The aim of the present study was to replicate earlier findings from NIRS studies investigating cortical correlates of ME and MI of swallowing (Kober, Bauernfeind, et al., 2015; Kober et al., 2014). Those studies demonstrated that ME and MI of swallowing lead to comparable activation patterns in the hemodynamic response function. More precisely, HbO and HbR levels were found to increase during ME, while during MI an increase in HbR and a decrease in HbO was observed. Concentration changes were most pronounced over the IFG (Kober & Wood, 2014). As activation of the temporal muscle, present during speaking, teeth clinching or swallowing, can lead to massive motion artifacts in the NIRS signal
(Schecklmann et al., 2017), this study attempted to extend and validate earlier findings by applying motion artifact correction methods during signal postprocessing. Motion artifact techniques, such as wavelet filtering and SD-channel regression, have been found to be efficient in reducing noise present in the NIRS signal, which is unrelated to the actual cortical activation (Brigadoi et al., 2014; Brigadoi & Cooper, 2015; Chen et al., 2020; Yücel et al., 2021). Moreover, as correction methods are assumed to be highly dependent on the type of data (Yücel et al., 2015), the applied correction methods were compared to a conventional, manual rejection of artifacts, used in previous studies investigating ME and MI of swallowing (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014).
4.1 Topographical Distribution
In the present study, the largest signal changes during ME and MI of swallowing were expected above the IFG, as indicated by previous research (Kober, Bauernfeind, et al., 2015;
Kober & Wood, 2014). The topographical distribution of concentration changes in the NIRS signal found during ME and MI partly confirmed this hypothesis. During ME, HbO and HbR levels showed the strongest signal changes in channels referring to the pars triangularis, pars orbitalis and pars opercularis, all part of the IFG. This is in line with previous research as the IFG has been frequently found to be activated during swallowing and is thought to be
involved in controlling non-linguistic movements of mouth and face (Kober, Grössinger, et al., 2019; Kober & Wood, 2014, 2018; Martin et al., 2001). As NIRS detects activation changes in upper cortical layers only (Chen et al., 2020), the insula, which is lying subjacent to the IFG, has been suspected to lead to more pronounced activation in the IFG during
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swallowing (Kober, Bauernfeind, et al., 2015; Kober, Grössinger, et al., 2019; Kober &
Wood, 2014). The insula serves as an integratory hub for sensory and gustatory information and is constantly activated during swallowing (Ertekin, 2011; Hamdy, Mikulis, et al., 1999;
Smits et al., 2007; Sorös et al., 2009).
Contrary to ME, a strong involvement of the IFG during MI was observed only in HbR concentration changes, which is reflected when projecting the data onto the cortex (Figures 4 and 5). Previous NIRS studies investigating MI of swallowing found a stronger congruency between ME and MI in HbR than in HbO levels (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014). Thus, the different distribution between HbO and HbR concentration changes found in this study, is comparable to earlier findings.
Activation in the IFG during MI of different body parts has been frequently found (Hétu et al., 2013). Part of the IFG (BA 44) responds to mirror neurons, which are assumed to activate motor representations during action observation (Koski et al., 2002). A previous study comparing brain activity during visual and kinesthetic MI, revealed activations in the IFG, but only when kinesthetic strategies were used (Guillot et al., 2009). As MI of
swallowing is limited to kinesthetic approaches, this could further explain the involvement of the IFG.
HbO levels during MI revealed activation changes mainly above channels
corresponding with premotor areas and SMA, as well as the dorsolateral prefrontal cortex.
The SMA is another swallowing related area, involved in planning and preparation of swallowing (Hamdy, Rothwell, et al., 1999; Martin et al., 2001) and other sequential
movements (Nachev, Kennard, & Husain, 2008). In the present study observed concentration changes in HbR during active swallowing underline these findings. Activation in the SMA and has been consequently found during MI of different movements (Guillot et al., 2009;
Hétu et al., 2013; Lotze & Halsband, 2006) and MI of swallowing (Kober, Grössinger, et al., 2019; Kober & Wood, 2014). In Addition, MI of complex movements has led to stronger activation in premotor regions compared with simple ones (Guillot et al., 2009; Kuhtz-Buschbeck et al., 2003; Mizuguchi & Kanosue, 2017). Hence, SMA activation during MI could reflect the processing of complex information of movement sequences (Hétu et al., 2013). The dorsolateral prefrontal cortex is involved in timing aspects of movement initiation and has been shown to be activated during MI as well (Guillot et al., 2009). The observed concentration changes over the dorsolateral prefrontal cortex during MI of swallowing could
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therefore reflect efforts towards the correct timing of the imagined swallows (e.g., not too fast, or too slow).
