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Neural activities behind the influence of sensorimotor incongruence on dysesthesia and motor control
Neuroscience Letters 698 (2019) 19–26
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Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Research article
Neural activities behind the influence of sensorimotor incongruence on dysesthesia and motor control
T
⁎
Osamu Katayamaa,b, , Yuki Nishia, Michihiro Osumia,c, Yusaku Takamuraa, Takayuki Kodamad, Shu Moriokaa,c a
Department of Neurorehabilitation, Graduate School of Health Sciences, Kio University, 4-2-2 Umami-naka, Koryo-cho, Kitakatsuragi-gun, Nara 635-0832, Japan Department of Rehabilitation, Watanabe Hospital, 45-2 Noma-kamikawada, Mihama-cho, Chita-gun, Aichi 470-3235, Japan c Department of Neurorehabilitation Research Center, Kio University, 4-2-2 Umami-naka, Koryo-cho, Kitakatsuragi-gun, Nara 635-0832, Japan d Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, Kyoto 607-8175, Japan b
A R T I C LE I N FO
A B S T R A C T
Keywords: Bimanual coordination Dysesthesia Electroencephalogram motor control Motor-Related areas Sensorimotor incongruence
Sensorimotor incongruence (SMI) is associated with pathological pain, such as phantom limb pain. Additionally, patients with pathological pain and brain dysfunction typically present with movement disorders, including diminished voluntary control and increased variability in bimanual movement performance. In healthy subjects, SMI leads to dysesthesia and bimanual movement motor dysfunction. However, the brain localization of this activity remains unclear, particularly in SMI-induced dysesthesia and decrease in movement accuracy. In this study, 17 healthy participants were asked to perform repetitive flexion/extension exercises with their wrists in a congruent/incongruent position while viewing the activity in a mirror. Indeed, SMI induced dysesthesia and decreased bimanual movement accuracy. Moreover, beta band activities of the bilateral presupplementary (P < 0.01) and bilateral cingulate (P < 0.05) motor areas were decreased. Collectively, our findings indicate that SMI induces dysesthesia and movement disorders and reduces beta band activities in motor-related areas.
1. Introduction Evidence has suggested that mismatch between motor intention and sensory feedback, termed sensorimotor incongruence (SMI), is related to pathological pain, including phantom limb pain [1,2]. Altered limb perception such as loss of limb ownership and heaviness are important clinical features in pathological pain and stroke patients [3,4]. Recent studies have also shown that experimental SMI causes altered limb perceptions and emotions such as peculiarity and discomfort in people with pathological pain [5,6]. In fact, several studies on healthy subjects indicate that experimental SMI cause altered limb perceptions and emotions [7–9]. Dysesthesia is defined as altered limb perceptions and emotions and is evaluated using the following sensations: pain, itch, warmth, coldness, lightness, heaviness, lost limb, extra limb, peculiarity, pressure, shape, numbness, nausea, and other parts [9]. In addition, experimental SMI reduces movement accuracy [10–12]. Accordingly, SMI is considered an important factor influencing dysesthesia, motor control, and brain activity. However, the brain
localization of this activity during SMI-induced dysesthesia and motor control dysfunction remains unclear. We confirmed that various dysesthesias are induced in individuals during experimental SMI in a previous study performed with healthy subjects [9]. It was difficult to define the starting time of their dysesthesias because of the individual differences and the variation in strength: some subjects strongly experienced dysesthesia from the beginning, whereas others had various consecutive dysesthesia episodes at different times. Therefore, we concluded that it is important to clarify the localization of brain activity during SMI-induced dysesthesias. In this study, we sought to simultaneously analyze kinematic data and neural activity in healthy participants during experimental SMI. We aimed to determine whether SMI affects dysesthesia and motor control as well as investigated the brain localization of this activity.
Abbreviation: BA, Brodmann areas; CMA, Cingulate motor area; GFP, Global field power; MAD, Mean absolute deviation; MNI, Montreal Neurologic Institute; MRI, Magnetic resonance imaging; NRS, Numeric rating scale; pre-SMA, Presupplementary motor area; SMI, Sensorimotor incongruence; SnPM, Statistical nonparametric maps ⁎ Corresponding author. Present address: Kio University, 4-2-2 Umami-naka, Koryo-cho, Kitakatsuragi-gun, Nara 635-0832, Japan. E-mail address: [email protected] (O. Katayama). https://doi.org/10.1016/j.neulet.2019.01.010 Received 1 September 2018; Received in revised form 4 January 2019; Accepted 4 January 2019 Available online 06 January 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.
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2. Material and methods
sessions before the trial. In previous studies on bimanual coordination tasks, methods employing the metronome and those not employing it have been described. The metronome is used to verify the influence of movement pace on the behavioral data [14], whereas the metronome is not used in tests aimed to verify the natural variations in intervals and speeds of continuous movements in the task [12,15]. We followed the latter method as we did not want to control the pace of periodic movements with the metronome but we wanted to verify the natural variation of movement accuracy in each experimental condition. Each participant performed at the exercise pace they practiced beforehand and with their maximum ranges of flexion and extension and vice versa for 35 movements in every condition. All participants performed a total of 20 sequences (five replicates and four conditions). All conditions were pseudo-randomly applied, and electroencephalograms (EEGs) were recorded during each condition. During the break, participants rated intensities of related altered limb perceptions and emotions for 14 different dysesthesia sensations on an 11-point numeric rating scale (NRS), from 0 (not at all) to 10 (very strong) [8,9]. Among the 14 dysesthesia sensations, we calculated the number of sensations for which the participants reported an NRS score of ≥1.
2.1. Participants We recruited 17 healthy participants (5 women and 12 men; mean age, 26.2 ± 4.6 years; range, 18–39 years) with no current or past physical or mental illness and with normal vision. Demographic characteristics and a brief medical history (including hand dominance) were obtained from participants to ensure that inclusion and exclusion criteria were satisfied. All participants were right-handed, as confirmed by the Edinburgh Handedness Inventory [13]. This study was approved by the ethics committee of Kio University (approval number H28-30) and was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki. All participants provided written informed consent for the use of their data and any accompanying images.
2.2. Bimanual coordination test The test apparatus and associated assessment procedure have been described previously [7,9]. Briefly, each participant was instructed to remove any jewelry. A mirrored device that could be turned around easily and constituted a large mirror on one side and a white, nonreflecting control surface on the other side was used (Fig. 1). The whiteboard surface eliminated any form of visual feedback from the intended or performed movement, which was considered a control condition, whereas the mirrored surface was used for experiments (experimental condition). Participants were instructed to place their right limb on the right side of the whiteboard or mirror aligned with the sagittal plane and the left limb on the other side hidden by the whiteboard or mirror. They were instructed to sequentially perform flexion/ extension exercises with their wrist in a congruent/incongruent manner while looking at the whiteboard or mirror. In the whiteboard congruence condition, participants performed the flexion/extension exercises with both wrists simultaneously and symmetrically in the same direction. In the whiteboard incongruence condition, participants performed flexion/extension exercises with one wrist flexing while the other was extended asymmetrically. In the mirror congruence condition, participants performed flexion/extension exercises with both wrists simultaneously and symmetrically in the same direction. Motor intention was consistent with the direction of left wrist movement. In the mirror incongruence condition (i.e., the intervention condition), participants performed flexion/extension exercises with one wrist flexing while the other was extended asymmetrically. This condition fulfilled the incongruence between proprioception associated with motor intention and visual feedback for the left hand. In mirror conditions, participants viewed the reflection of their hand in the mirror. Each participant performed flexion/extension exercises at a frequency of 1 Hz, which was guided by a metronome during the practice
2.3. Kinematic data collection and analysis We collected kinematic data using an electronic goniometer (Biometrics Model SG110 A, Gwent, UK) placed on the dorsal sides of both wrists during the flexion/extension exercises (sampling rate, 1000 Hz). The first and last four cycles of each trial were removed to eliminate possible transitory effects. Cycle durations was defined as the time between successive peak extension positions. Mean absolute deviation (MAD) of the relative cycle duration was used as an estimate of movement accuracy. 2.4. EEG recording and preprocessing Standard EEG recording and data preprocessing procedures were followed as described elsewhere [5]. A high-resolution 32-channel portable EEG system (Active Two; BioSemi, Amsterdam, The Netherlands) was used for data acquisition (sampling rate, 1024 Hz). Electrodes were fit into an elastic head cap (BioSemi) to record continuous EEG data from the following 32 scalp locations organized according to the 10–20 system: Fp1, AF3, F3, F7, FC5, FC1, C3, T7, CP5, CP1, P3, P7, PO3, O1, Oz, Pz, Fp2, AF4, Fz, F4, F8, FC6, FC2, Cz, C4, T8, CP6, CP2, P4, P8, PO4, and O2. Each electrode was filled with Signal® Electrode Gel (Parker Laboratories, Fairfield, NJ, USA) for adequate signal transduction. ActiView software (BioSemi) was used for data collection. EEG data were analyzed using a multimodal EEG analysis software (EMSE Suite 5.4, Source Signal Imaging, La Mesa, CA, USA), band-pass filtered in the range 1.0–70.0 Hz, and applied to a common average reference
Fig. 1. Experimental setup. The mirror device was placed between the participant’s right and left upper limbs. The four study conditions: whiteboard congruence (A), whiteboard incongruence (B), mirror congruence (C), and mirror incongruence (D) are shown. 20
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Fig. 2. Degree of dysesthesia. Bars (mean [standard error]) represent numeric rating scale (NRS) scores for dysesthesia symptoms (A) and number of sensations (B) under the whiteboard congruence (white bar), whiteboard incongruence (gray bar), mirror congruence (diagonal-lines bar), and mirror incongruence (black bar) conditions.
