Primary somatosensory contextual modulation is encoded by oscillation frequency change

Clinical Neurophysiology xxx (2015) xxx–xxx

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Primary somatosensory contextual modulation is encoded by oscillation frequency change T. Götz a,b,1, T. Milde c,1, G. Curio d, S. Debener e, T. Lehmann c, L. Leistritz c, O.W. Witte f,b, H. Witte c, J. Haueisen a,g,⇑ a

Biomagnetic Center, Hans Berger Department of Neurology, Jena University Hospital, Erlanger Allee 101, 07747 Jena, Germany Center for Sepsis Control and Care, Jena University Hospital, Erlanger Allee 101, 07747 Jena, Germany Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Bachstrasse 18, 07740 Jena, Germany d Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité – University Medicine Berlin, Hindenburgdamm 30, 12200 Berlin, Germany e Faculty VI, Department of Psychology, Neuropsychology Lab, University of Oldenburg, 26111 Oldenburg, Germany f Hans Berger Department of Neurology, Jena University Hospital, Erlanger Allee 101, 07747 Jena, Germany g Institute of Biomedical Engineering and Informatics, Faculty of Computer Science and Automation, Technical University Ilmenau, Gustav-Kirchhoff-Straße 2, 98693 Ilmenau, Germany b c

a r t i c l e

i n f o

Article history: Accepted 1 December 2014 Available online xxxx Keywords: Electroencephalography Magnetoencephalography Primary somatosensory cortex Median nerve 600 Hz wavelets Auditory oddball

h i g h l i g h t s  Very early somatosensory evoked responses show context-dependent modulation by auditory

stimuli.  Our delay differential equation model strongly suggests cortico-thalamic feedback in primary somato-

sensory processing.  Frequency encoding, in contrast to network coupling, seems to play an important role in contextual

modulation in the somatosensory thalamo-cortical network.

a b s t r a c t Objective: This study characterized thalamo-cortical communication by assessing the effect of contextdependent modulation on the very early somatosensory evoked high-frequency oscillations (HF oscillations). Methods: We applied electrical stimuli to the median nerve together with an auditory oddball paradigm, presenting standard and deviant target tones representing differential cognitive contexts to the constantly repeated electrical stimulation. Median nerve stimulation without auditory stimulation served as unimodal control. Results: A model consisting of one subcortical (near thalamus) and two cortical (Brodmann areas 1 and 3b) dipolar sources explained the measured HF oscillations. Both at subcortical and the cortical levels HF oscillations were significantly smaller during bimodal (somatosensory plus auditory) than unimodal (somatosensory only) stimulation. A delay differential equation model was developed to investigate interactions within the 3-node thalamo-cortical network. Importantly, a significant change in the eigenfrequency of Brodmann area 3b was related to the context-dependent modulation, while there was no change in the network coupling. Conclusion: This model strongly suggests cortico-thalamic feedback from both cortical Brodmann areas 1 and 3b to the thalamus. With the 3-node network model, thalamo-cortical feedback could be described. Significance: Frequency encoding plays an important role in contextual modulation in the somatosensory thalamo-cortical network. Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

⇑ Corresponding author at: Technical University Ilmenau, Institute for Biomedical Engineering and Informatics, P.O. Box 100565, 98684 Ilmenau, Germany. Tel.: +49 3677 69 2861; fax: +49 3677 69 1311. E-mail address: [email protected] (J. Haueisen). 1 These authors contributed equally.

1. Introduction Neuronal oscillations are assumed to temporally link neurons into assemblies and therefore facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and

http://dx.doi.org/10.1016/j.clinph.2014.12.028 1388-2457/Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Please cite this article in press as: Götz T et al. Primary somatosensory contextual modulation is encoded by oscillation frequency change. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2014.12.028

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long-term consolidation and the transfer of information (Buzsaki and Draguhn, 2004). The analysis of oscillatory processes provides an insight into the unique link between basic neurophysiological processing and higher cognitive functions. A better understanding of the so called bottom-up and top-down mechanisms is the key to better access the functionality of our brain (Engel et al., 2001). While bottom-up mechanisms are those which are mainly influenced by stimulus parameters, top-down mechanisms represent high-level cognitive processing. The focus of this study was how cognitive task context affects information flow in a bottom-up process. We have chosen and combined two well established paradigms: the auditory oddball paradigm as contextual and the primary somatosensory processing after electrical median nerve stimulation as bottom-up process. Transcutaneous electrical median nerve stimulation permits to study afferent information flow within the somatosensory system, differentiating contributions from thalamic and cortical brain areas as reflected in somatosensory evoked fields or potentials (SEFs, SEPs). The first cortical response, N20, originates primarily in Brodmann area (BA) 3b at the posterior wall of the postcentral gyrus (Allison et al., 1989a,b, 1991) and is followed by the P22, which has been ascribed to BA 1 (Allison et al., 1989b). An even earlier positive peak at approximately 16 ms probably originates from deep thalamo-cortical axons (Gobbele et al., 1999; Jaros et al., 2008). Owing to the predominantly radial orientation of the P16 and P22, both components show a higher signal-to-noise ratio (SNR) in electroencephalogram (EEG) as compared to magnetoencephalogram (MEG) recordings (Wood et al., 1985; Haueisen et al., 1995). On the other hand MEG typically outperforms EEG regarding the SNR for tangentially oriented sources (Haueisen et al., 2012). Accordingly, the combination of both recording modalities affords more detailed insights into early somatosensory thalamo-cortical information processing and information flow. Previous studies indicated that median nerve stimulation evokes brain activity in two frequency ranges (Cracco and Cracco, 1976; Curio et al., 1994). Activity in a low-frequency (LF) range up to 250 Hz contains the early evoked components P16, N20, and P22. Activity in a high-frequency (HF) range at approximately 450–750 Hz has been identified using time-domain and time-frequency analyses (Emori et al., 1991; Curio et al., 1994, 1997; Haueisen et al., 2001). HF oscillations show properties different from LF components, however, the exact physiological origins of the HF oscillations are still debated e.g. (Curio et al., 1994; Maegaki et al., 2000; Haueisen et al., 2001). Most sensory information, including signals from median nerve stimulation, is initially transmitted via the feedforward pathway from afferent ascending thalamo-cortical fibers. Yet, corticothalamic feedback projections even exceed ascending projections 3–10 times (Guillery, 1969). With the help of oscillatory network modelling, Haueisen et al. (2007) recently introduced a method to analyze information processing in the primary somatosensory system by means of a set of coupled oscillators to model source activation time courses. Milde et al. (2009) were able to additionally verify significant feedback from the Brodmann area 3b to the thalamus. Despite this verifiable feedback from cortex to the thalamus, context-related paradigms have been rarely investigated in conjunction with somatosensory high frequency oscillations. For example, due to multi-sensory integration such paradigms are behaviourally relevant. In this study we investigated the possible influence of contextual effects on HF oscillations. Specifically it was studied how the somatosensory system processes and evaluates incoming signals by means of its internal connectivity. Aiming to manipulate and study the role of the context on early somatosensory network information flow, auditory target and non-target stimuli were presented shortly before electrical median nerve stimulation. The time

