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NEUROSCIENCE |
1 Newcastle University, Sir James Spence Institute, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne NE1 4LP, UK
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Abstract |
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20 Hz LFP oscillations was assessed during periods of steady holding, when such oscillations have previously been shown to be maximal in M1. Oscillations were strongest in area 5 and weakest in the DCN. Using a previously developed method, the postspike distance-to-threshold trajectory was determined from the interspike interval histogram for each cell. Many cells had significant peaks, suggesting an intrinsic tendency towards rhythmic firing. Surprisingly, trajectory peaks were most common for M1 PTNs (115/146 cells) and rarest for area 5 neurons (12/82 cells). The extent of intrinsic spiking rhythmicity is not therefore simply related to the strength of 20 Hz oscillations in the sensorimotor system. These results suggest that intrinsic rhythmicity is not required for the generation and maintenance of oscillatory activity.
(Received 7 November 2006;
accepted after revision 1 February 2007;
first published online 8 February 2007)
Corresponding author S. Baker: Sir James Spence Institute, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK. Email: stuart.baker{at}ncl.ac.uk
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Introduction |
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There is increasing evidence for the involvement of oscillations in the somatosensory system. Early on, Murthy & Fetz (1992) showed oscillatory synchrony between pre- and postcentral sites in monkey, and this was confirmed by Brovelli et al. (2004). Using Granger causality methods, Brovelli et al. (2004) also demonstrated that the information flow in the beta-band is strongest in the direction from area S1 to M1. Additionally, oscillations are present in posterior parietal cortex (MacKay & Mendonca, 1995; Brovelli et al. 2004) and in peripheral afferents (Baker et al. 2006). Corticomuscular coherence may involve both feedback (sensory) as well as feedforward (motor) pathways (Riddle & Baker, 2005), although this is controversial (Gerloff et al. 2006).
In computer simulations, stable oscillatory activity is an emergent property of small networks of inhibitory (GABAergic) interneurons (Wang & Buzsáki, 1996; Pauluis et al. 1999; Traub et al. 1999). Enhancing GABAA receptor transmission by administering diazepam increases the power of 20 Hz EEG oscillations in humans (Baker & Baker, 2003), supporting the hypothesis that inhibitory networks form the basis of oscillatory rhythmogenesis. At the single-cell level, rhythmic discharge at a particular frequency could be due to regular synaptic input (e.g. from local inhibitory networks), and/or to intrinsic properties of the cell. Neurons with an intrinsic tendency to rhythmic firing at a particular frequency could augment synchronous network oscillations (Gray & McCormick, 1996). Such cells have been discovered in M1 (Wetmore & Baker, 2004; Chen & Fetz, 2005), S1 (Lebedev & Nelson, 1995) and the deep cerebellar nuclei (DCN; Thach, 1968; Jahnsen, 1986).
If cells with an intrinsic tendency to rhythmic firing are important in assisting the generation of oscillatory activity, they might be found preferentially in brain areas with large amplitude oscillations. In this study, firstly we compare the magnitude of local field potential (LFP) oscillations in several areas of sensorimotor cortex, as well as the deep cerebellar nuclei. Using invasive recordings with penetrating microelectrodes in monkeys that were awake, we are able to provide definitive comparisons of oscillatory power, free of the possible confounding effects of signal spread which affect non-invasive work in humans. Secondly, we apply an interspike interval statistical analysis method pioneered by Matthews (1996), and refined by Wetmore & Baker (2004), to determine whether cells in each of these areas show a propensity to rhythmic firing. Surprisingly, we find that whereas oscillations are strongest in somatosensory areas, intrinsic rhythmicity is most prevalent for the pyramidal tract output cells in M1.
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Methods |
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Four female rhesus macaques (M. mulatta) were trained to perform behavioural tasks for food reward. Two monkeys (E and T) were trained on a bimanual precision grip task (for full details see Wetmore & Baker, 2004; Soteropoulos & Baker, 2006). Two animals (M and L) performed an index finger flexion task. The finger was inserted into a narrow tube, which splinted the finger and constrained movement to the metacarpo-phalangeal (MCP) joint. The tube was mounted on a lever that rotated on an axis aligned to the MCP joint. Lever movement was sensed by an optical encoder, and a motor exerted torque in a direction to oppose flexion. This was programmed to act like a spring (initial torque, 48 mN m). The task required movement into target (between 6 and 24 deg flexion) and holding for 2 s (torque required at target either 64 or 128 mN m). Motor torque then rose, and the animal released the lever to obtain its reward. The majority of the analysis reported here focuses on the hold period of both the precision grip and finger flexion tasks, as this has previously been shown to contain the strongest beta-band activity (Baker et al. 1997, 2001).
Surgical preparation
Following behavioural training, each monkey was implanted under general anaesthesia and aseptic conditions with a headpiece (to allow head fixation) and a recording chamber placed over the central sulcus (Lemon, 1984; Baker et al. 1999). The anaesthesia consisted of either 2–2.5% isoflurane inhalation in 50% N2O–50% O2 (monkeys E and T) or 3.0–5.0% sevoflurane inhalation in 100% O2 supplemented with a continuous infusion of intravenous alfentanil (0.025 mg kg–1 h–1; monkeys M and L). A full programme of postoperative analgesia (10 µg kg–1 buprenorphine (Vetergesic), Reckitt and Colman Products; 5 mg kg–1 carprofen (Rimadyl), Pfizer) and antibiotic care (coamoxyiclav 140/35, 1.75 mg kg–1 clavulanic acid, 7 mg kg–1 amoxycillin (Synulox), Pfizer; 10 mg kg–1 cefalexin (Ceporex), Schering-Plough Animal Health or 15 mg kg–1 amoxycillin (Clamoxyl LA), Pfizer) followed surgery. Two stimulating electrodes placed in the pyramidal tract permitted antidromic identification of M1 pyramidal tract neurons (PTNs) (Lemon, 1984). In a further surgical procedure a chamber was implanted, centred over stereotaxic coordinates P8.5, ML4, to allow access to the deep cerebellar nuclei (DCN). All procedures were carried out under the authority of licences issued by the UK Home Office under the Animals (Scientific Procedures) Act 1986.
Recording
In daily experiments, a 16-channel microdrive, loaded with either glass-insulated platinum electrodes (M1) or tetrodes (area 3a, area 2, area 5 and DCN), was used to record single units and LFPs (Eckhorn & Thomas, 1993). For the DCN, six sharpened guide tubes penetrated the dura in order to avoid electrode deviation (Soteropoulos & Baker, 2006). Spike waveforms (300 Hz–10 kHz bandpass) were sampled continuously at 25 kHz, and saved to hard disk together with lever position, LFPs (bandpass, 1–100 Hz; sampling rate, 500 Hz) and task behavioural markers. Spike occurrence times were discriminated off-line using custom-written cluster cutting software (Getspike, S.N. Baker; SpikeLab, Dyball & Bhumbra, 2003). Only clean single units with consistent wave shapes and no interspike intervals below 1 ms were used for subsequent analysis.
The different brain areas were identified by a clinical examination of unit receptive fields and by noting the motor responses to microstimulation. Neurons were identified as PTNs when they showed a constant latency antidromic response to electrical stimulation of the pyramidal tract. Identification was confirmed using a collision test (Lemon, 1984).
Power spectral analysis
To compute one-sided power spectra for LFPs, data were first split into 1.024 s long (512 sample point) non-overlapping sections taken from the hold period of the task. Analysis used only monkeys M and L; these performed the finger flexion task, where one trial yielded two sections as the hold period was 2 s long. Denoting the Fourier transform of the ith section at frequency f as Fi(f), the power spectrum P(f) was estimated by:
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| (1) |
Using this normalization, P(f) has units of µV2 (Press et al. 1989), permitting direct comparison of recordings from different areas.
Measure of spike train irregularity
Davies et al. (2006) have recently introduced a measure of spike train irregularity. This has advantages over alternatives such as the coefficient of variation of the interspike intervals (CV), because it is resistant to changes in firing rate over the length of data analysed. We used this measure as an initial means of assessing spiking regularity. Given a series of interspike intervals Ii, the measure IR was calculated as:
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| (2) |
Davies et al. (2006) showed that IR can be modulated in a task-dependent way. Because the aim was to compare cells from different areas, only intervals falling in the hold phase of the task were used in the calculation. This provided a single summary statistic of the spiking irregularity for that cell under standardized behavioural conditions.
Calculation of distance-to-threshold trajectory
Figure 1 shows the steps involved in calculating the distance-to-threshold trajectory using methods described in more detail by Wetmore & Baker (2004). First the population interspike interval (ISI) histogram (Fig. 1A, binwidth 1 ms) is used to calculate the population death rate curve (Fig. 1B). The death rate is the probability that an interval will end during a particular time bin after the previous spike, given that it has not already ended by the start of the bin. The death rate for a Poisson process, for example, is constant. The death rate curve is then transformed into the distance-to-threshold trajectory (Fig. 1C), using a monotonic transform determined on the basis of simulations of an integrate-and-fire model neuron. This transform allows the distance-to-threshold trajectory to be scaled in terms of the standard deviation of the membrane voltage noise (‘noise units’). It reflects the excitability of the cell membrane as a function of time after the spike.
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Significance testing of peaks
Several distance-to-threshold trajectories exhibited peaks (Wetmore & Baker, 2004). The significance of these peaks was tested by comparing the peak to the surrounding points (usually the 3rd to 10th points before and after the peak). Denoting trajectory estimates and their 95% confidence limits at two postspike latencies as x
±
x and y
±
y, the points were considered significantly different (P < 0.05) if:
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| (3) |
To correct for multiple comparisons, a binomial distribution with the probability of single trial success P(hit) = 0.05 was used to calculate the minimum number of significant points needed before the peak was considered statistically significant.
Histology
At the end of experiments, monkeys were deeply anaesthetized (60 mg kg–1 pentobarbitone) and perfused through the heart with phosphate-buffered saline (pH 7.2) followed by 4% formal saline fixative. For monkey M, 50 µm sagital sections of the sensorimotor cortex were cut and stained with cressyl violet. These were used to check the location of the different cortical areas and to estimate the depths of the cortical layers in area 2. In three animals, the location of pyramidal tract stimulating electrodes was confirmed histologically.
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Results |
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Figure 2 illustrates the differences in LFP oscillations between the five areas recorded for monkey M. Representative traces from each of the areas are shown in Fig. 2A. These traces have been centred on the task-hold period, because this has the strongest 20 Hz oscillations (Baker et al. 1997). Even in these raw data, clear differences are evident in the amplitude of oscillations between different brain regions.
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In order to compare the different areas, power in the 17.5–22.5 Hz range was summed for each recording site. Figure 2C presents the distribution of this power as a box plot; results for monkeys M and L are shown side by side. The 20 Hz oscillations were stronger in the postcentral areas 2 and 5 than in the precentral M1 (Kruskal–Wallis test, P < 0.05, Tukey–Kramer post hoc test, P < 0.05).
It is of considerable interest to know how oscillation strength varies with cortical layer. Unfortunately, the folded nature of the cortex in this region makes it difficult to reconstruct, in our chronic recordings, the cortical layer with any confidence. Small surface deviations of the electrodes could lead to considerable differences in placement at the depth of a sulcus, and the fine nature of the electrodes precludes histological reconstruction of individual tracks (Mountcastle et al. 1991). However, area 2 lies on the brain surface, mid-way between central and intraparietal sulci, so that it is possible to make approximate assignments of cortical laminae to the recording sites. To do this, we used the depth of electrodes below the cortical surface noted during the experiment, and the thickness of layers measured in post mortem histology. Figure 2D shows the distributions of 20 Hz power for the different layers. There were significant differences between layers (Kruskal–Wallis test, P < 0.05). Oscillations in layer V had significantly higher amplitude than those in layers II/III (Tukey–Kramer post hoc test, P < 0.05).
Unit firing properties
A total of 370 M1 cells (146 PTNs and 224 unidentified neurons; UIDs) and 230 DCN cells were recorded from monkeys E, T, M and L. A further 144 area 3a cells, 92 area 2 cells and 82 area 5 cells were recorded from monkeys M and L. The mean number of spikes per cell was 89 777 (range, 10 065–584 678) and the mean recording duration per cell was 4011 s (range, 515–10 259 s). PTNs and UIDs from M1 were treated separately in the following analyses.
