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Repetition suppression in the cortical motor and auditory systems resemble each other – A combined TMS and evoked potential study
Neuroscience 243 (2013) 40–45
REPETITION SUPPRESSION IN THE CORTICAL MOTOR AND AUDITORY SYSTEMS RESEMBLE EACH OTHER – A COMBINED TMS AND EVOKED POTENTIAL STUDY O. LO¨FBERG, a* P. JULKUNEN, a P. TIIHONEN, a A. PA¨A¨KKO¨NEN a AND J. KARHU a,b
responses at 100 ms after any sound decrease in amplitude and habituate (Fruhstorfer, 1971). RS is an essential factor in controlling the visual memory (Desimone, 1996), level of arousal and sensory memory trace of acoustic surroundings (Na¨a¨ta¨nen and Picton, 1987). Impairment of RS has been demonstrated, e.g., in schizophrenia (Moriwaki et al., 2009) and in ageassociated memory impairment (Soininen et al., 1995). The neuronal mechanisms of RS and arousal control are not yet resolved (Rankin et al., 2009) though multiple models have been suggested, including the neuronal fatigue model, the sharpening model and the facilitation model (Grill-Spector et al., 2006). The fatigue model explains the RS with simple suppression of neuronal response to a repeated stimulus via adaptation (Miller and Desimone, 1994), while according to the sharpening model the size of the neuron population which responds to a stimulus is optimized as the stimulus is repeated (Wiggs and Martin, 1998). The facilitation model suggests that cortical areas upstream from the primary receiving cortex start processing the repeated stimulus faster and the required neuronal firing-time in receiving cortex shortens (Friston, 2005). Although all of these models have gained support, the fatigue model fails to explain the performance enhancing effect of repetition (Grill-Spector et al., 2006). Recently, RS has been demonstrated to occur also during mental imaging of movement (Hohlefeld et al., 2011). Blood-oxygen-level-dependent contrast suppression has been shown during repetition of hand gestures in functional MRI (Hamilton and Grafton, 2009). Although these studies suggest that RS could have a role in motor control, they do not provide undeniable evidence of causal relation between neuroimaging data and cortical motor output. Transcranial magnetic stimulation (TMS) is a noninvasive method for direct cortical stimulation (Barker et al., 1985). It enables examination of causality between artificial activation of a cortical area e.g., muscle contraction recorded in the periphery (Barker, 1991). If a weaker or conditioning TMS stimulus is given 1–300 ms prior to the test stimulus, the effect of the test stimulus is facilitated or inhibited depending on the interstimulus interval (ISI) (Di Lazzaro et al., 2004; Ferreri et al., 2011). Long-term modulation of cortical excitability can be induced with repetitive transcranial magnetic stimulation (rTMS). High-frequency rTMS is known to increase the cortical excitability, but the evidence of the inhibitory effect of low-frequency rTMS
a Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland b
Nexstim Oy, Helsinki, Finland
Abstract—Repetition suppression (RS) in cortical sensory systems optimizes the size of neuronal ensemble reacting to repetitive stimuli such as sounds. Recently RS has also been demonstrated to occur with mental imaging of movement. We studied the existence of RS in the motor system using transcranial magnetic stimulation (TMS). Six healthy subjects participated in this study. TMS was focused on the primary motor cortex with neuronavigation and RS was studied by measuring the motor-evoked potentials from the contralateral first dorsal interosseous muscle. At the same time, we measured TMS-induced cortical responses using electroencephalography (EEG). For a comparison baseline, we evaluated RS by recording EEG responses to sounds with the same stimulation protocol as with TMS. Each stimulus train included four identical stimuli repeated at 1-s intervals, and the stimulation trains were repeated at 20-s intervals. The response amplitude was reduced significantly (p < .01) after the first stimulus in all stimulus trains. This suggests that RS may be a general mechanism for adaptation of neuronal population responses in the human cortex. Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved.
Key words: transcranial magnetic stimulation, neurophysiology, repetition suppression, evoked potentials.
INTRODUCTION Repetition suppression (RS) (Grill-Spector et al., 2006) or habituation (Groves and Richard, 1970; Rankin et al., 2009) of auditory-evoked potentials (AEPs) and the initial startle reaction have been widely studied (Fruhstorfer, 1971; Na¨a¨ta¨nen and Picton, 1987). As the stimulus is repeated, AEPs and especially the N100 *Corresponding author. Address: Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 1777, FI-70211 Kuopio, Finland. Tel: +358-405389077. E-mail address: [email protected].fi (O. Lo¨fberg). Abbreviations: AEP, auditory-evoked potential; EEG, electroencephalography; EMG, electromyography; EOG, electrooculogram; FDI, first dorsal interosseous; ISI, inter-stimulus interval; ITI, inter-train interval; MEP, motor-evoked potential; nTMS, navigated transcranial magnetic stimulation; rMT, resting motor threshold; RS, repetition suppression; rTMS, repetitive transcranial magnetic stimulation; TMS, transcranial magnetic stimulation.
