NDLI: EEG based inference of causal cortical network dynamics in reward-based decision making

Content Provider	IEEE Xplore Digital Library
Author	Courellis, H.S. Peterson, D. Poizner, H. Cauwenberghs, G. Iversen, J.
Copyright Year	2015
Description	Author affiliation: Swartz Center for Comput. Neurosci., Univ. of California - San Diego, La Jolla, CA, USA (Iversen, J.) \|\| Inst. for Neural Comput., Univ. of California - San Diego, La Jolla, CA, USA (Peterson, D.; Poizner, H.) \|\| Bioeng. Dept., Univ. of California - San Diego, La Jolla, CA, USA (Courellis, H.S.; Cauwenberghs, G.)
Abstract	Experimental investigation and monitoring of reward signaling implicated in addiction and neurological disorders has traditionally been limited to invasive measurement of deep-brain dopamine activity. Here we introduce a systematic methodology and algorithmic pipeline to quantify causal relationships between regions of interest (ROIs) in the cerebral cortex revealing reward-based signaling pathways involved in human decision making using only non-invasive scalp electroencephalography (EEG). The data is processed by extracting epochs around time-locked stimuli of interest and performing independent component analysis (ICA) on individual datasets to remove artifacts and identify cortical sources. The pipeline entails identifying ROI's with the Measure Projection Toolbox (MPT) through clustering of ICs, localizing current sources in these ROIs using Bayesian inference based constrained low resolution electromagnetic tomography (cLORETA), and computing causal relationships between ROI's using the Source Information Flow Toolbox (SIFT). The proposed methodology and pipeline are demonstrated on 64-channel scalp EEG signals recorded from healthy adults performing a reward-based decision making task conducted through a brain computer interface (BCI) framework. In comparison to a standard method for Group-ICA, our pipeline generates far more biologically plausible and consistent causal connections between ROIs.
Starting Page	1
Ending Page	4
File Size	1357806
Page Count	4
File Format	PDF
e-ISBN	9781479972340
DOI	10.1109/BioCAS.2015.7348346
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain-computer interface Visualization Computational modeling Pipelines Decision making Independent component analysis Brain modeling Electroencephalography Orbits Causality LORETA
Content Type	Text
Resource Type	Article

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