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: $ 2.64万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764258
• 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

认知神经科学主要专注于定位感知、注意力和记忆等点状过程所涉及的区域和网络。这种静态的观点忽略了这样一个事实，即这些认知过程在正常的心理操作中随着时间的推移是紧密交织在一起的。同样，认知过程的神经基础也会动态地相互作用。我们提出了一种更慎重的方法，使用经验神经成像数据的多尺度分析和使用TheVirtualBrain(TVB：theVirtualbrain.org)的大规模大脑模拟来更好地表征认知过程和支持这些流动的功能大脑架构之间的流动。我们将把复杂的大脑动力学分解成概率功能模式。这些模式在数学上被操作为流形，随着嵌入在低维空间中的动力学展开，轨迹沿着流形演变(流形上的结构化流动[SFM])。神经网络中可用的功能模式的集合构成了它的功能曲目，这些功能模式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，大多数认知行为指标都是单点的，比如反应时间或反应的准确性。我们将评估使用每时每刻的行为测量来构建流，并将这些流与由脑磁图(MEG)测量的神经生理学得出的类似的流联系起来。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，眼球运动轨迹将在人们扫描场景时进行测量，扫描模式可能与注意力和记忆过程有关。在第二个系列中，参与者将根据音乐片段的演变记录对它们的判断。行为轨迹(流)将被组合以创建流形，即流形上特定于主体的结构化流(SFM)。然后，行为SFM和大脑SFM可以分析地结合在一起，以确定一个流形上的试验特定流是如何通过另一个流形上的试验特定流来预测的。进一步深入了解SFM之间的联系将使用经验数据作为TVB中单个大规模大脑网络模型的约束。TVB产生生物受限的脑网络动力学，其输出可以实例化为神经生理信号，如局部场电位、脑磁图和BOLD-fMRI数据。每个模型的参数都将适合个人独特的大脑SFM。然后将重建各个流，以确定预测逐个试验变化的模型参数。大脑和行为SFM的联系使得能够跨时空尺度推断大脑动力学，从而支持认知过程的流动