Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition
| العنوان: | Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition |
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| المؤلفون: | Laura M. Rueda-Delgado, Kathy L. Ruddy, Robert Whelan, Nadja Enz |
| المصدر: | J Neurosci |
| بيانات النشر: | Society for Neuroscience, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Male, Models, Neurological, Stop signal, Electroencephalography, Biology, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Text mining, Basal ganglia, medicine, Humans, Beta Rhythm, Beta (finance), Research Articles, 030304 developmental biology, 0303 health sciences, medicine.diagnostic_test, business.industry, General Neuroscience, Brain, Inhibition, Psychological, medicine.anatomical_structure, Scalp, Female, business, Neuroscience, 030217 neurology & neurosurgery, Motor cortex |
| الوصف: | In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesised that the right inferior frontal cortex plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalography-derived beta rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal beta power often observed prior to successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of beta activity. There is evidence that the rate of beta bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of beta burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) electroencephalography Stop Signal Task dataset (n=218) to examine averaged normalised beta power, beta burst rate and beta burst ‘volume’ (which we defined as burst duration x frequency span x amplitude). We first sought to optimise the beta burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful vs. failed stopping and to (2) predict individual Stop Signal Reaction Time. Beta burst volume was significantly more predictive of successful and fast stopping than beta burst rate and normalised beta power. The classification model generalised to an external dataset (n=201). We suggest beta burst volume is a sensitive and reliable measure for investigation of human response inhibition. SIGNIFICANCE STATEMENT The electroencephalography-derived beta rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of beta activity that contribute to successful and fast inhibition. In order to search for the most relevant features of beta activity – across the whole scalp and with high temporal precision – we employed machine learning on two large datasets. Spatial and temporal features of beta burst ‘volume’ (duration x frequency span x amplitude) predicted response inhibition outcomes in our data significantly better than beta burst rate and normalised beta power. These findings suggest that multidimensional measures of beta bursts, such as burst volume, can add to our understanding of human response inhibition. |
| تدمد: | 1529-2401 0270-6474 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d129818519e50ad4b942b3d7f6412d58 https://doi.org/10.1523/jneurosci.2231-20.2021 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....d129818519e50ad4b942b3d7f6412d58 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15292401 02706474 |
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