Qucosa
- 18.04.2024
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0