0306-4522/13 $36.00 Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neuroscience.2013.03.060 40
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on excitability still remains controversial (Fitzgerald et al., 2006). The mechanism behind rTMS is thought to be the modulation of interneuron circuits of the cortex (Fitzgerald et al., 2006). Brasil-Neto et al. have demonstrated postexercise decrement of motor-evoked potentials (MEPs) with 0.3-Hz TMS after a 30-s voluntary contraction of the target muscle. The responsible mechanism was suggested to be central fatigue in the motor pathways (Brasil-Neto et al., 1994). Our aim was to study with TMS whether the output of central motor network is optimized by RS. We hypothesized that RS could be a part of motor control mechanisms. This would indicate that RS is a general mechanism of the brain for adapting cortical responses to both internal and external stimuli.
EXPERIMENTAL PROCEDURES Subjects, equipment setup and study protocol Six right-handed healthy subjects (five male and one female) aged 22–58 years participated in the study. The study was conducted in accordance with the Declaration of Helsinki and all procedures were conducted with the adequate understanding and with consent of the subjects. Each subject was scanned beforehand with a Siemens Magnetom Avanto (Erlangen, Germany) 1.5 T scanner to receive T1-weighted high-resolution 3D MRimages for the navigated transcranial magnetic stimulation (nTMS) (Ruohonen and Karhu, 2010) (Fig. 1). We measured RS of the cortical N100 responses with electroencephalography (EEG) (Na¨a¨ta¨nen and Picton, 1987). For this, the hearing threshold for each subject was determined by gradually decreasing the sound intensity until the subject could not hear it anymore. The duration of each 800-Hz tone was 84 ms including 7-ms rise and fall times and the ISI between the pulses was 1 s. Then, we utilized a standard protocol in studying
auditory habituation. Tones were delivered to the subject’s right ear at 60 dB above the hearing threshold. The paradigm comprised of 160 tones (800 Hz) in 40 trains, four tones within a train. The inter-train interval (ITI) was 20 s while the ISI between the tones within a train was 1 s (Furubayashi et al., 2000). The subjects listened passively and watched a silent video to occupy their attention. Neuroscan Stim Audio System P/N 1105 was used for auditory stimulation. During the auditory habituation study, EEG was recorded with a 60-channel TMS compatible EEG device (Nexstim Oy, Helsinki, Finland). The EEG was recorded with a 1450-Hz sampling frequency and 16-bit precision and 350-Hz hardware low-pass cut-off. The EEG electrodes were referenced to an electrode placed on the right mastoid. Vertical electro-oculogram (EOG) was recorded from electrodes placed above and below the right eye. Ag/ AgCl-electrodes were used with EEG. The nTMS setup consisted of a navigation system, a stimulator and a figure-of-eight TMS-coil with biphasic pulse-form (Nexstim Oy, Helsinki, Finland). During nTMS, electromyography (EMG) was recorded to store muscle activity online with a system-integrated EMG-device at a 3-kHz sampling rate. EMG was measured from pre-gelled disposable Ag/AgCl electrodes attached to the right first dorsal interosseous (FDI) muscle. The TMS-induced MEPs were measured from the resting muscle EMG as peak-to-peak response between the highest positive and negative deflection from the baseline (Fig. 1). After the auditory experiment, the primary motor cortex area was comprehensively mapped to locate the optimal cortical representation area of FDI muscle (Fig. 1) by finding the highest amplitude MEP. A compound muscle action potential with amplitude greater than 50 lV was taken as a reliable EMG response (Rossini et al., 1994). The resting motor threshold (rMT) intensity was determined for each subject at the mapped stimulus
TMS 1st stimulus
100%
2nd stimulus
46%
3rd stimulus
58%
4th stimulus
58%
600 µV 50 ms
Fig. 1. The right hand representation area on the left hemisphere was mapped using neuronavigated TMS. The location inducing the highest amplitude motor-evoked potential (MEP) in one subject that is represented as a turquoise circle, and the mapped area is outlined with a yellow line. White-dashed line indicates the central sulcus. On the right, average MEP responses (black line) within the stimulation train are presented for the same subject. The gray area represents standard deviation of the repeated MEPs. Percentages on the right are the normalized MEP amplitudes with respect to the mean of first MEPs within the stimulus trains. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
O. Lo¨fberg et al. / Neuroscience 243 (2013) 40–45 Auditive N100
A
F = 7.38, p = .003
N100 amplitude (-)
normalized to 1st stimulus
1.0 0.8
**
0.6
**
*
0.4 0.2
*p <.05 **p <.01
0.0 1st
2nd
3rd
4th
Stimulus order TMS N100
B
F = 14.13, p < .001
1.0
N100 amplitude (-)
target. For that we used threshold hunting paradigm (Awiszus, 2003) applying Motor Threshold Assessment Tool (MTAT 2.0) with 20 stimuli (Awiszus and Borckardt, 2012). Interval between the consecutive stimuli was 5–10 s. After determining the rMT, we conducted a stimulation protocol with TMS using 120 stimuli at 120% of the rMT. The stimuli were given in trains of four stimuli with ISI of 1 s and ITI of 20 s, similar to that used in the auditory stimulation. During this, EEG and EMG responses were recorded. MEPs with preceding muscle activity were rejected from the analysis. The subjects were instructed to keep their hands at rest and not to focus their attention on the stimulation or muscle contraction. During the nTMS experiment, the subjects watched a muted video to occupy their attention. Recorded EEGs were processed by using MATLAB 7.2 (MathWorks Inc., Natick, MA, USA). The continuous EEG was band-pass filtered at 2–40 Hz and segmented to 500-ms epochs. The epochs were averaged with a 100-ms pre-stimulus baseline. Epochs with blinkartefacts were removed from the data. From the EEG data, we only utilized the channels FCz or Cz depending on which channel best showed the N100 response to the stimulus (auditory or TMS) in each subject. The N100 amplitude in the EEG was measured as peak-to-peak amplitude between the N100 and following positive deflection.
