A comprehensive decoding of cognitive load

Q2 Health Professions Smart Health Pub Date : 2022-12-01 DOI:10.1016/j.smhl.2022.100336
Xishi Zhu , Soroush Korivand , Kittson Hamill , Nader Jalili , Jiaqi Gong
{"title":"A comprehensive decoding of cognitive load","authors":"Xishi Zhu ,&nbsp;Soroush Korivand ,&nbsp;Kittson Hamill ,&nbsp;Nader Jalili ,&nbsp;Jiaqi Gong","doi":"10.1016/j.smhl.2022.100336","DOIUrl":null,"url":null,"abstract":"<div><p><span>The extent of neurophysiological activation, such as brain activities<span><span>, eye movement, and skin conductance, can vary as a joint function of cognitive load. These functions are the basis of models that describe human behavior and neural mechanisms for diagnosing and treating </span>cognitive disorders, such as Alzheimer’s disease, </span></span>mild cognitive impairment<span>, and stroke-related cognitive dysfunction. Such models can enhance our understanding of the disease processes and enable crucial applications like predicting cognitive trajectories for early diagnosis. However, despite the success of these models in predicting early-stage cognitive impairment and decline, their practical use is limited in clinics because most of the models focus on utilizing one or two factors of the neurophysiological activation to achieve prediction. Still, little is known about the mechanisms through which the task difficulty and cognitive demands affect the expression of neurophysiological activation and whether there is an expression difference under cognitive task demands. The purpose of this paper is to provide a comprehensive examination of the neurophysiological expression difference and mechanisms under various cognitive loads. We designed an experimental protocol and developed a data processing framework to explicitly examine brain activity and eye movement under various levels of cognitive task difficulty and find that (1) eye movement is a readout of cognitive processes, but it is a joint function of task difficulty, brain activity, and skin conductance; (2) brain activity has specific patterns related to the various levels of cognitive load and exerts its influence on predicting the dynamics of cognitive processes. These findings suggest that neuroimaging studies comparing task-related neurophysiological activation must be examined and interpreted in a holistic view of neural mechanisms.</span></p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"26 ","pages":"Article 100336"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352648322000708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Health Professions","Score":null,"Total":0}
引用次数: 6

Abstract

The extent of neurophysiological activation, such as brain activities, eye movement, and skin conductance, can vary as a joint function of cognitive load. These functions are the basis of models that describe human behavior and neural mechanisms for diagnosing and treating cognitive disorders, such as Alzheimer’s disease, mild cognitive impairment, and stroke-related cognitive dysfunction. Such models can enhance our understanding of the disease processes and enable crucial applications like predicting cognitive trajectories for early diagnosis. However, despite the success of these models in predicting early-stage cognitive impairment and decline, their practical use is limited in clinics because most of the models focus on utilizing one or two factors of the neurophysiological activation to achieve prediction. Still, little is known about the mechanisms through which the task difficulty and cognitive demands affect the expression of neurophysiological activation and whether there is an expression difference under cognitive task demands. The purpose of this paper is to provide a comprehensive examination of the neurophysiological expression difference and mechanisms under various cognitive loads. We designed an experimental protocol and developed a data processing framework to explicitly examine brain activity and eye movement under various levels of cognitive task difficulty and find that (1) eye movement is a readout of cognitive processes, but it is a joint function of task difficulty, brain activity, and skin conductance; (2) brain activity has specific patterns related to the various levels of cognitive load and exerts its influence on predicting the dynamics of cognitive processes. These findings suggest that neuroimaging studies comparing task-related neurophysiological activation must be examined and interpreted in a holistic view of neural mechanisms.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
认知负荷的综合解码
神经生理激活的程度,如大脑活动、眼动和皮肤传导,可以作为认知负荷的联合功能而变化。这些功能是描述人类行为和诊断和治疗认知障碍(如阿尔茨海默病、轻度认知障碍和中风相关认知功能障碍)的神经机制的模型的基础。这些模型可以增强我们对疾病过程的理解,并使预测早期诊断的认知轨迹等关键应用成为可能。然而,尽管这些模型在预测早期认知障碍和衰退方面取得了成功,但它们在临床中的实际应用受到限制,因为大多数模型都侧重于利用神经生理激活的一两个因素来实现预测。然而,任务难度和认知需求对神经生理激活表达的影响机制以及认知任务需求下是否存在表达差异尚不清楚。本文旨在全面探讨不同认知负荷下的神经生理表达差异及其机制。我们设计了实验方案并开发了数据处理框架,明确考察了不同认知任务难度下的脑活动和眼动,发现:(1)眼动是认知过程的一个读出,但它是任务难度、脑活动和皮肤电导的共同作用;(2)大脑活动与不同水平的认知负荷相关,具有特定的模式,并对认知过程的动态预测产生影响。这些发现表明,比较任务相关的神经生理激活的神经影像学研究必须在神经机制的整体观点中进行检查和解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Smart Health
Smart Health Computer Science-Computer Science Applications
CiteScore
6.50
自引率
0.00%
发文量
81
期刊最新文献
Editorial Board Smart health practices: Strategies to improve healthcare efficiency through digital twin technology Human knowledge-based artificial intelligence methods for skin cancer management: Accuracy and interpretability study SAFE: Sound Analysis for Fall Event detection using machine learning Latent Space Representation of Adversarial AutoEncoder for Human Activity Recognition: Application to a low-cost commercial force plate and inertial measurement units
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1