Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

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.

Changes in frontal and posterior cortical activity underlie the early emergence of executive function

“…DNF models simulate cognitive and behavioral processes using neural population dynamics, uniquely situating such models as bridges between behavioral and neural data (Buss, Wifall et al., ; Wijeakumar et al., ). Following the approach above, we created a DNF‐LFP measure by summing the absolute value of all terms contributing to the rate of change in activation within each component of the model, excluding the stability term and the two factors that impact the neuronal resting level—a resting‐level parameter and the memory traces.…”

“…DNF models simulate cognitive and behavioral processes using neural population dynamics, uniquely situating such models as bridges between behavioral and neural data (Buss, Wifall et al, 2014;Wijeakumar et al, 2017). Following the approach above, we created a DNF-LFP measure by summing the absolute value of all terms con- and "old" models.…”

“…We use a model-based approach to neuroimaging (Buss, Wifall, Hazeltine, & Spencer, 2014;Wijeakumar, Ambrose, Spencer, & Curtu, 2017) to simultaneously generate behavioral and hemodynamic data from the DNF model to explain and predict brain-behavior relationships as EF develops. Typically, in task-based functional neuroimaging, researchers examine how neural activity varies across conditions.…”

The Development of Working Memory

“…The first is to better understand the neural changes that underlie the early development of working memory. On this front, we have recently proposed ways to simulate brain data directly from dynamic field models, opening up tests of this modeling framework using functional MRI (Wijeakumar, Ambrose, Spencer, & Curtu, 2017) and, early in development, functional near-infrared spectroscopy (Wijeakumar, Kumar, Reyes, Tiwari, & Spencer, 2019).…”

Model-based cognitive neuroscience

“…Wijeakumar, Ambrose, Spencer, and Curtu (2017) provide a review and tutorial of an approach to model-based cognitive neuroscience using a theoretical framework called Dynamic Field Theory (Erlhagen & Schöner, 2002) applied to functional brain imaging (Buss, Wifall, Hazeltine, & Spencer, 2009). They outline the assumptions of DFT and how it is applied to behavioral data, describe how parameters of the model can be used in brain imaging analyses, and compare the model-based cognitive neuroscience approach to standard brain imaging analyses of the same dataset.…”