Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses

Abstract Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether “classical” ERPs are truly the best reflection or even causal to observable var...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Amirali Vahid, Moritz Mückschel, Andres Neuhaus, Ann-Kathrin Stock, Christian Beste
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Neurophysiological Features Event-related Potentials (ERP) Behavioral Performance Sequential Floating Forward Selection (SFFS) NoGo Trials Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9de6ebc91344907a2f8bc1bdf5ba4bb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e9de6ebc91344907a2f8bc1bdf5ba4bb
record_format	dspace
spelling	oai:doaj.org-article:e9de6ebc91344907a2f8bc1bdf5ba4bb2021-12-02T15:08:27ZMachine learning provides novel neurophysiological features that predict performance to inhibit automated responses10.1038/s41598-018-34727-72045-2322https://doaj.org/article/e9de6ebc91344907a2f8bc1bdf5ba4bb2018-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-34727-7https://doaj.org/toc/2045-2322Abstract Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether “classical” ERPs are truly the best reflection or even causal to observable variations in behavior. Here, we used a data-driven strategy to extract features from neurophysiological data of n = 240 healthy young individuals who performed a Go/Nogo task and used machine learning methods in combination with source localization to identify the best predictors of inter-individual performance variations. Both Nogo-N2 and Nogo-P3 yielded predictions close to chance level, but a feature in between those two processes, associated with motor cortex activity (BA4), predicted group membership with up to ~68%. We further found two Nogo-associated features in the theta and alpha bands, that predicted behavioral performance with up to ~78%. Notably, the theta band feature contributed most to the prediction and occurred at the same time as the predictive ERP feature. Our approach provides a rigorous test for established neurophysiological correlates of response inhibition and suggests that other processes, which occur in between the Nogo-N2 and P3, might be of equal, if not even greater, importance.Amirali VahidMoritz MückschelAndres NeuhausAnn-Kathrin StockChristian BesteNature PortfolioarticleNeurophysiological FeaturesEvent-related Potentials (ERP)Behavioral PerformanceSequential Floating Forward Selection (SFFS)NoGo TrialsMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-15 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neurophysiological Features Event-related Potentials (ERP) Behavioral Performance Sequential Floating Forward Selection (SFFS) NoGo Trials Medicine R Science Q
spellingShingle	Neurophysiological Features Event-related Potentials (ERP) Behavioral Performance Sequential Floating Forward Selection (SFFS) NoGo Trials Medicine R Science Q Amirali Vahid Moritz Mückschel Andres Neuhaus Ann-Kathrin Stock Christian Beste Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
description	Abstract Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether “classical” ERPs are truly the best reflection or even causal to observable variations in behavior. Here, we used a data-driven strategy to extract features from neurophysiological data of n = 240 healthy young individuals who performed a Go/Nogo task and used machine learning methods in combination with source localization to identify the best predictors of inter-individual performance variations. Both Nogo-N2 and Nogo-P3 yielded predictions close to chance level, but a feature in between those two processes, associated with motor cortex activity (BA4), predicted group membership with up to ~68%. We further found two Nogo-associated features in the theta and alpha bands, that predicted behavioral performance with up to ~78%. Notably, the theta band feature contributed most to the prediction and occurred at the same time as the predictive ERP feature. Our approach provides a rigorous test for established neurophysiological correlates of response inhibition and suggests that other processes, which occur in between the Nogo-N2 and P3, might be of equal, if not even greater, importance.
format	article
author	Amirali Vahid Moritz Mückschel Andres Neuhaus Ann-Kathrin Stock Christian Beste
author_facet	Amirali Vahid Moritz Mückschel Andres Neuhaus Ann-Kathrin Stock Christian Beste
author_sort	Amirali Vahid
title	Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
title_short	Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
title_full	Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
title_fullStr	Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
title_full_unstemmed	Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
title_sort	machine learning provides novel neurophysiological features that predict performance to inhibit automated responses
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/e9de6ebc91344907a2f8bc1bdf5ba4bb
work_keys_str_mv	AT amiralivahid machinelearningprovidesnovelneurophysiologicalfeaturesthatpredictperformancetoinhibitautomatedresponses AT moritzmuckschel machinelearningprovidesnovelneurophysiologicalfeaturesthatpredictperformancetoinhibitautomatedresponses AT andresneuhaus machinelearningprovidesnovelneurophysiologicalfeaturesthatpredictperformancetoinhibitautomatedresponses AT annkathrinstock machinelearningprovidesnovelneurophysiologicalfeaturesthatpredictperformancetoinhibitautomatedresponses AT christianbeste machinelearningprovidesnovelneurophysiologicalfeaturesthatpredictperformancetoinhibitautomatedresponses
_version_	1718388159215042560

Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses

Ejemplares similares