Artifact Minimization in the Hemodynamic Response Function during Motor Execution and Motor Imagery of Swallowing: A Near-infrared Spectroscopy Study

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Veronika Isabella Engele

Artifact Minimization

in the Hemodynamic Response Function during Motor Execution and Motor Imagery of Swallowing: A Near-infrared Spectroscopy Study

MASTERARBEIT

zur Erlangung des akademischen Grades Master of Science (MSc) an der naturwissenschaftlichen Fakultät der

Karl-Franzens-Universität Graz (University of Graz)

Betreuung und Begutachtung:

Priv.-Doz.

ⁱⁿ

Mag.

^a

Dr.

ⁱⁿ

rer.nat. Silvia Erika Kober Karl-Franzens-Universität Graz

Institut für Psychologie Arbeitsbereich Neuropsychologie

Graz, Juli 2021

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Danksagung (Acknowledgments)

Allen voran möchte ich mich bei meiner Betreuerin Frau Priv.-Doz.ⁱⁿMag.^aDr.ⁱⁿSilvia Kober bedanken, die mich trotz allen Herausforderungen des letzten Jahres, immer freundlich und kompetent durch meine Masterarbeit begleitet hat. Ihr Verständnis, ihre Geduld und

Hilfsbereitschaft, haben mir dabei geholfen meine Motivation für diese Arbeit nicht zu verlieren. Ein großes Danke geht auch an alle Teilnehmer und Teilnehmerinnen der Studie, denn ohne sie wäre diese Arbeit nicht möglich gewesen. Ganz besonders möchte ich mich bei meinen Kollegen und Kolleginnen bedanken, mit denen ich gemeinsam durch dieses Studium gegangen bin und die mittlerweile für mich Freunde und Freundinnen geworden sind. Der größte Dank gilt dabei Thomas Kanatschnig, denn mit seiner Fähigkeit komplizierte Dinge einfach erscheinen zu lassen und nicht zuletzt seinen Humor, ist er mir das ganze Studium über zur Seite gestanden und hat mich nun mit seinem Rat auch bei der Erstellung dieser Arbeit unterstützt. Abschließend möchte ich mich noch bei den Personen bedanken, die mir am nächsten stehen. Bei meiner Mutter, die mich immer unterstützt, bei meinem Vater, der alles relativieren und damit leichter machen kann, bei meiner Schwester, die mir immer ein Vorbild sein wird und bei meinem Onkel, der mir die Welt und die Welt der Wissenschaft gezeigt hat. Und schließlich bedanke ich mich insbesondere bei meinem Freund, der mich durch alle kleinen und großen Herausforderungen dieser Arbeit hindurch gestützt hat.

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Abstract

As the imagination and execution of a movement evoke similar brain activation, motor imagery (MI) of limb movements is rapidly gaining importance in neurorehabilitation and psychological research. Previous studies using near-infrared spectroscopy (NIRS) have found comparable brain activation during motor execution (ME) and MI of swallowing, indicating a potential for its usage in treating dysphagia. Movements close to the scalp induce artifacts in the NIRS signal, interfering with the hemodynamic response function of the brain. Thus, the origin of hemodynamic concentration changes found during swallowing has been challenged.

This study aimed to replicate earlier findings and to address their limitations, by applying motion artifact correction methods (wavelet filter and short-distance channel regression).

Correction methods were compared to a conventional rejection approach, to investigate their effects on the NIRS signal during swallowing. Thirty-three participants executed and

imagined saliva swallowing while hemodynamic concentration changes were assessed with NIRS. After applying a wavelet filter and short-distance channel regression, results showed that ME and MI of swallowing led to pronounced activation above the inferior frontal gyrus.

Findings were largely comparable to previous research, yet in contrast HbR increased stronger during MI than during ME of swallowing. Wavelet filtering led to a decrease in the signal compared to manual rejection, reflecting a reduction of motion artifacts. No significant effect of short-distance channels was found. While some differences to earlier studies were

observed, this study showed that the general pattern of hemodynamic activation elicited by ME and MI of swallowing, is still present after motion artifact correction. Thus, results

underline the potential of MI for clinical settings. Suitable methods to correct motion artifacts, while maintaining the hemodynamic response of swallowing, should further be investigated.

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Zusammenfassung (German Abstract)

Die Vorstellung und Ausführung von Bewegungen aktivieren vergleichbare Areale im Gehirn. Bewegungsvorstellung ist daher ein vielversprechender Ansatz in der

Neurorehabilitation und Untersuchungsgegenstand psychologischer Forschung. Da auch ähnliche Gehirnaktivierung bei der Vorstellung und Ausführung von Schluckbewegungen mittels Nah-infrarot Spektroskopie (NIRS) gefunden wurde, könnte Bewegungsvorstellung nützlich in der Behandlung von Dysphagie sein. Bewegungen von Kopf und Gesicht können jedoch Artefakte im NIRS Signal verursachen und dabei die eigentliche Gehirnaktivierung überdecken. Ziel dieser Studie war es, die Ergebnisse früherer Forschung zu replizieren und Bewegungsartefakte durch den Einsatz von Korrekturmethoden zu minimieren. Verschiedene Korrekturmethoden (Wavelet, Short-Distance Kanäle und manuelle Korrektur) wurden miteinander verglichen. Dreiunddreißig Personen schluckten ihren Speichel und versuchten den Vorgang imaginär nachzufühlen, während ihre Gehirnaktivierung mittels NIRS gemessen wurde. Die stärkste Aktivierung wurde über dem inferioren frontalen Gyrus gefunden und Anstiege im Signal waren langsamer als bei anderen Bewegungen. Trotz Signalkorrektur mit Wavelet und Short-Distance Kanälen, waren die Ergebnisse daher größtenteils vergleichbar mit früheren Studien. Eine Ausnahme war die Beobachtung eines höheren HbR Anstiegs während der Vorstellung im Vergleich zu aktivem Schlucken. Von den verwendeten Korrekturmethoden führte einzig der Wavelet Filter zu einer Reduktion der

Konzentrationsveränderungen, welche für eine Verminderung von Artefakten spricht. Die hämodynamische Reaktion während der Vorstellung und Ausführung von

Schluckbewegungen konnte trotz der Korrektur von Bewegungsartefakten größtenteils repliziert werden und unterstreicht damit das Potenzial für den Einsatz in klinischen Settings.

Besonders geeignete Korrekturmethoden für Schluckparadigmen sollten in folgenden Studien näher untersucht werden.

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1 Introduction

1.1 Motor Imagery

Motor Imagery (MI) is specified as the mental imagination of a movement without the actual execution or the activation of any involved muscles (Decety & Grèzes, 1999;

Jeannerod, 1994). Imaging a movement shows characteristics akin to the execution of the same movement (Decety, Jeannerod, Germain, & Pastene, 1991; Guillot & Collet, 2005;

Aymeric Guillot, Di Rienzo, Macintyre, Moran, & Collet, 2012; Papaxanthis, Schieppati, Gentili, & Pozzo, 2002). Earlier studies found a similar time course between MI and motor execution (ME), which adapts to varying conditions. For example, lifting one arm and

imaging it to be lifted take the same amount of time (Guillot & Collet, 2005). Further, MI and ME underlie the same physical constraints. Putting more weight on the arm increases the duration of both ME and MI (Papaxanthis et al., 2002). Vegetative responses, like heart rate and respiratory activity, also increase during MI, however to smaller degree than during ME (Decety, Jeannerod, Germain, & Pastene, 1991).

Considering these findings, it is not surprising that MI leads to similar brain activation patterns as the movement itself (Hétu et al., 2013; Jeannerod, 2001; Munzert, Lorey, &

Zentgraf, 2009; Neuper, Scherer, Reiner, & Pfurtscheller, 2005). Hence, MI can be used to enhance neuroplasticity and improve motor functions (Faralli, Bigoni, Mauro, Rossi, &

Carulli, 2013; Pfurtscheller & Neuper, 1997; Ruffino, Papaxanthis, & Lebon, 2017).