Fig. 3. Kinematic data. Boxes (mean [standard error]) represent the whiteboard congruence (white box), whiteboard incongruence (gray box), mirror congruence (diagonal-lines box), and mirror incongruence (black box) conditions. Means of cycle durations (A) and MAD (B) are shown. WCc, whiteboard congruence condition; WIc, whiteboard incongruence condition; MCc, mirror congruence condition; MIc, mirror incongruence condition.
were calculated and used for intracerebral spatial analyses in conjunction with eLORETA analysis [20]. This method allows for a threedimensional image display of intracerebral neural activity. Voxel values in the brain areas displaying neural activity under each condition were calculated as neural activity (current source density, μA/mm2*10−3) and further identified in terms of BA and MNI coordinates [21]. Neural activity was calculated as the global field power value [20] for each condition.
montage. Artifacts generated by blinking, eye movements, facial muscle activity, or body movements were removed using a specially designed spatial filter in EMSE Suite 5.4 and by visual inspection of the frontal EEG trace (Fp1 and Fp2) [9,16].
2.5. EEG data analysis We used exact low-resolution brain electromagnetic tomography (eLORETA) to compute the cortical electrical distribution from the scalp electrical potentials [17]. This method is a weighted minimum norm inverse solution, in which weights are unique and endow the inverse solution, with the property of the exact localization for any point-source in the brain. Considering the principles of linearity and superposition, any arbitrary distribution can thus be correctly localized, albeit with low spatial resolution. In the eLORETA version we used, the solution space comprised 6239 cortical gray matter voxels at a 5-mm spatial resolution in a realistic head model [18]. During this process, the Montreal Neurologic Institute (MNI) average magnetic resonance imaging brain (MNI152) template [19] was used with anatomic labels corresponding to Brodmann areas (BAs) used to compute the lead field. For imaging analysis, five epochs were extracted from the data obtained under each condition (the duration of each epoch was 20 s), and frequency analyses were performed. Data within the frequency bands of interest (delta [1.5–6.0 Hz], theta [6.5–8.0 Hz], alpha 1 [8.5–10.0 Hz], alpha 2 [10.5–12.0 Hz], beta 1 [12.5–18.0 Hz], beta 2 [18.5–21.0 Hz], beta 3 [21.5–30.0 Hz], and gamma [30.5–70.0 Hz])
2.6. Statistical analysis Data normality was confirmed using the Shapiro–Wilk test. Results of dysesthesia NRS and the number of sensations were compared using the Friedman test. When differences were < 5%, Wilcoxon test was performed six times for comparison among the four conditions. Therefore, the significance level was set to 0.8% (5/6). Statistical analysis of data regarding the extension of both wrists was conducted in the same manner as that for cycle duration and MAD. In addition, intracerebral neural activities of each condition were compared using the eLORETA statistical nonparametric maps [22]. Neural areas showing significant differences in activities were colored, calculated, and drawn. The significance threshold was based on testing with 5000 permutations using log-transformed LORETA values with subject-wise normalization (assuming individual differences in baseline activity). Correlation analyses on dysesthesia NRS score, EEG data, and kinematic data under the mirror incongruence condition were performed using 21
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Fig. 4. Differences in electroencephalographic activities under the mirror congruence and mirror incongruence conditions. Statistical nonparametric maps (SnPMs) of exact low-resolution brain electromagnetic tomography of the beta 3 band comparing the mirror congruence and mirror incongruence conditions are shown. Blue indicates decreased activity under the mirror incongruence condition. Images depicting SnPMs from different perspectives are based on voxel-by-voxel t-values of differences. MCc, mirror congruence condition; MIc, mirror incongruence condition (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
3.2. Comparison of kinematic data
Spearman’s rank correlation coefficient. For all calculations, SPSS version 25.0 (IBM Corp., Armonk, NY, USA) was used.
Cycle durations of bimanual coordination tests are presented in Fig. 3. On the left limb (i.e., the intervention limb), Friedman test revealed a significant main effect for the mean (χ2 = 83.26, P < 0.001) and MAD (χ2 = 45.67, P < 0.001) (Fig. 3A). Post hoc test revealed that the mirror congruence condition had a significantly longer average cycle duration than did the whiteboard congruence (P < 0.001), whiteboard incongruence (P < 0.001), and mirror incongruence (P < 0.001) conditions. Moreover, the mirror incongruence condition had a significantly longer average cycle duration than did the whiteboard congruence (P < 0.001) and whiteboard incongruence (P < 0.001) conditions. The mirror incongruence condition had a significantly higher MAD of cycle duration than did the whiteboard congruence (P < 0.001), whiteboard incongruence (P < 0.001), and mirror congruence (P < 0.001) conditions (Fig. 3B).
3. Results 3.1. Comparison of dysesthesia NRS scores and number of sensations Descriptive NRS results are summarized in Fig. 2. Friedman test for the comparison of dysesthesia NRS scores and number of sensations revealed a significant main effect for itch (chi-square [χ]2 = 10.01, P = 0.018), peculiarity (χ2 = 18.50, P < 0.001), nausea (χ2 = 7.93, P = 0.048), other part (χ2 = 9.92, P = 0.019), and number of sensations (χ2 = 18.87, P < 0.001) (Fig. 2A). Post hoc test revealed that itch scores for the mirror incongruence condition were not higher than those for the other conditions and that peculiarity scores for the mirror incongruence condition were significantly higher those for the whiteboard congruence (P = 0.002), whiteboard incongruence (P = 0.002), and mirror congruence (P = 0.005) conditions (Fig. 2A). Furthermore, nausea scores for the mirror incongruence condition were not higher than those for the other conditions (Fig. 2A); other parts scores for the mirror incongruence condition were not higher than those for the other conditions (Fig. 2A); and the number of sensations for the mirror incongruence condition was significantly higher than that for the whiteboard congruence (P = 0.021), whiteboard incongruence (P = 0.011), and mirror congruence (P = 0.001) conditions (Fig. 2B).
3.3. Comparison of EEG data For the mirror incongruence condition, beta 3 band (21.5–30.0 Hz) activities of the bilateral presupplementary (pre-SMA) (BA6) and bilateral cingulate (CMA) (BA32) motor areas were significantly lower than those for the mirror congruence and whiteboard incongruence conditions (T = 3.744, P < 0.01; T = 3.432; P < 0.05) (Figs. 4 and 5, Table 1). For the mirror incongruence condition, beta 1 band (12.5–18.0 Hz) activities of the left posterior insula (BA13), bilateral angular gyrus (BA39), and supramarginal gyrus (BA40) were 22
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Fig. 5. Differences in the electroencephalographic activities under the whiteboard incongruence and mirror incongruence conditions. Statistical nonparametric maps (SnPMs) of exact low-resolution brain electromagnetic tomography of the beta 3 band comparing the whiteboard incongruence and mirror incongruence conditions are shown. Blue indicates decreased activity under the mirror incongruence condition. Images depicting SnPMs from different perspectives are based on voxel-byvoxel t-values of differences. WIc, whiteboard incongruence condition; MIc, mirror incongruence condition (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Table 1 Differences in beta 3 and beta 1 activities under the four condition. Condition
Frequency band
Statistical value (t)
MCc vs. MIc
Beta 3
3.744
WIc vs. MIc
Beta 3
3.432
WCc vs. MIc
Beta 1
3.315
MNI coordinates x
y
z
5 −5 5 −5 5 −5 −5 −5 −30 55 −55 60 −50
45 45 35 35 35 35 35 35 −30 −55 −60 −55 −30
40 40 30 30 40 40 30 25 15 10 10 25 25
Brodmann area
Region
6 6 32 32 6 6 32 32 13 39 39 40 40
Pre-SMA Pre-SMA CMA CMA Pre-SMA Pre-SMA CMA CMA Posterior insula Angular gyrus Angular gyrus Supramarginal gyrus Supramarginal gyrus
MCc, Mirror congruence condition; MIc, Mirror incongruence condition; WCc, Whiteboard congruence condition; MIc, Whiteboard incongruence condition; MNI, Montreal Neurologic Institute; Pre-SMA, Presupplementary motor area; CMA, Cingulate motor area.
significantly higher than those for the whiteboard congruence condition (T = 3.315; P < 0.05) (Fig. 6, Table 1).
activity was lower in the mirror incongruence condition than in the mirror congruence and whiteboard incongruence conditions in terms of peculiarity (r = −0.42, P = 0.090; r = −0.54, P = 0.025), pressure (r = −0.52 P = 0.031; r = −0.61, P = 0.010), and number of sensations (r = −0.52, P = 0.034; r = −0.58, P = 0.015). A significant positive correlation of MAD with warmth (r = 0.50, P = 0.043), heaviness (r = 0.57, P = 0.016), and numbness (r = 0.52, P = 0.034) was observed.