Figure 3A shows the distribution of the mean firing rate during the task-hold phase, for the different cell classes. In all of the cortical areas recorded, neurons fired close to 20 Hz. The similarity of the firing rate to the beta-band frequency may make it easier for cells to phase-lock to the synchronous oscillations. Cells in the DCN fired at significantly higher rates (Kruskal–Wallis test, P < 0.05; Tukey–Kramer post hoc test, P < 0.05).
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Distance-to-threshold trajectories
Figure 4 shows the ISI histograms, death rate curves and composite trajectories for six example cells; one from each area. These illustrate two of the three trajectory categories described by Wetmore & Baker (2004): significant peaks (Fig. 4A–C) and exponentially increasing trajectories (Fig. 4D–F). Cells with significant peaks have a tendency to fire rhythmically at a particular frequency. These two categories accounted for over 90% of all the cells analysed. A few cells in each area had exponentially decreasing trajectories (29/928 cells) or did not fit any of these patterns (62/928 cells). The area 3a cell illustrated had a large number of short intervals (1–4 ms; Fig. 4Ca) suggesting a tendency to burst. This is a common feature of S1 cells (see Baker et al. 2003a). However, the bursting was taken account of by the distance-to-threshold analysis, which yielded a trajectory with larger values just after time zero than a little later (Fig. 4Cc). This represents the increased excitability at short times after the previous spike, presumably caused by intrinsic depolarizing inward currents.
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2 test, P < 0.001). Almost 80% of M1 PTNs had significant peaks in their composite trajectories (115/146 cells) compared with approximately 40% of M1 UIDs, area 3a cells and DCN cells (104/224, 58/144 and 92/230 cells, respectively) and less than 20% of area 2 and area 5 cells (15/92 and 12/82 cells, respectively). Peak height
For all cells, peak height was measured from the composite distance-to-threshold trajectory. This was calculated as the greatest height difference between the highest point of the trajectory, and the region 3–10 ms later. Figure 5 shows, for each brain region, the distribution of peak heights. Filled bars indicate those cells with a significant peak, assessed as described in the Methods. Most of the significant peaks were of moderate height (0.284 ± 0.013 noise units; median ± standard error of the median) although six cells did have peaks larger than 1 noise unit (one in area 3a, one in area 2 and four in DCN). The distribution for M1 PTNs was clearly shifted to the right compared to the other regions (median peak height across all cells: M1 PTNs, 0.282 ± 0.015; M1 UIDs, 0.151 ± 0.011; area 3a, 0.168 ± 0.026; area 2, 0.075 ± 0.019; area 5, 0.084 ± 0.038; DCN, 0.181 ± 0.021 noise units). M1 PTNs had significantly larger peaks than all other cell classes (Kruskal–Wallis test, P < 0.05; Tukey–Kramer post hoc test, P < 0.05).
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In agreement with the work of Wetmore & Baker (2004), the peak latency showed considerable variability across the recorded cell population (Fig. 6A). M1 cells (both PTNs and UIDs) had significantly longer peak latency (PTN, 37.1 ± 0.8 ms; UID, 36.6 ± 1.1 ms) than area 3a cells (30.9 ± 1.5 ms) and DCN cells (19.0 ± 0.9 ms). Area 3a cells also had significantly longer peak latency than DCN cells (Kruskal–Wallis test, P < 0.05; Tukey–Kramer post hoc test, P < 0.05).
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Dependence of trajectory peaks on network oscillations
It is attractive to assume that the peaks in distance-to-threshold trajectories reflect the action of conductances intrinsic to the cells. However, our recordings contained robust network oscillations, at a frequency similar to the periodicity implied by the peak latency (Fig. 2B cf. Fig. 6A). Trajectory peaks could therefore simply represent cells synchronizing to the local beta-frequency oscillations (Wetmore & Baker, 2004). To investigate this possibility, we calculated the trajectories separately for task periods with high (hold period) and low (movement phase) beta-power in the LFP (Fig. 7A and B). Only cells that previously showed significant peaks were used.
