EEG-fMRI Based Information Theoretic Characterization of the Human Perceptual Decision System

• Published in: PloS one Vol. 7; no. 4; p. e33896
• Main Authors: Ostwald, Dirk, Porcaro, Camillo, Mayhew, Stephen D., Bagshaw, Andrew P.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.04.2012; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Analysis of Variance; Binomial Distribution; Biology; Brain; Brain mapping; Brain research; Coding; Cognitive ability; Computer Simulation; Data processing; Decision Making; Decision theory; EEG; Electroencephalography; Female; Functional magnetic resonance imaging; Humans; Information processing; Information theory; Magnetic resonance; Magnetic Resonance Imaging; Male; Medicine; Mental task performance; Models, Neurological; Neuroimaging; Neurosciences; Normal Distribution; Occipital Lobe - physiology; Ostwald ripening; Parietal Lobe - physiology; Perception; Quality; Social and Behavioral Sciences; Variables; Visual observation; Visual Perception; Visual tasks; Young Adult; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0033896

The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498-516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain.