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

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.

Feature-Based Change Detection Reveals Inconsistent Individual Differences in Visual Working Memory Capacity.

Visual working memory (VWM) is a key cognitive system that enables people to hold visual information in mind after a stimulus has been removed and compare past and present to detect changes that have occurred. VWM is severely capacity limited to around 3-4 items, although there are robust individual differences in this limit. Importantly, these individual differences are evident in neural measures of VWM capacity. Here, we capitalized on recent work showing that capacity is lower for more complex stimulus dimension. In particular, we asked whether individual differences in capacity remain consistent if capacity is shifted by a more demanding task, and, further, whether the correspondence between behavioral and neural measures holds across a shift in VWM capacity. Participants completed a change detection (CD) task with simple colors and complex shapes in an fMRI experiment. As expected, capacity was significantly lower for the shape dimension. Moreover, there were robust individual differences in behavioral estimates of VWM capacity across dimensions. Similarly, participants with a stronger BOLD response for color also showed a strong neural response for shape within the lateral occipital cortex, intraparietal sulcus (IPS), and superior IPS. Although there were robust individual differences in the behavioral and neural measures, we found little evidence of systematic brain-behavior correlations across feature dimensions. This suggests that behavioral and neural measures of capacity provide different views onto the processes that underlie VWM and CD. Recent theoretical approaches that attempt to bridge between behavioral and neural measures are well positioned to address these findings in future work.

Response control networks are selectively modulated by attention to rare events and memory load regardless of the need for inhibition.

Recent evidence has sparked debate about the neural bases of response selection and inhibition. In the current study, we employed two reactive inhibition tasks, the Go/Nogo (GnG) and Simon tasks, to examine questions central to these debates. First, we investigated whether a fronto-cortical-striatal system was sensitive to the need for inhibition per se or the presentation of infrequent stimuli, by manipulating the proportion of trials that do not require inhibition (Go/Compatible trials) relative to trials that require inhibition (Nogo/Incompatible trials). A cortico-subcortical network composed of insula, putamen, and thalamus showed greater activation on salient and infrequent events, regardless of the need for inhibition. Thus, consistent with recent findings, key parts of the fronto-cortical-striatal system are engaged by salient events and do not appear to play a selective role in response inhibition. Second, we examined how the fronto-cortical-striatal system is modulated by working memory demands by varying the number of stimulus-response (SR) mappings. Right inferior parietal lobule showed decreasing activation as the number of SR mappings increased, suggesting that a form of associative memory - rather than working memory - might underlie performance in these tasks. A broad motor planning and control network showed similar trends that were also modulated by the number of motor responses required in each task. Finally, bilateral lingual gyri were more robustly engaged in the Simon task, consistent with the role of this area in shifts of visuo-spatial attention. The current study sheds light on how the fronto-cortical-striatal network is selectively engaged in reactive control tasks and how control is modulated by manipulations of attention and memory load.