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Figure 7D shows the results across a population of M1 PTNs, M1 UIDs, area 3a cells and DCN cells. Area 2 and area 5 cells were not included in this analysis because of the low number of cells with significant peaks. Very few cells showed peaks in one task period and not the other (blue or red bars in Fig. 7D). Among the cortical cells, there was a slight bias towards larger peaks during the periods of low beta-power – the opposite of what would be expected if trajectory peaks were caused solely by network oscillations. This may be because larger peaks occur at high firing rates, as shown by Wetmore & Baker (2004), and rates were usually higher during the movement phase of the task. This tendency was less obvious in the DCN, where there was both less modulation in 20 Hz LFP power, and less modulation in firing rates, between movement and hold phases of the task.
Becaue there was no trend for peaks to be larger during periods of high 20 Hz network oscillations, trajectory peaks are probably the result of processes intrinsic to the single neuron.
Identification of neuron type by spike width
Lebedev & Nelson (1995) described a subset of S1 neurons that fired rhythmically during rest periods; they proposed that these were inhibitory interneurons. It is possible to distinguish some groups of inhibitory interneurons (fast spiking) from other groups of cortical cells (pyramidal cells and spiny stellate cells) by measuring the duration of the extracellularly recorded action potential (Swadlow, 1989, 1995). A suggested dividing line between the two distributions is 0.6 ms, although there is considerable overlap. We used this approach to see whether any of the unidentified cortical cells that we recorded were likely to be interneurons. Spike width was measured as illustrated in Fig. 8A; the distributions for different cortical areas are shown in Fig. 8B. The values for M1 PTNs (top panel in Fig. 8B) serve as a useful standard, because we know that these antidromically identified output neurons are pyramidal cells. As expected, all of these neurons have a spike width > 0.6 ms. For other regions, the distributions were centred at spike durations > 1.0 ms, overlapping with the M1 PTNs. This implies that the majority of cells were either pyramidal cells or spiny stellate neurons. There was no significant difference between the spike duration for cells with and without peaked trajectories (two-way ANOVA, P > 0.05). All the cells with peaked trajectories had durations above the 0.6 ms boundary, making it unlikely that they are fast-spiking inhibitory interneurons.
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M1 PTNs have a higher tendency for rhythmic firing than other cortical cells (Fig. 5). It is possible that this is a property of all PTNs, including those in S1, and not just those in M1. PTNs are substantially harder to locate and record in S1 than M1. There are only a few reports of S1 PTN activity in primates (Fromm & Evarts, 1982), even though parietal cortex accounts for up to 40% of the cells of origin of the pyramidal tract (Philips & Porter, 1977). In our recordings we were fortunate to record two S1 PTNs, one from area 3a and one from area 2. Figure 9 presents data from these two neurons, which were activated at fixed latency from the stimulating electrodes in the pyramidal tract (Fig. 9Aa and Ba). Spontaneous spikes were capable of colliding with evoked spikes if the stimulus was appropriately timed (Fig. 9Ab and Bb), confirming the antidromic nature of the activation and the identification as PTNs.
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Discussion |
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Functional significance of oscillations in somatosensory cortex
The present findings extend and support recent work on beta-band oscillations in the somatosensory system. Riddle & Baker (2005) argued that corticomuscular coherence is likely to be generated by sensory feedback pathways as well as descending motor tracts. Oscillations synchronized with those in EMG appear in peripheral afferents (Baker et al. 2006). In addition, S1 oscillations show strong Granger causal influences over those in M1 (Brovelli et al. 2004). Oscillatory networks therefore form a fully connected loop from cortex to the periphery and back. Such a system would be well suited to some form of proprioceptive processing, as suggested by Riddle & Baker (2006). It is interesting that lower frequency (10 Hz) oscillations have been previously suggested to play a role in decoding tactile information in both monkey hand (Ahissar & Vaadia, 1990) and rodent whisker systems (Ahissar et al. 1997).
