Modeling and Measuring Flows between Cognitive and Neural Processes

认知和神经过程之间的建模和测量流程

• 批准号: RGPIN-2018-04457
• 负责人: McIntosh, Anthony
• 金额: $ 4.01万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659300
• 关键词: Modeling; Measuring; Flows; between; Cognitive

Cognitive neuroscience has focused mainly on localizing regions and networks that are engaged by punctate processes such as perception, attention and memory. Such a static perspective misses the fact that these cognitive processes are intimately intertwined over time for normal mental operations. So too, the neural bases for the cognitive processes interact dynamically. We propose a more deliberate approach using multiscale analyses of empirical neuroimaging data and large-scale brain simulations with TheVirtualBrain (TVB: thevirtualbrain.org) to better characterize the flow between cognitive processes and the functional brain architectures that support these flows. We will decompose complex brain dynamics into probabilistic functional modes. These modes are mathematically operationalized as manifolds, along which trajectories evolve as the dynamics unfold embedded in a low-dimensional space (structured flows on manifolds [SFM]). The collection of functional modes available in a neural network constitutes its functional repertoire, which together instantiates a complete set of potential cognitive functions and overt behaviors. ******The difficulty with relating the brain network dynamics to cognition is that most behavioural measures of cognition are single points, such as reaction time or accuracy of responses. We will evaluate the use of moment-by-moment behavioural measures to construct flows and relate these to flows that are similarly derived from neurophysiology measured with magnetoencephalography (MEG). Simply stated, we will construct cognitive SFM that will relate to the corresponding brain SFM. In one series, eye-movement trajectories will be measured as people scan scenes, where the scan patterns can be related to attention and memory processes. In a second series, participants will register judgments of music clips as they evolve. The behavioural trajectories (flows) will be combined to create manifolds, subject-specific structured flows on manifolds (SFM). The behavioral and brain SFM can then be analytically combined to ascertain how the trial-specific flow on one manifold is predicted by the trial-specific flow on the other.******Further insights into the links between SFM's will be gathered using the empirical data as constraints for individual large-scale brain network models in TVB. TVB generates biologically-constrained brain network dynamics, and its outputs can be instantiated as neurophysiological signals, such as local-field potentials, MEG and BOLD-fMRI data. Parameters for each model will be fit to the person's unique brain SFM. The individual flows will then be reconstructed to identify the model parameters that predict trial-by-trial variation. The link of the brain and behaviour SFM enables inferences of the brain dynamics across spatiotemporal scales that support flow of cognitive processes

认知神经科学主要集中在定位区域和网络，参与点状过程，如感知，注意力和记忆。 这种静态的观点忽略了这样一个事实，即这些认知过程随着时间的推移而紧密交织在一起，以进行正常的心理操作。 认知过程的神经基础也是动态相互作用的。我们提出了一个更深思熟虑的方法，使用多尺度分析的经验神经成像数据和大规模的大脑模拟与TheVirtualBrain（TVB：thevirtualbrain.org），以更好地表征认知过程和功能的大脑架构，支持这些流量之间的流量。 我们将复杂的大脑动力学分解成概率功能模式。这些模式在数学上可操作为流形，沿着其轨迹随着嵌入低维空间中的动力学展开而演变（流形上的结构化流[SFM]）。神经网络中可用的功能模式的集合构成了其功能库，这些功能库一起实例化了一套完整的潜在认知功能和公开行为。** 将大脑网络动力学与认知联系起来的困难在于，大多数认知的行为测量都是单点的，例如反应时间或反应的准确性。我们将评估每时每刻的行为措施来构建流，并将这些与脑磁图（MEG）测量的神经生理学类似的流联系起来。 简单地说，我们将构建与相应的大脑SFM相关的认知SFM。 在一个系列中，当人们扫描场景时，将测量眼球运动轨迹，其中扫描模式可以与注意力和记忆过程相关。 在第二个系列中，参与者将随着音乐片段的演变而记录它们的判断。行为轨迹（流）将被组合以创建歧管，歧管上的特定主题结构化流（SFM）。 然后，可以将行为SFM和大脑SFM进行分析组合，以确定一个歧管上的试验特定流量如何通过另一个歧管上的试验特定流量进行预测。**进一步深入了解SFM之间的联系，将收集使用的经验数据作为限制的个人大规模的大脑网络模型在TVB。 TVB生成生物约束的大脑网络动力学，其输出可以实例化为神经生理信号，例如局部场电位，MEG和BOLD-fMRI数据。每个模型的参数将适合于人的独特的大脑SFM。 然后将重构各个流以识别预测逐个试验变化的模型参数。 大脑和行为的链接SFM使跨时空尺度的大脑动力学推断成为可能，支持认知过程的流动