window of electrical stimulation was temporally shifted to late processing stages (i.e. to the individual peak latency of the auditory target P3, which is contextually elicited to minimize bottom-up interference. The P3 is subdivided into the subcomponents P3a and P3b, whereas P3b is seen as classical P3 (Bledowski et al., 2004). We have chosen the P3b peak since it is mentioned in the context of orientation, memory and attention and therefore suitable for the studies of contextual modulation. 2. Methods 2.1. Participants 12 right-handed volunteers (2 males, all right handed) took part in the study (age: 25.1 ± 1.8 years; mean ± SD). All participants gave their written informed consent prior to the experiments. The study was approved by the ethics committee of the medical faculty of the Friedrich-Schiller-University Jena. All participants were paid 6 Euros/hour. The instructions included the announcement of an additional payment when performing the tasks as accurate as possible. Participants affirmed the absence of alcohol, nicotine and medication at each measurement day. 2.2. Stimulation procedure and data acquisition 2.2.1. Somatosensory stimulation The right median nerve was stimulated by electrical monophasic square wave constant current pulses with a duration of 200 ls delivered from a constant current stimulator (DS7A, Digitimer Ltd., Welwyn Garden City, Hertfordshire, GB). The electrode pair was attached to the right wrist. Current amplitude was adjusted individually according to the recommendations of the International Federation for Clinical Neurophysiology IFCN at motor plus sensory threshold (Mauguiere, 1999). 2.2.2. Auditory stimulation Binaural auditory stimuli were delivered through TIP-300 earphones (Nicolet Biomedical, C-300, Madison, WI, USA). Tones with a duration of 50 ms (1000 Hz standard-tone, 1050 Hz deviant tone, 100 ms rise and fall time) were presented 30 dB above hearing threshold. The fixed stimulus onset asynchrony was 800 ms similar to (Giesedavis et al., 1993). Task-relevant deviant tones were presented with a probability of 20%. Pure tone audiometry (frequencies 1000–1200 Hz) revealed individual hearing levels. 2.2.3. Data acquisition Magnetic fields (MEG) and scalp electric potentials (EEG) were simultaneously recorded with a 306-channel helmet-shaped neuromagnetometer (Vectorview, Elekta Neuromag Oy, Helsinki, Finland) and a 60-channel electrodes cap (Elekta Neuromag Oy, Helsinki, Finland). EEG data were recorded with a nose-tip reference and electrode impedances were kept below 20 kOhm prior acquisition. EEG and MEG data were sampled simultaneously with 5 kHz, following a low-pass filter at 1660 Hz and a high pass filter at 0.1 Hz. Two electrodes at the sulcus bicipitalis brachii was used to record the peripheral compound action potential (CAP), which confirmed reliable somatosensory input. A 3D Digitizer (3SPACE FASTRAK, Polhemus Inc., Colchester, VT, USA) was used to locate electrode positions, anatomical locations (nasion and preauricular points) and the MEG localization landmarks. Using the digitized landmark coordinates the MEG sensor coordinates were later transformed into an MRI-compatible coordinate system. Magnetic Resonance images (isotropic T1 weighted images with 1 mm resolution) were acquired with a 1.5 T Siemens Magnetom (Siemens, Erlangen, Germany) for each volunteer.

Please cite this article in press as: Götz T et al. Primary somatosensory contextual modulation is encoded by oscillation frequency change. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2014.12.028

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2.2.4. Paradigm On the first measurement day each participant was screened for possible magnetic interferences with the measurement system. The individual P3 latency was determined by visual inspection in the EEG multichannel butterfly plot. This was done by recording 500 trials of standard tones and 100 trials of deviants, respectively. Participants were instructed to press a pneumatic button with their left hand whenever a target was detected (in the following referred to as auditory paradigm; AP). Next, electrical median nerve stimulation with 2 Hz repetition rate was performed (6000 trials). This measurement was divided into four blocks of 15 min. During each block the electric stimulation paused a few times randomly for 2 s (in the following referred to as somatosensory paradigm; SP). To ensure individuals attended the somatosensory stimulation, participants were instructed to count these pauses and report the number to the experimenter after a block. During the whole measurement the arm was covered with a towel to prevent cooling and thus possible propagation delay. Volunteers were advised to keep still and avoid blinks during the measurement as much as possible. The main somatosensory-auditory-oddball paradigm (in the following referred to as multisensory paradigm; MP) took place on days 2 and 3. Auditory and somatosensory stimulation was applied at the same time with the same stimulus parameters as in SP and AP. The somatosensory stimulus was applied at a latency of 20 ms prior to the individual P3 peak latency, as determined on day 1, when the auditory oddball experiment (AP) and the somatosensory experiment (SP) took place. Fig. 1 displays the sequence of stimuli. In the MP, attention to the somatosensory stimulation was required to detect rare missing somatosensory stimuli, which were introduced randomly after standard tones. Hence, participants had to count the missing somatosensory stimuli and respond to target tones by pressing a pneumatic button with their left. Performance was determined for each block by calculating the number of correct targets (Ct), correct non-targets (Cn), missed targets (Mt) and false positives (Fp):

Performance ¼

Ct þ Cn Ct þ Cn þ Mt þ Fp

Each session lasted approximately 3.5 h per day. This resulted in the acquisition of 3600 trials for standard tones, 900 target tones and 4500 trials for electrical stimulation (minus the omitted ones) per hour (see below). Participants were allowed to rest for about 2 min after each 15 min block. Head positions were estimated anew for each block. 2.3. Data preprocessing Raw MEG data were filtered with Maxfilter Version 2.0.21 (Elekta Neuromag Oy, Helsinki, Finland) using the time-domain extension. Afterwards the data were location-corrected and averaged. For Maxfilter, we used a length of 4 s for the raw data buffer and a subspace correlation of 98% (Taulu and Hari, 2009). For MEG data, all head positions were transformed onto the head position of the first block of MP. The average was calculated concatenating blocks from the whole MP. Blocks from SP were concatenated separately. Overall, 19,714 ± 1746 (mean ± SD) standard tones, 5063 ± 439 deviant tones and 20,748 ± 2075 electrical stimuli were recorded form each participant during the oddball paradigm. During the control/screening condition, 6000 trials per volunteer were recorded. Normally, eye blinks are statistically distributed over all trials. However, due to the high number of trials we suspect that the effect of eye blinks is negligible. Therefore we did not perform eye blink correction. Preprocessing revealed that only up to 4 EEG channels per dataset had to be discarded (different channels per

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participant, channels were discarded after visual inspection), whereas all MEG channels could be used for analysis. LF-SEF/SEP waveforms were extracted with a band-pass of 10– 300 Hz (fourth order Butterworth) and auditory evoked responses with a band-pass 0.3–30 Hz. HF oscillations were extracted with a band-pass of 450–750 Hz (fourth order Butterworth) in Matlab (The Mathworks, Inc., USA). Further data processing was performed with Curry Version 4.6 (Compumedics NeuroScan, Charlotte, NC, USA). Median nerve stimulation trials were averaged for the control condition (Cnt) and for the bimodal stimulation either time-locked to the standard tone (Std) or time-locked to the deviant tone (Dev). 2.4. Source localization Dipole source modelling of electrical potential and magnetic fields was done in one processing approach (Haueisen et al., 2001). For each volunteer a realistic three-compartment boundary element method (BEM) model was created for skin, skull, and liquor. This was derived by segmenting the individual MR images in Curry 4.6. The triangle size was 7 mm for liquor, 9 mm for skull and 10 mm for skin. A BEM model with approx. 4000 nodes and conductivities of 0.33, 0.0042 and 0.33 S/m was calculated. For the source localization a fixed dipole approach was chosen. Specifically, a cortical radial source (HFcr), a cortical tangential source (HFct), and a subcortical radial source (HFp) were fitted (Scherg and Buchner, 1993; Buchner et al., 1994, 1995; Haueisen et al., 2007; Jaros et al., 2008; Milde et al., 2009). The unexplained variance after the dipole fit for combined EEG and MEG dipoles was 39.4% (±4.6) for HF dipoles in a time range of 16–25 ms (average over all volunteers). While this at first glance suggests a poor model fit, lower unexplained variance reported by others are usually obtained by single modality fitting, and by focussing on a subset of channels (Bast et al., 2007). In this study all channels were included, which ensured better comparability of the results across subjects. Source waveforms for all three components (HFcr, HFct, HFp) in all three conditions (Cnt, Std, Dev) were obtained and submitted to further analysis. Grand averages of the dipole source waveforms were calculated for display purpose, following latency adjustment of the largest peak. 2.5. Statistical data analysis Parametric MANOVAs with the 3-staged within-subject factor condition (Cnt, Std, Dev) and the 3-staged factor component (HFcr, HFct, HFp) were used to assess possible latency and location differences separately for each component and condition. Significant pvalues and degrees of freedom were e-corrected according to Greenhouse–Geisser. Significant post hoc t-tests were corrected according to Bonferroni. All values are given by mean and its 95% confidence interval. To compare HF dipole waveforms, we obtained the six major peaks for the HFp and ten major peaks for HFcr and HFct, respectively. The ten peaks for HFcr and HFct were divided into two peak families of five peaks each corresponding to the first and second oscillation part (peaks 1–5: first peak family and peaks 6–10: second peak family). For each component and peak family, paired onesided student’s t-tests were used to test for differences between the three conditions. Significant p-values were Bonferroni–Holm corrected (Holm, 1979). 2.6. Cortico-thalamic network analysis 2.6.1. Computational model The main aim of the cortico-thalamic network analysis was the estimation of differences in the coupling properties from the

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Fig. 1. (a) Schematic protocol of the time course of the combination of bimodal (auditory and somatosensory) stimulation used in MP; (b) exemplary EEG channel (Cz) showing the P3 (P3a at about 300 ms and P3b at about 370 ms) time course for standard (blue) and deviant bimodal conditions (red); (c) butterfly plot of all EEG and magnetometer channels (MAG) of the low frequency responses of the corresponding median nerve stimulation.

cortical areas to the thalamus within the three stimulation conditions. The source waveforms for the three components (HFcr, HFct, HFp) were associated with three brain regions: BA 1, BA 3b, and thalamus respectively. An oscillatory delay differential equation model was adapted to the three source waveform time series for each volunteer and each condition.

