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Detection of Neural Activity in fMRI Using ... - Purdue University

Detection of Neural Activity in fMRI Using ... - Purdue University

Detection of Neural Activity in fMRI Using ... - Purdue

NeuroImage 15, 386–395 (2002) doi:10.1006/nimg.2001.0972, available online at http://www.idealibrary.com on Detection of Neural Activity in fMRI Using Maximum Correlation Modeling Ola Friman, Magnus Borga, Peter Lundberg,* and Hans Knutsson Department of Biomedical Engineering and *Departments of Radiation Physics and Diagnostic Radiology, Linköping University, Linköping, Sweden Received March 6, 2001 A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data. © 2002 Elsevier Science I. INTRODUCTION Two main problems make the detection of neural activity in fMRI difficult; the noisy character of the fMRI data and the variability of the hemodynamic response. The noise problem is traditionally addressed by smoothing the fMRI images with a Gaussian filter. Referring to the matched filter theorem, it is claimed that an increase in signal to noise ratio (SNR) is obtained (Worsley et al., 1996). However, Gaussian-shaped activation patterns are rarely seen at the current resolution of the fMRI images and certainly not at the sizes of the filter kernels frequently applied. Therefore, smoothing with a Gaussian-like kernel is far from optimal when the aim is to improve SNR and the effect can very well be the opposite, e.g., some small activated area is drowned by the surrounding noise when the filter is applied. The second problem, the nature of the hemodynamic response, has drawn much attention and several models has been proposed (Friston et al., 1994; Bullmore et al., 1996; Lange and Zeger, 1997; Josephs et al., 1997). The shape of the response has been shown to vary significantly (Aguirre et al., 1998), implying that methods that incorporate this variability have greater detection sensitivity. A detection problem in general can be divided into two separate problems, feature extraction and classification. In fMRI, feature extraction amounts to the calculation of a map of some statistical parameter, for example a correlation map or a t map. The classification problem is usually solved by thresholding the statistical parameter map in order to classify each voxel as either active or nonactive. The success in solving the classification problem crucially depends on the performance of the preceding feature extraction step. Much research on detecting brain activity in fMRI data has been devoted to the classification problem, where advanced methods for finding the correct threshold has been developed (Worsley and Friston, 1995; Poline et al., 1997). In contrast, although there is an awareness that a fixed Gaussian filter may not provide optimal detection sensitivity (Kruggel et al., 1999; Shafie et al., 1998), methods such as basic cross-correlation analysis and t-tests accompanied with Gaussian smoothing have been the dominating way of producing the statistical parameter maps. The aim in this paper is to develop a method that improves the feature extraction step, making the ensuing classification part of the detection problem simpler. The technique presented is a further development of an earlier reported method based on canonical correlation analysis (Friman et al., 2001). The feature extraction problem is approached in a somewhat unconventional way by viewing the correlation between two timeseries as an objective function to be maximized with respect to some given parameters. Prior knowledge is built in by imposing constraints on these parameters. Using this approach we overcome uncertainties in hemodynamic response modelling and which spatial filter to apply in order to improve SNR. Instead of routinely smoothing with a Gaussian filter and using a fixed model for the hemodynamic response the proposed approach adaptively finds the best response model and spatial filter to obtain maximum correlation. Therefore the name maximum correlation modeling has been adopted (Tofallis, 1999). 1053-8119/02 $35.00 © 2002 Elsevier Science All rights reserved. 386

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