Despite many overlapping processes there are also differences between ME and MI, resulting from the fact that during MI no actual movements are performed. Inhibitory

mechanisms are believed to stop the execution of the movement at a certain time (Bart, Koch,

& Rieger, 2021; Guillot et al., 2012; Rieger, Dahm, & Koch, 2016). Electromyographic (EMG) activity has been previously found during MI, but with significantly reduced

magnitude compared to ME, probably reflecting incomplete movement inhibition (Guillot et al., 2012; Jeannerod, 1994). Moreover, EMG activity increased with the mental effort required for an imagination (Guillot et al., 2007), and in neurologic patients, who show reduced inhibitory control (Guillot et al., 2012). Recent studies demonstrated that global (inhibition of all movements) and selective (inhibition of specific movements that are

imagined) inhibitory mechanisms are active during MI (Bart et al., 2021; Rieger et al., 2016).

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8 1.1.1 Neuronal Network of Motor Imagery

Various studies using positron-encephalography (PET), transcranial magnet

stimulation (TMS), electro-encephalography (EEG), functional magnet-resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) found similar brain activation in MI compared to ME (Batula, Mark, Kim, & Ayaz, 2017; Guillot, Rienzo, & Collet, 2014; Jeannerod, 2001;

Kraeutner, El-Serafi, Lee, & Boe, 2019; Malouin, Richards, Jackson, Dumas, & Doyon, 2003;

Munzert et al., 2009). The activated regions depend partly on the type of movement that has been imagined, but there is also consistent activation found during MI of different parts of the body (Ehrsson, Geyer, & Naito, 2003; Hétu et al., 2013). A meta-analysis conducted by Hetu et al. (2013) revealed a large fronto-parietal network as well as subcortical and cerebellar regions that were activated during MI of both, upper and lower limb movements. More precisely, frontal regions included premotor regions, like the inferior frontal gyri (IFG) and supplementary motor area (SMA), as well as the anterior insula and precentral and middle frontal gyri. In the parietal lobes, the bilateral superior parietal lobule, the supramarginal gyrus and the left inferior parietal lobule were activated. Activation in the basal ganglia (left putamen and pallidum), the right thalamus and the cerebellum was linked to general MI (Hétu et al., 2013). Therefore, MI, similar to ME, appears to rely on a large functional neural

network, much like during ME, and requires not only motor representations but also further processing steps (Hétu et al., 2013; Solodkin, Hlustik, Chen, & Small, 2004).

Although an overlay in activated brain regions has been shown by neuroimaging studies, levels of brain activation tend to be lower in MI than during actual movements (Jeannerod, 2001; Solodkin et al., 2004). Other than for ME, the role of the primary motor cortex (M1) in MI is rather controversial. While some studies reported activation in M1 during MI (Jeannerod, 2001; Munzert et al., 2009) others failed to detect involvement an involvement of the M1 (Binkofski et al., 2000; Hardwick, Caspers, Eickhoff, & Swinnen, 2018; Hétu et al., 2013). It has been suggested that M1 activation is due to increased muscle activity (Berman, Horovitz, Venkataraman, & Hallett, 2012) or could depend on the applied imagination approach (discussed in the following section 1.1.2), the given instructions, or the individual’s MI abilities (Guillot et al., 2012; Lotze & Zentgraf, 2012; Mizuguchi & Kanosue, 2017; Van der Meulen, Allali, Rieger, Assal, & Vuilleumier, 2014).

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9 1.1.2 Strategies

Two different strategies can be used to imagine a movement: visual MI approaches and kinesthetic MI approaches. Visual strategies can further be categorized into internal, first- person perspectives (perception of the movement from one self’s perspective) and external, third-person perspectives (watching oneself on a video tape or from another person’s view during the execution of the movement). In contrast, kinesthetic approaches require the imagination of how the used body parts feel during the movement (Guillot et al., 2009;

Neuper et al., 2005).

Various studies account for an overlapping yet distinct construct of visual and

kinesthetic strategies as they activate different brain regions. Both internal and external visual strategies involve activation of occipital regions, the superior parietal lobulus and further visual pathways, whereas kinesthetic strategies mainly lead to an increase in motor related areas and the inferior parietal lobule, which is suggested to be important for mirror neurons (Binkofski et al., 2000; Stephan Frederic Dahm, 2020; Guillot et al., 2009; Lorey et al., 2009).

In case of the intraparietal sulcus, kinesthetic imagery elicits stronger activation in the anterior part, whereas visual imagery activates the posterior part (Binkofski et al., 2000). Furthermore, it has been shown that kinesthetic strategies lead to stronger brain activation than visual ones (Guillot et al., 2009; Ridderinkhof & Brass, 2015) and that visual strategies are more effective in younger subjects and become less effective with increasing age (Hovington & Brouwer, 2010). Thus, the most suitable strategy used for MI depends on the type of the movement, the characteristics of the population and the expected outcome (Guillot & Collet, 2008).

Despite those differences, the connection between the superior parietal lobe and the SMA is similar for visual and kinesthetic MI strategies and illustrates the importance of this neuronal connection for MI (Jeannerod, 2001; Solodkin, Hlustik, Chen, & Small, 2004).

1.1.3 Psychological Correlates of MI

Compared to the numbers of studies investigating neurophysiological correlates of MI, little is still known about psychological correlates or other influences, leading to

interindividual differences in MI. Studies have pointed out the importance of individual MI ability, measured with questionnaires, for neuronal activation during MI of different

movements (Guillot et al., 2008; Seiler, Newman-Norlund, & Monsma, 2017; Van der Meulen et al., 2014). Good imagers have been found to elicit more focused brain activation

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during MI of finger tapping compared to novices, indicating more efficient recruitment of motor relevant areas (Guillot & Collet, 2008). Moreover, good MI abilities lead to stronger neuronal activation in motor related brain areas during MI of arm rotation (Seiler et al., 2017) and MI of gait (Van der Meulen et al., 2014). Similarly, subjective ratings of quality and vividness of imaginations were found to correlate with neuronal activation in premotor areas as well as increased corticospinal excitability. The authors suggest that stronger activation of the premotor cortex modulates the activation of the primary motor cortex, which in turn leads to a more elaborate and vivid perception of the imagination (Moriuchi et al., 2020; Zabicki et al., 2019).

Subirats, Allali, Briansoulet, Salle, & Perrochon (2018) investigated age and gender dependent effects on MI. Their findings show a transfer in elderly people from a visual to kinesthetic approach, but no differences between men and women concerning the used strategy. A gender effect was revealed regarding the corresponding timing of ME and MI, with women showing a slightly improved (closer to ME) time course (Subirats et al., 2018).

Further, influences of motivation and emotional states on MI are assumed (Tinaz et al., 2018). Therefore, the usage of positive associated mental imageries, especially in

rehabilitative patients, should be considered (Guillot et al., 2014; Tinaz et al., 2018). This is in line with neurofeedback and brain-computer-interface (BCI) studies, suggesting a positive effect of motivation on BCI and MI-BCI performance (Hammer et al., 2012; Jeunet, N’Kaoua, & Lotte, 2016; Kleih et al., 2011).

A study conducted by Tinaz et al. (2018) revealed an interesting approach. They found correlations between successful kinesthetic MI neurofeedback, body awareness and insula- dorsomedial frontal cortex connectivity in patients with Parkinson’s disease (PD). The authors outlined, that the quality of imagined movements, as well as the emotional and motivational context of MI determined the amplitude of connectivity between both regions.