€xðtÞ ¼ m1 xðtÞ _  ð2p e11 Þ2 xðtÞ þ e12 yðt  d3 Þ þ e13 zðt  d1 Þ; _ ¼0 xð0Þ ¼ xð0Þ _  ð2p e21 Þ2 yðtÞ þ e22 xðt  d4 Þ þ e23 zðt  d2 Þ; €ðtÞ ¼ m2 yðtÞ y _ ¼0 yð0Þ ¼ yð0Þ €zðtÞ ¼ m3 z_ ðtÞ  ð2p e31 Þ2 zðtÞ þ e32 yðt  d5 Þ þ e33 xðt  d6 Þ þ e34 uðtÞ;

zð0Þ ¼ z_ ð0Þ ¼ 0

u(t) = cos(2pxt + u)b(t  d7), where b(t) is the smoothed propagated stimulation signal measured at the biceps. x(t) and y(t) describe the activity in BA 1 and BA 3b, respectively, and z(t) describes the thalamic activity. The damping parameters mi (i = 1, . . ., 3) were assumed to be greater than or equal to zero. The values ei1, (i = 1, . . ., 3) are frequency parameters and represent the eigenfrequencies of the model components. The remaining eij refer to coupling between the oscillators. The values di (i = 1, . . ., 7) represent the time delay parameters; e.g. d1 represents the time delay of the thalamic influence term on the BA 1 activity. Further details of this model, especially the justification of the input term u(t), can be found in Milde et al. (2009). The difference between the former (Milde et al., 2009) and the present model is the occurrence of both feedback terms e32y(t  d5) and e33x(t  d6) which represent the influence of the cortical activity (HFcr and HFct, respectively) on

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the subcortical activity (HFp). Thus, two analysis steps were carried out (see below): the cortico-thalamic feedback analysis and the complete network coupling parameter analysis. The model was fitted to the dipole activation curves via a combination of a global (Differential Evolution (Storn and Price, 1997)) and a subsequent local optimization procedure (Nelder Mead algorithm (Nelder and Mead, 1965)). Three conditions (Cnt, Std, Dev) and eleven volunteers yielded 33 sets of 22 parameters each. Because of different amplitudes of the three activation courses, the coupling parameters could not be compared directly. To normalize the amplitude of x, y, or z, the envelopes H(x), H(y), or H(z) of the model curves were calculated and the average amplitude of the envelopes used as weight (factor) for the corresponding coupling parameter. The amplitude corrected coupling parameter is denoted by Eij. 2.6.2. Cortico-thalamic feedback analysis In this study we investigated whether feedback from one or both cortical areas to the thalamus could be identified. The quantification of thalamo-cortical feedback was motivated by the interest of contextual modulation driven by the cortex. Due to individual differences between the volunteers, a large variation of the feedback parameters Eij was observed. Hence, an intra-individual analysis of the coupling properties within the three stimulation conditions was performed, using a resampling technique (Sato et al., 2009). Specifically, one of the 33 cases was fixed. Then, the three dipole waveforms described by the model above are considered. As illustrated in Fig. 2, the model waveforms fitted well to the measured activation. The residuals at each time point were collected as triplet-points (values for x, y and z, resp. HFcr, HFct, and HFp) in an error set F. Subsequently, 30 new data sets were created via adding at each sample point a randomly sampled residual with replacement out of F to the original model curve. These randomly changed curves were on average identical with the original model curves, but small deviations, depending on the distribution of the error vectors in F, changed the shape of the curves. Starting with the original optimal parameter set, a local adaptation of the model equations via the Nelder–Mead (Nelder and Mead, 1965) algorithm leads to 30 new parameter sets based on the 30 resampled data curves. This approach made it possible to study the strength of deviation of the original parameters when the data set changes

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only a little. This sequence of computational steps was repeated for each of the 33 cases. For each of them, the amplitude corrected feedback parameters corresponding with the feedback from BA 1 or BA 3b to the thalamus, respectively, were computed. This resulted 33 times in 30 pairs of real numbers which were evaluated in a scatter plot. The resulting point cloud was used to compute the principal axes and, based on the cloud-dependent Mahalanobis metric, the surrounding ellipsoid with size equal to the 95% quantile of the Mahalanobis distances between the cloud points and their barycenter was extracted. Note that the ellipsoid can be considered as a confidence ellipsoid for the mean pair of amplitude corrected feedback parameters. Hence, if the origin lies within such an ellipsoid, the null hypothesis of no feedback from the cortex to the thalamus cannot be rejected. If the ellipsoid covers both axes but not the origin, the null hypothesis of absent feedback must be rejected but one of the possible feedbacks might drop out of the model. The choice, which of the possible feedback terms might drop out, is only restrictive in the case that just one of the axes is covered by the ellipsoid. If no axis intersects with the ellipsoid, the null hypothesis of absent feedback must be rejected and both feedback terms are necessary for the model. The resulting constellations of axis and ellipsoids were analyzed for each of the 33 cases. 2.6.3. Network coupling parameters In a subsequent analysis step, the absolute values of the amplitude corrected coupling parameters Eij were analyzed to identify differences between the three stimulation conditions. Since three values of the amplitude were measured on the same individual, the correlation of these measurements had to be modeled in the statistical analysis. Generalized estimating equations (GEE) are semi-parametric regression models accounting for the problem of correlated data. Therefore, an appropriate working correlation structure has to be selected a priori for the data set. We have chosen the compound symmetry structure, which implies that the responses for each simulation condition of an individual are equally correlated. This is not a critical assumption, because even if the correlation structure is misspecified, parameter estimates of the GEE model are consistent and inferences regarding the difference of the three simulation conditions are still unbiased (Burton et al., 1998). However, a working correlation structure fitting the data well increases the efficiency of the estimation. The parameters of the GEE model were estimated with the Software IBM SPSS Statistics 20.0. 3. Results 3.1. Behavioural data Volunteers performed the auditory task (deviant detection from the MP) with an accuracy of 88.8 (±3)%. There was no significant improvement of performance from the MP (t(10) = 0.43; p = 0.68). Auditory target detection significantly improved on the second day of MP in terms of reaction time from 398 (±37) ms to 371 (±37) ms (t(10) = 4.4; p = 0.001). Considering somatosensory stimuli, there were significant differences in the number of counted pauses between the three measurement days (F(1.4;13.7) = 28; p < 0.001). During the SP, volunteers correctly counted 96 (±3.3)% of the pauses. This percentage decreased to 61 (±11.3)% on the first day of the MP and 62 (±4.2)% on the second day of the MP. 3.2. P3

