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Computational and Mathematical Methods in Medicine - Volume 2015 2015, Article ID 830849, 13 pages -

Research Article

Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH 03755, USA

Institute for Quantitative Biomedical Sciences, One Medical Center Drive, Lebanon, NH 03756, USA

Received 24 November 2014; Revised 3 February 2015; Accepted 4 February 2015

Academic Editor: Phaneendra K. Yalavarthy

Copyright © 2015 M. Tanveer Talukdar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic EEG and near-infrared spectroscopic NIRS data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data.

Autor: M. Tanveer Talukdar, H. Robert Frost, and Solomon G. Diamond



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