Abstract：
:Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Lessa PS,Sato JR,Cardoso EF,Neto CG,Valadares AP,Amaro E Jrdoi
10.1016/j.jneumeth.2010.09.005subject
Has Abstractpub_date
2011-01-15 00:00:00pages
350-7issue
2eissn
0165-0270issn
1872-678Xpii
S0165-0270(10)00517-0journal_volume
194pub_type
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