normalized to 1st stimulus
42
Statistical analyses
0.8 0.6 0.4 0.2
***
***
**
**p <.01 ***p <.001
0.0
RS was evident in the cortical N100 response to auditory stimuli. The N100 response exhibited a 31–37% decrease in amplitude from the first to the subsequent stimuli in the train (Fig. 2A). Similar observation was also made in the cortical N100 response (Nikouline et al., 1999) measured during the TMS protocol (Fig. 2B). The N100 amplitude in the TMS experiment decreased by 43–58% from the first to the subsequent stimuli in the train. Most importantly, we also observed RS in the MEPs (Fig. 2C). The MEPs of the second, third and fourth stimulus in the train were 42–46% lower in amplitude than that of the first stimulus. The effect was similar in all subjects for both the N100 and MEP responses. In a control measurement with peripheral medianus nerve stimulation with constant current, we found no such effect in the measured thenar muscle responses. The control measurement was conducted afterward with six healthy subjects (three of whom were not part of the original study population).
2nd
3rd
4th
Stimulus order TMS MEP
C
F = 12.28, p < .001
1.0
MEP amplitude (-)
RESULTS
1st
normalized to 1st stimulus
Average response amplitude (MEP, TMS N100 or auditory N100) from each subject was utilized in the statistical analysis as the dependent variable. By using general linear model, we analyzed the fixed effect of stimulus order on each of the dependent variables. To account for the inter-individual variation, we used subject identifier as the random factor. Post-hoc analysis with Sidak adjustment was used to test the differences between the different stimuli within a stimulation train. We considered p < .05 as statistically significant difference.
0.8 0.6
***
***
***
0.4 0.2
***p <.001 0.0 1st
2nd
3rd
4th
Stimulus order Fig. 2. Response amplitudes during the habituation test routines with respect to the first response of each stimulus train: during auditory stimulation observed in cortical N100 amplitude (A), during TMS observed in cortical N100 amplitude (B) and during TMS observed in MEPs of the contralateral hand (C). The effect size and p-value has been indicated with pair-wise post hoc comparison p-values.
DISCUSSION We studied RS, previously well documented in the auditory system, in the motor system by focusing nTMS on the primary motor cortex, more specifically on the exact individual representation area of the right-hand FDI muscle. We found that RS in the motor system was
O. Lo¨fberg et al. / Neuroscience 243 (2013) 40–45
very similar to that of the auditory N100 responses, as indicated by reduced MEP amplitudes. This novel finding is crucial to understanding the general control mechanisms of the motor cortex. Decrement of MEPs has been previously demonstrated to occur after voluntary muscle contraction lasting 30 s, (Brasil-Neto et al., 1994) but decrement of MEPs during 1-Hz stimulation of a resting unexercised muscle has not been shown. Peripheral causes for MEP decrement were ruled out with a control measurement with electric stimulation of the medianus nerve. It showed no such effect, which is in line with a previous study of BrasilNeto et al. (1994). As demonstrated, RS of MEPs takes place in the central motor network, but the mechanism for it cannot be yet explained unequivocally. It appears that like more complex sensory networks, basic motor network in M1 is able to habituate to repeated stimuli. The recorded suppression of MEPs (Fig. 2C) and TMS–EEG activity (Fig. 2B) resembles the auditory habituation (Fig. 2A), which suggests that they all share same neuronal mechanisms of suppression. Hohlefeld et al. (2011) suggested that RS might be modulated by intention of action rather than by motor programming. However, intention cannot play any role in explaining our result of significant suppression in MEPs elicited by artificial stimulation of M1. Neuronal fatigue and hyperpolarization of individual neurons are unlikely to cause suppression after only one stimulus and ISI of 1000 ms (Brasil-Neto et al., 1993). Hypothetically, the suprathreshold TMS may modulate the release of synaptic transmitters of the motor cortex and hence affect the cortical excitability.
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The possibility that the suppression is induced by TMS modulation can be excluded. The decrease in amplitude of the second MEP resembles a paired-pulse phenomenon, but such a phenomenon should not occur with ISIs longer than 300 ms (Di Lazzaro et al., 2004; Ferreri et al., 2011). It is important to differentiate RS, caused by fast intrinsic neuronal mechanisms, from artificial modulation induced by trains of rTMS. rTMS at 1 Hz is demonstrated to have an inhibitory effect on cortical excitability after a train of multiple stimulations but not with 1–2 stimuli (Chen et al., 1997; Fierro et al., 2001; Inghilleri et al., 2006). Pascual-Leone et al. observed an increase of MEP amplitudes during rTMS at 5 Hz. However, they did not observe any such modulatory phenomenon at 1-Hz rTMS at any stimulation intensity (Pascual-Leone et al., 1994). The presented results imply a trend towards recovery of responses at the fourth stimulus (Fig 2A–C). However the trend was not statistically verified. Future effort should be put in investigating the duration of the RS effect and recovery of MEP and TMS-induced N100 amplitudes with longer stimulation trains. With auditory N100 habituation such a recovery trend does not exist (Fruhstorfer, 1971). One limitation considering the TMS-induced N100 responses should be addressed. We did not include noise masking in our study to avoid auditory cortical response to coil-induced clicks (Nikouline et al., 1999). Our reason for this was to avoid affecting the cortical excitability with monotonous sound. Although the subjects wore ear-plugs, click sound may still have affected our finding with TMS N100. Nikouline et al. (1999) have shown that acoustic clicks from TMS coil
Fig. 3. Comparison of the spatial distributions of TMS-induced N100 and auditory N100 cortical potentials after the first and second stimulus within the stimulus train. The spatial distributions are presented as means over all subjects. The TMS-induced N100 potential was normalized such that the global-field-power of the TMS N100 response was matched with that of the auditory N100. The difference of the two spatial distributions of the cortical potentials demonstrates the interhemispheric asymmetry present in TMS-induced N100 responses. Notice that the cortical N100 potentials were lower globally between the first and the second stimuli. The white dot represents the stimulation site. The auditory stimulus was given to the right ear.
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cause large AEPs, although this effect can be reduced by the use of ear-plugs. However, it has been demonstrated that distinct TMS-induced N100 responses do occur unrelated to coil click (Nikulin et al., 2003). To further investigate the differences between the auditory N100 and TMS-induced N100, we looked at the spatial distribution of the cortical potentials at the N100 timepoint. TMS-induced N100 responses were clearly lateralized to the stimulated hemisphere whereas auditory N100 showed no lateralization (Fig. 3). Nevertheless, auditory habituation to TMS coil-clicks (Fig. 2B) might alter the excitability of the motor cortex or that of subcortical motor ensembles contributing to RS behavior. A loud auditory stimulus has been demonstrated to suppress the excitability of motor cortex for a period of 30–50 ms (Furubayashi et al., 2000). On the other hand, the amplitudes of N100 potentials elicited by auditory ‘‘Go’’ stimuli in a Go/NoGo task have been shown to correlate with shorter reaction times indicating a facilitating link between the sensory and motor systems (Karlin et al., 1971). Thus intermodal factors cannot be excluded when discussing motor RS. RS in the cortical motor system is probably caused by immediate feedback from cortical network nodes which control motor execution; when the size of the neuron population recruited by TMS is optimized by feedback during the stimulation train, the movement can be executed with a lower number of firing neurons (Wiggs and Martin, 1998) This would be in line with the sharpening model of perceptual priming (Wiggs and Martin, 1998) and explain the similarity between RS in the cortical motor and auditory systems. On the other hand, negative feedback from other associated motor – or sensory – areas could increase the intracortical inhibition after a movement executed without prefrontal planning (Herrero et al., 2002; Stinear and Byblow, 2003; Friston, 2005) and could thereby suppress the excitability of M1 to prevent meaningless movements. Movement causes afferent sensory feedback to the primary somatosensory cortex (S1) (Huttunen and Lauronen, 2012). Recent imaging studies of sensorimotor cortico-cortical connectivity provide anatomical substrate for direct and fast feedback of individual muscle movements and positions required, e.g., for grasping (Catani et al., 2012). It is possible that feedback from the S1 could modulate the M1 and therefore suppress or sharpen its activity. However, we consider it unlikely that a fast sensory feedback mechanism would play a major role in motor RS after a relatively long interval of 1000 ms (Huttunen and Lauronen, 2012). Although our experimental group consisted of only six subjects the statistical power of the finding was strong. In the future we intend to expand the study with more subjects and a patient group suffering from a disease affecting the cortical motor system, e.g., stroke. Stroke causes changes in cortical excitability and transcallosal inhibition (Liepert et al., 2000). We are interested in how stroke affects motor RS and are the changes reversible after stroke recovery (Ward and Cohen, 2004). We
should also attempt to modulate RS by parallel stimuli for instance to further investigate the relation between the auditory and motor cortex excitability, and to study the focality of the RS effect in the motor system when induced with TMS.
CONCLUSION We have demonstrated the RS of TMS induced MEPs and cortical responses. These resemble the known suppression of auditory stimulus induced cortical responses through RS. Thus we hypothesize that RS could be a general mechanism for adapting neuronal population responses in the cortex, which is not limited to perception and control of the cortical input. However, further studies are required to verify our hypothesis of a general RS mechanism.
CONFLICT OF INTEREST P.J. has received consulting fees from Nexstim Oy prior to the year 2012, unrelated to this study. J.K. works as parttime chief medical officer at Nexstim Oy, manufacturer of navigated TMS systems. Acknowledgements—Funding from Kuopio University Hospital (EVO 5041730), Orion-Farmos Research Foundation and Emil Aaltonen Foundation is acknowledged.
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(Accepted 30 March 2013) (Available online 6 April 2013)
REPETITION SUPPRESSION IN THE CORTICAL MOTOR AND AUDITORY SYSTEMS RESEMBLE EACH OTHER – A COMBINED TMS AND EVOKED POTENTIAL STUDY O. LO¨FBERG, a* P. JULKUNEN, a P. TIIHONEN, a A. PA¨A¨KKO¨NEN a AND J. KARHU a,b
responses at 100 ms after any sound decrease in amplitude and habituate (Fruhstorfer, 1971). RS is an essential factor in controlling the visual memory (Desimone, 1996), level of arousal and sensory memory trace of acoustic surroundings (Na¨a¨ta¨nen and Picton, 1987). Impairment of RS has been demonstrated, e.g., in schizophrenia (Moriwaki et al., 2009) and in ageassociated memory impairment (Soininen et al., 1995). The neuronal mechanisms of RS and arousal control are not yet resolved (Rankin et al., 2009) though multiple models have been suggested, including the neuronal fatigue model, the sharpening model and the facilitation model (Grill-Spector et al., 2006). The fatigue model explains the RS with simple suppression of neuronal response to a repeated stimulus via adaptation (Miller and Desimone, 1994), while according to the sharpening model the size of the neuron population which responds to a stimulus is optimized as the stimulus is repeated (Wiggs and Martin, 1998). The facilitation model suggests that cortical areas upstream from the primary receiving cortex start processing the repeated stimulus faster and the required neuronal firing-time in receiving cortex shortens (Friston, 2005). Although all of these models have gained support, the fatigue model fails to explain the performance enhancing effect of repetition (Grill-Spector et al., 2006). Recently, RS has been demonstrated to occur also during mental imaging of movement (Hohlefeld et al., 2011). Blood-oxygen-level-dependent contrast suppression has been shown during repetition of hand gestures in functional MRI (Hamilton and Grafton, 2009). Although these studies suggest that RS could have a role in motor control, they do not provide undeniable evidence of causal relation between neuroimaging data and cortical motor output. Transcranial magnetic stimulation (TMS) is a noninvasive method for direct cortical stimulation (Barker et al., 1985). It enables examination of causality between artificial activation of a cortical area e.g., muscle contraction recorded in the periphery (Barker, 1991). If a weaker or conditioning TMS stimulus is given 1–300 ms prior to the test stimulus, the effect of the test stimulus is facilitated or inhibited depending on the interstimulus interval (ISI) (Di Lazzaro et al., 2004; Ferreri et al., 2011). Long-term modulation of cortical excitability can be induced with repetitive transcranial magnetic stimulation (rTMS). High-frequency rTMS is known to increase the cortical excitability, but the evidence of the inhibitory effect of low-frequency rTMS
a Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland b
Nexstim Oy, Helsinki, Finland
Abstract—Repetition suppression (RS) in cortical sensory systems optimizes the size of neuronal ensemble reacting to repetitive stimuli such as sounds. Recently RS has also been demonstrated to occur with mental imaging of movement. We studied the existence of RS in the motor system using transcranial magnetic stimulation (TMS). Six healthy subjects participated in this study. TMS was focused on the primary motor cortex with neuronavigation and RS was studied by measuring the motor-evoked potentials from the contralateral first dorsal interosseous muscle. At the same time, we measured TMS-induced cortical responses using electroencephalography (EEG). For a comparison baseline, we evaluated RS by recording EEG responses to sounds with the same stimulation protocol as with TMS. Each stimulus train included four identical stimuli repeated at 1-s intervals, and the stimulation trains were repeated at 20-s intervals. The response amplitude was reduced significantly (p < .01) after the first stimulus in all stimulus trains. This suggests that RS may be a general mechanism for adaptation of neuronal population responses in the human cortex. Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved.
Key words: transcranial magnetic stimulation, neurophysiology, repetition suppression, evoked potentials.
INTRODUCTION Repetition suppression (RS) (Grill-Spector et al., 2006) or habituation (Groves and Richard, 1970; Rankin et al., 2009) of auditory-evoked potentials (AEPs) and the initial startle reaction have been widely studied (Fruhstorfer, 1971; Na¨a¨ta¨nen and Picton, 1987). As the stimulus is repeated, AEPs and especially the N100 *Corresponding author. Address: Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 1777, FI-70211 Kuopio, Finland. Tel: +358-405389077. E-mail address: [email protected].fi (O. Lo¨fberg). Abbreviations: AEP, auditory-evoked potential; EEG, electroencephalography; EMG, electromyography; EOG, electrooculogram; FDI, first dorsal interosseous; ISI, inter-stimulus interval; ITI, inter-train interval; MEP, motor-evoked potential; nTMS, navigated transcranial magnetic stimulation; rMT, resting motor threshold; RS, repetition suppression; rTMS, repetitive transcranial magnetic stimulation; TMS, transcranial magnetic stimulation.