Fig. 2. Visualization of the optimal model output (green lines) for the three dipole source waveforms (brown lines). The objective function for the optimization was evaluated only in the time window indicated by the grey boxes, defining regions of sufficient signal to noise ratio of the dipole curves (cf. (19)).

P3a and P3b peak latencies were at 242 ± 15 and 361 ± 12 ms post stimulus. Somatosensory stimulation was applied at an average latency of 320 ± 13 ms. The field topography of the P3 showed

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Fig. 3. HF components evoked after median nerve stimulation for the three reconstructed sources (subcortical, cortical tangential and radial source), as obtained in one representative volunteer during unimodal stimulation. Left: 3D representation of the head with electrodes and pickup coils and localized dipoles (yellow spheres). Right: source waveforms and the corresponding magnetic field (HF MEG; 500 aT/Inc.) and the electric potential topographies (HF EEG; 10 nV/Inc.).

Fig. 4. (a) Grand average source waveforms (top: HFp, middle: HFct: bottom: HFcr) obtained from 11 volunteers (note that the latency was corrected to the largest peak, i.e. peak family I in HFct and peak family II in HFcr, respectively). Black: unimodal stimulation (control), blue: bimodal after standard tone, red: bimodal after deviant tone; separation in peak families I and II (grey boxes). (b) The size of the bubbles represents relative signal strength, the different shades of green represent the p-values; grey: n.s.

a central positivity in the EEG. There were significant amplitude differences when comparing the three measurement days (F(1.8;26.7) = 16.7; p < 0.001). In the MP, the P3-amplitude was 62 ± 11% smaller than in AP (p = 0.001), whereas it was only 41 ± 14% smaller on the second day in the MP (p < 0.001). The difference between the first and the second days of the MP was not significant (p = 0.99).

3.3. Source localization and latency In 11 out of the 12 participants all three HF components could be clearly identified. Thus, in the following only the data from 11 participants are presented. Specifically, the subcortical radial source was localized near the thalamus, the cortical tangential source was localized near the somatosensory BA 3b, and the

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cortical radial source, was localized near BA 1 (see Fig. 3). The median peak latency of the Hilbert envelope was 16.6 ± 0.6 ms for the HFp, 19.8 ± 0.33 ms for HFct, and 21.9 ± 0.8 ms for HFcr. In one participant we could only extract the HFct and the HFp. There was no condition effect on the latency of the components in HF (F(1.5;14.6) = 0.07; p = 0.89). Fig. 2 shows an example of the HF waveforms for the three components of interest in one volunteer. The median frequencies of the subcortical HF oscillations were 586 Hz (±18.2) and for the cortical HF oscillations 641 Hz (±15.3). The HF localization was similar across the three experimental conditions and not significantly different (F(1.9;18.5) = 0.3; p = 0.73). 3.4. Peak amplitudes In the low frequency (LF) range, condition had a significant influence on the peak amplitudes of the N20 (F(2;21.4) = 4.43; p = 0.025). The amplitude for condition Dev was significantly higher than for Cnt (p = 0.048). There was neither a difference between Dev and Std (p = 0.7), nor a difference between Std and Cnt (p = 0.34). There was not influence of condition on the P16 (F(1.4;13.7) = 0.77; p = 0.44) or the P22 (F(2;19.8) = 0.14; p = 0.87). Fig. 4 shows the grand average across 11 volunteers for the 3 high frequency components (HFcr, HFct, HFp) and the 3 conditions (Cnt, Std, Dev). Table 1 shows the results of the statistical analysis of the peak families of the three source activation curves HFp, HFcr and HFct. HFp: Highest amplitudes were found for Cnt. Statistical analysis revealed a trend for the comparison between Cnt and Dev (t(11) = 1.97; p = 0.07). No difference was found for Std and Dev, and for Cnt and Std. HFct: For the first five peaks, the highest amplitudes were again found for Cnt, which were significantly higher than in condition Dev (t(11) = 4.3; p = 0.001) and Std (t(11) = 2.8; p = 0.017). In the second five peaks, Std resulted in higher amplitudes than Cnt and Dev. Moreover, there was a trend towards a difference between conditions Std and Dev. HFcr: There were no significant differences within the two peak families, although condition Dev showed the highest and Cnt the lowest amplitudes within the second peak family. There was no