1.1.4 Applications of Motor Imagery

Because of its profound relationship to ME, the concepts of MI are applied in many different disciplines, including sports, to improve the performance of athletes or to maintain it during times of injury (Holmes & Calmels, 2008; McNeill, Ramsbottom, Toth, & Campbell, 2020; Ridderinkhof & Brass, 2015; Wei & Luo, 2010), for surgeons to train complex

procedures (Cocks, Moulton, Luu, & Cil, 2014) and even for musicians (Lotze, 2013). MI is also a promising tool in neurorehabilitation, in addition to physical therapy or while actual

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movements are not possible or too painful. Several studies have shown the potential of MI to improve the recovery of motor functions after brain injuries, stroke or other neurological disorders (Faralli et al., 2013; Hovington & Brouwer, 2010; Jackson, Lafleur, Malouin, Richards, & Doyon, 2001; Lim et al., 2006; Lotze & Cohen, 2006). MI guided neurofeedback and BCI of upper and lower limb movements are gaining importance in practice with

neurological patients and amputees (Ahn, Cho, Ahn, & Jun, 2018; Chholak et al., 2019;

Chiew, Laconte, & Graham, 2012; Kober, Hinterleitner, Bauernfeind, Neuper, & Wood, 2018; Saruco et al., 2017).

So far, most studies investigating MI focused on simple movements like hand, finger or foot movements. (Mizuguchi, Nakamura, & Kanosue, 2017; Neuper et al., 2005;

Pfurtscheller & Neuper, 1997). Some analyzed imagination of more complex movements (O’shea & Moran, 2019), musical performances like piano and trombone playing (Coffman, 1990; Ross, 1985) or different kinds of sports as golf or dart performances (Kremer, Spittle, McNeil, & Shinners, 2009; Lutz, Landers, & Linder, 2001). There is also a smaller number of studies focusing on less conscious motions like jaw and tongue movements (Ehrsson et al., 2003; Morash, Bai, Furlani, Lin, & Hallett, 2008) or swallowing (Kober, Gressenberger, Kurzmann, Neuper, & Wood, 2015; Kober, Grössinger, & Wood, 2019; Kober & Wood, 2014; H. Yang, Ang, Wang, Phua, & Guan, 2016).

1.2 Swallowing

Swallowing is a complex sequence of movements which we perform voluntarily during eating and drinking but also spontaneously, without awareness between meals, during sleep or in stressful situations (Cuevas, Cook, Richter, McCutcheon, & Taub, 1995; Ertekin, 2011; Fonagy & Calloway, 1986).

The swallowing process consists of three different phases, oral, pharyngeal, and esophageal. It begins with a mainly voluntarily controlled preparatory phase, in which the bolus is prepared for swallowing and tongue movements entrap it between tongue and palate (oral phase). The pharyngeal phase is considered the transfer phase and starts with tip and sides of the tongue pressing against the palate, while the posterior part of it relaxes to allow the bolus to pass into the oropharynx. Contractions of the tongue and pharyngeal wall move the bolus further into the pharynx. To seal the airway during this process, the tongue closes the oral cavity off, the soft palate and proximal wall close the nasopharynx, and vocal cords, arytenoids and epiglottis close the laryngeal opening and vestibule. Next, the larynx positions

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itself outside the path of the bolus and the pharynx starts contracting to move it into the esophagus. The final phase is the esophageal phase, or transport phase. The esophagus relaxes to allow the bolus to pass into the stomach. Fluids are transported mainly through gravity, while peristaltic contractions move solids downwards. Unlike the oral phase, the pharyngeal and esophageal phase are reflexive and involuntary (Ertekin, 2011; Goyal & Mashimo, 2006).

1.2.1 Neuronal Activation of Swallowing

The process of swallowing relies on a large network inside the brain, the exact activation loci depend on the size and properties (consistency, flavor, water or saliva) of the bolus as well as on the type of initiation (voluntary vs. spontaneous) of swallowing (Ertekin, 2011; Humbert & Robbins, 2007; Sorös, Inamoto, & Martin, 2009). The brainstem has been found to play a crucial role in the initiation of the pharyngeal and esophageal phases as well as in swallowing control (Bautista, Sun, & Pilowsky, 2014; Ertekin, 2011; Jean, 2001) and is activated during both voluntary and reflexive swallowing (Ertekin, 2011). Multiple studies found a strong activation of the IFG, which, besides its role in language production, has been linked to the control of non-linguistic mouth and face movements (Kober, Grössinger, et al., 2019; Kober & Wood, 2018; Martin, Goodyear, Gati, & Menon, 2001). The SMA, which plays a role in planning of complex or sequential movements (Satow et al., 2004), has frequently been found to be activated during swallowing (Martin et al., 2001; Sorös et al., 2009). The insula is involved in integrating sensory and gustatory information received from different brain regions, which are active during swallowing (Ertekin, 2011; Hamdy, Mikulis, et al., 1999; Smits, Peeters, Hecke, & Sunaert, 2007; Sorös et al., 2009). The lateral precentral gyrus in M1 is crucial to initiate voluntary swallowing and movements of tongue, jaws and lips, but has also been found to be activated during spontaneous swallowing, whereas the postcentral gyrus in the somatosensory cortex processes sensory inputs in mouth and pharynx (Hamdy, Mikulis, et al., 1999; Hamdy, Rothwell, et al., 1999). The superior temporal gyrus addresses perception of taste sensations and swallowing noises (Martin, 2001).

Compared to other motor actions such as hand movements (Wriessnegger, Kurzmann,

& Neuper, 2008), NIRS signal changes and blood oxygenation dependent (BOLD) signal during active swallowing have been found to be slower and to peak later (Hamdy, Mikulis, et al., 1999; Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014, 2018). During swallowing, peak activation of NIRS signal changes was reported at around 15 seconds after task onset (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014, 2018), whereas during hand

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movements, the maximum activation of the hemodynamic response was at 5 seconds after task onset (Wriessnegger et al., 2008). As the whole swallowing process takes between 8 and 12 seconds and incorporates secondary motor activity in the esophagus, this prolonged time course could result from sensory and motoric feedback loops leading from cortical to peripheric areas (Hamdy, Mikulis, et al., 1999; Kober & Wood, 2014; Martin et al., 2001).

Regarding different bolus types, several studies showed stronger activation in swallowing related brain areas for saliva than for water swallowing (Humbert & Robbins, 2007; Kober & Wood, 2018). Further, a lateralization effect indicating higher activation during water swallowing in the right inferior parietal lobe, the right postcentral gyrus and the right insula has been found, whereas activation patterns for saliva swallowing were more bilaterally distributed (Sorös et al., 2009). In a NIRS study, signal changes during water swallowing were most pronounced over the right IFG and during saliva swallowing over the left IFG, indicating a similar lateralization effect (Kober & Wood, 2018). Another finding of this study was that compared to water swallowing, oxy hemoglobin (HbO) levels showed a prolonged time course and deoxy hemoglobin (HbR) decreased steady during saliva

swallowing, reflecting a higher task demands of saliva swallowing (Kober & Wood, 2018).

1.2.2 Dysphagia

Dysphagia is a pathological difficulty in swallowing, which can affect all phases of the movement sequence. Dysphagia can result from brain injuries or neurological diseases like Alzheimer’s dementia, Multiple Sclerosis, amyotrophy lateral sclerosis (ALS), strokes or traumatic brain injuries, but it is also present in the healthy aging population. In the normal elderly population, prevalence for suffering from dysphagia is expected between 15 and 23%

while for clinical populations the prevalence can be up to 85.9% (Barczi, Sullivan, &

Robbins, 2000; Eslick & Talley, 2008; Espinosa-Val et al., 2020; Wilkins, Gillies, Thomas, &

Wagner, 2007). Difficulties in the swallowing process are not only troublesome in daily life but can also lead to severe lung damage through choking and inhalation. Patients with

dysphagia are more likely to develop anxiety disorders or depression and often report impacts on their social life. Hence, dysphagia highly affects health and overall quality of life (Eslick &

Talley, 2008).

