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Subsampling characterization of event-related fMRI data
NemoImage
11, Number
5, 2000,
Part 2 of 2 Parts
IOE~P
METHODS
- ACQUISITION
Subsampling characterization
of event-related fMFU data
GCrard R. Crelier, Xavier Golay, Thomas Jiirmann, Institute of Neuroradiology,
Hatem Alkadhi, Spyros S. Kollias
University Hospital, Zurich, Switzerland
In the past years, event-related fMRI has become a very important tool for the understanding of transient activation patterns in the brain (1). However, until recently, very little post-processing methods were proposed to both detect the area of activation together with the shape and behavior of the haemodynamic responses (2). In this study we propose a two-steps method allowing for the complete post-processing of event-related fMR1 data, using no a priori knowledge of the location of the activation. Furthermore, the proposed method permits the extraction of important haemodynamic response parameters, such as the delay, amplitude and length of the measured activation signal, as well as their representation as parametric maps. Methods Functional experiments: To demonstrate the post-processing technique a set of simple visual functional experiments was performed. For each experiment, 10 slices (5 mm thickness) were acquired every second for a total of 256 time frames using a conventional EPI BOLD sequence (TE=4Oms, flip angle=70”). Visual stimuli consisted in presentation of a reversing checkerboard pattern for 2 seconds followed by 14 seconds of rest condition. This sequence was repeated 16 times to match the duration of the image acquisition. For each experiment, the timing of the last 8 cycles of stimulus presentation was modified with respect to the first 8 cycles. The onset of the checkerboard pattern was delayed by lOOOms, 5OOms. and lfklms, respectively. The total duration of each stimulation cycle remained constant. Analysis method: Post-processing was performed on a workstation running Linux and using custom made C+ + and MATLAB software. Firstly, the individual slices in the functional volumes were corrected for the delays resulting from the multi-slice acquisition by linear interpolation over time. Phase correction in Fourier space was also considered for this step, but added a noticeable amount of artificial frequencies to the temporal signal. Subsequently, the individual volumes were spatially filtered by convolution with a gaussian kernel of 6mm FWHM. Each experiment was then splitted into two to include only 8 identical cycles of stimulation. Activated voxels were determined by seeking the frequency of the activation paradigm (in our case 0.0625 Hz) out of the power spectra of the individual time courses using an F statistics (3). The resulting statistical maps were then thresholded, and only highly significantly (p < 1.10-5) activated pixels were retained for further analysis. Individual voxel time courses were corrected for linear drift using the Gram-Schmidt algorithm (4). The average time course for a single stimulus cycle was calculated from the 8 cycles in each experiment. In order to characterize the temporal behavior of the heamodynamic response signal, a gamma function (2) was fitted numerically pixel-by-pixel to the individual averaged time courses. Time courses were thus modeled by the titted response signal S: S(t)=exp(-(t-s)/r)k((t-8)lr)’ We defined 8 as corresponding to the haemodynamic delay. The absolute amplitude A of the BOLD signal and the width of activation FWHM were defined. (We approximated FWHM as the distance between the two inflexion points of the Gamma function (null crossings of the second derivative)). Using these three variables (6, A and FWHM), parametric maps were created, color-encoded and overlaid on co-registered high-resolution Tl-weighted anatomical images. To test for the accuracy of our fitting method, a pixel-by-pixel comparison of the delay d in the two parts of each experiment was performed by subtraction. By averaging over the activated voxels contained in a region of interest inside the primary visual cortex, we were able to measure the differences in stimulation onset or duration of the two parts of our experiments. Results In all experiments F-maps revealed similar activation patterns located in the visual cortex. The measured Individual experiments were 1256+-4(X? ms (1OOOms). 5562 183mq (SoOms) and 216 i- 107ms (looms).
onset differences
in the
Discussion The proposed analysis method offers a robust way to characterize in detail the heamodynamic response signal in event-related tMR1 studies with a temporal resolution superior to the actual signal sampling. In particular, the generation of parametric maps describing the delay and duration of the haemodynamic signal response to activation allows to study transient activation patterns in functional experiments. References 1. 2. 3. 4.
R.L. Buckner et al., PNAS USA, 93, 14878-14883, 1996; M.S. Cohen, Neuroimage, 6, 93-103. 1997; R.B.H. Tootell et al., Neuron, 21, 1409-1422, 1998; P.A. Bandettini et al., MRM, 30, 161-173, 1993.
s544
11, Number
5, 2000,
Part 2 of 2 Parts
IOE~P
METHODS
- ACQUISITION
Subsampling characterization
of event-related fMFU data
GCrard R. Crelier, Xavier Golay, Thomas Jiirmann, Institute of Neuroradiology,
Hatem Alkadhi, Spyros S. Kollias
University Hospital, Zurich, Switzerland
In the past years, event-related fMRI has become a very important tool for the understanding of transient activation patterns in the brain (1). However, until recently, very little post-processing methods were proposed to both detect the area of activation together with the shape and behavior of the haemodynamic responses (2). In this study we propose a two-steps method allowing for the complete post-processing of event-related fMR1 data, using no a priori knowledge of the location of the activation. Furthermore, the proposed method permits the extraction of important haemodynamic response parameters, such as the delay, amplitude and length of the measured activation signal, as well as their representation as parametric maps. Methods Functional experiments: To demonstrate the post-processing technique a set of simple visual functional experiments was performed. For each experiment, 10 slices (5 mm thickness) were acquired every second for a total of 256 time frames using a conventional EPI BOLD sequence (TE=4Oms, flip angle=70”). Visual stimuli consisted in presentation of a reversing checkerboard pattern for 2 seconds followed by 14 seconds of rest condition. This sequence was repeated 16 times to match the duration of the image acquisition. For each experiment, the timing of the last 8 cycles of stimulus presentation was modified with respect to the first 8 cycles. The onset of the checkerboard pattern was delayed by lOOOms, 5OOms. and lfklms, respectively. The total duration of each stimulation cycle remained constant. Analysis method: Post-processing was performed on a workstation running Linux and using custom made C+ + and MATLAB software. Firstly, the individual slices in the functional volumes were corrected for the delays resulting from the multi-slice acquisition by linear interpolation over time. Phase correction in Fourier space was also considered for this step, but added a noticeable amount of artificial frequencies to the temporal signal. Subsequently, the individual volumes were spatially filtered by convolution with a gaussian kernel of 6mm FWHM. Each experiment was then splitted into two to include only 8 identical cycles of stimulation. Activated voxels were determined by seeking the frequency of the activation paradigm (in our case 0.0625 Hz) out of the power spectra of the individual time courses using an F statistics (3). The resulting statistical maps were then thresholded, and only highly significantly (p < 1.10-5) activated pixels were retained for further analysis. Individual voxel time courses were corrected for linear drift using the Gram-Schmidt algorithm (4). The average time course for a single stimulus cycle was calculated from the 8 cycles in each experiment. In order to characterize the temporal behavior of the heamodynamic response signal, a gamma function (2) was fitted numerically pixel-by-pixel to the individual averaged time courses. Time courses were thus modeled by the titted response signal S: S(t)=exp(-(t-s)/r)k((t-8)lr)’ We defined 8 as corresponding to the haemodynamic delay. The absolute amplitude A of the BOLD signal and the width of activation FWHM were defined. (We approximated FWHM as the distance between the two inflexion points of the Gamma function (null crossings of the second derivative)). Using these three variables (6, A and FWHM), parametric maps were created, color-encoded and overlaid on co-registered high-resolution Tl-weighted anatomical images. To test for the accuracy of our fitting method, a pixel-by-pixel comparison of the delay d in the two parts of each experiment was performed by subtraction. By averaging over the activated voxels contained in a region of interest inside the primary visual cortex, we were able to measure the differences in stimulation onset or duration of the two parts of our experiments. Results In all experiments F-maps revealed similar activation patterns located in the visual cortex. The measured Individual experiments were 1256+-4(X? ms (1OOOms). 5562 183mq (SoOms) and 216 i- 107ms (looms).
onset differences
in the
Discussion The proposed analysis method offers a robust way to characterize in detail the heamodynamic response signal in event-related tMR1 studies with a temporal resolution superior to the actual signal sampling. In particular, the generation of parametric maps describing the delay and duration of the haemodynamic signal response to activation allows to study transient activation patterns in functional experiments. References 1. 2. 3. 4.
R.L. Buckner et al., PNAS USA, 93, 14878-14883, 1996; M.S. Cohen, Neuroimage, 6, 93-103. 1997; R.B.H. Tootell et al., Neuron, 21, 1409-1422, 1998; P.A. Bandettini et al., MRM, 30, 161-173, 1993.
s544