3.5. Cortico-thalamic feedback analysis Fig. 2 shows the dipole source waveforms and the model predicted curves for the 3 components (HFcr, HFct, HFp) for condition Dev for one volunteer. On the basis of the confidence ellipsoids, each of the possible null hypotheses regarding the feedback terms in the model was tested in all 33 cases. Table 2 shows the results of this approach. Entry ‘‘1’’ means: the feedback from BA 1 to the thalamus is required, entry ‘‘3b’’ means: the feedback from BA 3b to the thalamus is required, entry ‘‘1 or 3b’’ means: either the feedback from BA 1 to the thalamus or the feedback from BA 3b to the thalamus is required, entry ‘‘both’’ means: both feedback terms are required. It can be seen that within the standard condition for almost every volunteer, the full model was necessary to achieve adequate model fit. A possible model reduction could be identified for the Deviant condition. In 9 of the 33 cases, feedback from BA 3b to the Thalamus was not crucial, and in 9 of the 33 cases feedback from BA 1 to the Thalamus could be ignored. Two of the eleven volunteers (1 and 9) allowed no model reduction within the three stimulation conditions. For three volunteers (6, 7, 10), only the BA 3b feedback could be left out of the model and for two volunteers (5 and 8), only the BA 1 feedback could be left out of the model. Accordingly, for ten of the eleven volunteers the full feedback model was necessary for at least one of the stimulation conditions. Hence, modelling feedback from cortex to thalamus was necessary for an adequate modelling of the dipole activation curves. 3.6. Network coupling parameters Stimulus condition had no significant model effect on the coupling parameters (Table 3). Mean thalamic feedforward connections (rows 2 and 4 of Table 3) as well as mean connections between both cortical areas (rows 1 and 3 of Table 3) were almost identical in all conditions.

Table 3 Estimated means of the GEE model for all conditions.

Table 1 Comparison of peak families for all conditions and sources. Peaks 1–5

difference between Cnt and Dev in the second peak family (t(10) = 1.8; p = 0.1).

Peaks 6–10

Description

p-value

HFp

Cnt > Std Cnt > Dev Std > Dev

0.13 0.07(⁄) 0.367

Description

HFct

Cnt > Std Cnt > Dev Std > Dev

0.017⁄ 0.001⁄⁄ 0.13

Cnt < Std Cnt > Dev Std > Dev

0.25 0.16 0.06(⁄)

HFcr

Cnt > Std Cnt > Dev Std = Dev

0.34 0.24 0.44

Cnt < Std Cnt < Dev Std < Dev

0.44 0.10(⁄) 0.14

Variable

Estimated mean of Eij (standard error) Control

Deviant

Standard

HFct to HFcr

0.538 (0.108) 0.340 (0.078) 1.684 (0.360) 1.464 (0.176) 0.159 (0.039) 0.198 (0.049)

0.595 (0.125) 0.283 (0.038) 1.646 (0.341) 1.595 (0.330) 0.152 (0.040) 0.205 (0.063)

0.741 (0.180) 0.350 (0.107) 1.404 (0.319) 1.568 (0.412) 0.259 (0.077) 0.435 (0.137)

p-value

HFp to HFcr HFcr to HFct HFp to HFct HFcr to HFp HFct to HFp

(⁄)

p  0.1. p < 0.05. ⁄⁄ p < 0.01. ⁄

#

p-Value#

0.598 0.724 0.504 0.865 0.249 0.127

Test of model effects (Wald).

Table 2 Significant feedback from cortical areas (BA 1 and BA 3b) to the thalamus. Volunteer

1

2

3

4

5

6

7

8

9

10

11

Control Deviant Standard

Both Both Both

3b 3b or 1 Both

None 1 Both

Both 3b 3b or 1

3b Both Both

1 Both Both

1 1 Both

Both 3b Both

Both Both Both

Both 1 1

3b or 1 3b 3b

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T. Götz et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

Fig. 5. Schematics of time delay and frequency parameters with significantly different delays for d1 and d4 and frequency parameter e21 demonstrating the higher eigenfrequency in HFct for the deviant condition in comparison to unimodal and bimodal standard condition.

Higher means were evident for the standard condition and for both feedback connections from cortical areas to the thalamus (rows 5 and 6 of Table 3). As one main result of this study, the frequency parameter e21 was found significantly higher (condition effect: p = 0.015) for the deviant condition compared to the control (p = 0.04) and standard condition (p = 0.01); specifically, the eigenfrequency in HFct was higher in the deviant condition (average difference Dev–Cnt: 77 Hz; average difference Dev–Std: 75 Hz). The frequency parameter e11 was smaller at trend level (condition effect: p = 0.09) for the deviant condition compared to the control (p = 0.1) and standard condition (p = 0.15). Additionally, condition had a significant effect on the two time delay parameters d1 and d4 (p = 0.04 and p = 0.047), where d1 is larger in the control condition compared to standard condition (average difference: 0.84 ms, p = 0.03), meaning that the time delay of the influence of the thalamus on the activity in BA 1 was larger in the control condition. d4 was smaller in the standard condition compared to control (average difference: 0.29 ms, p = 0.01) and deviant condition (average difference: 0.28 ms, p = 0.04). This reflected that the time delay of the influence of the BA1 activity on BA 3b was smaller in the standard condition. These results are illustrated in Fig. 5. 4. Discussion In this study, we characterized thalamo-cortical communication by assessing the influence of contextual regulation on somatosensory high frequency oscillations (a) on source activity itself and (b) on feed-forward and feedback communication between subcortical and cortical sources. The identification of one subcortical and two cortical HF source activities served as basis for this analysis. We found (a) significant amplitude modulations during bimodal stimulation for HF evoked activity, (b) a significant feedback from both cortical areas to the thalamus, (c) no significant differences in the coupling parameters of the thalamo-cortical network for the three conditions, and (d) frequency and time delay parameters to be sensitive to stimulus context. 4.1. Localization of HF dipoles The localization of the HF dipoles is in accordance with previous studies. The tangentially oriented HFct (high frequency cortical tangential source) showed a stable localization near BA 3b, while

