  • 1Recordings were made of local field potential (slow waves) and pyramidal tract neurone (PTN) discharge from pairs of sites separated by a horizontal distance of up to 1.5 mm in the primary motor cortex of two conscious macaque monkeys performing a precision grip task.
  • 2In both monkeys, the slow wave recordings showed bursts of oscillations in the 20–30 Hz range. Spectral analysis revealed that the oscillations were coherent between the two simultaneously recorded cortical sites. In the monkey from which most data were recorded, the mean frequency of peak coherence was 23.4 Hz.
  • 3Coherence in this frequency range was also seen between cortical slow wave recordings and rectified EMG of hand and forearm muscles active during the task, and between pairs of rectified EMGs.
  • 4The dynamics of the coherence were investigated by analysing short, quasi-stationary data segments aligned relative to task performance. This revealed that the 20–30 Hz coherent oscillations were present mainly during the hold phase of the precision grip task.
  • 5The spikes of identified PTNs were used to compile spike-triggered averages of the slow wave recordings. Oscillations were seen in 11/17 averages of the slow wave recorded on the same electrode as the triggering spike, and 11/17 averages of the slow wave recorded on the distant electrode. The mean period of these oscillations was 45.8 ms.
  • 6It is concluded that oscillations in the range 20–30 Hz are present in monkey motor cortex, are coherent between spatially separated cortical sites, and encompass the pyramidal tract output neurones. They are discernable in the EMG of active muscles, and show a consistent task-dependent modulation.

A number of recent studies have reported oscillatory activity in the cerebral cortex, in the range 20–50 Hz. Initial reports in the visual cortex (Singer & Gray, 1995, for review) showed that these oscillations appeared following a specific pattern of stimuli, and that coherence between oscillations at spatially separate sites could be interpreted as having a role in the perceptual ‘binding’ of stimulus attributes. Murthy & Fetz (1992, 1997a) and Sanes & Donoghue (1993) recorded oscillations in local field potentials in the primary motor cortex during the performance of a variety of tasks. These oscillations were most pronounced in the bandwidth 25–35 Hz. Murthy & Fetz (1992, 1997a) did not find any consistent modulation with an overtrained wrist flexion–extension task, but reported that the oscillations were strongest during performance of novel tasks, including use of fine finger movements to retrieve small food rewards that were out of the monkey's sight. Sanes & Donoghue (1993) found that oscillations were consistently present while monkeys held their wrist in a target zone, but declined markedly just before and during wrist movement to another pre-cued wrist position. Oscillations persisted somewhat longer in a task requiring the monkey to press a force plate with its digits. Oscillations were widespread in motor cortex, and synchronized over large distances (up to 14 mm; Murthy & Fetz, 1992, 1997a).

A recent report by Conway et al. (1995) has demonstrated coherence in the 15–35 Hz range between magneto-encephalographic (MPG) signals recorded over human primary motor cortex, and PMG recorded from hand muscles during a steady-state contraction. This result may reflect functional linkages between motor cortex neurones and motoneurones supplying the hand muscles. In the monkey, motor cortical neurones with outputs to the spinal cord undergo very pronounced modulation in their activity during skilled hand tasks such as the precision grip (Lemon, Mantel & Muir, 1986; Bennett & Lemon, 1996). We have therefore searched for coherence between oscillations in cortical slow wave activity and PMG activity during precision grip. We demonstrate that such coherence is clearly present, that it is strongly modulated during the task, and that this coherent activity includes identified motor cortex output neurones.

A preliminary report has been published previously (Baker, Olivier & Lemon, 1996).

Behavioural task

The results reported here were gathered from cortical and EMG recordings made in two purpose-bred macaque monkeys (both adult females, one Macaca, nemestrina, one M. mulatta) trained to do the precision grip task (Lemon et al. 1986) for a fruit reward. Confirmatory analysis of EMG data was carried out on recordings obtained from a further four macaques (one male, three females, all M. nemestrina), also performing the precision grip task. The task required that two independently pivoted levers were squeezed between index finger and thumb, and held in electronically defined position windows for around 1 s. Performance on the task was cued to the animal by three tones. The first indicated that the levers were in target, the second that they had been held for long enough and should be released, and the third that a reward would follow. The onset of the second tone (‘End hold’) was used as a trigger in the analysis of task-related effects.

After the animal reached a high level of performance (ca 1000 trials per experimental session), it was implanted under general anaesthesia (isoflurane in 50:50 O2 : N2O) and aseptic conditions with a stainless steel headpiece to allow head fixation, and a circular chamber positioned over the hand representation of area 4 of the left hemisphere. During the same surgery, two fine epoxy insulated tungsten wire electrodes (impedance 15–25 kΩ at 1 kHz) were also implanted in the ipsilateral medullary pyramid, at stereotaxic co-ordinates A3 and P2. Their location was confirmed during the surgery by the presence of an antidromic field potential recorded from the surface of the motor cortex following stimulation through the electrodes, and by post-mortem histology. Antibiotics (20 mg kg−1i.m. Terramycin LA; Pfizer, Sandwich, Kent, UK) and an analgesic (5–10 μg kg−1i.m. Vetergesic (buprenorphine hydrochloride); Reckitt & Colman, York, UK) were administered for the first 2–3 postoperative days. All procedures were carried out under appropriate licences from the UK Home Office.

At the conclusion of recordings in the left hemisphere, the animal was retrained to perform the task with the left hand, and further surgery carried out in which a chamber and pyramidal electrodes were implanted on the right side.

At the start of each experimental session, the monkey was prepared to record EMG from up to seven hand and forearm muscles. Muscles abductor pollicis brevis (AbPB), adductor pollicis (AdP) and flexor digitorum profundis (FDP) were recorded from intramuscular electrodes inserted percutaneously into the muscle using a hypodermic needle at the start of each session (Lemon et al. 1986). First dorsal interosseous (1DI), abductor digiti minimi (AbDM), flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC) muscles were recorded using adhesive surface electrodes.

Cortical slow wave and single unit recordings, together with EMG data, were obtained from two monkeys (29 and 30), and quantitative analysis was carried out on data recorded from one of these monkeys (29). In this monkey, two glass-insulated platinum–iridium microelectrodes (impedance 500 kΩ to 1 MΩ at 1 kHz) were then inserted into the motor cortex using hydraulic micro-drives, which allowed independent positioning of each electrode. The horizontal interelectrode distance was held constant at 1.5 mm. The electrodes were lowered until trains of intracortical micro-stimuli (ICMS, 13 pulses, 0.2 ms pulse width, 300 Hz) elicited EMG responses or overt movements of the hand or digits with a threshold less than 20 μA. The electrodes were then moved to search for pyramidal tract neurones, identified antidromically from the pyramidal electrodes using conventional criteria (Lemon et al. 1986).

Each electrode was connected to the inverting input of a head stage preamplifier (Neurolog NL100AK). The output from these was amplified (gain, 5 or 10K), and then filtered (Neurolog NL125). Filters were set to a bandpass of 10–120 Hz for the slow wave portion and 300 Hz to 10 kHz for spikes. The slow wave data were amplified by further DC amplifiers in some sessions. Spikes were recorded on a Racal Store 14 tape-recorder using direct record modules (300 Hz to 10 kHz); the slow waves were recorded using FM modules (DC to 600 Hz).

Off-line, cell spikes were discriminated into TTL pulses using a double amplitude–time window discriminator. Spike occurrence tunes were digitized by a PC-compatible computer fitted with 1401 Laboratory Interface (CED Ltd, Cambridge, UK), together with rectified EMG (sampled at 5kHz) and the slow wave signals (sampled at 250 Hz).

Data were recorded from a total of ten penetrations in the two hemispheres of monkey 29. In monkey 30, slow wave recordings were made simultaneously from up to four sites separated by 300–1300 μm, using a 16-channel ‘Eckhorn’ drive (Thomas Recording Ltd, Marburg, Germany). Data from eleven penetrations were analysed. In the remaining four monkeys (which were used primarily for quite separate studies) only EMG data were available for analysis.

Following the end of the experimental period, the monkey was killed by an overdose (50 mg kg−1i.v.) of pentobarbitone (Sagatal; Rhone Merieux, Harlow, UK) and perfused through the heart. The brain and spinal cord were removed for histological analysis.

Local field potential spectral analysis

Analysis in the frequency domain, whilst mathematically equivalent to that in the time domain, serves to emphasize different features of the data, and is more appropriate when dealing with oscillatory data (Rosenberg, Amjad, Breeze, Brillinger & Halliday, 1989). Accordingly, the interactions between the slow wave recordings at different sites, and between slow wave and EMG, were assessed using spectral analysis. Two separate analyses were carried out. The first used all data recorded during an experiment, including all phases of task performance and periods in between successful trial performance. The objective was to characterize the frequency of oscillations. The second used sections of data selected according to the phase of task performance; this aided investigation of any task-related modulation of oscillatory activity.

The first analysis was performed as follows. For a single analog data channel, the power spectrum was calculated using a Fast Fourier Transform (FFT) algorithm (Press, Flannery, Teukolsky & Vetterling, 1989) with a 256 point window. In the case of the slow wave potentials, all FFT calculations were performed on the raw tune series, sampled at 250 Hz. For the rectified EMG, which had been sampled at 5kHz, the signal was smoothed by averaging successive sets of twenty points, producing a series also effectively sampled at 250 Hz. Under these conditions, the maximum discernable frequency is 125 Hz (Nyquist Theorem; Press et al. 1989).

In order to measure the extent to which signals were correlated in the frequency domain, coherence was calculated (Farmer, Bremner, Halliday, Rosenberg & Stephens, 1993). This is a measure of the proportion of the signal within a particular frequency band which has a constant phase relationship between the two sources, and is bounded between 0 and 1. It was calculated as follows. If the FFT derived from signal i (i= 1,2) over the lth 256 point section (l= 1…L)is FI,l(f), then the cross-spectrum is:

display math(1)

where* denotes complex conjugate; both F and X are, in general, complex numbers. The autospectrum of one of the signals is given by an analogous expression:

display math(2)

The power spectrum is simply :Ai(f):2, and the coherence is given by:

display math(3)

Before calculating C12, the cross-spectrum X12 and autospectra Ai were smoothed using a Hanning window in which each point was replaced with the weighted sum of its value and that of the two surrounding points, with weights 0.25, 0.5 and 0.25 (Farmer et al. 1993).

Significance of the coherence measures was estimated using the test provided by Rosenberg et al. (1989). On the null hypothesis that the two signals were independent, coherence above Z was considered significant at P < α, where

display math(4)

and L is the number of disjoint sections used in the estimation of the auto- and cross-spectra in eqns (1) and (2).

In order to investigate how the coherence and power spectra varied during performance of the precision grip task, sections of data 2.048 s before and l.024 s after the ‘End hold’ marker (see above) were used. These 768 sample series were split into twelve sections of sixty-four points each. The coherence and power were then calculated separately using the first section from all available trials, the second and so on, with an FFT length of sixty-four points. This allowed investigation of task-related changes in spectral measurements with a frequency resolution of 3.9 Hz and time resolution of 0.256 s. Measures were plotted as two-dimensional colour maps to aid visualization.

In addition to spectral analysis, time-domain analysis over all data recorded in a session was applied to those combinations of cortical potentials and EMGs which showed coherence peaks at around 20 Hz. This involved calculating the cross-correlation function r(τ):

display math(5)

where f1 and f2 are the two signals to be analysed, inline image1 and inline image2 are their means and σl and σ2 are their standard deviations. So defined, r is bounded between –1 and 1. The time of the first peak of this function after time zero (with positive τ indicating EMG lagging the cortical signal) was measured to estimate the time delays between the coherent portion of the two signals.

Spike-triggered averages of local field potentials

Local field potentials recorded as described above are effectively recordings of the mean postsynaptic potentials of cells in the vicinity of the electrode tip (Lemon, 1984). Spike-triggered averaging of these field recordings is expected to be a powerful means of revealing synchronous activity between the trigger cell and the neurones making synapses at the slow wave recording site (Murthy & Fetz, 1997b). Such averages in the present study frequently showed oscillations, in agreement with observations of Engel, Konig, Gray & Singer (1990) in visual cortex, where averages were compiled with multiunit activity as the trigger. A quantitative method was used, similar to the technique proposed by Engel et al. (1990).

Averages were compiled for a period 200 ms before and after the triggering spike. The standard error time course was also computed from the standard deviation time course, divided by the square root of the number of triggers. A Gabor function (Gabor, 1946) was fitted to the average using the Levenberg–Marquardt non-linear least-squares algorithm (Press et al. 1989); the algorithm took account of the standard error of each point. The function G(t) had the form:

display math(6)

where B, k, T, τ and t0 are parameters. Any fitting algorithm can suffer from the problem of local minima, which prevent it from reaching the true global minimum. In order to overcome this, the fitting was carried out in an interactive manner. The program implementing the Levenberg–Marquardt algorithm first carried out an initial fit, which was displayed along with the raw data. The parameters were adjusted manually, and a fit starting from these values was then calculated. This proceeded until the smallest value of the mean square error between data and fitted function was found; these final parameters were used in subsequent analysis.

The Levenberg–Marquardt algorithm calculates the covariance matrix Cij for the fitted parameters. This was used to calculate the standard error σG(t) in the value of the fitted Gabor function G(t) as:

display math(7)

where the five parameters of the Gabor function in eqn (6) have been rewritten as pi (i= 1…5) for simplicity of notation. The nth peak of the Gabor function was considered significant if:

display math(8)

This provided an objective means of assessing whether the average showed significant oscillations (i.e. whether eqn (8) held for n= 1, the first peak), and of how many cycles of the oscillation were present (the largest n for which eqn (8) was true).

