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Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

Packham, B; Barnes, G; Dos Santos, GS; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; (2016) Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography. Physiological Measurement , 37 (6) pp. 951-967. 10.1088/0967-3334/37/6/951. Green open access

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Abstract

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Type: Article
Title: Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/0967-3334/37/6/951
Publisher version: http://dx.doi.org/10.1088/0967-3334/37/6/951
Language: English
Additional information: Copyright © 2016 Institute of Physics and Engineering in Medicine. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (CC BY 3.0) (https://creativecommons.org/licenses/by/3.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Keywords: EIT, SPM, permutation testing, neuroimaging, functional brain imaging
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1496322
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