|Appears in Collections:||Psychology Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach|
Ambrose, Joseph P
Spencer, John P
|Keywords:||Dynamic field theory modeling|
Integrative cognitive neuroscience
Functional magnetic resonance imaging
|Citation:||Wijeakumar S, Ambrose JP, Spencer JP & Curtu R (2017) Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach, Journal of Mathematical Psychology, 76 (Part B), pp. 212-235. https://doi.org/10.1016/j.jmp.2016.11.002.|
|Abstract:||A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus–response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.|
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