Reference number : GC-2017-Q1-03498, amendment number : , agreement type : G, recipient legal name : McIntosh, Anthony (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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&rsquo;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&rsquo;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., program purpose french : 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&rsquo;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&rsquo;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., agreement title english : Modeling and Measuring Flows between Cognitive and Neural Processes, agreement title french : Modeling and Measuring Flows between Cognitive and Neural Processes, agreement number : RGPIN, agreement value : 32000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada