The dependence of the BOLD response transients on stimulus type and echo time
Martin Havlicek1, Dimo Ivanov1, Benedikt A Poser1, and Kamil Uludag1

1Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands

### Synopsis

Adaptation and post-stimulus undershoot observed in the BOLD signal can originate from both active neuronal and passive vascular mechanisms. Using a multi-echo GE-EPI functional MRI sequence with two distinct visual stimuli, we are able to show that not only the BOLD response magnitude but also its shape is affected by the echo-time (TE). As pure oxygenation changes predict approximately a linear scaling of the BOLD signal with TE, this finding indicates that the composition of the underlying physiological mechanisms contributing to the fMRI signal (i.e. CBF, CBV, and CMRO2) varies over time.

### Purpose

Transients, such as adaptation of the positive response and the post-stimulus undershoot, are common features of the BOLD response1. Their origins can be linked to both active mechanisms induced by changes in neuronal activity2 and to passive mechanisms resulting from vascular3 or/and metabolic uncoupling4. The aim of this study is to investigate the dependence of both active and passive mechanisms underlying BOLD response transients on stimulus type and echo-time (TE).5 Being able to disentangle active and passive mechanisms from TE dependency of the BOLD response may provide an additional window for studying the physiological origins of the BOLD response.

### Methods

Subjects (n=2) performed six runs of passive visual task, each 715 s long. Within each run, we alternated between static and reduced-contrast flickering (4 Hz) checkerboard conditions (both 55 s long), interleaved with resting periods (110 s long). Functional data were acquired on a Siemens 3T Magnetom Prisma scanner with multi-echo GE-EPI sequence6 with the following parameters: TE1/2/3/4/5/6 = 8/21/33/46/58/71 ms and TR = 1100 ms, voxel size = 3 mm isotropic. Additionally, we acquired T1-weighted (MPRAGE, 1 mm isotropic) anatomical data. Activated voxels (p<0.05; FWE corrected; restricted to gray matter) from left and right V1, based on BOLD data with TE = 33 ms, were used to generate ROIs for quantitative analysis of the six-TE data. The average percent signal change time courses were plotted for six TEs and for both static and flickering conditions. Next, to visualize the effect of TE on BOLD response transients over time, we normalized all responses to the amplitude of the BOLD response at t = 18 s. Finally, to evaluate differences between two conditions independent of TE effect, we used a linear model of (the non-normalized) percent signal change ($\triangle S/S=-\triangle R_2^*\times TE+intercept$) and fitted at early (t = 18 s) and late (t = 58 s) phases of positive BOLD responses and at early phase of the post-stimulus BOLD undershoot (t = 72 s).

### Results

The amplitudes of the positive BOLD response and post-stimulus BOLD undershoot in both stimulus conditions increase linearly with TE (see Fig.1). However, there are significant differences in the responses to static and flickering conditions. During the static condition, the positive BOLD responses reach their peaks at 18 s and then decline during the stimulation period. The magnitude of the decline from the peak is dependent on TE: amplitude difference between the peak and the end of stimulation increases with TE (see Fig.2). There is no significant post-stimulus undershoot in the BOLD response with the shortest TE, but it becomes more pronounced with higher TE. During the flickering condition, the positive responses continue to rise after 18 s until they reach a steady-state plateau (see Fig.2). This increase is least­ for the shortest TE and maximal for the longest TE. The post-stimulus undershoot for flickering stimulus is significant even with the shortest TE and scales linearly with increasing TE. Differences between static and flickering conditions and also for different time points of the BOLD response are described by regression analysis (Fig.3). The slope of the fitted line is almost identical between static and flickering conditions in the early phase of positive response (110 and 111 s-1, respectively). In the late phase, the slope is smaller for static stimulus (72 s-1) compared to flickering stimulus (110 s-1). Finally, for the post-stimulus BOLD undershoot after static stimulus the slope is about half (-29 s-1) of that after flickering stimulus (-72 s-1).

### Discussion

We demonstrate that the BOLD response transients can be modulated by both stimulus type and echo-time. First, we confirmed previous results2 that the post-stimulus undershoot can be varied independently of the positive BOLD response amplitude. In addition, TE modulates the response transients differently over time. Differences in the slopes suggest that different physiological mechanisms operate at different phases of the BOLD signal. That is, for a pure susceptibility (i.e. blood oxygenation) change but with different amplitudes, it is expected that the slope of the amplitude vs. TE remains the same. Therefore, differences in the slope indicate either time-varying mismatch between CBF and CBV or between CBF and CMRO2. We, therefore, suggest that TE dependence of the BOLD signal provides additional information on the underlying physiological mechanisms and, thus, provides a window for studying brain physiology. We are currently evaluating biophysical BOLD signal models in order to disentangle neuronal and vascular mechanisms from multi-echo data.

### Acknowledgements

No acknowledgement found.

### References

1. Havlicek M, Roebroeck A, Friston K, et al. Physiologically informed dynamic causal modeling of fMRI data. Neuroimage 2015; 122:355-372.

2. Sadaghiani S, Ugurbil K, Uludag K. Neural activity-induced modulation of BOLD poststimulus undershoot independent of the positive signal. Magn Reson Imaging. 2009; 8:1030-1038.

3. Mandeville J, Marota, J, Ayata, C, et al. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J Cereb Blood Flow Metab. 1999; 19:679-689.

4. Frahm J, Kruger, K, Merboldt, K, et al. Dynamic uncoupling and recoupling of perfusion and oxidative metabolism during focal brain activation in man. Magn Reson Med. 1996; 35:143-148.

5. Zhao F, Jin T, Wang P, et al. Improved spatial localization of post-stimulus BOLD undershoot relative to positive BOLD. Neuroimage 2007; 34(3):1084-1092.

6. Poser B, Versluis M, Hoogduin J, et al. BOLD contrast sensitivity enhancement and artifact reduction with multiecho EPI: parallel-acquired inhomogeneity-desensitized fMRI. Magn Reson Med. 2006; 55:1227–1235.

### Figures

Average time courses from both left and right V1 areas for six different TEs obtained during static (left) and flickering (right) visual stimulation. Black bar under the time courses indicates the 55 s visual stimulation period.

Normalized average time courses from both left and right V1 areas for six different TEs obtained during static (left) and flickering (right) visual stimulation. This figure emphasizes time-varying BOLD response dependence on TE.

The percentage signal change vs. TE for early and late phase of the positive BOLD response and post-stimulus BOLD undershoots for both static and flickering conditions. Additionally, linear fits are displayed and parameters shown (slopes in 1/s and intercepts in %).

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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