In contrast to previous neuroimaging studies investigating swallowing, concentration changes in channels above the frontal eye fields (FEF) were pronounced in HbO/HbR during MI as well as in HbR during ME. The FEF are crucial for eye movements and are assumed to play an important role in visual attention and focusing (Vernet, Quentin, Chanes, Mitsumasu,
& Valero-Cabré, 2014). Quite a few participants reported closing their eyes during imagery tasks. The observed concentration changes may reflect repeated opening of the eyes and focusing on the screen, as no auditory signals indicated the ending of the MI task. Participants had been seated inside a cabin; therefore, no visual observation of the participants was
possible during the task.
For the most part, the topographical distribution of the HRF was comparable between ME and MI, especially for changes in HbR as illustrated in Figures 4 and 5. In line with previous studies (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014), pronounced concentration changes have been found above the IFG, during ME and in HbR during MI.
4.2 Hemodynamic Signal Changes during ME vs. MI
When comparing hemodynamic signal changes during ME and MI directly, similar findings as in previous research investigating ME and MI of swallowing (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014) were observed. In the present study, concentration
changes in HbO were higher during ME than MI. This finding is in line, not only with previous swallowing studies (Kober, Bauernfeind, et al., 2015; Kober, Grössinger, et al., 2019; Kober & Wood, 2014), but also with various studies investigating MI of other
movements, which have found stronger neuronal activation during ME than MI (Hétu et al., 2013). Weakened activation during MI could originate from the absence of actual movements and consequent sensory sensations (Guillot et al., 2012). HbO during MI even decreased during the task, before increasing slowly, yet steadily during the resting period. Contrary to MI of other movements, which usually lead to an increase in HbO during ME and MI (Batula et al., 2017; Wriessnegger et al., 2008), this is a common finding in MI of swallowing. As swallowing is a more complex and reflexive movement than e.g. limb movements, previous studies suggested effortful inhibitory mechanisms that prevent actual movement during MI and may cause the decrease in HbO (Kober & Wood, 2014). Moreover, a decrease in HbO has been found in healthy young adults, but not in elderly (Kober, Bauernfeind, et al., 2015;
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Kober & Wood, 2014). Considering that executive functions like inhibition are thought to decline with increasing age (Andrés, Guerrini, Phillips, & Perfect, 2008), this seems a plausible explanation. Moreover, a significant role of inhibition processes in MI has been demonstrated various times (Bart et al., 2021; Rieger et al., 2016). Clenching teeth, has been found to lead to an increase in HbR accompanied by a simultaneous decrease in HbO in inferior frontal areas (Schecklmann et al., 2017). As the participants could not be observed during the tasks and no supplementary EMG was applied to control for muscle activity, unconscious teeth clenching while trying to avoid active swallowing could be another explanation for the inversed direction of HbO and HbR in MI of swallowing.
Conversely to HbO, MI led to stronger concentration changes in HbR compared to ME; HbR even decreased over time during ME. Previous research investigating ME and MI of swallowing found a comparable effect, but only during the pause interval, whereas during the task HbR was higher during ME than MI (Kober & Wood, 2014). As Kober and Wood (2014) did not use an enhanced motion artifact correction method, such as wavelet filtering and SD-channel regression in the present study, the higher HbR concentration during active swallowing could have been due to motion artifacts, elicited by the temporal muscle during swallowing (Schecklmann et al., 2017; Zanato, Chiari, Vieira, & Bommarito, 2016).
Conversely, and further addressed in the following section, the settings chosen for wavelet filtering could have been too conservative, leading to an underestimation of the hemodynamic response during active swallowing (Di Lorenzo et al., 2019). However, in a neurofeedback study participants were able to successfully upregulate HbR and downregulate HbO during MI of swallowing, but not in the opposite direction (Kober, Gressenberger, et al., 2015; Kober et al., 2018; Kober, Spörk, et al., 2019). Hence, higher HbR concentration changes during MI of swallowing could also indicate a more successful imagination.
Concentration changes in HbO were significantly higher during the pause interval following ME and MI, indicating a prolonged time course compared to ME and MI of other movements (Wriessnegger et al., 2008). Longer duration and later peak activation of the NIRS signal have been frequently reported in earlier studies investigating swallowing (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014). A prolonged time course during swallowing might be explained by the duration and complexity of swallowing itself (Hamdy, Mikulis, et al., 1999).