the HFcr (high frequency cortical radial source) was located near BA 1 (Allison et al., 1989a, 1991). Furthermore, the subcortical HFp (high frequency subcortical radial source) was located near the thalamus (Gobbele et al., 1999; Jaros et al., 2008). 4.2. Amplitude modulation of the HF activity The activity of the two cortical high frequency sources (HFct and HFcr) revealed significant effects in all three conditions. During the processing of the auditory deviant, HFct amplitudes were significantly lower in comparison to the unimodal control condition. A contextual modulation of the SEF signals in terms of a smaller HFct amplitude for condition Dev in the second peak family was also found. However, the HFcr results showed a tendency towards higher amplitudes for the deviant in comparison to the standard stimulus presentation, but the control condition was not statistically different from the two bimodal conditions. It is possible that fitting sources near the centre of the head leads to a higher deviation and therefore no significant differences could be found for the HFp. In contrast to the present study, Hashimoto and colleagues (1999) reported no amplitude enhancement but a significant increase of HF oscillation peak numbers, leading to a longer duration of the burst during somatosensory interference. Besides the unimodal design of the Hashimoto study (1999), HF analysis was performed on the sensor level and therefore no direct comparison between tangential and radial contributions to HF could be made. Thus it cannot be excluded that their increase in peak number was due to an otherwise covert enhancement of the HF amplitude during interference. Another auditory-somatosensory interference study by Gobbele et al. (2003) investigated the influence of selective attention and arousal on four dipole sources (brainstem, near thalamic and two cortical sources) elicited after electrical median nerve stimulation. In contrast to our study, Gobbele et al. (2003) found no attentional modulation on the source level, but low arousal significantly attenuated the HF oscillation source amplitudes. However, due to the asynchronously applied stream of interfering somatosensory and acoustic targets, a direct comparison with our study is not possible. Furthermore, a study by Restuccia et al. (2011) showed that habituation influences low frequency components but not the high frequency oscillations, indicating different functional relevance of high and low frequency responses.

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T. Götz et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

4.3. Network analysis The oscillatory delay differential equation model was successfully adapted to the dipole waveforms for each of the three stimulation conditions and each of the eleven volunteers. We found that the inclusion of feedback from the cortex to the thalamus (either HFct to HFp or HFcr to HFp) into the model equations was required. In addition, there was no significant difference for the experimental conditions evident in the coupling parameters of the network considered. Physiologically, the inclusion of feedbacks from the cortex to the thalamus into the model equations is reasonable since there exist massive feedback projections from cortex to thalamus (Li and Ebner, 2007; Porcaro et al., 2013). Functional connectivity between subcortical and cortical structures in the somatosensory pathway have been investigated using partial directed coherence (Porcaro et al., 2013). Moreover, information flow between thalamus and cortex is bidirectional (Milde et al., 2009; Lam and Sherman, 2010, 2011). We speculate that the described enhanced feedback from cortex to thalamus in the condition Std lessens the signal transfer from thalamus to the cortex during the standard condition in the sense of a gating effect (Ermutlu et al., 2007). When attention is drawn to the more salient stimulus, such as the deviant tone, no such attenuation in feedback is necessary, possibly reflecting a more detailed processing at the potentially task-relevant stimulus. These results are partly in line with the results obtained from the amplitude analyses, where we found smaller amplitudes of HFct and HFp during standard and deviant conditions. Interestingly, we could not find any significant amplitude differences regarding the thalamic components. It is important to keep in mind that fitting sources near the centre of the head leads to a higher uncertainty. Therefore, the approach to model feedback by means of coupled oscillators might be more sensitive than analysing amplitudes alone, since it provides information about the kind of feedback, i.e. whether there is a difference between different (attentional) conditions. However, in our case, the stimulus condition did not significantly influence the coupling parameters. Interestingly, significant changes in time delay parameters and in one eigenfrequency were found. In the control condition, the information transfer from the thalamus to BA 1 is delayed. Moreover, in line with the enhanced feedback from BA 3 to thalamus during the standard condition, we also found a smaller temporal delay from BA 1 to BA 3b. In line with these observations, temporal delays in the context of HFOs were also reported by Porcaro et al. (2013). While, in our case, there was no change in the frequency of the source waveforms at the two cortical areas (x(t) and y(t)), we observed a change in the intrinsic eigenfrequencies of the coupled oscillators (e11 and e21) in the deviant condition (significant frequency increase in BA 3b and trend-level decrease in BA1; cf. Fig. 5 and the single terms in the network equations). This might indicate a change in the internal processing in both areas for the deviant condition. A similar effect of context related change of oscillation frequencies was found in a visual animal study by Funke and Kerscher (2000) who demonstrated that the oscillation frequency increased with the stimulus contrast. Inhibition however, probably occurring during the standard condition, reduced the oscillation frequency. Note that the increase in the eigenfrequency in BA 3b was found contrasting the deviant and both control and standard conditions. Thus, neither the bimodal stimulation as such nor the counting task was responsible for this change. The assumed change in internal processing (delays and eigenfrequencies) is in line with the above discussed change in amplitudes and also with a study by Barth, who found that the precise timing of action potentials is essential for coding the time-varying features of an ongoing stimulus and the spatial structure of an environmental object (Barth, 2003). Neurons in cortical layer IV,

