This is the first fMRI study on the effect of scanner noise on brain activation during cognitive tasks. The major finding of the study is that acoustic noise affected brain activation during working memory tasks. Previous studies used sensory-motor tasks (Bandettini, et al., 1998
, Bilecen, et al., 1998
, Cho, et al., 1998
, Elliott, et al., 1999
), and found that scanner noise increases activation in auditory cortices, but its effects on visual and motor cortices is still controversial (Cho, et al., 1998
, Elliott, et al., 1999
In fMRI studies, it is desirable to use MR sequences that are as silent as possible, in order to reduce subjects' stress, and minimize potential stress-related activations. However, fMRI protocols are typically very loud, vary from scanner to scanner, and the effect of the scanner noise on brain activation cannot be underestimated. Study designs using different levels of scanner noise can access the quantification of noise-related changes in BOLD responses.
To increase acoustic noise, Cho et al. (Cho, et al., 1998
) played back previously recorded scanner noise to the subjects, using sound guides. This approach was not used in the present study because we aimed to achieve a precise and reproducible 12dBA spl
-difference between “Loud” and “Quiet” scans for all subjects. In Cho's study, the relative contribution of two sound sources (i.e. sound guides and the scanner) strongly depends on the distance between sound guide and ear canal and earplug-to-ear canal and/or earmuff-to-ear adaptation, which can significantly increase the spl
-variability across subjects. In the present study, the earplug-to-ear attenuation may have been variable across subjects, but not across conditions (“Loud” and “Quiet”), assuring an spl
-difference of 12dBA between “Loud” and “Quiet” scans for all subjects. On the other hand, Bandettini et al. (Bandettini, et al., 1998
), Bilecen et al. (Bilecen, et al., 1998
), and Elliot et al. (Elliott, et al., 1999
) used additional gradient pulses to enhance scanner noise. This approach also was not used in the present study because we try to maximize the spl
-difference between “Loud” and “Quiet” scans. Preliminary studies in our scanner showed that additional readout gradient pulses (1.5s) before radio frequency excitation pulses only produce a 3dBA increase in spl
, which was considered insufficient to significantly interfere with cognitive tasks. An alternative approach for changing the spl
at subjects ears could have been to let the subjects use earplugs in one of the sessions (
), but do not let them do it during the other session (
); therefore the average spl
-difference could have been up to 28 dBA. We did not use this approach because it may increase the variability of the spl
-difference across subjects due to the variability in the earplug-to-ear canal attenuation.
The larger BOLD signals in the FusG, SFG, medFG, IFG and the cerebellum for “Loud” scans compared to “Quiet” scans ( and ) suggest increased requirements for attention-network resources during louder scans to compensate for the interference of scanner noise. During WM, the FusG may play an important role as a visual cache (Baddeley, 2003
), and the SFG may be involved in general high-level functions (Wager and Smith, 2003
), while the medFG, IFG, and the cerebellum could perform general attentional processing required by WM tasks. All of these functions appear to be affected by the louder scans, and thus require additional neural resources during WM.
The reduced BOLD responses in the putamen and the ACG during louder scans suggest minimization of neural processes in task-irrelevant brain to minimize competition and maximize resources for attention processing in noisier conditions. The reduced responses in the ACG and putamen are unlikely the result of “blood stealing” phenomena (local reduction of CBF in less active adjacent brain regions to compensate for CBF-increases in those brain regions more active during louder scans), since these regions are not directly adjacent to other activated brain regions.
The AN-related increase of BOLD signals in the LG is consistent with the fMRI studies on perceptual, cognitive, and affective processing performed at different field strengths (Krasnow, et al., 2003
). These studies found significant activation in the LG for experiments performed at 3 Tesla field strength but not those performed at 1.5 Tesla. This discrepancy could have resulted from the increased scanner noise [approximately 6−10 dBA spl
-increase, (Price, et al., 2001
)] or the higher sensitivity at 3T compared to 1.5T. Increased BOLD signals in the LG during louder scans, but not with increased WM-load, demonstrates that the LG is more specific for general attention processing but less so for WM-load processing.
AN, and WM-load related increases in brain activation in the cerebellum and the IFG (, ) suggest increased attentional modulation in these brain regions during louder scans. These areas might have a general role for attention, and commonly activate during verbal and visuospatial WM tasks (LaBar, et al., 1999
), as well as visual attention tasks (Chang, et al., 2004
). Therefore, fMRI-acoustic noise might lead to an increased requirement for attentional modulation to perform a given task, causing corresponding increases in fMRI-signals. However, the lack of increases in BOLD signals in the FusG and IPL with increased WM load in the setting of louder scanner noise suggests that the capacity in these brain regions might be saturated already with the interference from the louder noise, and that these regions cannot further activate with the additional WM load.
The negative correlations between load- and AN-related changes in BOLD responses () support the theoretical notion that the working memory network is a limited capacity system (Baddeley, 2003
). According to , there is a ceiling effect in the BOLD responses; - α ΔBOLD (AN) + ΔBOLD (WM-load) = constant
, where the value of the weighting factor α, which is the negative slope of linear fits in , varies across brain regions. The behavioral data during the study demonstrate that the 2-back task is very demanding, and some subjects probably require the use of near-full network capacity (performance accuracy drops from 98% to 92%, and reaction time increases from 540 to 610 ms from 1-back to 2-back). Other fMRI studies using similar WM tasks (Kumari, et al., 2003
) have shown a more severe drop in performance accuracy (to approximately 50%) during the even more demanding 3-back WM task. Therefore, when WM-resources are allocated to compensate for decreased efficiency due to increased acoustic noise, the reserve capacity may be exhausted for even more demanding WM-processing (i.e. further increased WM-load).
The different AN-related activation patterns between men and women are consistent with a previous fMRI study on gender differences in brain activation during WM tasks (Speck, et al., 2000
). In agreement with our previous study, the present investigation shows that brain activation during WM tasks is greater in the right hemisphere for male, but greater in the left hemisphere for female subjects. In contrast, AN-activation was larger in the opposite hemisphere relative to that observed with WM activation for each sex, i.e. in the left hemisphere for males, and in the right hemisphere for females. These gender-specific differences in noise-related activation during WM tasks further emphasize the importance of careful gender matching on fMRI studies.
Finally, the observed AN-related differences in brain activation are not a consequence of small differences in acquisition bandwidth because there was no difference in brain activation between “Quiet” and “Loud” scans for the less demanding task (0-back task).
In summary, we studied the effect of increased acoustic noise on fMRI activation using a verbal working memory paradigm with graded levels of task difficulty. The study was conducted at high field strength in a large cohort (30) of healthy volunteers. The spl
of scanner noise was increased by 12 dBA from “Quiet” to “Loud” EPI scans by taking advantage of the resonant modes of vibration of our gradient coil (Tomasi and Ernst, 2003
). Increased scanner noise produced increased BOLD responses bilaterally in temporal (FusG), occipital (LG), and prefrontal (SFG, medFG, IFG) cortices and the cerebellum, and decreased BOLD responses bilaterally in frontal cortices (ACG) and subcortical gray matter regions (putamen). These findings support greater recruitment of neural resources from the attention network to compensate for interference due to increased scanner noise. WM-load dependent increases in BOLD signals correlated negatively with increased scanner noise throughout the WM network, which suggests that the WM network is a limited capacity system. Furthermore, this study demonstrates that MR noise can alter brain activation patterns. Therefore, comparisons of fMRI studies performed at different magnetic field strengths (i.e. > 3 Tesla vs. 1.5 Tesla) or on different systems or with different pulse sequences (e.g. EPI vs. spiral) should measure and control for acoustic noise.