3.4. Correlation of dysesthesia NRS score, EEG data, and kinematic data under the mirror incongruence condition We observed a significant negative correlation in the pre-SMAs (x = 5, y = 30, z = 60, x = 5, y = 35, and z = 40), in which brain 23
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Fig. 6. Differences in the electroencephalographic activities under the whiteboard congruence and mirror incongruence conditions. Statistical nonparametric maps (SnPMs) of exact low-resolution brain electromagnetic tomography of the beta 1 band comparing the whiteboard congruence and mirror incongruence conditions are shown. Red indicates increased activity under the mirror incongruence condition. Images depicting SnPMs from different perspectives are based on voxel-by-voxel tvalues of differences. WCc, whiteboard congruence condition; MIc, mirror incongruence condition (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
4. Discussion
congruence, the mean cycle duration was close to the most practiced pace (1 Hz = 1000 ms). Therefore, it was considered that the mirror congruence condition was an appropriate control condition. We observed significantly higher scores for the MAD (movement accuracy) of the cycle duration for the mirror incongruence condition than for the other conditions; thus, SMI modulated dysesthesia as well as motor control. The whiteboard incongruence condition showed significantly higher MAD than the whiteboard congruence condition. Coordination variability is higher during asymmetrical than symmetrical bimanual coordination movements [14], with lack of visual feedback leading to degraded movement accuracy [26]. Thus, in the whiteboard incongruence condition, blocked visual feedback may have reduced movement accuracy. Consequently, reduced movement accuracy in the whiteboard incongruence condition may be caused by asymmetrical movement and blocked visual feedback. Our findings indicated that SMI decreased the activity of pre-SMA and CMA in beta 3 band motor-related areas during bimanual coordination tasks. These areas are important for bimanual coordination tasks [27,28] as well as self-initiated movements [29] and action monitoring and sequencing [30,31]. Changes in beta band activity have been reported in previous electroencephalographic studies related to bimanual coordination tasks [32,33]. The significance of the beta band for movement-related function has been demonstrated by cortex-muscle coherence [34], suggesting that SMI disrupts distortion in motor control, resulting in decreased movement accuracy.
In this study, we simultaneously analyzed kinematic data and neural activities in experimental SMI using a bimanual coordination task in healthy participants. Our novel findings indicated that SMI reduced beta band activity in motor-related areas. Dysesthesia symptoms appeared in all conditions during this study, with NRS scores for peculiarity being the highest, as reported previously [9]. The highest number of sensations was observed in the mirror incongruence condition. Peculiarity may thus be caused by incongruence between the predicted and actual sensory information. Consistent with previous reports, dysesthesia was only marginally induced in other conditions [7–9]. In the whiteboard congruence/incongruence condition, when vision was blocked by the whiteboard, dysesthesia was caused by even a slight conflict between the predicted and actual proprioception. Although there were symmetric movements in the mirror congruence condition, we believed that increasing attention to sensory information in the left hand hidden in the mirror and exercising while watching the mirror image. Thus, we considered that dysesthesia was caused by these factors. Movement accuracy is controlled by sensory feedback [23–25], and experimental disturbances reduce movement accuracy [10–12]. We did not control the pace of periodic movements during conditions with the metronome and we verified the natural variation of movement accuracy. In the mirror congruence condition with sensorimotor 24
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Beta 1 band activities of the bilateral angular gyrus, supramarginal gyrus, and left posterior insula were significantly elevated in the mirror incongruence condition compared with those in the whiteboard congruence condition. These inferior parietal lobe areas play roles in the integration of sensory feedback [35–37] and detection of SMI [38,39], and neuronal responses in the beta band are associated with sensorimotor network processing [40]. Hence, we considered the necessity of multisensory integration involving the bilateral angular gyrus, supramarginal gyrus, and left posterior insula in the mirror incongruence condition. Thus, motor-related SMI may be involved in movement accuracy, with the inferior parietal lobule in the beta band involved in the multisensory integration of SMI. We found a significant negative correlation between pre-SMAs and dysesthesia in the mirror incongruence condition, which is consistent with results of a previous study of temporal SMI [15]. Neural activities in these areas may be associated with dysesthesia induced by SMI. Movement accuracy is controlled by sensory feedback [23,24], and experimental disturbances in sensory feedback reportedly reduce movement accuracy [10–12], with subjective heaviness being altered with SMI [4]. There may be a similar mechanism between reduced movement accuracy and altered heaviness, as indicated by a significant correlation between heaviness and MAD. Therefore, altered visual feedback may lead to sensorimotor integration failure, increased heaviness, and decreased movement accuracy.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
5. Limitations [15]
Dysesthesia was measured after the task; In the future, it will be necessary to investigate whether a relationship exists among dysesthesia, motor control, and the activity localization in the brain. If this is the case, we would like to analyze the event-related potential and define the time point at which a specific sensation occurs using a foot pedal. Additionally, sensations and dysesthesia intensity were subjectively reported by participants, and the sample size of this study was relatively small compared with that of similar studies.
[16]
[17]
[18]
6. Conclusions
[19]
Movement accuracy in SMI and brain activity associated with dysesthesia are not well understood. However, we report for the first time that SMI induces dysesthesia and decreased movement accuracy as well as along with reduced beta band activity in motor-related areas and increased inferior parietal lobule activity.
[20]
[21]
[22]
Declarations of interest None.
[23]
Acknowledgments [24]
The authors would like to thank students of the Graduate School of Kio University for their participation in this study. We would also like to thank Ammor Adam Karim for the English translation.