0306-4522/13 $36.00 Ó 2013 IBRO. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neuroscience.2013.03.060 40
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on excitability still remains controversial (Fitzgerald et al., 2006). The mechanism behind rTMS is thought to be the modulation of interneuron circuits of the cortex (Fitzgerald et al., 2006). Brasil-Neto et al. have demonstrated postexercise decrement of motor-evoked potentials (MEPs) with 0.3-Hz TMS after a 30-s voluntary contraction of the target muscle. The responsible mechanism was suggested to be central fatigue in the motor pathways (Brasil-Neto et al., 1994). Our aim was to study with TMS whether the output of central motor network is optimized by RS. We hypothesized that RS could be a part of motor control mechanisms. This would indicate that RS is a general mechanism of the brain for adapting cortical responses to both internal and external stimuli.
EXPERIMENTAL PROCEDURES Subjects, equipment setup and study protocol Six right-handed healthy subjects (five male and one female) aged 22–58 years participated in the study. The study was conducted in accordance with the Declaration of Helsinki and all procedures were conducted with the adequate understanding and with consent of the subjects. Each subject was scanned beforehand with a Siemens Magnetom Avanto (Erlangen, Germany) 1.5 T scanner to receive T1-weighted high-resolution 3D MRimages for the navigated transcranial magnetic stimulation (nTMS) (Ruohonen and Karhu, 2010) (Fig. 1). We measured RS of the cortical N100 responses with electroencephalography (EEG) (Na¨a¨ta¨nen and Picton, 1987). For this, the hearing threshold for each subject was determined by gradually decreasing the sound intensity until the subject could not hear it anymore. The duration of each 800-Hz tone was 84 ms including 7-ms rise and fall times and the ISI between the pulses was 1 s. Then, we utilized a standard protocol in studying
auditory habituation. Tones were delivered to the subject’s right ear at 60 dB above the hearing threshold. The paradigm comprised of 160 tones (800 Hz) in 40 trains, four tones within a train. The inter-train interval (ITI) was 20 s while the ISI between the tones within a train was 1 s (Furubayashi et al., 2000). The subjects listened passively and watched a silent video to occupy their attention. Neuroscan Stim Audio System P/N 1105 was used for auditory stimulation. During the auditory habituation study, EEG was recorded with a 60-channel TMS compatible EEG device (Nexstim Oy, Helsinki, Finland). The EEG was recorded with a 1450-Hz sampling frequency and 16-bit precision and 350-Hz hardware low-pass cut-off. The EEG electrodes were referenced to an electrode placed on the right mastoid. Vertical electro-oculogram (EOG) was recorded from electrodes placed above and below the right eye. Ag/ AgCl-electrodes were used with EEG. The nTMS setup consisted of a navigation system, a stimulator and a figure-of-eight TMS-coil with biphasic pulse-form (Nexstim Oy, Helsinki, Finland). During nTMS, electromyography (EMG) was recorded to store muscle activity online with a system-integrated EMG-device at a 3-kHz sampling rate. EMG was measured from pre-gelled disposable Ag/AgCl electrodes attached to the right first dorsal interosseous (FDI) muscle. The TMS-induced MEPs were measured from the resting muscle EMG as peak-to-peak response between the highest positive and negative deflection from the baseline (Fig. 1). After the auditory experiment, the primary motor cortex area was comprehensively mapped to locate the optimal cortical representation area of FDI muscle (Fig. 1) by finding the highest amplitude MEP. A compound muscle action potential with amplitude greater than 50 lV was taken as a reliable EMG response (Rossini et al., 1994). The resting motor threshold (rMT) intensity was determined for each subject at the mapped stimulus
TMS 1st stimulus
100%
2nd stimulus
46%
3rd stimulus
58%
4th stimulus
58%
600 µV 50 ms
Fig. 1. The right hand representation area on the left hemisphere was mapped using neuronavigated TMS. The location inducing the highest amplitude motor-evoked potential (MEP) in one subject that is represented as a turquoise circle, and the mapped area is outlined with a yellow line. White-dashed line indicates the central sulcus. On the right, average MEP responses (black line) within the stimulation train are presented for the same subject. The gray area represents standard deviation of the repeated MEPs. Percentages on the right are the normalized MEP amplitudes with respect to the mean of first MEPs within the stimulus trains. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
O. Lo¨fberg et al. / Neuroscience 243 (2013) 40–45 Auditive N100
A
F = 7.38, p = .003
N100 amplitude (-)
normalized to 1st stimulus
1.0 0.8
**
0.6
**
*
0.4 0.2
*p <.05 **p <.01
0.0 1st
2nd
3rd
4th
Stimulus order TMS N100
B
F = 14.13, p < .001
1.0
N100 amplitude (-)
target. For that we used threshold hunting paradigm (Awiszus, 2003) applying Motor Threshold Assessment Tool (MTAT 2.0) with 20 stimuli (Awiszus and Borckardt, 2012). Interval between the consecutive stimuli was 5–10 s. After determining the rMT, we conducted a stimulation protocol with TMS using 120 stimuli at 120% of the rMT. The stimuli were given in trains of four stimuli with ISI of 1 s and ITI of 20 s, similar to that used in the auditory stimulation. During this, EEG and EMG responses were recorded. MEPs with preceding muscle activity were rejected from the analysis. The subjects were instructed to keep their hands at rest and not to focus their attention on the stimulation or muscle contraction. During the nTMS experiment, the subjects watched a muted video to occupy their attention. Recorded EEGs were processed by using MATLAB 7.2 (MathWorks Inc., Natick, MA, USA). The continuous EEG was band-pass filtered at 2–40 Hz and segmented to 500-ms epochs. The epochs were averaged with a 100-ms pre-stimulus baseline. Epochs with blinkartefacts were removed from the data. From the EEG data, we only utilized the channels FCz or Cz depending on which channel best showed the N100 response to the stimulus (auditory or TMS) in each subject. The N100 amplitude in the EEG was measured as peak-to-peak amplitude between the N100 and following positive deflection.
