title: Intentional signal in prefrontal cortex generalizes across different sensory modalities.
abstract: Biofeedback-EEG training to learn the mental control of an external device (e.g., a cursor on the screen) has been an important paradigm to attempt to understand the involvements of various areas of the brain in the volitional control and the modulation of intentional thought processes. Often the areas to adapt and to monitor progress are selected a priori. Less explored, however, has been the notion of automatically emerging activation in a particular area or subregions within that area recruited above and beyond the rest of the brain. Likewise, the notion of evoking such a signal as an amodal, abstract one remaining robust across different sensory modalities could afford some exploration. Here we develop a simple binary control task in the context of brain-computer interface (BCI) and use a Bayesian sparse probit classification algorithm to automatically uncover brain regional activity that maximizes task performance. We trained and tested 19 participants using the visual modality for instructions and feedback. Across training blocks we quantified coupling of the frontoparietal nodes and selective involvement of visual and auditory regions as a function of the real-time sensory feedback. The testing phase under both forms of sensory feedback revealed automatic recruitment of the prefrontal cortex with a parcellation of higher strength levels in Brodmann's areas 9, 10, and 11 significantly above those in other brain areas. We propose that the prefrontal signal may be a neural correlate of externally driven intended direction and discuss our results in the context of various aspects involved in the cognitive control of our thoughts.
authors.affiliation: Psychology Department, Rutgers University, Piscataway, New Jersey; Center for Computational Biomedicine Imaging and Modeling, Computer Science Department, Rutgers University, Piscataway, New Jersey; and Rutgers Center for Cognitive Science, Rutgers University, Piscataway, New Jersey ebtorres@rci.rutgers.edu.
authors.forename: Kyuwan
authors.initials: K
authors.lastname: Choi
authors.affiliation: Psychology Department, Rutgers University, Piscataway, New Jersey; Center for Computational Biomedicine Imaging and Modeling, Computer Science Department, Rutgers University, Piscataway, New Jersey; and.
authors.forename: Elizabeth B
authors.initials: EB
authors.lastname: Torres
mesh_terms: D000328:Adult; D001931:Brain Mapping / Q000295:instrumentation / Q000379:methods*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005072:Evoked Potentials, Auditory; D005074:Evoked Potentials, Visual; D056228:Feedback, Sensory; D005260:Female; D005793:Generalization, Psychological; D006801:Humans; D008297:Male; D017397:Prefrontal Cortex / Q000502:physiology*
keywords: brain computer interface; coadaptation; intentional control; prefrontal cortex; volitional control