[25]
References [26]
[1] A.J. Harris, Cortical origin of pathological pain, Lancet 354 (1999) 1464–1466, https://doi.org/10.1016/S0140-6736(99)05003-5. [2] S. Don, L. Voogt, M. Meeus, M. De Kooning, J. Nijs, Sensorimotor incongruence in people with musculoskeletal pain: a systematic review, Pain Pract. 17 (2017) 115–128, https://doi.org/10.1111/papr.12456. [3] J. Schwoebel, H.B. Coslett, J. Bradt, R. Friedman, C. Dileo, Pain and the body schema: effects of pain severity on mental representations of movement, Neurology 59 (2002) 775–777, https://doi.org/10.1212/WNL.59.5.775. [4] A. Kuppuswamy, E. Clark, J. Rothwell, N.S. Ward, Limb heaviness: a perceptual phenomenon associated with poststroke fatigue? Neurorehabil. Neural Repair 30 (2016) 360–362, https://doi.org/10.1177/1545968315597071. [5] C.S. McCabe, H. Cohen, D.R. Blake, Somaesthetic disturbances in fibromyalgia are
[27] [28]
[29]
25
exaggerated by sensory motor conflict: implications for chronicity of the disease? Rheumatology 46 (2007) 1587–1592, https://doi.org/10.1093/rheumatology/ kem204. L. Daenen, J. Nijs, N. Roussel, K. Wouters, M. Van Loo, P. Cras, Sensorimotor incongruence exacerbates symptoms in patients with chronic whiplash associated disorders: an experimental study, Rheumatology 51 (2012) 1492–1499, https://doi. org/10.1093/rheumatology/kes050. C.S. McCabe, R.C. Haigh, P.W. Halligan, D.R. Blake, Simulating sensory-motor incongruence in healthy volunteers: implications for a cortical model of pain, Rheumatology 44 (2005) 509–516, https://doi.org/10.1093/rheumatology/ keh529. J. Foell, R. Bekrater-Bodmann, C.S. McCabe, H. Flor, Sensorimotor incongruence and body perception: an experimental investigation, Front. Hum. Neurosci. 7 (2013) 310, https://doi.org/10.3389/fnhum.2013.00310. O. Katayama, M. Osumi, T. Kodama, S. Morioka, Dysesthesia symptoms produced by sensorimotor incongruence in healthy volunteers: an electroencephalogram study, J. Pain Res. 9 (2016) 1197–1204, https://doi.org/10.2147/JPR.S122564. J. Brandes, F. Rezvani, T. Heed, Abstract spatial, but not body-related, visual guidance of bimanual coordination relies on movement direction, Sci. Rep. 7 (2016) 16732, https://doi.org/10.1038/s41598-017-16860-x. N. Lebar, J. Danna, S. Moré, L. Mouchnino, J. Blouin, On the neural basis of sensory weighting: alpha, beta and gamma modulations during complex movements, Neuroimage 150 (2017) 200–212, https://doi.org/10.1016/j.neuroimage.2017.02. 043. M. Osumi, S. Nobusako, T. Zama, M. Taniguchi, S. Shimada, S. Morioka, Sensorimotor incongruence alters limb perception and movement, Hum. Mov. Sci. 57 (2018) 251–257, https://doi.org/10.1016/j.humov.2017.09.003. R.C. Oldfield, The assessment and analysis of handedness: the Edinburgh inventory, Neuropsychologia 9 (1971) 97–113, https://doi.org/10.1016/0028-3932(71) 90067-4. N. Dounskaia, K.G. Nogueira, S.P. Swinnen, E. Drummond, Limitations on coupling of bimanual movements caused by arm dominance: when the muscle homology principle fails, J. Neurophysiol. 103 (2010) 2027–2038, https://doi.org/10.1152/ jn.00778.2009. O. Katayama, T. Tsukamoto, M. Osumi, T. Kodama, S. Morioka, Neural mechanism of altered limb perceptions caused by temporal sensorimotor incongruence, Front. Behav. Neurosci. 12 (2018) 282, https://doi.org/10.3389/fnbeh.2018.00282. R. Debnath, E.A. Franz, Perception of hand movement by mirror reflection evokes brain activation in the motor cortex contralateral to a non-moving hand, Cortex 81 (2016) 118–125, https://doi.org/10.1016/j.cortex.2016.04.015. R.D. Pascual-Marqui, D. Lehmann, M. Koukkou, K. Kochi, P. Anderer, B. Saletu, H. Tanaka, K. Hirata, E.R. John, L. Prichep, R. Biscay-Lirio, T. Kinoshita, Assessing interactions in the brain with exact low-resolution electromagnetic tomography, Philos, Trans. A Math. Phys. Eng. Sci. 369 (2011) 3768–3784, https://doi.org/10. 1098/rsta.2011.0081. M. Fuchs, J. Kastner, M. Wagner, S. Hawes, J.S. Ebersole, A standardized boundary element method volume conductor model, Clin. Neurophysiol. 113 (2002) 702–712, https://doi.org/10.1016/S1388-2457(02)00030-5. V. Fonov, A.C. Evans, K. Botteron, C.R. Almli, R.C. McKinstry, D.L. Collins, Unbiased average age-appropriate atlases for pediatric studies, Neuroimage 54 (2011) 313–327, https://doi.org/10.1016/j.neuroimage.2010.07.033. R.D. Pascual-Marqui, Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, methods find, Exp. Clin. Pharmacol. 24 (2002) 5–12. D.L. Collins, C.J. Holmes, T.M. Peters, A.C. Evans, Automatic 3-D model-based neuroanatomical segmentation, Hum. Brain Mapp. 3 (1995) 190–208, https://doi. org/10.1002/hbm.460030304. R.D. Pascual-Marqui, Instantaneous and Lagged Measurements of Linear and Nonlinear Dependence Between Groups of Multivariate Time Series: Frequency Decomposition, arXiv:0711.1455 [stat.ME], Available online at: (2007) http:// arxiv.org/abs/0711.1455. R.W. Sperry, Neural basis of the spontaneous optokinetic response produced by visual inversion, J. Comp. Physiol. Psychol. 43 (1950) 482–489, https://doi.org/10. 1037/h0055479. D.M. Wolpert, Z. Ghahramani, M.I. Jordan, An internal model for sensorimotor integration, Science. 269 (1995) 1880–1882, https://doi.org/10.1126/science. 7569931. E. Von Holst, H. Mittelstaedt, (1950/1973) Daz Reafferezprincip. Wechselwirkungen zwischen Zentralnerven-system und Peripherie, Naturwissenschaften 37 (1950) 467–476. The reference principle. In: The behavioral physiology of animals and man. The collected papers of Erich von Holst. Martin R (translator) University of Miami Press, Coral Gables, Florida, pp. 139–173.. F. Debaere, N. Wenderoth, S. Sunaert, P. Van Hecke, S.P. Swinnen, Internal vs external generation of movements: differential neural pathways involved in bimanual coordination performed in the presence or absence of augmented visual feedback, Neuroimage 19 (2003) 764–776, https://doi.org/10.1016/S1053-8119(03) 00148-4. N. Picard, P.L. Strick, Imaging the premotor areas, Curr. Opin. Neurobiol. 11 (2001) 663–672, https://doi.org/10.1016/S0959-4388(01)00266-5. T. Paus, M. Petrides, A.C. Evans, E. Meyer, Role of the human anterior cingulate cortex in the control of oculomotor, manual, and speech responses: a positron emission tomography study, J. Neurophysiol. 70 (1993) 453–469, https://doi.org/ 10.1152/jn.1993.70.2.453. R.E. Passingham, S.L. Bengtsson, H.C. Lau, Medial frontal cortex: from self-generated action to reflection on one’s own performance, Trends Cogn. Sci. (Regul. Ed.)
Neuroscience Letters 698 (2019) 19–26
O. Katayama et al.
14 (2010) 16–21, https://doi.org/10.1016/j.tics.2009.11.001. [30] F. Bonini, B. Burle, C. Liegeois-Chauvel, J. Regis, P. Chauvel, F. Vidal, Action monitoring and medial frontal cortex: leading role of supplementary motor area, Science 343 (2014) 888–891, https://doi.org/10.1126/science.1247412. [31] J. Tanji, Sequential organization of multiple movements: involvement of cortical motor areas, Annu. Rev. Neurosci. 24 (2001) 631–651, https://doi.org/10.1146/ annurev.neuro.24.1.631. [32] D.J. Serrien, P. Brown, The functional role of interhemispheric synchronization in the control of bimanual timing task, Exp. Brain Res. 147 (2002) 268–272, https:// doi.org/10.1007/s00221-002-1253-z. [33] E. Formaggio, S.F. Storti, I. Boscolo Galazzo, M. Gandolfi, C. Geroin, N. Smania, L. Spezia, A. Waldner, A. Fiaschi, P. Manganotti, Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements, J. Neuroeng. Rehabil. 26 (2013) 24, https://doi.org/10.1186/1743-0003-10-24. [34] P. Brown, Cortical drives to human muscle: the piper and related rhythms, Prog. Neurobiol. 60 (2000) 97–108, https://doi.org/10.1016/S0301-0082(99)00029-5. [35] J. Decety, J.A. Sommerville, Shared representations between self and other: a social
[36]
[37]
[38]
[39]
[40]
26
cognitive neuroscience view, Trends Cogn. Sci. (Regul. Ed.) 7 (2003) 527–533, https://doi.org/10.1016/j.tics.2003.10.004. J.R. Augustine, Circuitry and functional aspects of the insular lobe in primates including humans, Brain Res. Rev. 22 (1996) 229–244, https://doi.org/10.1016/ S0165-0173(96)00011-2. F.G. Flynn, D.F. Benson, A. Ardila, Anatomy of the insula functional and clinical correlates, Aphasiology 13 (1999) 55–78, https://doi.org/10.1080/ 026870399402325. C. Farrer, C.D. Frith, Experiencing oneself vs another person as being the cause of an action: the neural correlates of the experience of agency, Neuroimage 15 (2002) 596–603, https://doi.org/10.1006/nimg.2001.1009. D. Balslev, F.A. Nielsen, O.B. Paulson, I. Law, Right temporoparietal cortex activation during visuo-proprioceptive conflict, Cereb. Cortex 15 (2005) 166–169, https://doi.org/10.1093/cercor/bhh119. A. Brovelli, M. Ding, A. Ledberg, Y. Chen, R. Nakamura, S.L. Bressler, Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality, Proc. Natl. Acad. Sci. U. S. A. 101 (2004) 9849–9854, https://doi.org/10.1073/pnas.0308538101.