Conventional therapy methods for dysphagia focus mainly on compensatory strategies or on training of in swallowing involved muscular structures (Langmore & Pisegna, 2015;

Szynkiewicz, Nobriga, & Donoghue, 2018; Vose, Nonnenmacher, Singer, & González-

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Fernández, 2014). Compensatory strategies are mostly used to prevent health consequences of dysphagia like dehydration, malnutrition or pneumonia and do not lead to any physiological improvements. Examples for such strategies are different postures of head and tongue during swallowing, as well as modification of the texture of fluids and food (Vose et al., 2014).

Contrary, exercises for swallowing related muscle structures aim to improve swallowing function directly and therefore enhance neuroplasticity (Langmore & Pisegna, 2015; Vose et al., 2014). Along those exercises, some can also serve as compensatory strategy (Vose et al., 2014).

Previous research showed that it is possible to enhance neuroplasticity in swallowing related brain areas (Robbins et al., 2008), thus the usage of MI could be a promising tool to support conventional therapy methods in dysphagia patients (Szynkiewicz et al., 2018).

1.2.3 MI of Swallowing

Despite the rapidly increasing number of studies in motor imagery, there is still a shortage of investigations about the imagination of swallowing (for a review, see Szynkiewicz et al., 2018; Yang et al., 2016). For the use in neurorehabilitation, it is important to ensure that MI of swallowing activates similar brain regions as swallowing itself in order to enhance neuroplasticity and thus improve the recovery of patients with dysphagia (Faralli et al., 2013;

Ruffino et al., 2017; Szynkiewicz et al., 2018). The absence of visual cues for swallowing outlines a huge difference to MI of limb movements and forces users to rely on kinesthetic imagery strategies. In addition, various muscle groups have to be imagined. Yet, Yang et al.

(2014) showed in an EEG study that the imagination of swallowing and tongue movements are distinct constructs.

Neuroimaging studies revealed similar brain activation patterns between MIand ME of swallowing, including the IFG, basal ganglia, insula, SMA, bilateral pre- and postcentral gyrus, and the cerebellum (Kober, Bauernfeind, et al., 2015; Kober, Grössinger, et al., 2019;

Kober & Wood, 2014). Previous NIRS studies located strongest signal changes during ME and MI of swallowing above the IFG bilaterally in healthy young adults, healthy elderly and dysphagia patients with brain stem lesions (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014). For dysphagia patients with cerebral lesions, a more unilateral activation pattern was revealed. (Kober, Bauernfeind, et al., 2015). A recent fMRI study extended those findings for additional activation in deeper brain structures (Kober, Grössinger, et al., 2019). Further, and similar as during ME of swallowing (see section 1.2.1), a prolonged time course of the NIRS

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signal compared to ME and MI of other movements (Wriessnegger et al., 2008) has been found for MI of swallowing (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014).

Increases in HbR during MI of swallowing were largely comparable to ME, but the tasks differed fundamentally regarding changes in HbO. While a significant increase was observed during ME, HbO levels even decreased during MI (Kober & Wood, 2014). A decrease in HbO has been associated with improved motor inhibition (Gentili, Shewokis, Ayaz, & Contreras-Vidal, 2013), therefore Kober and Wood (2014) interpreted their finding as a sign of movement inhibition. As mentioned before (see section 1.1), MI contains motor plans of the movement, but on some point they have to be stopped to prevent actual

movement (Guillot et al., 2012). However, no such decrease in HbO levels has been observed during MI of other movements (Wriessnegger et al., 2008). As swallowing is partly a

reflexive movement (Ertekin, 2011), more effort could be needed to inhibit active swallowing during MI, leading to a decrease in HbO (Kober & Wood, 2014). Small, but not significant increases in EMG activity during MI support this assumption of increased effort for inhibiting swallowing (Kober & Wood, 2014). In healthy elderly and dysphagia patients with brain stem lesions, increases in HbR were observed during MI of swallowing, but along with a

simultaneous increase in HbO, which could indicate insufficient movement inhibition in this population (Kober, Bauernfeind, et al., 2015).

Neurofeedback studies showed that healthy young adults were able to upregulate HbR but not HbO over the IFG during MI of swallowing (Kober, Gressenberger, et al., 2015;

Kober et al., 2018). Moreover it has been demonstrated that it is possible to voluntary downregulate HbO and upregulate HbR over the IFG during MI of swallowing, but not vice versa (Kober et al., 2018). In contrast, healthy elderly were only able to upregulate HbR (Kober, Spörk, Bauernfeind, & Wood, 2019). MI of swallowing usually leads to a decrease in HbO and an increase in HbR in healthy young adults (Kober & Wood, 2014) and to an

increase in both, HbO and HbR, in healthy elderly (Kober, Bauernfeind, et al., 2015). Hence, the trainability of these parameters during neurofeedback suggests that only this natural course of the NIRS signal can be modulated (Kober et al., 2018).

1.3 NIRS

NIRS (or fNIRS; functional-near-infrared-spectroscopy) is a non-invasive neuroimaging method, based on light in the near-infrared spectrum to measure the

hemodynamic response function (HRF) in the cortex and therefore infer cerebral activity. A

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NIRS system consists of source optodes, directing the light into the head, and detector optodes, which are positioned several centimeters apart and measure the intensity of back scattered light. From these changes in signal intensity, HbO and HbR can be calculated (Huppert, Diamond, Franceschini, & Boas, 2009; Scholkmann et al., 2014). The temporal resolution of NIRS is quite good, better than in fMRI, but lower than in EEG. Conversely, the spatial resolution is relatively poor, better than in EEG, but much lower than in fMRI

(Gagnon et al., 2011). As the light reaches maximally 3 centimeters inside the head, NIRS is limited to activity changes in upper cortical layers only (Chen et al., 2020). A huge advantage of NIRS is its portability. Therefore, it can be used in different settings inside and outside of the laboratory; it is cheap in its maintenance, easy to apply and relatively robust against body movements. Thus, NIRS systems are gaining importance especially in the study of motor and MI tasks (Batula et al., 2017) and in the use with challenging populations that might be unsuitable for fMRI measurements (e.g., infants or stroke patients; Di Lorenzo et al., 2019;

Obrig, 2014; Sood, McLaughlin, & Cortez, 2015).

1.3.1 Artifacts

Although NIRS is a relatively robust method considering movements of the body, there are some limitations to the method, resulting in unsystematic variations present in the signal. The light emitted by the source probe travels from the surface of the scalp to the cortical layer of the brain. On its way to the cortex and back, the light passes through the upper layers of the scalp and the skull. As a result, the signal contains not only the hemodynamic response resulting from neuronal activation, but also physiological noise present in the extra cortical layers. The noise arises from the cardiac signal, oscillations in arterial blood pressure (Meyer waves), respiration as well as other physiological changes in the extra cortical layers (Kirilina et al., 2012). Cardiac and respiratory signals can easily be filtered out of the NIRS signal whereas lower frequency changes, such as Meyer waves, are hard to detect and can correlate with the signal (Brigadoi et al., 2014; Gagnon, Yücel, Boas, &

Cooper, 2014; Jahani, Setarehdan, Boas, & Yücel, 2018).

Additionally, the NIRS signal can also contain motion artifacts, appearing when the optodes are losing contact with the skull. To ensure constant contact, the NIRS probes should be mounted on a cap, fitting tightly to the head of subjects or glued directly to the skin

(Gagnon et al., 2014; Yücel, Selb, Boas, Cash, & Cooper, 2014). Despite proper mounting, motion artifacts can still occur due to motions of the scalp resulting from eyebrow or jaw

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movements or movements close to the head. Motion artifacts occur in different shapes, frequencies, content, and different timing. Generally, there are three different types of shapes in the signal: Spikes, baseline shifts and low frequency variations. Further, motion artifacts can occur as isolated events or they can be temporally correlated with the hemodynamic response function, due to task related movements and physiological changes (Jahani et al., 2018). For instance, jaw movements, as carried out during speaking or swallowing, lead to massive temporal muscle activity and can produce task related motion artifacts in speech studies (Schecklmann, Mann, Langguth, & Ehlis, 2017). Hence, motion artifacts can be either identified easily or hard to distinguish from the signal, too. As motion artifacts differ in their appearance, the efficacy of correction techniques varies with the type of motion artifacts and the choice of the best method is data dependent (Brigadoi et al., 2014; Cooper et al., 2012;

Yücel et al., 2021).

