ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

心理科学进展 ›› 2024, Vol. 32 ›› Issue (6): 873-885.doi: 10.3724/SP.J.1042.2024.00873

• 研究构想 •    下一篇

老年遗忘型轻度认知障碍执行功能的神经机制及数字干预

刘海宁1(), 董现玲2, 刘海虹1, 刘艳丽2, 李现文3   

  1. 1承德医学院心理学系
    2承德医学院生物医学工程系, 河北 承德 067000
    3南京医科大学护理学院, 南京 211166
  • 收稿日期:2023-10-13 出版日期:2024-06-15 发布日期:2024-04-07
  • 通讯作者: 刘海宁, E-mail: liuhn0401@sina.com
  • 基金资助:
    国家自然科学基金项目(32300931);教育部人文社科基金项目(23C10093001);河北省自然科学基金项目(C2022406010)

Neural mechanisms and digital promotion of executive function in older adults with amnestic mild cognitive impairment

LIU Hai-ning1(), DONG Xian-ling2, LIU Hai-hon1, LIU Yan-li2, LI Xian-wen3   

  1. 1Department of Psychology, Chengde Medical University, Chengde 067000, China
    2Department of Biomedical Engineering, Chengde Medical University, Chengde 067000, China
    3School of Nursing, Nanjing Medical University, Nanjing 211166, China
  • Received:2023-10-13 Online:2024-06-15 Published:2024-04-07

摘要:

阿尔茨海默病具有极高的发病率和致死率。遗忘型轻度认知障碍(Amnestic Mild Cognitive Impairment, aMCI)作为临床前驱期, 探究其形成和发展机制有助于预防阿尔茨海默病的发生。现有研究显示, 多个执行域缺陷与aMCI记忆衰退密切相关, 但尚未回答何种执行域是关键致病因子、关键干预治疗靶标等科学问题。为突破以往研究将执行功能视作整体抑或割裂元素的局限性, 本研究拟从执行功能结构全貌着眼, 在提出aMCI执行功能与记忆损害关系假说的基础上, 利用脑电技术系统考察aMCI抑制、刷新和转换三种执行功能子成分的时域、时频和动态脑网络特征; 并结合三维卷积神经网络筛选、识别执行功能缺陷的特异性神经靶标, 探索将抑制域相关神经标记物加入aMCI早期识别的可能性; 最后, 通过纵向因果设计分析不同靶向数字干预对aMCI患者的训练效果及神经基础, 以揭示抑制域相关额顶控制网络在干预中的重要作用。本研究有望从计算认知神经视角阐明抑制是aMCI执行功能缺损和干预的认知新靶点, 进而为aMCI早期识别和制定精准化诊疗方案提供循证依据。

关键词: 执行功能, 遗忘型轻度认知障碍, 认知神经机制, 数字干预, 深度学习

Abstract:

Amnestic mild cognitive impairment (aMCI), a clinical prodromal stage, has a high probability of progressing to Alzheimer’s disease (AD). Therefore, new methods for the early identification and intervention are urgently required. Executive function deficits are characteristic of the initial stage of aMCI and may cause or aggravate memory symptoms, thereby increasing the risk of AD. Therefore, considering executive function as the entry point, it is possible to identify new targets for the early identification and intervention in aMCI. However, the neuropathological mechanisms underlying executive function in patients with aMCI remain unclear. The three subcomponents of inhibition, updating, and switching show both structural identity and specificity. To overcome the limitations of previous studies that viewed executive function as a whole or as separate elements, this study aims to focus on the overall structure of executive function and propose a hypothesis regarding the relationship between executive function and memory impairment in older patients with aMCI. It is also speculated that training the inhibitory subcomponents involving a larger range of frontoparietal control networks may be more helpful in attenuating or delaying memory impairment in patients with aMCI. Therefore, this study will follow the path of "assessment of spatiotemporal characteristics of cranial nerves - identification of characteristic targets - targeted digital intervention" for executive function in patients with aMCI, and will conduct verification by combining behavioral and ERP technology with deep learning and longitudinal intervention.
Study 1 will use high temporal resolution electroencephalography (EEG) technology as the main research method and adopt a two-factor mixed design; aMCI group vs. cognitively normal older individual group, and two stimulation types. By comparing the performance of Go/No-go, N-back, and set-switching tasks between the aMCI and normal cognitive older individual groups, the time-domain, time-frequency, and dynamic brain network characteristics of the patients with aMCI in the three executive function subcomponents of inhibition, updating, and switching will be systematically examined. Using this method, the neural mechanisms of these executive function subcomponents in patients with aMCI can be revealed, providing a new perspective for understanding their roles in memory impairment.
Study 2 will use deep learning to integrate the advantages of EEG temporal and spatial multidimensional data to construct a three-dimensional convolutional neural network classification model for the three executive function subcomponents of inhibition, updating, and switching in patients with aMCI. The focus is to examine the convolutional neural network classification model constructed based on the EEG characteristics of inhibitory function, which is a potential common cognitive process of different executive function subcomponents, namely the G factor. This study will analyze spatiotemporal features by extracting frequency-domain features, multi-layer convolution, and pooling layers from EEG data as inputs to the classifier. Through feature combination cluster analysis and 5-fold cross-validation, a model for aMCI inhibition, updating, and switching will be constructed, and cross-classification and cross-population validation conducted to evaluate the accuracy, sensitivity, and specificity of the model. It aims to more accurately identify and differentiate different executive function deficits in patients with aMCI and provide a new theoretical and practical basis for the early identification and treatment of aMCI.
To explore effective intervention strategies, this study also includes an analysis of the training effects and neural basis of different targeted digital interventions in patients with aMCI. Study 3 will adopt a 4 (inhibition group/refresh group/switching group/active control group) × 3 (measurement time: pretest/post-test 1/post-test 2) two-factor mixed design. The inhibition group, refresh group and switching group will use the difficulty-adaptive targeted digital interventions of "Whacking a Mole,” "Picturesque,” and "Dual Purpose,” respectively, while the active control group will only use processing speed training. Participants will be trained three times a week for 30 min each time, and post-tests will be conducted at weeks four and eight to examine intervention, transfer, and dose effects, as well as changes in corresponding neuroelectrophysiological indicators and dynamic brain network connections. The focus is on whether digital interventions targeting the inhibitory domain can improve episodic memory in patients with aMCI better than refreshing and switching targeted digital interventions.
This study aims to clarify that inhibition is a new cognitive target for executive function impairment and intervention in aMCI from a computational cognitive neural perspective, thereby providing an evidence-based basis for the early identification of aMCI and the formulation of precise diagnosis and treatment plans.

Key words: executive function, amnestic mild cognitive impairment, cognitive neural mechanism, digital intervention, deep learning

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