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Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Research article
Neural activities behind the influence of sensorimotor incongruence on dysesthesia and motor control
T
⁎
Osamu Katayamaa,b, , Yuki Nishia, Michihiro Osumia,c, Yusaku Takamuraa, Takayuki Kodamad, Shu Moriokaa,c a
Department of Neurorehabilitation, Graduate School of Health Sciences, Kio University, 4-2-2 Umami-naka, Koryo-cho, Kitakatsuragi-gun, Nara 635-0832, Japan Department of Rehabilitation, Watanabe Hospital, 45-2 Noma-kamikawada, Mihama-cho, Chita-gun, Aichi 470-3235, Japan c Department of Neurorehabilitation Research Center, Kio University, 4-2-2 Umami-naka, Koryo-cho, Kitakatsuragi-gun, Nara 635-0832, Japan d Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, Kyoto 607-8175, Japan b
A R T I C LE I N FO
A B S T R A C T
Keywords: Bimanual coordination Dysesthesia Electroencephalogram motor control Motor-Related areas Sensorimotor incongruence
Sensorimotor incongruence (SMI) is associated with pathological pain, such as phantom limb pain. Additionally, patients with pathological pain and brain dysfunction typically present with movement disorders, including diminished voluntary control and increased variability in bimanual movement performance. In healthy subjects, SMI leads to dysesthesia and bimanual movement motor dysfunction. However, the brain localization of this activity remains unclear, particularly in SMI-induced dysesthesia and decrease in movement accuracy. In this study, 17 healthy participants were asked to perform repetitive flexion/extension exercises with their wrists in a congruent/incongruent position while viewing the activity in a mirror. Indeed, SMI induced dysesthesia and decreased bimanual movement accuracy. Moreover, beta band activities of the bilateral presupplementary (P < 0.01) and bilateral cingulate (P < 0.05) motor areas were decreased. Collectively, our findings indicate that SMI induces dysesthesia and movement disorders and reduces beta band activities in motor-related areas.
1. Introduction Evidence has suggested that mismatch between motor intention and sensory feedback, termed sensorimotor incongruence (SMI), is related to pathological pain, including phantom limb pain [1,2]. Altered limb perception such as loss of limb ownership and heaviness are important clinical features in pathological pain and stroke patients [3,4]. Recent studies have also shown that experimental SMI causes altered limb perceptions and emotions such as peculiarity and discomfort in people with pathological pain [5,6]. In fact, several studies on healthy subjects indicate that experimental SMI cause altered limb perceptions and emotions [7–9]. Dysesthesia is defined as altered limb perceptions and emotions and is evaluated using the following sensations: pain, itch, warmth, coldness, lightness, heaviness, lost limb, extra limb, peculiarity, pressure, shape, numbness, nausea, and other parts [9]. In addition, experimental SMI reduces movement accuracy [10–12]. Accordingly, SMI is considered an important factor influencing dysesthesia, motor control, and brain activity. However, the brain
localization of this activity during SMI-induced dysesthesia and motor control dysfunction remains unclear. We confirmed that various dysesthesias are induced in individuals during experimental SMI in a previous study performed with healthy subjects [9]. It was difficult to define the starting time of their dysesthesias because of the individual differences and the variation in strength: some subjects strongly experienced dysesthesia from the beginning, whereas others had various consecutive dysesthesia episodes at different times. Therefore, we concluded that it is important to clarify the localization of brain activity during SMI-induced dysesthesias. In this study, we sought to simultaneously analyze kinematic data and neural activity in healthy participants during experimental SMI. We aimed to determine whether SMI affects dysesthesia and motor control as well as investigated the brain localization of this activity.
Abbreviation: BA, Brodmann areas; CMA, Cingulate motor area; GFP, Global field power; MAD, Mean absolute deviation; MNI, Montreal Neurologic Institute; MRI, Magnetic resonance imaging; NRS, Numeric rating scale; pre-SMA, Presupplementary motor area; SMI, Sensorimotor incongruence; SnPM, Statistical nonparametric maps ⁎ Corresponding author. Present address: Kio University, 4-2-2 Umami-naka, Koryo-cho, Kitakatsuragi-gun, Nara 635-0832, Japan. E-mail address: [email protected] (O. Katayama). https://doi.org/10.1016/j.neulet.2019.01.010 Received 1 September 2018; Received in revised form 4 January 2019; Accepted 4 January 2019 Available online 06 January 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.
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2. Material and methods
sessions before the trial. In previous studies on bimanual coordination tasks, methods employing the metronome and those not employing it have been described. The metronome is used to verify the influence of movement pace on the behavioral data [14], whereas the metronome is not used in tests aimed to verify the natural variations in intervals and speeds of continuous movements in the task [12,15]. We followed the latter method as we did not want to control the pace of periodic movements with the metronome but we wanted to verify the natural variation of movement accuracy in each experimental condition. Each participant performed at the exercise pace they practiced beforehand and with their maximum ranges of flexion and extension and vice versa for 35 movements in every condition. All participants performed a total of 20 sequences (five replicates and four conditions). All conditions were pseudo-randomly applied, and electroencephalograms (EEGs) were recorded during each condition. During the break, participants rated intensities of related altered limb perceptions and emotions for 14 different dysesthesia sensations on an 11-point numeric rating scale (NRS), from 0 (not at all) to 10 (very strong) [8,9]. Among the 14 dysesthesia sensations, we calculated the number of sensations for which the participants reported an NRS score of ≥1.
2.1. Participants We recruited 17 healthy participants (5 women and 12 men; mean age, 26.2 ± 4.6 years; range, 18–39 years) with no current or past physical or mental illness and with normal vision. Demographic characteristics and a brief medical history (including hand dominance) were obtained from participants to ensure that inclusion and exclusion criteria were satisfied. All participants were right-handed, as confirmed by the Edinburgh Handedness Inventory [13]. This study was approved by the ethics committee of Kio University (approval number H28-30) and was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki. All participants provided written informed consent for the use of their data and any accompanying images.
2.2. Bimanual coordination test The test apparatus and associated assessment procedure have been described previously [7,9]. Briefly, each participant was instructed to remove any jewelry. A mirrored device that could be turned around easily and constituted a large mirror on one side and a white, nonreflecting control surface on the other side was used (Fig. 1). The whiteboard surface eliminated any form of visual feedback from the intended or performed movement, which was considered a control condition, whereas the mirrored surface was used for experiments (experimental condition). Participants were instructed to place their right limb on the right side of the whiteboard or mirror aligned with the sagittal plane and the left limb on the other side hidden by the whiteboard or mirror. They were instructed to sequentially perform flexion/ extension exercises with their wrist in a congruent/incongruent manner while looking at the whiteboard or mirror. In the whiteboard congruence condition, participants performed the flexion/extension exercises with both wrists simultaneously and symmetrically in the same direction. In the whiteboard incongruence condition, participants performed flexion/extension exercises with one wrist flexing while the other was extended asymmetrically. In the mirror congruence condition, participants performed flexion/extension exercises with both wrists simultaneously and symmetrically in the same direction. Motor intention was consistent with the direction of left wrist movement. In the mirror incongruence condition (i.e., the intervention condition), participants performed flexion/extension exercises with one wrist flexing while the other was extended asymmetrically. This condition fulfilled the incongruence between proprioception associated with motor intention and visual feedback for the left hand. In mirror conditions, participants viewed the reflection of their hand in the mirror. Each participant performed flexion/extension exercises at a frequency of 1 Hz, which was guided by a metronome during the practice
2.3. Kinematic data collection and analysis We collected kinematic data using an electronic goniometer (Biometrics Model SG110 A, Gwent, UK) placed on the dorsal sides of both wrists during the flexion/extension exercises (sampling rate, 1000 Hz). The first and last four cycles of each trial were removed to eliminate possible transitory effects. Cycle durations was defined as the time between successive peak extension positions. Mean absolute deviation (MAD) of the relative cycle duration was used as an estimate of movement accuracy. 2.4. EEG recording and preprocessing Standard EEG recording and data preprocessing procedures were followed as described elsewhere [5]. A high-resolution 32-channel portable EEG system (Active Two; BioSemi, Amsterdam, The Netherlands) was used for data acquisition (sampling rate, 1024 Hz). Electrodes were fit into an elastic head cap (BioSemi) to record continuous EEG data from the following 32 scalp locations organized according to the 10–20 system: Fp1, AF3, F3, F7, FC5, FC1, C3, T7, CP5, CP1, P3, P7, PO3, O1, Oz, Pz, Fp2, AF4, Fz, F4, F8, FC6, FC2, Cz, C4, T8, CP6, CP2, P4, P8, PO4, and O2. Each electrode was filled with Signal® Electrode Gel (Parker Laboratories, Fairfield, NJ, USA) for adequate signal transduction. ActiView software (BioSemi) was used for data collection. EEG data were analyzed using a multimodal EEG analysis software (EMSE Suite 5.4, Source Signal Imaging, La Mesa, CA, USA), band-pass filtered in the range 1.0–70.0 Hz, and applied to a common average reference
Fig. 1. Experimental setup. The mirror device was placed between the participant’s right and left upper limbs. The four study conditions: whiteboard congruence (A), whiteboard incongruence (B), mirror congruence (C), and mirror incongruence (D) are shown. 20
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Fig. 2. Degree of dysesthesia. Bars (mean [standard error]) represent numeric rating scale (NRS) scores for dysesthesia symptoms (A) and number of sensations (B) under the whiteboard congruence (white bar), whiteboard incongruence (gray bar), mirror congruence (diagonal-lines bar), and mirror incongruence (black bar) conditions.
