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Neural markers of impending task performance

即将执行任务的神经标记

基本信息

批准号：
10218883
负责人：
THEODORE P ZANTO
金额：
$ 16.15万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10218883
关键词：
Address Adult Age Age-associated memory impairment Aging Alzheimer&apos s disease risk Applications Grants Attention Behavior Behavioral Brain Clinical Cognition Cognitive Coupling Data Data Set Development Discrimination Elderly Electroencephalogram Ensure Frequencies Future Goals Impaired cognition Individual Individual Differences Intervention Life Link Longevity Measures Memory Modeling Nature Neuronal Plasticity Older Population Outcome Parietal Participant Patients Pattern Performance Phase Population Population Heterogeneity Process Prophylactic treatment Protocols documentation Public Health Rehabilitation therapy Research Rest Risk Role Series Short-Term Memory Signal Transduction Task Performances Testing Therapeutic Training United States Vulnerable Populations age related aging population amnestic mild cognitive impairment baby boomer base behavioral outcome brain behavior cognitive ability cognitive enhancement cognitive function cognitive load cognitive performance cognitive task cognitive training deep learning deep learning algorithm demographics executive function improved indexing mild cognitive impairment neuromechanism novel predictive modeling rehabilitation paradigm relating to nervous system secondary analysis sustained attention targeted biomarker targeted treatment young adult

项目摘要

PROJECT ABSTRACT The neural mechanisms underlying successful cognitive performance are not well understood. In this R03 project, we will thoroughly investigate the ability to predict behavioral improvements through changes in shared underlying neural mechanisms. Identifying neural metrics that predict individual performance gains will ensure more reliable behavioral outcomes across diverse populations with varying cognitive capabilities and age ranges. Additionally, understanding the neural mechanisms underlying successful cognition will allow for rescuing of lost cognitive abilities and prophylaxis of cognitive decline in aging and other vulnerable populations such as in mild cognitive impairment (MCI). We recently identified several signals in the electroencephalogram (EEG) that correlated with cognitive performance improvements following and intervention with cognitive training and/or noninvasive neurostimulation. Specifically, frontal midline power in the theta band (4-8 Hz) correlates with improved divided attention ability and transfer to improved sustained attention ability in older adults (Anguera et al., 2013), and frontoparietal connectivity in the theta band is linked to improved attention (Anguera et al., 2013) and working memory (Jones et al., 2017). In addition to band- limited theta, we found that cross-frequency coupling between frontal theta oscillations and temporo-parietal gamma (>30 Hz) activity tracked individual performance gains in working memory following intervention with training and neurostimulation (Jones et al., 2020). These findings suggest that theta oscillations, as measured by power, connectivity, and cross-frequency coupling in task-relevant regions, underlie individual differences in cognitive performance. Indeed, the application of noninvasive neurostimulation confirmed the critical role of theta oscillations in divided attention by showing that direct entrainment of frontal theta oscillations further enhanced performance (Hsu et al., 2017, 2018). The proposed research will determine the mechanisms by which theta oscillations, assessed at baseline, predict subsequent cognitive performance outcomes in aging and vulnerable populations. Critically, we identified preliminary evidence that an individual's intrinsic peak theta frequency predicted the efficacy of theta entrainment with neurostimulation on divided attention ability. This important preliminary result demonstrates that a nuanced approach to tailoring an intervention to the individual is feasible. Here, our analyses seek to predict subsequent performance during divided attention prior to intervention (Aim 1) and before trial onset in a range of cognitive tasks (Aim 2). We will conduct comprehensive regularized multiple regression and deep learning analyses on 13 existing EEG datasets, several of which share the same divided attention task, yet vary in participant demographics, including age and cognitive capability (healthy older and younger adults, multi-domain amnestic MCI patients), to develop a predictive model for an individually-tailored cognitive intervention. The results will inform future research that seeks to maximize the reliability of intervention protocols across demographics at any age or cognitive capability.

项目摘要成功的认知表现背后的神经机制还没有得到很好的理解。在此R 03 项目，我们将彻底调查的能力，预测行为的改善，通过改变共享潜在的神经机制识别预测个人绩效收益的神经指标将确保在具有不同认知能力和年龄的不同人群中获得更可靠的行为结果范围.此外，理解成功认知背后的神经机制将允许挽救失去的认知能力和预防老年人和其他易受伤害者的认知能力下降如轻度认知障碍（MCI）人群。我们最近发现了几个信号，脑电图（EEG）与以下认知表现改善相关，通过认知训练和/或非侵入性神经刺激进行干预。具体来说，额叶中线功率在 θ波段（4-8 Hz）与改善的分散注意力能力和转移到改善的持续注意力能力相关。老年人的注意力能力（Anguera等人，2013年），和theta带的额顶叶连接是相关的提高注意力（Anguera等人，2013）和工作记忆（Jones等人，2017年）。除了乐队- 有限的θ，我们发现，交叉频率之间的耦合额θ振荡和颞顶叶伽马（>30 Hz）活动跟踪了在干预后工作记忆中的个体表现增益，训练和神经刺激（Jones等人，2020年）。这些发现表明，θ振荡，如测量在任务相关区域的功率，连接性和交叉频率耦合，是个体差异的基础，认知绩效事实上，非侵入性神经刺激的应用证实了 θ振荡在分散注意力，表明直接夹带正面θ振荡进一步增强的性能（Hsu等人，2017年、2018年）的情况。拟议的研究将确定机制，在基线时评估的θ振荡预测了随后的衰老认知表现结果，和易受伤害的人口。重要的是，我们发现了初步证据，表明一个人的内在峰值θ 频率预测了神经刺激θ夹带对分散注意能力的有效性。这重要的初步结果表明，一个细致入微的方法来调整干预个人是可行的在这里，我们的分析试图预测随后的表现，在分散注意力之前，干预（目标1）和试验开始前的一系列认知任务（目标2）。我们将进行全面的对13个现有EEG数据集进行正则化多元回归和深度学习分析，其中几个数据集共享相同的分散注意力任务，但参与者人口统计数据不同，包括年龄和认知能力能力（健康的老年人和年轻人，多领域遗忘型MCI患者），以制定一个预测一个个性化的认知干预模型。这些结果将为未来的研究提供信息，最大限度地提高干预方案在任何年龄或认知能力的人口统计学中的可靠性。