Dissociation of 20 Hz oscillations and intrinsic rhythmicity
Surprisingly, we have shown a clear difference between the areas with large beta-oscillations and those with a high proportion of peaked distance-to-threshold trajectories. The lack of peaks in areas 2 and 5 may be due to preferentially sampling the upper layers (II–IV) of the cortex rather than layer V. However, the one area 2 PTN recorded had no trajectory peak, and cells with significant peaks were evenly distributed throughout the layers (results not shown). The small proportion of neurons in areas 2 and 5 with peaks therefore suggests that intrinsic rhythmicity is not necessary for the generation and maintenance of strong oscillations. This agrees with much previous work suggesting that oscillations are generated by local networks of inhibitory interneurons (Wang & Buzsáki, 1996; Pauluis et al. 1999; Traub et al. 1999).
Peaks in PTN distance-to-threshold trajectories
Although significantly peaked distance-to-threshold trajectories were observed in all areas recorded, they were substantially more common among the motor cortical PTNs than all other cell classes. The distribution of peak heights for PTNs (Fig. 5) was unimodal, suggesting that intrinsic rhythmicity was a common feature even for cells where peaks failed to reach statistical significance. The absence of a peak in the single area 2 PTN recorded implies that this may be a feature particularly related to motor cortical PTNs. This could reflect the different functions of M1 and S1 PTNs; however, a larger database of S1 PTNs is needed before definitive conclusions can be drawn.
In a previous study we reported that PTN spiking was coherent with LFP oscillations in M1; however, the size of this coherence was low (Baker et al. 2003b). Using a computational model, we showed (Baker et al. 2003b) that the non-linearity inherent in spike generation markedly impairs the representation of oscillations in neural spiking, and therefore attenuates coherence. Even if 20% of the inputs to a simple integrate-and-fire model neuron are coherent with LFP oscillations, the coherence with output spiking is as low as 0.05. If PTNs have a tendency towards rhythmic discharge at the same frequency as the local network oscillations, this will increase the reliability with which these oscillations can be represented in their spike output. As PTNs are the output neurons of M1, transmission of the oscillatory signal to the spinal motoneurons will consequently be improved, boosting corticomuscular coherence. The preferential association of peaked trajectories with M1 PTNs therefore provides indirect evidence that the transmission of oscillations to the periphery has a functional role in motor control. By contrast, the processing of ascending oscillatory information by somatosensory areas does not appear to require an intrinsic tendency towards rhythmicity.
Rhythmicity and DCN neurons
It has previously been shown, both in vivo and in vitro, that cells in DCN fire spontaneously in a rhythmic pattern (Thach, 1968; Jahnsen, 1986). This is reflected by the relatively high proportion of DCN cells with peaked distance-to-threshold trajectories in the current dataset (92/230 cells). Beta-frequency oscillations have previously been reported in the cerebellar cortex and DCN (Aumann & Fetz, 2004; Courtemanche & Lamarre, 2005; Soteropoulos & Baker, 2006). However, the short latency of the trajectory peaks (19.0 ± 0.9 ms; significantly shorter than the other areas) makes it difficult to link them to the oscillations. The major input to the DCN, from the cerebellar cortex, is inhibitory. The function of intrinsic conductances that produce spontaneous, rhythmic firing may instead be to allow synaptic inputs to modulate the activity in either direction (Jahnsen, 1986).
Conclusions
The present results support the idea that beta-band oscillations have a role in sensorimotor integration, rather than purely in motor control (Riddle & Baker, 2006). Oscillations were present in both motor and somatosensory cortices, but stronger in the latter. The generation of these oscillations clearly does not require the intrinsic tendency to rhythmic firing at beta-band frequencies which we observed as peaks in the distance-to-threshold trajectories. However, the high incidence of such peaks in M1 PTNs will improve the fidelity of transmission of oscillations to the periphery. This argues that oscillatory coupling between cortex and motoneurons is functionally important, rather than a simple side effect of a primarily cortical oscillatory phenomenon. We also concluded previously (Baker & Baker, 2003), from a very different study, that corticomuscular coherence was likely to be playing a functional role over and above any assigned to cortical oscillations. The exact nature of the oscillatory interplay between cortex and periphery, and the specific advantage that it confers on sensorimotor processing, remains to be elucidated.
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References |
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