Fig. 3. Kinematic data. Boxes (mean [standard error]) represent the whiteboard congruence (white box), whiteboard incongruence (gray box), mirror congruence (diagonal-lines box), and mirror incongruence (black box) conditions. Means of cycle durations (A) and MAD (B) are shown. WCc, whiteboard congruence condition; WIc, whiteboard incongruence condition; MCc, mirror congruence condition; MIc, mirror incongruence condition.
were calculated and used for intracerebral spatial analyses in conjunction with eLORETA analysis [20]. This method allows for a threedimensional image display of intracerebral neural activity. Voxel values in the brain areas displaying neural activity under each condition were calculated as neural activity (current source density, μA/mm2*10−3) and further identified in terms of BA and MNI coordinates [21]. Neural activity was calculated as the global field power value [20] for each condition.
montage. Artifacts generated by blinking, eye movements, facial muscle activity, or body movements were removed using a specially designed spatial filter in EMSE Suite 5.4 and by visual inspection of the frontal EEG trace (Fp1 and Fp2) [9,16].
2.5. EEG data analysis We used exact low-resolution brain electromagnetic tomography (eLORETA) to compute the cortical electrical distribution from the scalp electrical potentials [17]. This method is a weighted minimum norm inverse solution, in which weights are unique and endow the inverse solution, with the property of the exact localization for any point-source in the brain. Considering the principles of linearity and superposition, any arbitrary distribution can thus be correctly localized, albeit with low spatial resolution. In the eLORETA version we used, the solution space comprised 6239 cortical gray matter voxels at a 5-mm spatial resolution in a realistic head model [18]. During this process, the Montreal Neurologic Institute (MNI) average magnetic resonance imaging brain (MNI152) template [19] was used with anatomic labels corresponding to Brodmann areas (BAs) used to compute the lead field. For imaging analysis, five epochs were extracted from the data obtained under each condition (the duration of each epoch was 20 s), and frequency analyses were performed. Data within the frequency bands of interest (delta [1.5–6.0 Hz], theta [6.5–8.0 Hz], alpha 1 [8.5–10.0 Hz], alpha 2 [10.5–12.0 Hz], beta 1 [12.5–18.0 Hz], beta 2 [18.5–21.0 Hz], beta 3 [21.5–30.0 Hz], and gamma [30.5–70.0 Hz])
2.6. Statistical analysis Data normality was confirmed using the Shapiro–Wilk test. Results of dysesthesia NRS and the number of sensations were compared using the Friedman test. When differences were < 5%, Wilcoxon test was performed six times for comparison among the four conditions. Therefore, the significance level was set to 0.8% (5/6). Statistical analysis of data regarding the extension of both wrists was conducted in the same manner as that for cycle duration and MAD. In addition, intracerebral neural activities of each condition were compared using the eLORETA statistical nonparametric maps [22]. Neural areas showing significant differences in activities were colored, calculated, and drawn. The significance threshold was based on testing with 5000 permutations using log-transformed LORETA values with subject-wise normalization (assuming individual differences in baseline activity). Correlation analyses on dysesthesia NRS score, EEG data, and kinematic data under the mirror incongruence condition were performed using 21
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Fig. 4. Differences in electroencephalographic activities under the mirror congruence and mirror incongruence conditions. Statistical nonparametric maps (SnPMs) of exact low-resolution brain electromagnetic tomography of the beta 3 band comparing the mirror congruence and mirror incongruence conditions are shown. Blue indicates decreased activity under the mirror incongruence condition. Images depicting SnPMs from different perspectives are based on voxel-by-voxel t-values of differences. MCc, mirror congruence condition; MIc, mirror incongruence condition (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
3.2. Comparison of kinematic data
Spearman’s rank correlation coefficient. For all calculations, SPSS version 25.0 (IBM Corp., Armonk, NY, USA) was used.
Cycle durations of bimanual coordination tests are presented in Fig. 3. On the left limb (i.e., the intervention limb), Friedman test revealed a significant main effect for the mean (χ2 = 83.26, P < 0.001) and MAD (χ2 = 45.67, P < 0.001) (Fig. 3A). Post hoc test revealed that the mirror congruence condition had a significantly longer average cycle duration than did the whiteboard congruence (P < 0.001), whiteboard incongruence (P < 0.001), and mirror incongruence (P < 0.001) conditions. Moreover, the mirror incongruence condition had a significantly longer average cycle duration than did the whiteboard congruence (P < 0.001) and whiteboard incongruence (P < 0.001) conditions. The mirror incongruence condition had a significantly higher MAD of cycle duration than did the whiteboard congruence (P < 0.001), whiteboard incongruence (P < 0.001), and mirror congruence (P < 0.001) conditions (Fig. 3B).
3. Results 3.1. Comparison of dysesthesia NRS scores and number of sensations Descriptive NRS results are summarized in Fig. 2. Friedman test for the comparison of dysesthesia NRS scores and number of sensations revealed a significant main effect for itch (chi-square [χ]2 = 10.01, P = 0.018), peculiarity (χ2 = 18.50, P < 0.001), nausea (χ2 = 7.93, P = 0.048), other part (χ2 = 9.92, P = 0.019), and number of sensations (χ2 = 18.87, P < 0.001) (Fig. 2A). Post hoc test revealed that itch scores for the mirror incongruence condition were not higher than those for the other conditions and that peculiarity scores for the mirror incongruence condition were significantly higher those for the whiteboard congruence (P = 0.002), whiteboard incongruence (P = 0.002), and mirror congruence (P = 0.005) conditions (Fig. 2A). Furthermore, nausea scores for the mirror incongruence condition were not higher than those for the other conditions (Fig. 2A); other parts scores for the mirror incongruence condition were not higher than those for the other conditions (Fig. 2A); and the number of sensations for the mirror incongruence condition was significantly higher than that for the whiteboard congruence (P = 0.021), whiteboard incongruence (P = 0.011), and mirror congruence (P = 0.001) conditions (Fig. 2B).
3.3. Comparison of EEG data For the mirror incongruence condition, beta 3 band (21.5–30.0 Hz) activities of the bilateral presupplementary (pre-SMA) (BA6) and bilateral cingulate (CMA) (BA32) motor areas were significantly lower than those for the mirror congruence and whiteboard incongruence conditions (T = 3.744, P < 0.01; T = 3.432; P < 0.05) (Figs. 4 and 5, Table 1). For the mirror incongruence condition, beta 1 band (12.5–18.0 Hz) activities of the left posterior insula (BA13), bilateral angular gyrus (BA39), and supramarginal gyrus (BA40) were 22
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Fig. 5. Differences in the electroencephalographic activities under the whiteboard incongruence and mirror incongruence conditions. Statistical nonparametric maps (SnPMs) of exact low-resolution brain electromagnetic tomography of the beta 3 band comparing the whiteboard incongruence and mirror incongruence conditions are shown. Blue indicates decreased activity under the mirror incongruence condition. Images depicting SnPMs from different perspectives are based on voxel-byvoxel t-values of differences. WIc, whiteboard incongruence condition; MIc, mirror incongruence condition (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Table 1 Differences in beta 3 and beta 1 activities under the four condition. Condition
Frequency band
Statistical value (t)
MCc vs. MIc
Beta 3
3.744
WIc vs. MIc
Beta 3
3.432
WCc vs. MIc
Beta 1
3.315
MNI coordinates x
y
z
5 −5 5 −5 5 −5 −5 −5 −30 55 −55 60 −50
45 45 35 35 35 35 35 35 −30 −55 −60 −55 −30
40 40 30 30 40 40 30 25 15 10 10 25 25
Brodmann area
Region
6 6 32 32 6 6 32 32 13 39 39 40 40
Pre-SMA Pre-SMA CMA CMA Pre-SMA Pre-SMA CMA CMA Posterior insula Angular gyrus Angular gyrus Supramarginal gyrus Supramarginal gyrus
MCc, Mirror congruence condition; MIc, Mirror incongruence condition; WCc, Whiteboard congruence condition; MIc, Whiteboard incongruence condition; MNI, Montreal Neurologic Institute; Pre-SMA, Presupplementary motor area; CMA, Cingulate motor area.
significantly higher than those for the whiteboard congruence condition (T = 3.315; P < 0.05) (Fig. 6, Table 1).
activity was lower in the mirror incongruence condition than in the mirror congruence and whiteboard incongruence conditions in terms of peculiarity (r = −0.42, P = 0.090; r = −0.54, P = 0.025), pressure (r = −0.52 P = 0.031; r = −0.61, P = 0.010), and number of sensations (r = −0.52, P = 0.034; r = −0.58, P = 0.015). A significant positive correlation of MAD with warmth (r = 0.50, P = 0.043), heaviness (r = 0.57, P = 0.016), and numbness (r = 0.52, P = 0.034) was observed.
