Tagged: neuroimaging

Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling

In the last few decades, noninvasive neuroimaging has revealed macroscale brain dynamics that underlie perception, cognition, and action. Advances in noninvasive neuroimaging target two capabilities: 1) increased spatial and temporal resolution of measured neural activity; and 2) innovative methodologies to extract brain–behavior relationships from evolving neuroimaging technology. We target the second. Our novel methodology integrated three neuroimaging methodologies and elucidated expertise-dependent differences in functional (fused EEG-fMRI) and structural (dMRI) brain networks for a perception–action coupling task. A set of baseball players and controls performed a Go/No-Go task designed to mimic the situation of hitting a baseball. In the functional analysis, our novel fusion methodology identifies 50-ms windows with predictive EEG neural correlates of expertise and fuses these temporal windows with fMRI activity in a whole-brain 2-mm voxel analysis, revealing time-localized correlations of expertise at a spatial scale of millimeters. The spatiotemporal cascade of brain activity reflecting expertise differences begins as early as 200 ms after the pitch starts and lasts up to 700 ms afterwards. Network differences are spatially localized to include motor and visual processing areas, providing evidence for differences in perception–action coupling between the groups. Furthermore, an analysis of structural connectivity reveals that the players have significantly more connections between cerebellar and left frontal/motor regions, and many of the functional activation differences between the groups are located within structurally defined network modules that differentiate expertise. In short, our novel method illustrates how multimodal neuroimaging can provide specific macroscale insights into the functional and structural correlates of expertise development.

Quality of Evidence for Perceptual Decision Making is Indexed by Trial-to-Trial Variability of the EEG

A fundamental feature of how we make decisions is that our responses are variable in the choices we make and the time it takes to make them. This makes it impossible to determine, for a single trial of an experiment, the quality of the evidence on which a decision is based. Even for stimuli from a single experimental condition, it is likely that stimulus and encoding differences lead to differences in the quality of evidence. In the research reported here, with a simple “face”/”car” perceptual discrimination task, we obtained late (decision-related) and early (stimulus-related) single-trial EEG component amplitudes that discriminated between faces and cars within and across conditions. We used the values of these amplitudes to sort the response time and choice within each experimental condition into more-face-like and less-face-like groups and then fit the diffusion model for simple decision making (a well-established model in cognitive psychology) to the data in each group separately. The results show that dividing the data on a trial-by-trial basis by using the late-component amplitude produces differences in the estimates of evidence used in the decision process. However, dividing the data on the basis of the early EEG component amplitude or the times of the peak amplitudes of either component did not index the information used in the decision process. The results we present show that a single-trial EEG neurophysiological measure for nominally identical stimuli can be used to sort behavioral response times and choices into those that index the quality of decision-relevant evidence.