Artif Intell Med - Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit.

Tópicos

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Resumo

JECTIVE: We propose a method for preprocessing event-related functional magnetic resonance imaging (fMRI) data that can lead to enhancement of template-free activation detection. The method is based on using a figure of merit to guide the wavelet shrinkage of a given fMRI data set.BACKGROUND: Several previous studies have demonstrated that in the root-mean-square error setting, wavelet shrinkage can improve the signal-to-noise ratio of fMRI time courses. However, preprocessing fMRI data in the root-mean-square error setting does not necessarily lead to enhancement of template-free activation detection. Motivated by this observation, in this paper, we move to the detection setting and investigate the possibility of using wavelet shrinkage to enhance template-free activation detection of fMRI data.METHODOLOGY: The main ingredients of our method are (i) forward wavelet transform of the voxel time courses, (ii) shrinking the resulting wavelet coefficients as directed by an appropriate figure of merit, (iii) inverse wavelet transform of the shrunk data, and (iv) submitting these preprocessed time courses to a given activation detection algorithm. Two figures of merit are developed in the paper, and two other figures of merit adapted from the literature are described.RESULTS: Receiver-operating characteristic analyses with simulated fMRI data showed quantitative evidence that data preprocessing as guided by the figures of merit developed in the paper can yield improved detectability of the template-free measures. We also demonstrate the application of our methodology on an experimental fMRI data set.CONCLUSIONS: The proposed method is useful for enhancing template-free activation detection in event-related fMRI data. It is of significant interest to extend the present framework to produce comprehensive, adaptive and fully automated preprocessing of fMRI data optimally suited for subsequent data analysis steps.

Resumo Limpo

jectiv propos method preprocess eventrel function magnet reson imag fmri data can lead enhanc templatefre activ detect method base use figur merit guid wavelet shrinkag given fmri data setbackground sever previous studi demonstr rootmeansquar error set wavelet shrinkag can improv signaltonois ratio fmri time cours howev preprocess fmri data rootmeansquar error set necessarili lead enhanc templatefre activ detect motiv observ paper move detect set investig possibl use wavelet shrinkag enhanc templatefre activ detect fmri datamethodolog main ingredi method forward wavelet transform voxel time cours ii shrink result wavelet coeffici direct appropri figur merit iii invers wavelet transform shrunk data iv submit preprocess time cours given activ detect algorithm two figur merit develop paper two figur merit adapt literatur describedresult receiveroper characterist analys simul fmri data show quantit evid data preprocess guid figur merit develop paper can yield improv detect templatefre measur also demonstr applic methodolog experiment fmri data setconclus propos method use enhanc templatefre activ detect eventrel fmri data signific interest extend present framework produc comprehens adapt fulli autom preprocess fmri data optim suit subsequ data analysi step

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