3.4. Correlation of dysesthesia NRS score, EEG data, and kinematic data under the mirror incongruence condition We observed a significant negative correlation in the pre-SMAs (x = 5, y = 30, z = 60, x = 5, y = 35, and z = 40), in which brain 23
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Fig. 6. Differences in the electroencephalographic activities under the whiteboard congruence and mirror incongruence conditions. Statistical nonparametric maps (SnPMs) of exact low-resolution brain electromagnetic tomography of the beta 1 band comparing the whiteboard congruence and mirror incongruence conditions are shown. Red indicates increased activity under the mirror incongruence condition. Images depicting SnPMs from different perspectives are based on voxel-by-voxel tvalues of differences. WCc, whiteboard congruence condition; MIc, mirror incongruence condition (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
4. Discussion
congruence, the mean cycle duration was close to the most practiced pace (1 Hz = 1000 ms). Therefore, it was considered that the mirror congruence condition was an appropriate control condition. We observed significantly higher scores for the MAD (movement accuracy) of the cycle duration for the mirror incongruence condition than for the other conditions; thus, SMI modulated dysesthesia as well as motor control. The whiteboard incongruence condition showed significantly higher MAD than the whiteboard congruence condition. Coordination variability is higher during asymmetrical than symmetrical bimanual coordination movements [14], with lack of visual feedback leading to degraded movement accuracy [26]. Thus, in the whiteboard incongruence condition, blocked visual feedback may have reduced movement accuracy. Consequently, reduced movement accuracy in the whiteboard incongruence condition may be caused by asymmetrical movement and blocked visual feedback. Our findings indicated that SMI decreased the activity of pre-SMA and CMA in beta 3 band motor-related areas during bimanual coordination tasks. These areas are important for bimanual coordination tasks [27,28] as well as self-initiated movements [29] and action monitoring and sequencing [30,31]. Changes in beta band activity have been reported in previous electroencephalographic studies related to bimanual coordination tasks [32,33]. The significance of the beta band for movement-related function has been demonstrated by cortex-muscle coherence [34], suggesting that SMI disrupts distortion in motor control, resulting in decreased movement accuracy.
In this study, we simultaneously analyzed kinematic data and neural activities in experimental SMI using a bimanual coordination task in healthy participants. Our novel findings indicated that SMI reduced beta band activity in motor-related areas. Dysesthesia symptoms appeared in all conditions during this study, with NRS scores for peculiarity being the highest, as reported previously [9]. The highest number of sensations was observed in the mirror incongruence condition. Peculiarity may thus be caused by incongruence between the predicted and actual sensory information. Consistent with previous reports, dysesthesia was only marginally induced in other conditions [7–9]. In the whiteboard congruence/incongruence condition, when vision was blocked by the whiteboard, dysesthesia was caused by even a slight conflict between the predicted and actual proprioception. Although there were symmetric movements in the mirror congruence condition, we believed that increasing attention to sensory information in the left hand hidden in the mirror and exercising while watching the mirror image. Thus, we considered that dysesthesia was caused by these factors. Movement accuracy is controlled by sensory feedback [23–25], and experimental disturbances reduce movement accuracy [10–12]. We did not control the pace of periodic movements during conditions with the metronome and we verified the natural variation of movement accuracy. In the mirror congruence condition with sensorimotor 24
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Beta 1 band activities of the bilateral angular gyrus, supramarginal gyrus, and left posterior insula were significantly elevated in the mirror incongruence condition compared with those in the whiteboard congruence condition. These inferior parietal lobe areas play roles in the integration of sensory feedback [35–37] and detection of SMI [38,39], and neuronal responses in the beta band are associated with sensorimotor network processing [40]. Hence, we considered the necessity of multisensory integration involving the bilateral angular gyrus, supramarginal gyrus, and left posterior insula in the mirror incongruence condition. Thus, motor-related SMI may be involved in movement accuracy, with the inferior parietal lobule in the beta band involved in the multisensory integration of SMI. We found a significant negative correlation between pre-SMAs and dysesthesia in the mirror incongruence condition, which is consistent with results of a previous study of temporal SMI [15]. Neural activities in these areas may be associated with dysesthesia induced by SMI. Movement accuracy is controlled by sensory feedback [23,24], and experimental disturbances in sensory feedback reportedly reduce movement accuracy [10–12], with subjective heaviness being altered with SMI [4]. There may be a similar mechanism between reduced movement accuracy and altered heaviness, as indicated by a significant correlation between heaviness and MAD. Therefore, altered visual feedback may lead to sensorimotor integration failure, increased heaviness, and decreased movement accuracy.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
5. Limitations [15]
Dysesthesia was measured after the task; In the future, it will be necessary to investigate whether a relationship exists among dysesthesia, motor control, and the activity localization in the brain. If this is the case, we would like to analyze the event-related potential and define the time point at which a specific sensation occurs using a foot pedal. Additionally, sensations and dysesthesia intensity were subjectively reported by participants, and the sample size of this study was relatively small compared with that of similar studies.
[16]
[17]
[18]
6. Conclusions
[19]
Movement accuracy in SMI and brain activity associated with dysesthesia are not well understood. However, we report for the first time that SMI induces dysesthesia and decreased movement accuracy as well as along with reduced beta band activity in motor-related areas and increased inferior parietal lobule activity.
[20]
[21]
[22]
Declarations of interest None.
[23]
Acknowledgments [24]
The authors would like to thank students of the Graduate School of Kio University for their participation in this study. We would also like to thank Ammor Adam Karim for the English translation.