1.3.2 Artifact Correction

So, what options are there to gain a clear, artifact free signal? The first possibility is to exclude identified artifacts from the signal. Obviously, this implies a loss of data, which can be especially problematic in single trials, when the artifacts correlate with the stimulus onset or when dealing with data with large numbers of artifacts are present. The second option is the correction of the signal. Roughly, two different types of approaches can be distinguished:

Those relying only on the data commonly collected with NIRS and those which require additional sources (Chen et al., 2020; Nguyen, Yoo, Bhutta, & Hong, 2018; Yücel et al., 2021).

1.3.2.1 Correction Methods

As motion artifacts can significantly reduce the number of useful trials, distort the initial HRF and thereby introduce artificial effects, it is crucial to correct the signal (Selb et al., 2015). Correction methods aim to restore the original hemodynamic response resulting from brain activation for the parts of the signal where motion artifacts have been identified and excluded. There are several approved mathematical approaches, but no established standard procedure, as the best method depends on type and number of motion artifacts (Brigadoi et al., 2014). The following sections presents an overview of commonly used correction techniques.

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PCA uses an orthogonal transformation to gain uncorrelated components, equal to the number of measurements in the original data set and related to its variance. The components are arranged in descending order regarding the proportion of variance they explain. As motion artifacts often evoke bigger changes in amplitude, it is presumed that the first couple of components represent the artifacts, causing the variance in the signal. Therefore, those components should be removed and consequently ensure correction for the motion artifacts.

(Zhang, 2005). The components to be removed can either be fixed, chosen for each subject separately or by defining a specific percentage of variance as threshold for removal. PCA is quite effective for data, in which motion artifacts are the main source of variance and consist out of high amplitude spikes (e.g. in studies with infants), but in data where the amplitude of motion artifacts is similar to the one of the cortical signal, it tends to remove too much variance of the evoked hemodynamic response (Brigadoi et al., 2014). To handle this

problem, PCA can be applied to segments of data, identified as motion artifacts, only (tPCA;

Yücel, Selb, Cooper, & Boas, 2014).

Spline Interpolation

Spline interpolation (Scholkmann, Spichtig, Muehlemann, & Wolf, 2010) is a channel- by-channel approach, which only operates on identified motion artifacts instead of the entire signal. First, the motion artifacts have to be detected by a reliable technique, before a

polynomial model is fitted into the period of the motion artifact and then subtracted from the original signal. Afterwards, the time series has to be reconstructed, based on the mean of the signal section following the artifact and the mean of the previous section. Spline interpolation is capable to correct for baseline shifts in the signal, however, if the artifacts cannot be

appropriately detected, the technique will not improve the signal quality. (Brigadoi et al., 2014)

Wavelet Filtering

Wavelet filtering constitutes another channel-by-channel approach, based on the assumption, that the components of the signal function follow a normal distribution, whereas outliers will be due to motion artifacts (Molavi & Dumont, 2012). Outliers are defined by the interquartile range, which can be set depending on the amount and type of motion artifacts

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(Brigadoi et al., 2014; Di Lorenzo et al., 2019). The coefficients of the outliers are set to zero and the original signal is recomposed with an inversed discrete wavelet transformation.

Wavelet filtering allows to maintain most of the frequency content and is a suitable method to correct for spikes (Brigadoi et al., 2014; Yücel et al., 2021). Moreover, it has proven efficient in a speech study, where motion artifacts occurred in dependence of the stimuli (Brigadoi et al., 2014). On the other hand, wavelet filtering does not correct for baseline shifts, it takes a lot of time to be computed compared to other techniques, and the parameter determining outliers varies between populations (Brigadoi et al., 2014; Yücel et al., 2021).

Correlation-based Signal Improvement (CBSI)

CBSI is based on the assumptions that HbO and HbR are negatively correlated during functional activation and positively correlated in the presence of motion artifacts (Cui, Bray,

& Reiss, 2010). Further, the true physiological signal and the motion artifact are assumed to be uncorrelated. To correct for motion artifacts, CBSI simply forces a negative correlation between HbO and HbR. This approach is effective especially when motion artifacts occur simultaneously with stimulus onsets and as it is quite simple, it also enables online correction of the signal during measurements. CBSI is limited due to the assumptions it relies on. A negative correlation between HbO and HbR may not always be present during cortical activation and therefore CBSI itself can produce artifacts in the HRF (Brigadoi et al., 2014).

As results from previous NIRS studies indicate that HbO and HbR are not inversely related during active swallowing (see section 1.2.2; Kober & Wood, 2014, 2018), CBSI may not be a useful motion artifact correction method for swallowing.

1.3.2.2 Additional Sources

Additional sources allow to extract the interfering measure from the NIRS signal. This can be any auxiliary external instrument, that measures physiological parameters (Raggam, Bauernfeind, & Wriessnegger, 2020), for instance, a pulse sensor, a respiratory belt or EMG (Chen et al., 2020; Kirilina et al., 2012, 2013). Another source which is recently gaining attention are so called short-separation, or short-distance (SD) channels (Gagnon et al., 2011;

Saager & Berger, 2008). SD-channels result from the observation that the depth of the NIRS signal varies as a function of the distance between source and detector optodes. The further corresponding probes are apart from each other, the deeper the light travels into the brain, along with a simultaneous increase in the number of artifacts (see section 1.3.1; Chen et al.,

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2020; Cooper et al., 2012; Gagnon et al., 2011). Whereas normal NIRS detectors are about 3 cm apart from the corresponding emitter (so-called long-distance channels), SD detectors are placed close to the light source. Thus, the emitted light reaches extracortical layers only (e.g., scalp, skull) before being detected by a SD-detector. As regular long-distance channels contain information from both, the cortex and the extra-cortical, measuring multiple distances at the same time enables to regress the signal from the extra-cortical layers from the cortical activation (Brigadoi & Cooper, 2015; Gagnon et al., 2011).

1.4 Present study

1.4.1 Aim of the Study and Research Questions

Previous NIRS studies investigating the hemodynamic response of ME and MI of swallowing found that during ME there is an increase in frontal areas in HbO and in HbR, whereas during MI there is a decrease in HbO and an increase in HbR, probably reflecting inhibition of active swallowing. The strongest activations could be found over the IFG for both ME and MI (Kober, Bauernfeind, et al., 2015; Kober, Grössinger, et al., 2019; Kober &

Wood, 2014). Further neurofeedback studies showed that it is possible to upregulate HbR in the IFG during MI of swallowing. Hence, the usage of MI based neurofeedback could be a promising tool in treatment of patients suffering from dysphagia. As jaw movements result in massive temporal muscle activity (Schecklmann et al., 2017), it is assumed that execution of swallowing also leads to strong motion artifacts in the signal. So far, studies investigating neuronal correlates of swallowing with NIRS, relied on visual inspection of the signal and manual rejection of obviously visible motion artifacts (Kober, Bauernfeind, et al., 2015;

Kober & Wood, 2014, 2018). Manual rejection of artifacts is highly subjective and some motion artifacts are hard to distinguish from the HRF in the signal (Brigadoi et al., 2014;

Jahani et al., 2018; Yücel, Selb, Cooper, et al., 2014). Therefore, brain activation patterns found during ME of swallowing (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014) could still have been affected by artifacts, which questions the comparability between ME and MI of swallowing. In order to use MI of swallowing for the treatment of dysphagia, due to neural plasticity, it is important to ensure that MI leads to comparable brain activation patterns as ME of swallowing (Faralli et al., 2013; Ruffino et al., 2017; Szynkiewicz et al., 2018;

Huijuan Yang, Guan, Ang, Wang, & Yu, 2012). The latest advancements in NIRS, such as SD-channels and correction techniques, enable to gain a better insight into the real

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hemodynamic response in signals containing massive motion artifacts (Brigadoi et al., 2014;

Chen et al., 2020; Cooper et al., 2012; Yücel et al., 2021). Therefore, the first aim of this study is to validate earlier findings (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014) with a newer NIRS system using additional short distance detectors and wavelet filtering.

