A non-parametric approach for fMRI analysis: application for single subject data analysis

Abstract

Event Abstract Back to Event A non-parametric approach for fMRI analysis: application for single subject data analysis Andor V. Gál1, 2*, Adám Bíró1, 2 and Zoltán Vidnyánszky1, 2, 3 1 Pázmány Péter Catholic University, Faculty of Information Technology, Hungary 2 Semmelweis University, MR Research Center, Szentágothai J. Knowledge Center, Hungary 3 Pázmány Péter Catholic University - Semmelweis University, Neurobionics Research Group, Hungarian Academy of Sciences, Hungary The classical analytical method in functional neuroimaging, Statistical Parametric Mapping is prone to violation of a set of assumptions about homogeneity of variance, normality, and independent errors. Prospective bias in the statistical decision escalates as the number of inspected voxels grow (due to multiple comparisons) and in case of fixed effect (single subject) analysis. In clinical applications (typical single subject studies) the appropriate control for potential statistical errors is primary concern. We developed a new, non-parametric method that requires only minimal assumptions for validity and can be used to assess statistical significance in voxelwise analysis accounting for the multiple comparisons problem. The core concept is to create a null-hypothesis sampling distribution of GLM beta-weights for each experimental condition separately. The sampling distribution is formed using randomly generated alternative temporal condition sequences that meet design optimization constraints. Unlike other nonparametric permutation or surrogate data generation based algorithms, this technique does not require multiple experimental conditions and additional assumptions about the structure of the data are minimal. We validated our non-parametric approach on the data obtained from two single subject fMRI experiments and the results were compared to the results obtained using standard parametric approaches on the same data set. It was found that when the appropriate assumptions hold, the nonparametric permutation approach gives more stable results as compared to those obtained from a parametric analysis: i.e. variability in voxel-activations between different studies of the same subject is lower. Conference: IBRO International Workshop 2010, Pécs, Hungary, 21 Jan - 23 Jan, 2010. Presentation Type: Poster Presentation Topic: Abstracts Citation: Gál AV, Bíró A and Vidnyánszky Z (2010). A non-parametric approach for fMRI analysis: application for single subject data analysis. Front. Neurosci. Conference Abstract: IBRO International Workshop 2010. doi: 10.3389/conf.fnins.2010.10.00228 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 May 2010; Published Online: 04 May 2010. * Correspondence: Andor V Gál, Pázmány Péter Catholic University, Faculty of Information Technology, Budapest, Hungary, gviktor@digitus.itk.ppke.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andor V Gál Adám Bíró Zoltán Vidnyánszky Google Andor V Gál Adám Bíró Zoltán Vidnyánszky Google Scholar Andor V Gál Adám Bíró Zoltán Vidnyánszky PubMed Andor V Gál Adám Bíró Zoltán Vidnyánszky Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.