In the present study, HbO levels during the resting period were higher in the right hemisphere, but during the tasks no differences between hemispheres were found. Other
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studies investigating swallowing found mainly bilateral activation patterns (Kober & Wood, 2014), yet neuroimaging studies found lateralization effects depending on the type of bolus that has been swallowed. Those studies found a stronger activation in the right hemisphere during water swallowing and more left pronounced activation for saliva (Kober et al., 2018;
Sorös et al., 2009). Martin (2001) observed a dominance in the left hemisphere during spontaneous swallowing of saliva, but during voluntary saliva swallowing, as applied in the present study, a stronger right lateralization of the insula was found. The lateralization effect could therefore originate from subjacent insula activation elicited by voluntary saliva
swallowing and may have been masked by motion artifacts in previous studies. Moreover, the lateralization effects could highly depend on the context of swallowing (Martin et al., 2001).
The effect of the observed right lateralization during resting periods was quite small and could also originate from other, more random factors, such as interindividual differences in
gustatory sensations. The anterior insula is involved in processing of gustatory stimuli (Sorös et al., 2009), as the taste of saliva varies with consumed food, beverages or cigarette smoking, gustatory sensations between participants may have differed.
During swallowing, the temporal muscle is highly active (Zanato et al., 2016) which in turn produces massive motion artifacts in the NIRS signal (Schecklmann et al., 2017).
Previous NIRS studies investigating swallowing relied on manual rejection of motion
artifacts, which is highly subjective and can be insufficient when dealing with great amounts of motion artifacts (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014, 2018). In the present study those challenges were addressed by applying motion artifact methods (wavelet filtering and SD-channel regression) to gain a clear NIRS signal. For the most part results were highly comparable to previous studies (Kober, Bauernfeind, et al., 2015; Kober &
Wood, 2014), thus the present study can validate those earlier findings and prove that the previously found activation patterns in the HRF did not result mainly from motion artifacts.
4.3 Comparison of Motion Artifact Correction Methods
In the present study different motion artifact correction methods were directly compared with each other. The aim was to investigate effects of wavelet filtering and SD-channel regression and a conventional rejection of motion artifacts on the NIRS signal during swallowing. Wavelet filtering was found to lead to significant smaller signal changes in HbO levels than manual rejection methods. A smaller magnitude of the averaged signal also indicates less motion artifacts. Wavelet filtering has so far proven as a valuable method for
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signal correction in speech studies, where motion artifacts occur coincidently with stimulus onsets (Brigadoi et al., 2014), just like in the present study. For HbR levels, such an effect was not observed, neither for wavelet filtering nor for SD-channels. Other studies also reported a greater efficiency of wavelet filtering and PCA with SD-regression in removing motion artifacts in HbO than in HbR (Kirilina et al., 2013; Sato et al., 2016). Task related artifacts are primarily present in HbO levels, while HbR, despite its smaller magnitude is more robust against physiological noise (Kirilina et al., 2012, 2013). HbO may therefore benefit more from motion artifact correction techniques than HbR.
Overall, wavelet filtering let to a strong decrease in the signal intensity compared to manual rejection (see Figure 9). The iqr serves as a tuning parameter in wavelet filtering and its choice depends on the characteristics of motion artifacts and whether they are correlated with the task. While a high iqr increases the risk of remaining motion artifacts in the signal, choosing a low iqr can result in filtering out the hemodynamic response as well (Di Lorenzo et al., 2019). The iqr (0.1) was chosen in regard of a previous speech study, where wavelet filtering was highlighted as efficient in correcting task-related motion artifacts (Brigadoi et al., 2014). In the present study, however, it could have led to an overcorrection of the signal and a reduction of the HRF itself. Using a slightly less conservative iqr could be beneficial in maintaining the characteristics of the hemodynamic concentration changes during swallowing (see Appendix B.3).
For the SD-channel regression, no significant differences in the signal have been found. However, in combination with manual rejection a small decrease in signal changes can be assumed (see time course manual and manual + SD in Figure 9 and Appendix B.2). As SD-channel regression is performed after applying wavelet filtering, it is likely that no further benefits could be achieved on already strongly corrected data. SD-channels aim to extract physiological noise, due to blood flow in the scalp, and have been frequently found to improve the NIRS signal (Chen et al., 2020; Gagnon et al., 2014; Sato et al., 2016; Yücel et al., 2015). However, their contribution in correcting artifacts, mainly resulting from contact loss between optodes and scalp, due to muscle activity, is still unclear.