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pyramidal cells and inhibitory interneurons receive direct thalamic input (Jones, 1975). The connectivity of the interneurons is bidirectional, strongly connecting different layers (Jones, 1993). Therefore, GABAergic inhibition may play an important role in the shaping of physiologic response profiles of the pyramidal neurons (Hashimoto, 2000). We assume that these interneurons might also play a role in attentional contextual settings and contribute to the observed eigenfrequency changes. According to Buzsaki and Draguhn (2004), neuronal oscillations play functionally distinct roles in cortical information processing, since they are modulated in response to sensory input and cognitive demands. Evidence exists that the brain is able to effectively control its synchrony for the appropriate processing of sensory information. This has been demonstrated e.g. for visual stimuli (Paik and Glaser, 2010). Oscillation frequencies may change very fast and this could be an effective method of regulating dynamic sensory responses to varying stimuli. It has been demonstrated that the synaptic plasticity of thalamo-cortical neurons can control gamma resonance by the varying frequency of spontaneous oscillation and therefore control the processing of information (Paik and Glaser, 2010). We also speculate that the locus coeruleus (LC) as part of the reticular system might play a role in explaining our results. It exhibits a high number of projection areas, amongst others also to the somatosensory cortex. Recent literature suspects the locus coeruleus – noradrenergic system to explain the functional significance of the P3 (for a review see (Nieuwenhuis et al., 2005). The authors suggest that the LC works as an attentional cortical filter, which specifically filters for the content of a stimulus. Its response behaviour allows the regulation of the state of cognitive processing. LC stimulation enhances excitatory and post excitatory inhibitory responses after forepaw stimulation in the rat (Waterhouse et al., 1998) and increased the signal to noise ratio of SI neuronal responses (Snow et al., 1999). Thus, LC activation mediated by stimulus context might be responsible for the different processing of high frequency oscillations in this study. A limitation of our model is the lumped assignment of all subcortical activity to one node in the network. Multisensory integration takes place already at the level of the thalamus and certainly different thalamic nuclei are involved (Hackett et al., 2007; Cappe et al., 2009; Kimura et al., 2010). However, given the limited spatial resolution of non-invasive measurements, it seems impossible to resolve these contributions. 4.4. Experimental design From a theoretical point of view, the performance of a demanding task might increase the arousal level, and, of consequence, HFO amplitudes. Conversely, our data converge in demonstrating that the amount of high-frequency somatosensory input is not only the mere result of the overall arousal level (HFO components are predominantly seen as exogenous); yet, it probably depends on corticofugal influences, which modulate somatosensory input according to its contextual relevance. Studies revealed that high arousal significantly enhances the HFOs (Gobbele et al., 2000a; Restuccia et al., 2004). Changes in arousal and changes in HFO amplitudes were observed by simple eyes opening/eyes closing experiments. In our study, volunteers were requested to keep their eyes open during the experiments. Sleep and arousal are thought to be dominated by subcortical bottom-up regulation via the brainstem reticular formation and the reticular thalamic nucleus (Gobbele et al., 2000b, 2007; Halboni et al., 2000). To control arousal, two measures were taken: (a) the introduction of a counting and a detecting task; and (b) all our measurements were performed at identical times of the day (in the morning) and under the same conditions (i.e. approx. the same duration of measurement, no intake of alcohol or coffee, no sedation, which was asked

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T. Götz et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

anew every measurement day), although measurements on different days/day times do not seem to have any influence on the HFOs (Haueisen et al., 2000; Gobbele et al., 2007). To induce contextual modulation, we changed the background stimulation to modify cognitive processing, such as target detection and/or memory updating processes, and to assess their possible influence on somatosensory function represented by the HF dipole waveforms. The dual-task demands were substantial for the volunteers since they had to attend to both sensory modalities. The somatosensory counting task was important to maintain the vigilance of the volunteers. However, the predominant task was to respond to target sounds. The P3, which is a stable and reliably reproducible component, was chosen as the underlying cognitive context co-occurring with the somatosensory stimulation (Pineda et al., 1997; Conroy and Polich, 2007). According to (Ishii et al., 2009) frontocentral–parietal event related synchronisation (related to the P3) is functionally engaged in auditory attention and also in memory updating processes (Debener et al., 2002). Our results show that the main part of the interaction seemed to take place on the cortical level, since no modulation could be reported on the subcortical level, neither regarding LF dipoles nor the HF dipoles. Admittedly, it remains still unclear whether the occurrence of the P3 as low-level cognitive component or the mere meaning of the preceding auditory stimulus itself (standard or deviant) critically influenced the modulation of the somatosensory activity. Nevertheless, the significant decrease of the P3 amplitude during MP is also a proof for the intensity of the experimental context. The fact that the standard condition also marginally differed from the control, which means that cognitive requirements were also present during the standard condition, points to the fact that the somatosensory sources were influenced by the differing background and not exclusively by the occurrence of the P3. To assess the influence of the meaning of the stimulus, an experiment is needed which analyses the influence of contextual changes on HF oscillations in different time windows after an interfering standard or deviant stimulus (before or even after the P3). In conclusion, we found out that the cognitive stimulus context did neither influence coupling parameters of the target network, nor HFO amplitudes. The latter is in line with the results of Gobbele et al. (2000b). However, frequency encoding and temporal delay of the influence of one region to another seem to play an important role in contextual modulation in the somatosensory thalamo-cortical network. Conflict of interest statement There are no competing interests. Funding This work was supported by the German Research Foundation (DFG Ha 2899/14-1) and the German Federal Ministry of Science (BMBF 3IPT605A). References Allison T, Mccarthy G, Wood CC, Darcey TM, Spencer DD, Williamson PD. Human cortical potentials-evoked by stimulation of the median nerve. 1. Cytoarchitectonic areas generating short-latency activity. J Neurophysiol 1989a;62:694–710. Allison T, Mccarthy G, Wood CC, Jones SJ. Potentials-evoked in human and monkey cerebral-cortex by stimulation of the median nerve – a review of scalp and intracranial recordings. Brain 1991;114:2465–503. Allison T, Mccarthy G, Wood CC, Williamson PD, Spencer DD. Human cortical potentials-evoked by stimulation of the median nerve. 2. Cytoarchitectonic areas generating long-latency activity. J Neurophysiol 1989b;62:711–22.

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