normalized to 1st stimulus
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Statistical analyses
0.8 0.6 0.4 0.2
***
***
**
**p <.01 ***p <.001
0.0
RS was evident in the cortical N100 response to auditory stimuli. The N100 response exhibited a 31–37% decrease in amplitude from the first to the subsequent stimuli in the train (Fig. 2A). Similar observation was also made in the cortical N100 response (Nikouline et al., 1999) measured during the TMS protocol (Fig. 2B). The N100 amplitude in the TMS experiment decreased by 43–58% from the first to the subsequent stimuli in the train. Most importantly, we also observed RS in the MEPs (Fig. 2C). The MEPs of the second, third and fourth stimulus in the train were 42–46% lower in amplitude than that of the first stimulus. The effect was similar in all subjects for both the N100 and MEP responses. In a control measurement with peripheral medianus nerve stimulation with constant current, we found no such effect in the measured thenar muscle responses. The control measurement was conducted afterward with six healthy subjects (three of whom were not part of the original study population).
2nd
3rd
4th
Stimulus order TMS MEP
C
F = 12.28, p < .001
1.0
MEP amplitude (-)
RESULTS
1st
normalized to 1st stimulus
Average response amplitude (MEP, TMS N100 or auditory N100) from each subject was utilized in the statistical analysis as the dependent variable. By using general linear model, we analyzed the fixed effect of stimulus order on each of the dependent variables. To account for the inter-individual variation, we used subject identifier as the random factor. Post-hoc analysis with Sidak adjustment was used to test the differences between the different stimuli within a stimulation train. We considered p < .05 as statistically significant difference.
0.8 0.6
***
***
***
0.4 0.2
***p <.001 0.0 1st
2nd
3rd
4th
Stimulus order Fig. 2. Response amplitudes during the habituation test routines with respect to the first response of each stimulus train: during auditory stimulation observed in cortical N100 amplitude (A), during TMS observed in cortical N100 amplitude (B) and during TMS observed in MEPs of the contralateral hand (C). The effect size and p-value has been indicated with pair-wise post hoc comparison p-values.
DISCUSSION We studied RS, previously well documented in the auditory system, in the motor system by focusing nTMS on the primary motor cortex, more specifically on the exact individual representation area of the right-hand FDI muscle. We found that RS in the motor system was
O. Lo¨fberg et al. / Neuroscience 243 (2013) 40–45
very similar to that of the auditory N100 responses, as indicated by reduced MEP amplitudes. This novel finding is crucial to understanding the general control mechanisms of the motor cortex. Decrement of MEPs has been previously demonstrated to occur after voluntary muscle contraction lasting 30 s, (Brasil-Neto et al., 1994) but decrement of MEPs during 1-Hz stimulation of a resting unexercised muscle has not been shown. Peripheral causes for MEP decrement were ruled out with a control measurement with electric stimulation of the medianus nerve. It showed no such effect, which is in line with a previous study of BrasilNeto et al. (1994). As demonstrated, RS of MEPs takes place in the central motor network, but the mechanism for it cannot be yet explained unequivocally. It appears that like more complex sensory networks, basic motor network in M1 is able to habituate to repeated stimuli. The recorded suppression of MEPs (Fig. 2C) and TMS–EEG activity (Fig. 2B) resembles the auditory habituation (Fig. 2A), which suggests that they all share same neuronal mechanisms of suppression. Hohlefeld et al. (2011) suggested that RS might be modulated by intention of action rather than by motor programming. However, intention cannot play any role in explaining our result of significant suppression in MEPs elicited by artificial stimulation of M1. Neuronal fatigue and hyperpolarization of individual neurons are unlikely to cause suppression after only one stimulus and ISI of 1000 ms (Brasil-Neto et al., 1993). Hypothetically, the suprathreshold TMS may modulate the release of synaptic transmitters of the motor cortex and hence affect the cortical excitability.