Previous NIRS studies investigating swallowing (Kober, Bauernfeind, et al., 2015;

Kober & Wood, 2014, 2018) handled motion artifacts with visual detection and manual rejection. In recent years, more and more methods addressing the problem of motion artifacts have been proposed. As motion artifact correction is always data dependent (Brigadoi et al., 2014) and there is no established standard procedure, it is important to specifically investigate appropriate correction method for swallowing. For real speech, wavelet filtering appears to be a promising approach (Brigadoi et al., 2014). Considering that both, jaw movements and swallowing lead to similar muscular activity and therefore create similar motion artifacts (Schecklmann et al., 2017), wavelet filtering could present a suitable approach for swallowing as well. Furthermore, SD channels showed to be a valuable addition for signals confounded with a lot of motion (Brigadoi & Cooper, 2015; Gagnon et al., 2014). The second aim of the study is, therefore, to investigate the effects of wavelet filtering and the application of SD- channels on the NIRS signal assessed during executing swallowing movements and if these artifact correction methods could present benefits over conventional manual approaches.

So far, little is known about influences leading to interindividual differences in brain activation patterns, elicited by MI. As swallowing can only be imagined via a kinesthetic MI approach, influences of visual strategies on activated brain regions (Guillot et al., 2009) can be ruled out for MI of swallowing. Besides the effects of different strategies, individuals with high MI ability have been found to produce more focused activation in motor related brain areas during MI than novices (Guillot et al., 2008; Seiler et al., 2017; Van der Meulen et al., 2014). Additionally, enhanced activation in premotor areas has been associated with higher subjective ratings of the imagination quality (Moriuchi et al., 2020; Zabicki et al., 2019) and psychological factors such as motivational aspects, as well as an individual’s body awareness seem to influence the neuronal activation during MI (Tinaz et al., 2018). Although outlining first insights into correlates of brain activation during MI, previous studies differed in types of movements that had been imagined. For the use of MI in neurorehabilitation, it is important to identify individuals that profit most from the approach (Stephan Frederic Dahm, 2020; Guillot et al., 2008). Thus, the third aim of this study is to explore relationships between brain

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activation patterns elicited by MI of swallowing and possible correlates such as kinesthetic motor imagery ability, subjective quality of imagination, motivation and body awareness.

1.4.2 Hypotheses

Hemodynamic Response of ME and MI of Swallowing

Based on the previous results from Kober & Wood (2014) it is expected that during ME there will be an increase in HbO and in HbR, whereas during MI there will be a decrease in HbO and an increase in HbR. The strongest signal changes are expected over the IFG for both tasks.

Influence of Correction Methods and SD-Detectors on NIRS Signal during Swallowing Wavelet filtering has been found to be effective in a task involving comparable motion artifacts than swallowing (Brigadoi et al., 2014). As motion artifact correction is data

dependent, and so far, no standard procedure for swallowing is established, no clear

hypothesis can be made. However, it is expected that the usage of a correction method such as Wavelet filtering and regressing SD-channels out of the NIRS signal contribute to a clearer NIRS signal compared to manual artifact rejection only. As motion artifacts lead to stronger activation changes in the NIRS signal, a clearer signal should be reflected by lower HbO and HbR levels.

Correlates of the Hemodynamic Response of MI of Swallowing

Considering the lack of literature investigating interindividual differences in brain activation elicited by MI, the third hypothesis is of an explorative nature. As earlier studies indicated possible relations between MI ability, motivation, subjectively perceived quality of MI and body awareness with neuronal activation patterns, correlations between those

variables will be further explored.

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2 Method

2.1 Participants

In sum, 33 healthy young adults (20 women and 13 men) aged between 18 and 35 years (M = 25.03, SD = 4.40) volunteered to participate in the study. They had no history of psychiatric, neurological, respiratory, or swallowing disorders and were all suitable for NIRS measurements (no wounds on the scalp, no hyper sensibility of the skin, etc.). Participants were included in the study regardless of their self-reported handedness (29 right-handed and 4 left-handed). Recruitment of participants was conducted via social media, email distribution and personal contacts. Except of one subject, all participants had graduated from high school (Matura) or had achieved an even higher level of education.

Due to COVID-19, the study took place under strict safety regulations. To reduce times of personal contact, part of the study was conducted via online survey. In Addition, participants received documents for written informed consent, information about the

procedure and the safety guidelines, including a symptom checklist for COVID-19 symptoms prior to the laboratory measurements via email. The measurements took place in a laboratory room at the University of Graz in consideration of hygienic guidelines (e.g., usage of hand sanitizer, wearing an FFP-2 mask all the time, usage of gloves and additional facial shield during positioning of the NIRS equipment, cleaning of the laboratory and used equipment after each participant).

All participants gave written informed consent and were free of COVID-19 symptoms for at least seven days prior to the measurement. Psychology students received course credits for their participation in the study. The study procedures were approved by the ethics

commission of the University of Graz and data privacy was ensured in line with the European General Data Protection Regulation (DSGVO).

2.2 Material

2.2.1 Edinburgh Handedness Inventory – Short Form

The Edinburgh-Handedness-Inventory – Short Form (EHI short form; Veale, 2014) is a well-established tool to assess handedness, which asks on a five-point scale for the

preferably used hand for four different activities and objects (writing, throwing, toothbrush,

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spoon). A laterality quotient is calculated from the mean of the responses, ranging from -100 (fully left-handed) to 100 (fully right-handed). Scores between -60 and 60 are considered as mixed handed.

2.2.2 Short Questionnaire to Assess Body Awareness (KEKS)

The short questionnaire to assess body awareness (original: Kurzer Fragebogen zur Eigenwahrnehmung des Körpers, KEKS; Pöhlmann, Berger, von Jarnim & Joraschky, 2009) was used to assess body awareness of the participants. The KEKS is based on the dimensions of body awareness used in functional relaxation (original: funktionelle Entspannung, FE;

(Fuchs, 1997). The authors report construct validity assessed via factor analysis and good reliability of the KEKS (Cronbach’s ⍺ = .71 - .93). Further, medium convergent validity is assumed as it correlates (r = .42) with another questionnaire measuring self-attention (SAM;

Filipp & Freudenberg, 1989). Regarding discriminant validity, the KEKS showed to

differentiate between Hatha Yoga practitioners (which are assumed to have high awareness of their body) and non-yoga practitioners. The questionnaire consists of three scales (parts of skeleton, body cavities and skin) and two control items. Examples for included body parts are tongue (body cavities), shoulder blades (skeleton), eyelids (skin), or cerebellum (control).

Participants rate on a scale from “1” (no awareness) to “5” (detailed awareness) how distinct they can feel 20 different parts of their body at that moment. In this study the mean score for body awareness was used but scores for the scales can be calculated as well. As the KEKS aims to measure body awareness at the time of implementation, it was used before the ME/MI task and afterwards (see section 2.4).