[25]
References [26]
[1] A.J. Harris, Cortical origin of pathological pain, Lancet 354 (1999) 1464–1466, https://doi.org/10.1016/S0140-6736(99)05003-5. [2] S. Don, L. Voogt, M. Meeus, M. De Kooning, J. Nijs, Sensorimotor incongruence in people with musculoskeletal pain: a systematic review, Pain Pract. 17 (2017) 115–128, https://doi.org/10.1111/papr.12456. [3] J. Schwoebel, H.B. Coslett, J. Bradt, R. Friedman, C. Dileo, Pain and the body schema: effects of pain severity on mental representations of movement, Neurology 59 (2002) 775–777, https://doi.org/10.1212/WNL.59.5.775. [4] A. Kuppuswamy, E. Clark, J. Rothwell, N.S. Ward, Limb heaviness: a perceptual phenomenon associated with poststroke fatigue? Neurorehabil. Neural Repair 30 (2016) 360–362, https://doi.org/10.1177/1545968315597071. [5] C.S. McCabe, H. Cohen, D.R. Blake, Somaesthetic disturbances in fibromyalgia are
[27] [28]
[29]
25
exaggerated by sensory motor conflict: implications for chronicity of the disease? Rheumatology 46 (2007) 1587–1592, https://doi.org/10.1093/rheumatology/ kem204. L. Daenen, J. Nijs, N. Roussel, K. Wouters, M. Van Loo, P. Cras, Sensorimotor incongruence exacerbates symptoms in patients with chronic whiplash associated disorders: an experimental study, Rheumatology 51 (2012) 1492–1499, https://doi. org/10.1093/rheumatology/kes050. C.S. McCabe, R.C. Haigh, P.W. Halligan, D.R. Blake, Simulating sensory-motor incongruence in healthy volunteers: implications for a cortical model of pain, Rheumatology 44 (2005) 509–516, https://doi.org/10.1093/rheumatology/ keh529. J. Foell, R. Bekrater-Bodmann, C.S. McCabe, H. Flor, Sensorimotor incongruence and body perception: an experimental investigation, Front. Hum. Neurosci. 7 (2013) 310, https://doi.org/10.3389/fnhum.2013.00310. O. Katayama, M. Osumi, T. Kodama, S. Morioka, Dysesthesia symptoms produced by sensorimotor incongruence in healthy volunteers: an electroencephalogram study, J. Pain Res. 9 (2016) 1197–1204, https://doi.org/10.2147/JPR.S122564. J. Brandes, F. Rezvani, T. Heed, Abstract spatial, but not body-related, visual guidance of bimanual coordination relies on movement direction, Sci. Rep. 7 (2016) 16732, https://doi.org/10.1038/s41598-017-16860-x. N. Lebar, J. Danna, S. Moré, L. Mouchnino, J. Blouin, On the neural basis of sensory weighting: alpha, beta and gamma modulations during complex movements, Neuroimage 150 (2017) 200–212, https://doi.org/10.1016/j.neuroimage.2017.02. 043. M. Osumi, S. Nobusako, T. Zama, M. Taniguchi, S. Shimada, S. Morioka, Sensorimotor incongruence alters limb perception and movement, Hum. Mov. Sci. 57 (2018) 251–257, https://doi.org/10.1016/j.humov.2017.09.003. R.C. Oldfield, The assessment and analysis of handedness: the Edinburgh inventory, Neuropsychologia 9 (1971) 97–113, https://doi.org/10.1016/0028-3932(71) 90067-4. N. Dounskaia, K.G. Nogueira, S.P. Swinnen, E. Drummond, Limitations on coupling of bimanual movements caused by arm dominance: when the muscle homology principle fails, J. Neurophysiol. 103 (2010) 2027–2038, https://doi.org/10.1152/ jn.00778.2009. O. Katayama, T. Tsukamoto, M. Osumi, T. Kodama, S. Morioka, Neural mechanism of altered limb perceptions caused by temporal sensorimotor incongruence, Front. Behav. Neurosci. 12 (2018) 282, https://doi.org/10.3389/fnbeh.2018.00282. R. Debnath, E.A. Franz, Perception of hand movement by mirror reflection evokes brain activation in the motor cortex contralateral to a non-moving hand, Cortex 81 (2016) 118–125, https://doi.org/10.1016/j.cortex.2016.04.015. R.D. Pascual-Marqui, D. Lehmann, M. Koukkou, K. Kochi, P. Anderer, B. Saletu, H. Tanaka, K. Hirata, E.R. John, L. Prichep, R. Biscay-Lirio, T. Kinoshita, Assessing interactions in the brain with exact low-resolution electromagnetic tomography, Philos, Trans. A Math. Phys. Eng. Sci. 369 (2011) 3768–3784, https://doi.org/10. 1098/rsta.2011.0081. M. Fuchs, J. Kastner, M. Wagner, S. Hawes, J.S. Ebersole, A standardized boundary element method volume conductor model, Clin. Neurophysiol. 113 (2002) 702–712, https://doi.org/10.1016/S1388-2457(02)00030-5. V. Fonov, A.C. Evans, K. Botteron, C.R. Almli, R.C. McKinstry, D.L. Collins, Unbiased average age-appropriate atlases for pediatric studies, Neuroimage 54 (2011) 313–327, https://doi.org/10.1016/j.neuroimage.2010.07.033. R.D. Pascual-Marqui, Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, methods find, Exp. Clin. Pharmacol. 24 (2002) 5–12. D.L. Collins, C.J. Holmes, T.M. Peters, A.C. Evans, Automatic 3-D model-based neuroanatomical segmentation, Hum. Brain Mapp. 3 (1995) 190–208, https://doi. org/10.1002/hbm.460030304. R.D. Pascual-Marqui, Instantaneous and Lagged Measurements of Linear and Nonlinear Dependence Between Groups of Multivariate Time Series: Frequency Decomposition, arXiv:0711.1455 [stat.ME], Available online at: (2007) http:// arxiv.org/abs/0711.1455. R.W. Sperry, Neural basis of the spontaneous optokinetic response produced by visual inversion, J. Comp. Physiol. Psychol. 43 (1950) 482–489, https://doi.org/10. 1037/h0055479. D.M. Wolpert, Z. Ghahramani, M.I. Jordan, An internal model for sensorimotor integration, Science. 269 (1995) 1880–1882, https://doi.org/10.1126/science. 7569931. E. Von Holst, H. Mittelstaedt, (1950/1973) Daz Reafferezprincip. Wechselwirkungen zwischen Zentralnerven-system und Peripherie, Naturwissenschaften 37 (1950) 467–476. The reference principle. In: The behavioral physiology of animals and man. The collected papers of Erich von Holst. Martin R (translator) University of Miami Press, Coral Gables, Florida, pp. 139–173.. F. Debaere, N. Wenderoth, S. Sunaert, P. Van Hecke, S.P. Swinnen, Internal vs external generation of movements: differential neural pathways involved in bimanual coordination performed in the presence or absence of augmented visual feedback, Neuroimage 19 (2003) 764–776, https://doi.org/10.1016/S1053-8119(03) 00148-4. N. Picard, P.L. Strick, Imaging the premotor areas, Curr. Opin. Neurobiol. 11 (2001) 663–672, https://doi.org/10.1016/S0959-4388(01)00266-5. T. Paus, M. Petrides, A.C. Evans, E. Meyer, Role of the human anterior cingulate cortex in the control of oculomotor, manual, and speech responses: a positron emission tomography study, J. Neurophysiol. 70 (1993) 453–469, https://doi.org/ 10.1152/jn.1993.70.2.453. R.E. Passingham, S.L. Bengtsson, H.C. Lau, Medial frontal cortex: from self-generated action to reflection on one’s own performance, Trends Cogn. Sci. (Regul. Ed.)
Neuroscience Letters 698 (2019) 19–26
O. Katayama et al.
14 (2010) 16–21, https://doi.org/10.1016/j.tics.2009.11.001. [30] F. Bonini, B. Burle, C. Liegeois-Chauvel, J. Regis, P. Chauvel, F. Vidal, Action monitoring and medial frontal cortex: leading role of supplementary motor area, Science 343 (2014) 888–891, https://doi.org/10.1126/science.1247412. [31] J. Tanji, Sequential organization of multiple movements: involvement of cortical motor areas, Annu. Rev. Neurosci. 24 (2001) 631–651, https://doi.org/10.1146/ annurev.neuro.24.1.631. [32] D.J. Serrien, P. Brown, The functional role of interhemispheric synchronization in the control of bimanual timing task, Exp. Brain Res. 147 (2002) 268–272, https:// doi.org/10.1007/s00221-002-1253-z. [33] E. Formaggio, S.F. Storti, I. Boscolo Galazzo, M. Gandolfi, C. Geroin, N. Smania, L. Spezia, A. Waldner, A. Fiaschi, P. Manganotti, Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements, J. Neuroeng. Rehabil. 26 (2013) 24, https://doi.org/10.1186/1743-0003-10-24. [34] P. Brown, Cortical drives to human muscle: the piper and related rhythms, Prog. Neurobiol. 60 (2000) 97–108, https://doi.org/10.1016/S0301-0082(99)00029-5. [35] J. Decety, J.A. Sommerville, Shared representations between self and other: a social
[36]
[37]
[38]
[39]
[40]
26
cognitive neuroscience view, Trends Cogn. Sci. (Regul. Ed.) 7 (2003) 527–533, https://doi.org/10.1016/j.tics.2003.10.004. J.R. Augustine, Circuitry and functional aspects of the insular lobe in primates including humans, Brain Res. Rev. 22 (1996) 229–244, https://doi.org/10.1016/ S0165-0173(96)00011-2. F.G. Flynn, D.F. Benson, A. Ardila, Anatomy of the insula functional and clinical correlates, Aphasiology 13 (1999) 55–78, https://doi.org/10.1080/ 026870399402325. C. Farrer, C.D. Frith, Experiencing oneself vs another person as being the cause of an action: the neural correlates of the experience of agency, Neuroimage 15 (2002) 596–603, https://doi.org/10.1006/nimg.2001.1009. D. Balslev, F.A. Nielsen, O.B. Paulson, I. Law, Right temporoparietal cortex activation during visuo-proprioceptive conflict, Cereb. Cortex 15 (2005) 166–169, https://doi.org/10.1093/cercor/bhh119. A. Brovelli, M. Ding, A. Ledberg, Y. Chen, R. Nakamura, S.L. Bressler, Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality, Proc. Natl. Acad. Sci. U. S. A. 101 (2004) 9849–9854, https://doi.org/10.1073/pnas.0308538101.