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The possibility that the suppression is induced by TMS modulation can be excluded. The decrease in amplitude of the second MEP resembles a paired-pulse phenomenon, but such a phenomenon should not occur with ISIs longer than 300 ms (Di Lazzaro et al., 2004; Ferreri et al., 2011). It is important to differentiate RS, caused by fast intrinsic neuronal mechanisms, from artificial modulation induced by trains of rTMS. rTMS at 1 Hz is demonstrated to have an inhibitory effect on cortical excitability after a train of multiple stimulations but not with 1–2 stimuli (Chen et al., 1997; Fierro et al., 2001; Inghilleri et al., 2006). Pascual-Leone et al. observed an increase of MEP amplitudes during rTMS at 5 Hz. However, they did not observe any such modulatory phenomenon at 1-Hz rTMS at any stimulation intensity (Pascual-Leone et al., 1994). The presented results imply a trend towards recovery of responses at the fourth stimulus (Fig 2A–C). However the trend was not statistically verified. Future effort should be put in investigating the duration of the RS effect and recovery of MEP and TMS-induced N100 amplitudes with longer stimulation trains. With auditory N100 habituation such a recovery trend does not exist (Fruhstorfer, 1971). One limitation considering the TMS-induced N100 responses should be addressed. We did not include noise masking in our study to avoid auditory cortical response to coil-induced clicks (Nikouline et al., 1999). Our reason for this was to avoid affecting the cortical excitability with monotonous sound. Although the subjects wore ear-plugs, click sound may still have affected our finding with TMS N100. Nikouline et al. (1999) have shown that acoustic clicks from TMS coil
Fig. 3. Comparison of the spatial distributions of TMS-induced N100 and auditory N100 cortical potentials after the first and second stimulus within the stimulus train. The spatial distributions are presented as means over all subjects. The TMS-induced N100 potential was normalized such that the global-field-power of the TMS N100 response was matched with that of the auditory N100. The difference of the two spatial distributions of the cortical potentials demonstrates the interhemispheric asymmetry present in TMS-induced N100 responses. Notice that the cortical N100 potentials were lower globally between the first and the second stimuli. The white dot represents the stimulation site. The auditory stimulus was given to the right ear.
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cause large AEPs, although this effect can be reduced by the use of ear-plugs. However, it has been demonstrated that distinct TMS-induced N100 responses do occur unrelated to coil click (Nikulin et al., 2003). To further investigate the differences between the auditory N100 and TMS-induced N100, we looked at the spatial distribution of the cortical potentials at the N100 timepoint. TMS-induced N100 responses were clearly lateralized to the stimulated hemisphere whereas auditory N100 showed no lateralization (Fig. 3). Nevertheless, auditory habituation to TMS coil-clicks (Fig. 2B) might alter the excitability of the motor cortex or that of subcortical motor ensembles contributing to RS behavior. A loud auditory stimulus has been demonstrated to suppress the excitability of motor cortex for a period of 30–50 ms (Furubayashi et al., 2000). On the other hand, the amplitudes of N100 potentials elicited by auditory ‘‘Go’’ stimuli in a Go/NoGo task have been shown to correlate with shorter reaction times indicating a facilitating link between the sensory and motor systems (Karlin et al., 1971). Thus intermodal factors cannot be excluded when discussing motor RS. RS in the cortical motor system is probably caused by immediate feedback from cortical network nodes which control motor execution; when the size of the neuron population recruited by TMS is optimized by feedback during the stimulation train, the movement can be executed with a lower number of firing neurons (Wiggs and Martin, 1998) This would be in line with the sharpening model of perceptual priming (Wiggs and Martin, 1998) and explain the similarity between RS in the cortical motor and auditory systems. On the other hand, negative feedback from other associated motor – or sensory – areas could increase the intracortical inhibition after a movement executed without prefrontal planning (Herrero et al., 2002; Stinear and Byblow, 2003; Friston, 2005) and could thereby suppress the excitability of M1 to prevent meaningless movements. Movement causes afferent sensory feedback to the primary somatosensory cortex (S1) (Huttunen and Lauronen, 2012). Recent imaging studies of sensorimotor cortico-cortical connectivity provide anatomical substrate for direct and fast feedback of individual muscle movements and positions required, e.g., for grasping (Catani et al., 2012). It is possible that feedback from the S1 could modulate the M1 and therefore suppress or sharpen its activity. However, we consider it unlikely that a fast sensory feedback mechanism would play a major role in motor RS after a relatively long interval of 1000 ms (Huttunen and Lauronen, 2012). Although our experimental group consisted of only six subjects the statistical power of the finding was strong. In the future we intend to expand the study with more subjects and a patient group suffering from a disease affecting the cortical motor system, e.g., stroke. Stroke causes changes in cortical excitability and transcallosal inhibition (Liepert et al., 2000). We are interested in how stroke affects motor RS and are the changes reversible after stroke recovery (Ward and Cohen, 2004). We
should also attempt to modulate RS by parallel stimuli for instance to further investigate the relation between the auditory and motor cortex excitability, and to study the focality of the RS effect in the motor system when induced with TMS.
CONCLUSION We have demonstrated the RS of TMS induced MEPs and cortical responses. These resemble the known suppression of auditory stimulus induced cortical responses through RS. Thus we hypothesize that RS could be a general mechanism for adapting neuronal population responses in the cortex, which is not limited to perception and control of the cortical input. However, further studies are required to verify our hypothesis of a general RS mechanism.
CONFLICT OF INTEREST P.J. has received consulting fees from Nexstim Oy prior to the year 2012, unrelated to this study. J.K. works as parttime chief medical officer at Nexstim Oy, manufacturer of navigated TMS systems. Acknowledgements—Funding from Kuopio University Hospital (EVO 5041730), Orion-Farmos Research Foundation and Emil Aaltonen Foundation is acknowledged.
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(Accepted 30 March 2013) (Available online 6 April 2013)