DocumentCode :
754213
Title :
Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering
Author :
Hossein-Zadeh, Gholam-Ali ; Ardekani, Babak A. ; Soltanian-Zadeh, Hamid
Author_Institution :
Nathan Kline Inst. for Psychiatric Res., Orangeburg, NY, USA
Volume :
22
Issue :
7
fYear :
2003
fDate :
7/1/2003 12:00:00 AM
Firstpage :
795
Lastpage :
805
Abstract :
An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.
Keywords :
adaptive filters; biomedical MRI; brain; haemodynamics; medical image processing; spatial filters; vectors; activation detection; block design fMRI; event related fMRI; fMRI; false-alarm rate; filter coefficients; functional magnetic resonance imaging; generalized eigenproblem; medical diagnostic imaging; method specificity; null hypothesis; permutation; possible hemodynamic response functions; signal subspace; spatially smoothed data; wavelet domain; Adaptive filters; Eigenvalues and eigenfunctions; Hemodynamics; Information filtering; Information filters; Statistical analysis; Statistical distributions; Statistics; Testing; Wavelet domain; Algorithms; Brain; Brain Mapping; Computer Simulation; Evoked Potentials, Motor; Feedback; Fingers; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Biological; Models, Statistical; Movement; Neurons; Signal Processing, Computer-Assisted; Volition;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2003.815074
Filename :
1216203
Link To Document :
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