2.2.3 Vividness of Movement Imagery Questionaire 2

The Vividness of movement imagery questionnaire-2 (VMIQ-2; Roberts, Callow, Hardy, Markland, & Bringer, 2008) in its German version (Dahm, Bart, Pithan, & Rieger, 2020) was used to assess motor imagery ability. The VMIQ-2 is a commonly used

questionnaire measuring vividness of MI for the three imagery modalities (external visual imagery, internal visual imagery, and kinesthetic imagery), which were all included in this study. The authors report high internal consistency (Cronbachs ⍺ = .90 - .91) and a test-retest reliability between rtt = .64 and rtt = .69. The factorial validity is comparable to the English version of the questionnaire, as are the intercorrelation between the separate scales (r = .44 -

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.59) with exception for internal and external visual imagery, which scored higher than the original. The VMIQ-2 consists of 36 items, participants are asked to imagine twelve different movements for each of the three imagery strategies and rate their imagination on a scale from

“1” (perfectly clear and vivid) to “5” (no image at all, you only know that you are “thinking”

of the skill). A score below three means that the movement could not be imagined

successfully. A mean score, calculated for each scale separately, was used for further analysis.

2.2.4 Visual Analog Scale

Visual analog scales (VAS) were used to assess subjective ratings of the success of motor execution and imagination, as well as motivation, swallowing difficulties or sensed pain (see Appendix A.4). VAS are assumed to be reliable and valid (Reips & Funke, 2008).

Each item consists out of a horizontal line (10 cm) whose ends represent two extremes of a continuum. Participants are asked to mark the point on the line, which represents their position. The answers are measured in millimeters, whereas 1 indicates full agreement with the left-sided and 100 with the right-sided extreme of the item.

2.2.5 Motor Imagery Strategies

Additionally, participants were asked to describe the strategies they used during MI of swallowing and if those strategies where successful for the imagination (Appendix A.5).

2.3 Motor Execution and Imagery Task

A similar ME/MI task as in Kober & Wood (2014) was used in the present study, with saliva instead of water swallowing. During the task, participants had to swallow their own saliva (ME) and imagine how it feels to swallow it (MI). The task consisted out of 20 ME and 20 MI trials presented in a randomized order, with each one lasting for 15 seconds. A

computer script using PsychoPy3 (Peirce et al., 2019) was programmed to indicate

participants on a computer screen whether they should execute or imagine swallowing and to set triggers in the signal, referring to each task on- and offset. As shown in Figure 1, the letter

“A” indicated ME and the letter “V” indicated MI. Participants were asked to swallow between five and six times during each ME trial in a comfortable pace. During MI

participants were instructed to imagine swallowing in the same pace as during ME and to use a kinesthetic approach. In between the tasks a fixation cross was presented on the screen for a

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variable length of 28 to 32 seconds and participants were asked to avoid active swallowing as much as possible. Before the actual task all participants were briefly trained.

Figure 1. Schematic representation of the ME/MI task. ME (A) and MI (V) trials were presented in a randomized order.

2.4 NIRS Recordings

A NIRSport 2 system (NIRx Medizintechnik, GmbH, Berlin, Germany) was used to record the hemodynamic respond of participants during the ME/MI task. It is a continuous wave system with LED sources, emmiting light at wavelengths of 760 and 850 nm, and detectors measuring oxy- (HbO), deoxy- (HbR) and total hemoglobin changes in the cortex.

The probe-setup consisted of eight source- and eight detector-optodes which were mounted on EEG-caps with holders ensuring a distance of 30 mm between each source-detector pair (long-distance channels). Caps were available in different sizes (54, 56 and 58 cm), to guarantee a proper fitting for each participant. Additionally, to long-distance probes, eight short-distance detector probes were placed about 8 mm apart from the sources (short-distance channels). Therefore, one long-distance detector had to be sacrificed to serve as an optode for them. The sampling rate was set to 10.2 Hz. Two standard software packages provided by NIRx were used for channel configuration (NIRSSite 2.0) and recording (Aurora fNIRS 1.4).

The probe-setup depicted in Figure 2 was positioned above the IFG bilaterally,

according to previous NIRS studies investigating ME/MI of swallowing (Kober, Bauernfeind, et al., 2015; Kober & Wood, 2014) and further included dorsolateral prefrontal cortex,

premotor and supplementary motor cortex, frontal eye fields, middle and superior temporal gyrus, subcentral area, primary somatosensory cortex, supramarginal and angular gyrus (Wernicke) and the somatosensory association cortex (see also Table 1).

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AtlasViewer (Aasted et al., 2015), a free software package based on MATLAB (MathWorks, Natick, MA, USA), was used to generate visualizations of the probe-setup located above the cortex and the recorded NIRS data.

Figure 2. NIRS probe-setup over the left (LH) and right (RH) hemisphere and placement according to 10- 20 system, displayed with Aurora (right) and AtlasViewer (left). Red points indicate source probes, blue points indicate long- and short-distance detector probes. Yellow lines show corrsponding source-detector pairs.

2.5 Procedure

First, participants completed an online survey, generated with LimeSurvey

(LimeSurvey GmbH, Hamburg, Germany), comprised out of the EHI, the KEKS (pre-test) and the VMIQ-2. Participants received a personalized invitation link to the survey via email to prevent repeated completions of the questionnaires. The completion of the survey took about 20 minutes. Afterwards participants booked a timeslot for the laboratory session in an online calendar (Doodle AG, Zurich, Switzerland) and received further information and safety instructions (as described in section 2.2) via email.

Before entering the laboratory, participants gave written informed consent and handed over the COVID-19 symptom-checklists. In the laboratory participants were seated in a cabin

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in front of a computer screen, which allowed to darken the surrounding completely during NIRS measurements. The session started with participants filling out social-demographic data and a brief introduction to the NIRS system. Afterwards, a suitable cap containing the probes was mounted on participants’ heads and instructions for the ME/MI task were given including a short training session with one ME and one MI trial. During this training, participants were observed to ensure that they had understood the instructions and were following them. Then the lights in the cabin were switched off and the ME/MI paradigm as well as NIRS

measurements were started from a laboratory PC located outside the cabin. After completion of the task, participants were freed from the cap and filled out questionnaires regarding used strategies, VAS and the KEKS (post-test). The whole procedure in the laboratory took between 50 and 60 minutes.

2.6 Data Preprocessing

For NIRS data preprocessing the MATLAB (MathWorks, Natick, MA, USA) based program Homer2 (Huppert, Diamond, Franceschini, & Boas, 2009) was used. Four different processing pathways were used to compare artifact correction methods. An overview of the exact processing steps for each method is shown in Figure 3.

Raw optical density data was converted into changes in optical density (OD) for all techniques using the Homer2 function hmrIntensity2OD. Next, the enPruneChannels function was applied, to discard channels with extremely low OD. Then, data was either filtered with a wavelet transformation (Wav and Wav + SD) using the function hmrMotionCorrectWavelet (iqr = 0.1), or visually checked and manually corrected for motion artifacts (manual and manual + SD).

For four participants wavelet filtering was additionally performed with different settings for iqr (0.8 and 1.5), to exemplary investigate its impact on NIRS data during ME of swallowing (see Appendix B.3). Subsequently, the same settings as for Wav+SD were used.

The functions hmrMotionArtifact (STDEVthresh = 15.0; AMPthresh = 0.30) and enStimRejection (tRange = -10.0 – 10.0 seconds) were operated on processing options Wav and Wav + SD, to automatically detect and exclude trials, still containing motion artifacts after correction. A high-pass (0.01 Hz) and a low-pass filter (0.50 Hz) were then applied in all processing pathways via the function hmrBandpassFilt, to filter out physiological noise. Next, and again for all pathways, hmrOD2Conc (ppf = 6.0/6.0) was executed to convert the signal into HbO and HbR concentration changes. For pathways that should include SD-channel

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regression, the hmrDeconvHRF_DriftSS function (trange = -5.0 – 40.0; glmSolveMethod = 1;

idxBasis = 2; paramsBasis = 0.1/3.0/10.0/1.8/3.0/10.0; rhoSD_ssThresh = 15.0; flagSSmethod

= 0; driftOrder = 3; flagMotionCorrect = 0) was used to regress out general hemodynamic drift from the signal. In all pathways, HbO and HbR concentration changes during the tasks were referred to a baseline interval, 5 seconds prior to the stimulus onset (seconds -5 to 0).

