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Efficient modeling and inference for event-related fMRI data

Zhang, C., Lu, Y., Johnstone, T., Oakes, T. and Davidson, R.J. (2008) Efficient modeling and inference for event-related fMRI data. Computational Statistics and Data Analysis., 52 (10). pp. 4859-4871. ISSN 0167-9473

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To link to this article DOI: 10.1016/j.csda.2008.03.033

Abstract/Summary

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL. (C) 2008 Elsevier B.V. All rights reserved.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Psychology and Clinical Language Sciences
ID Code:13801
Uncontrolled Keywords:HEMODYNAMIC-RESPONSE

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