Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2025-07-08 eCollection Date: 2025-01-01 DOI:10.3389/fninf.2025.1563893
Karina J Maciejewska
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Abstract

Introduction: This paper describes an experimental work using machine learning (ML) as a "decoding for interpretation" to understand the brain's physiology better.

Methods: Multivariate pattern analysis (MVPA) was used to decode the patterns of event-related potentials (ERPs, brain responses to stimuli) in a visual oddball task. The ERPs were measured before (run 1) and after (30 min-run 2, 90 min-run 3) a single dose of an energy dietary supplement with only a small amount of caffeine.

Results: Its effect on ERPs was successfully decoded. Above-chance decoding accuracies were obtained between ∼350 and 450 ms (corresponds to P3 peak) after stimulus onset for both the placebo and study groups, whereas between ∼200 and 260 ms (corresponds to P2 waveform) only in the placebo group. Moreover, the decoding accuracies were significantly higher in the placebo than in the study group in the 200-250 ms and 450-500 ms time bins. Our previously reported findings showed an increase in P3 amplitude among the runs only in the placebo group, indicating a reduction of mental fatigue caused by the supplementation.

Discussion: Thus, this paper extends these results, showing that the dietary supplement affected the brain's neural activity related to the attention-related processing of the visual stimuli in the oddball task already at the early processing stage. This implies that inhibiting the fatigue-related brain changes after only a single dose of a dietary neurostimulant acts on early and late processing stages. This emphasizes the value of decoding for interpretation in ERP research. The results also point out the necessity of controlling the uptake of dietary supplements before the neurophysiological examinations.

解码事件相关电位:单剂量能量膳食补充剂作用于比我们想象的更早的大脑过程。
简介:本文描述了一项使用机器学习(ML)作为“解码解释”的实验工作,以更好地理解大脑的生理。方法:采用多元模式分析(Multivariate pattern analysis, MVPA)对视觉奇球任务中脑刺激反应的事件相关电位(event- correlation potential, ERPs)模式进行解码。在(跑1分钟)和(跑2分钟30分,跑3分钟90分)服用一剂只含少量咖啡因的能量膳食补充剂之前和之后测量erp。结果:成功解读了其对erp的影响。在刺激开始后,安慰剂组和研究组的解码准确率都在~ 350 ~ 450 ms(对应于P3峰)之间,而只有安慰剂组的解码准确率在~ 200 ~ 260 ms(对应于P2波形)之间。此外,在200-250毫秒和450-500毫秒的时间内,安慰剂组的解码准确率显著高于研究组。我们之前报道的研究结果显示,仅在安慰剂组中,P3振幅增加,表明补充引起的精神疲劳减少。讨论:因此,本文扩展了这些结果,表明膳食补充剂已经在早期加工阶段就影响了与视觉刺激的注意相关加工相关的大脑神经活动。这表明,仅在单一剂量的饮食神经兴奋剂作用于早期和晚期加工阶段后,抑制疲劳相关的大脑变化。这强调了解码解释在ERP研究中的价值。结果还指出了在神经生理检查前控制膳食补充剂摄取的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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