Delineation of a task-demand dependent rostrocaudal gradient in the salience network

显着网络中任务需求相关的轴尾梯度的描绘

• 批准号: RGPIN-2017-05854
• 负责人: Woodward, Todd
• 金额: $ 2.33万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710571
• 关键词: Delineation; task; demand; dependent; rostrocaudal

The salience network is characterized by co-activation of the dorsal anterior cingulate and the bilateral insula, and has been assigned fundamental cognitive processes, such as signalling that a task context is important, or switching between other large-scale networks to facilitate access to attention. The dACC and insula have been proposed to follow a roughly rostral/caudal division; therefore, the salience network should also follow a rostrocaudal gradient, and delineation of this, and its functional significance, is an important first step in beginning to understanding the cognitive functions associated with the vast array of functional brain networks involved in an array of cognitive tasks. We have observed such a task-demand dependent rostrocaudal gradient in the salience network in our published studies, such that coordinated salience network activity is most posterior when sensorimotor processing is required, intermediate when externally oriented attention is required, and most anterior when reasoning and inner speech cognitive operations are executed. This organization is congruent with evolutionary theories of brain organization, with more frontal cognitive processing corresponding to exclusively human cognitive processes like reasoning and inner speech. To date this theory of task-demand dependent rostrocaudal gradient in the salience network has been developed based on observations across experiments, across samples, and across data collection sites. The proposed 5 year plan involves demonstrating control over these different configurations of the salience network using within-subjects manipulations of modality of stimulus presentation (visual, verbal and auditory) and modality of data collection (fMRI and EEG) using variations of the N-back and Sternberg working memory task platforms. Displays in the verbal modality include series of letters, nonsense syllables and words, presented visually or auditorily. Stimulus displays in the non-verbal modality include designs, spatial configurations or motions presented visually, or tones or other non-verbal sounds presented auditorily, possibly also depicting motion or spatial configurations. All networks that co-activate with the various salience configuration are anticipated to be modality dependent. The effect of each variable, and combinations, on performance and the rostrocaudal gradient of the salience network, is an important first step in beginning to understanding the cognitive functions associated with the vast array of functional brain networks. This will be explored over 10 multimodal experiments proposed to extend over 5 years. This research is important to Canadians because a precise understanding of the function of brain networks is required for manipulation of those brain networks, through brain training and/or neuromodulation, to enhance cognitive functioning.

突出网络的特征是前扣带背侧和双侧脑岛的共同激活，并被分配到基本的认知过程中，例如信号表明任务上下文是重要的，或者在其他大规模网络之间切换以促进注意力的获取。dACC和岛叶大致遵循吻侧/尾侧的划分；因此，显著性网络也应该遵循背侧-尾侧梯度，对这一梯度及其功能意义的描述，是开始理解与一系列认知任务中涉及的大量功能性大脑网络相关的认知功能的重要第一步。在我们发表的研究中，我们已经观察到显著性网络中存在这样一种任务需求依赖的背侧侧梯度，因此，当需要感觉运动处理时，协调的显著性网络活动最后侧，当需要外部导向注意时，协调的显著性网络活动处于中间位置，而当执行推理和内部言语认知操作时，协调的显著性网络活动最前侧。这种组织与大脑组织的进化理论是一致的，更多的正面认知处理与人类的认知过程相对应，比如推理和内心语言。迄今为止，这一突出性网络中任务需求依赖的背侧方向梯度理论是基于跨实验、跨样本和跨数据收集点的观察而发展起来的。提出的5年计划包括通过使用N-back和Sternberg工作记忆任务平台的变化，在受试者内部操纵刺激呈现模式（视觉、言语和听觉）和数据收集模式（fMRI和EEG）来展示对这些不同配置的显著性网络的控制。在语态上的表现包括一系列的字母、无意义的音节和单词，以视觉或听觉的形式呈现。非语言形式的刺激显示包括视觉呈现的设计、空间结构或运动，或听觉呈现的音调或其他非语言声音，也可能描绘运动或空间结构。所有与各种显著配置共同激活的网络预计都是模态依赖的。每个变量和组合对显著性网络的表现和背侧-尾侧梯度的影响，是开始理解与大量功能性大脑网络相关的认知功能的重要第一步。这将在10个多模式实验中进行探索，计划持续5年以上。这项研究对加拿大人来说很重要，因为通过大脑训练和/或神经调节来提高认知功能，需要对大脑网络功能的精确理解。