Block averages where then calculated via hmrBlockAvg for seconds -5 to 40 around stimulus onset. After visual inspection of the resulting signal, two participants had to be excluded, due to poor data quality resulting in exclusion of almost all trials. The data for the remaining 31 participants were then exported and averaged for a time interval of seconds 5 to 15 (Task period) and seconds 15 to 25 (Pause period) after task onset, for ME and MI separately.

Averages were automatically calculated using Homer’s Export Mean Results function.

Table 1. List of channels (source [Src] - detector [Det] pairs; see also Figure 2) of right (RH) and left (LH) hemispheres and corresponding projections onto the brain surface. First number represents sources, second number detectors.

Src-Det LH

Src-Det RH

Broadman

area Description

1-1 5-5 8 Includes frontal eye fields

1-2 - 6 Pre-motor and supplementary motor cortex

1-3 5-6 9 Dorsolateral prefrontal cortex 2-1 6-5 46 Dorsolateral prefrontal cortex

2-3 6-6 9, 45 Dorsolateral prefrontal cortex, Pars triangularis (part of IFG and Broca's area)

2-4 6-7 45, 46 Pars triangularis (part of IFG and Broca's area), Dorsolateral prefrontal cortex

3-2 - 6 Pre-motor and supplementary motor cortex

3-3 7-6 6 Pre-motor and supplementary motor cortex 4-3 8-6 44 Pars opercularis (part of IFG an Broca's area) 4-4 8-7 45 Pars triangularis, Pars orbitalis (both part of IFG)

Note: Broadman areas and descriptions were adapted from Kober et al.(2015), as the same probe setup was used in the present study.

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Figure 3. Signal processing steps for wavelet filtering (orange) and manual rejection (green).

2.7 Statistical Analyses

To estimate the topographical distribution of significantly activated brain areas, t-tests were performed between the activation interval (seconds 5 to 25) and the baseline (seconds -5 to 0) for each channel, each condition (ME and MI) and HbO/HbR separately. Significant concentration changes were identified using the FDR-method (Singh & Dan, 2006). For further calculations, the same ROI’s as in Kober et al. (2015) were used to ensure comparability of the results with previous studies. ROI’s were defined for channels corresponding with left (source-detector pairs: 2-4, 4-3, and 4-4) and right IFG (source- detector pairs: 6-7, 8-6, 8-7; see Table 1 and Figure 2).

In accordance with Kober and Wood (2014) and to analyze changes in HbO and HbR during ME and MI, two separate 2x2x2 ANOVA’s for repeated measures were conducted with the within-subjects factors Task (ME vs. MI), Time (Task vs. Pause) and Hemi (left vs.

right) were conducted. The dependent variables for each ANOVA were HbO- and HbR- Concentration Change, respectively. For this analysis only data processed with wavelet filtering and SD-channels was used, to validate if earlier findings were due to motion artifacts.

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To compare motion artifact correction methods, two 2x2 ANOVA’s for repeated measures with the within-subject factors Correction Method (Wavelet transformation vs.

Manual rejection) and SD-Channel-Regression (yes vs. no) were conducted. As motion artifacts should occur especially during active swallowing, and therefore the greatest difference between correction methods is expected, HbO- and HbR-Concentration Change during METask (seconds 5 to 15) were used as dependent variables. In regard of the different scaling of the HRF for the diverse correction methods, HbO and HbR values were

standardized via z-transformation over all four methods within the subjects.

To investigate correlates of brain activation patterns elicited by MI of swallowing, bivariate correlations (Pearson) were calculated between HbO- and HbR-Concentration Changes during MI (pause period: 15-25s; correction method: Wav+SD) and the results from several questionnaires: Kinesthetic Imagery Ability (KMI subscale from VMIQ-2), Body Awareness (KEKSPre and KEKSPost), Motivation (motivation during MI; VAS subscale) and Quality (subjectively rated quality of imagined swallows; VAS subscale). The pause period after MI was chosen over the task period, as a prolonged time course for MI of swallowing has been indicated by previous studies (Kober & Wood, 2014; see section 1.2.1). As this hypothesis was of explorative nature, no correction for multiple comparisons was carried out.

All statistical calculations were performed using IBM SPSS Statistics 26 (IBM Corp., Armonk, NY, USA). All dependent variables were checked for extreme outliers through visual inspections of the boxplots, by checking for normal distribution of the Shapiro-Wilk- Test and by visual inspection of Q-Q plots. One participant was detected as an extreme outlier in almost all HbO- and HbR-concentration changes. As the participant had further consumed coffee and cigarettes less than an hour before the NIRS-measurements and his data could therefore been distorted, he was excluded from further analysis. Therefore, only 30

participants were included in all further analyses (see also exclusions after data processing, section 2.5). The level of significance was set to ⍺ = .05 (two-tailed).

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3 Results

3.1 Topographical Distribution

The FDR method, as described in the previous section, was used to investigate the

topographical distribution of NIRS signal changes during ME and MI of swallowing (seconds 5 to 25). For changes in HbO, all channels reached significance during ME and MI (p < FDR .05) indicating a significant signal increase compared to baseline activation. For changes in HbR levels, only source-detector pairs 5-5, 8-6, and 7-6 reached statistical significance during ME, while significant signal changes in MI were found in source-detector pairs 1-1, 5-5 and 8-6 (see Table 1 for a description of channels). No other channel reached statistical

significance in HbR concentration changes neither in ME nor MI (p > FDR .05). Table 2 shows the ranking of the strongest signal changes in HbO and HbR for both tasks. Overall, the topographical distribution over time indicated concentration changes of HbO and HbR during ME and MI, especially above the IGF (see Figure 4 and 5 for a visualization).

Table 2. Results of the FDR analysis listed in descending order. Significant signal changes are indicated with *. First number indicates source, second number detector (see Table 1/Figure 2, for probe placement).

HbO HbR

Rank ME MI ME MI

1 8-7 * 1-1* 5-5* 1-1*

2 4-4* 3-2* 8-6* 5-5*

3 2-3* 5-5* 7-6* 8-7

4 4-3* 5-6* 2-3 4-3

5 8-6* 1-3* 6-6 8-6*

6 1-3* 2-1* 6-7 6-7

7 6-6* 6-5* 1-2 4-4

8 7-6* 6-6* 3-2 1-3

9 2-4* 2-4* 1-1 3-2

10 5-5* 1-2* 3-3 2-4

11 5-6* 2-3* 2-4 1-2

12 6-7* 4-3* 4-3 6-5

13 6-5* 8-7* 8-7 6-6

14 3-3* 6-7* 5-6 3-3

15 2-1* 3-3* 1-3 2-1

16 3-2* 4-4* 2-1 2-3

17 1-2* 8-6* 4-4 7-6

18 1-1* 7-6* 6-5 5-6

(35)

33

3.2 Hemodynamic Activation Changes during ME and MI

Two separate 2x2x2 ANOVA’s for repeated measures, including the within-subjects factors Task, Time and Hemisphere, were calculated to investigate hemodynamic activation changes over the IFG during ME and MI, for HbO and HbR respectively. The time course of the signal during ME and MI in both hemispheres is displayed in Figure 6.

Figure 4. HbO concentration changes over the IFG during ME and MI for Task- (5-15 s) and Pause-interval (15-25 s) respectively.

Figure 5. HbR concentration changes over the IFG during ME and MI for Task- (5-15 s) and Pause- interval (15-25 s) respectively.

Artifact Minimization in the Hemodynamic Response Function during Motor Execution and Motor Imagery of Swallowing: A Near-infrared Spectroscopy Study