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Although functional magnetic resonance imaging (fMRI) methods yield rich temporal and spatial data for even a single subject, universally accepted data analysis techniques have not been developed that use all the potential information from fMRI of the brain. Specifically, temporal correlations and confounds are a problem in assessing change within pixels. Spatial correlations across pixels are a problem in determining regions of activation and in correcting for multiple significance tests. We propose methods that address these issues in the analysis of task-related changes in mean signal intensity for individual subjects. Our approach to temporally based problems within pixels is to employ a model based on autoregressive-moving average (ARMA or "Box-Jenkins") time series methods, which we call CARMA (Contrasts and ARMA). To adjust for performing multiple significance tests across pixels, taking into account between-pixel correlations, we propose adjustment of P values with "resampling methods." Our objective is to produce two- or three-dimensional brain maps that provide, at each pixel in the map, an estimated P value with absolute meaning. That is, each P value approximates the probability of having obtained by chance the observed signal effect at that pixel, given that the null hypothesis is true. Simulated and real data examples are provided.

@article{locascio_time_1997, title = {Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging}, volume = {5}, issn = {1065-9471}, doi = {10.1002/(SICI)1097-0193(1997)5:3<168::AID-HBM3>3.0.CO;2-1}, abstract = {Although functional magnetic resonance imaging (fMRI) methods yield rich temporal and spatial data for even a single subject, universally accepted data analysis techniques have not been developed that use all the potential information from fMRI of the brain. Specifically, temporal correlations and confounds are a problem in assessing change within pixels. Spatial correlations across pixels are a problem in determining regions of activation and in correcting for multiple significance tests. We propose methods that address these issues in the analysis of task-related changes in mean signal intensity for individual subjects. Our approach to temporally based problems within pixels is to employ a model based on autoregressive-moving average (ARMA or "Box-Jenkins") time series methods, which we call CARMA (Contrasts and ARMA). To adjust for performing multiple significance tests across pixels, taking into account between-pixel correlations, we propose adjustment of P values with "resampling methods." Our objective is to produce two- or three-dimensional brain maps that provide, at each pixel in the map, an estimated P value with absolute meaning. That is, each P value approximates the probability of having obtained by chance the observed signal effect at that pixel, given that the null hypothesis is true. Simulated and real data examples are provided.}, language = {eng}, number = {3}, journal = {Human Brain Mapping}, author = {Locascio, J. J. and Jennings, P. J. and Moore, C. I. and Corkin, S.}, year = {1997}, pmid = {20408214}, pages = {168--193}, file = {Submitted Version:/Users/jjallen/Zotero/storage/CIMI3DLX/Locascio et al. - 1997 - Time series analysis in the time domain and resamp.pdf:application/pdf} }

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