Besides, the used analysis could have been limited in its sensitivity to detect effects of correction techniques. Research focusing on motion artifact correction in NIRS signals, mainly uses different and multiple measures to assess advances of correction methods (Brigadoi et al., 2014; Cooper et al., 2012; Yücel, Selb, Cooper, et al., 2014). Comparing the signal to noise ratios (SNR) of the signals, or other parameters of signal variation could be
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better approaches. After a visual inspection of the signal, no rejected trials were found for wavelet filtering, whereas for the manual rejection approaches numerous trials had been excluded. A confined loss of trials is negligible when the number of trials is high, as in the present study (20 per condition). But especially in clinical populations the number of trials is limited, to prevent participants from overexertion (see Kober, Bauernfeind, et al., 2015).
Hence, including the number of sustained trials in analyses would be also valuable to identify a suitable motion artifact method for swallowing.
The present study demonstrated that wavelet filtering can improve the NIRS signal during swallowing, especially for HbO levels. Although no significant effects of SD-channel regression were observed, multi-distance measures have been previously proven to enhance signal quality (Yücel et al., 2021, 2015). As they are easy to apply and no decline in data quality is expected, SD-channels should be applied in future swallowing studies, if available.
4.4 Correlates of the Hemodynamic Response of MI
The present study aimed to explore possible correlates brain activation patterns elicited by MI of swallowing. No correlations have been found between HbO and HbR concentration changes of MI of swallowing, a kinesthetic MI ability and other suspected MI-related quantitative measurements. As questionnaires measuring MI ability are highly
subjective, and mainly high KMI scores were observed in the present study, an overestimation of their own abilities by the participants could have caused these results. Particularly
individuals untrained in MI tend to overestimate their actual abilities (Stephan Frederic Dahm, 2020). The VMIQ-2 requires participants to imagine sequential movements like walking, jumping or tossing a ball (Roberts et al., 2008). Even if those body movements are more comparable to the complexity of swallowing than simple finger tapping or hand movements, swallowing still represents a reflexive, less conscious type of movement (Ertekin, 2011;
Martin et al., 2001) and the imagery of it could therefore depend on other factors. Moreover, MI is considered to integrate multiple modalities (Cumming & Eaves, 2018), which are not all represented in MI questionnaires (Stephan Frederic Dahm, 2020). MI of swallowing could for instance include perceptions of taste, for which kinesthetic MI ability could be insufficient to detect individual differences. However, neurophysiological measures are not considered to assess MI ability accurately, as cortical arousal can differ between individuals independently from MI success (Stephan Frederic Dahm, 2020; Zabicki et al., 2019). Hence, adding another MI task of a different movement and comparing it to MI of swallowing could help to
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differentiate individuals with higher and lower kinesthetic MI abilities (Stephan Frederic Dahm, 2020; Zabicki et al., 2019).
Apart from neurophysiological measures, results indicate that participants with a higher awareness of their body possess higher abilities in kinesthetic MI. This is in line with the findings of Tinaz et al. (2018), who observed that in patients with PD successful
neurofeedback of kinesthetic MI was linked to the degree of body awareness. As MI demands the recalling of motor representations and sensations linked to ME (Milton, Small, &
Solodkin, 2008), it is not surprising that individuals with a better awareness of their body succeed better in MI as well. However, the exact relationship between kinesthetic MI and body awareness remains unclear (Tinaz et al., 2018).
In the present study correlations were found between the motivation and the subjective rating of quality of the imagined swallowing movements. Positive emotional and motivational states are assumed to enhance the quality of MI (Guillot et al., 2012; Tinaz et al., 2018). But there was no correlation of motivation with hemodynamic signal changes or kinesthetic imagery ability. Subsequent analyses suggest that participants were able to accurately rate the quality of their imagination of swallowing, as higher HbR concentration changes were yielded in persons with high quality ratings compared to those with low ratings. Hence, a simple overestimation of MI quality in motivated persons seems unlikely. However, no precise statements regarding the origin and relationship of the found effects can be made with the data provided by this study.
To conclude, motivational aspects and individual awareness of the body may shape kinesthetic MI abilities. However, the results in this study are equivocal and of an explorative nature. To detect individuals that profit most from MI of swallowing, future studies should investigate further aspects of differences in brain activation during MI.
4.5 Limitations and Future Research
Despite the attempt to address boundaries of earlier research, several limitations were present in this study. In this section those constraints are discussed and implications for future studies suggested.
Concerning the ME/MI task, multiple challenges were observed. As discussed before, participants were seated in a cabin to keep out light during NIRS measurements. Therefore, it was not possible to observe whether subjects followed the given instructions accurately and if movements were performed during MI or between tasks. Supplementary EMG measures