Tagged: Brain models

Advanced Technologies for Brain Research [Scanning the Issue]

We believe that this special issue will serve to increase the public awareness and foster discussions on the multiple worldwide BRAIN initiatives, both within and outside the IEEE, providing an impetus for development of long-term cost-effective healthcare solutions. We also believe that the topics presented in this special issue will serve as scientific evidence for health and policy advocates of the value of neurotechnologies for improving the neurological and mental health and wellbeing of the general population. Below we briefly highlight the papers and technologies in this special issue.

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.

Learning EEG Components for Discriminating Multi-Class Perceptual Decisions

Logistic regression has been used as a supervised method for extracting EEG components predictive of binary perceptual decisions. However, often perceptual decisions require a choice between more than just two alternatives. In this paper we present results using multinomial logistic regression (MLR) for learning EEG components in a 3-way visual discrimination task. Subjects were required to decide between three object classes (faces, houses, and cars) for images which were embedded with varying amounts of noise. We recorded the subjects’ EEG while they were performing the task and then used MLR to predict the stimulus category, on a single-trial basis, for correct behavioral responses. We found an early component (at 170ms) that was consistent across all subjects and with previous binary discrimination paradigms. However a later component (at 300-400ms), previously reported in the binary discrimination paradigms, was more variable across subjects in this three-way discrimination task. We also computed forward models for the EEG components, with these showing a difference in the spatial distribution of component activity for the different categorical decisions. In summary, we find that logistic regression, generalized to the arbitrary N-class case, can be a useful approach for learning and analyzing EEG components underlying multi-class perceptual decisions.