Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan

Brain Informatics

Research

Claudia Voelcker-Rehage¹  Stephanie Fröhlich¹  Julian Rudisch¹  Eva-Maria Reuter²  Christian Goelz³  Solveig Vieluf⁴  Ben Godde⁵ 

[1] Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster, Wilhelm-Schickard-Str. 8, 48149, Münster, Germany;Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany;Institute of Sports Medicine, Paderborn University, Paderborn, Germany;Institute of Sports Medicine, Paderborn University, Paderborn, Germany;Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA;School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany;

关键词: Machine learning;    Decoding;    EEG/ERP;    Flanker;    Selective attention;    Development;   

DOI  :  10.1186/s40708-023-00190-y

 received in 2023-01-20, accepted in 2023-04-01,  发布年份 2023

来源: Springer

【 摘 要 】

The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime.Graphical Abstract

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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