The technique originally proposed by Engel et al. (1990) relies on the use of the fitted parameters alone; it has been criticized, since it is susceptible to false positives caused by the Gabor function providing a poor fit to the average (Young, Tanaka & Yamane, 1992). The present method overcomes this limitation, since use of the standard deviation of the fitted Gabor function naturally takes into account the goodness of fit. Thus, even fitted Gabor functions with large amplitude (high B) will fail to satisfy eqn (8) if the standard deviation is large, as it will be in the case of a poor fit. In each average analysed here, the results of the quantitative analysis were the same or more conservative than a subjective judgement of the number of peaks present.

Synchronous oscillatory activity in slow wave recordings

Examination of the slow wave recordings often revealed bursts of oscillatory activity. Data from double-electrode recordings in monkey 29 which illustrated this are shown in Fig. 1. In this section of data, two bursts of oscillatory activity can be seen, occurring simultaneously on each electrode.

Figure 1.

Synchronous slow wave oscillations during precision grip task

Example of a section of recording showing slow waves from two cortical sites (SW1 and SW2), together with the position of finger and thumb levers. Oscillations appear during the hold period of the task, which appear synchronous between the two sites. Data in this and subsequent figures are from monkey 29, unless otherwise specified.

The oscillatory activity was quantified using spectral analysis techniques as described above. Figure 2 shows the results of such an analysis on the experimental session from which the data of Fig. 1 were taken. Figure 2A illustrates the power spectrum of the voltage in the two recordings. There are several peaks in these spectra. Two sharp peaks are evident at 50 and 100 Hz. These peaks are artefactual, and caused by contamination of the recordings by mains noise; such contamination is very difficult to avoid when the frequency range of interest is close to 50 Hz. A peak is also present at 4 Hz; however, this frequency was below the filter high-pass frequency, making interpretation unreliable. Finally, there is a broader, physiological peak at 21 Hz, which is present in the slow wave recordings from both sites.

Figure 2.

Power spectra and coherence of two simultaneously recorded cortical slow waves

A, example of the power spectra of two simultaneously recorded cortical slow waves, shown overlaid in continuous and dotted lines. Peaks at 50 and 100 Hz are artefactual due to contamination by mains noise. Note the logarithmic scale. The bar indicates the region where the amplifiers had a flat frequency response. B, coherence between the two signals whose power is plotted in A. Artefactual peaks at 50 and 100 Hz are again present, but a physiological peak can be seen around 20 Hz. All values are above the theoretical 5% significance limit, suggesting electrical cross-talk is present (see text). All available data from recordings at these sites were used in these calculations.

Figure 2B plots the coherence between the slow waves recorded on the two electrodes. The 5% significance level for the coherence calculation in this case is 0.00139; all points plotted in Fig. 2B exceed this. This probably represents common signals recorded by the two electrodes from distant generators, as they were only separated by 1.5 mm. However, three clear peaks can be seen in the coherence. Two are, unsurprisingly, at 50 and 100 Hz – the source of most power at these frequencies is mains noise, which is common to both channels. However, there is also a much broader peak at 21 Hz. This represents oscillatory activity in both recordings with a constant phase relationship between the two.

Similar results were seen in all ten double-electrode penetrations available for analysis in monkey 29. Measurements from the nine penetrations where the peak was clear showed a mean frequency of 23.4 Hz (range, 21.0–27.8 Hz, measured from the middle of the frequency bin with largest coherence), with a mean peak coherence of 0.46 (range, 0.26–0.82). Slow wave recordings were made in both hemispheres of monkey 29, with data collected from the right hemisphere about 3 months after that on the left, after the animal had been retrained to perform the precision grip with the left hand. Examination of the data separately for each hemisphere indicated that the frequency at which the maximum coherence occurred between EMG and slow waves was slightly higher for right hemisphere recordings than for left (29.7 Hz compared with 22-1 Hz). This difference was significant (P < 0.001, Student's t test). In monkey 30, in which recordings were made only from the left hemisphere, the mean frequency of the oscillations, measured over the 9/15 pairs of recordings where a peak was visible, was 22.8 Hz.

Task-related modulation of cortical oscillations

Figure 1 suggests that the oscillations in slow wave activity occur at a well-defined part of the precision grip task, namely the hold period after the monkey has correctly positioned the levers and is waiting for a reward. This was investigated quantitatively by calculating the power spectra and coherence of the slow wave recordings as they varied relative to the task ‘End hold’ marker. Figure 3 shows an example of this calculation for the same experimental session as illustrated in Figs 1 and 2. Figure 3A and B plots the power in a particular frequency range versus time relative to ‘End hold’. The frequency is given on the vertical axis, and peri-task time on the horizontal axis. Averages of the finger and thumb lever position traces are presented in Fig. 3D and E, respectively, for reference. All plots in Fig. 3 have the same horizontal scale, stretching from 2 s before to 1 s after ‘End hold’.

Figure 3.

Example of variation in spectral measures with task performance

A and B, the logarithm to base e of the power in each slow wave signal (scale bars to the right of each figure). The vertical axis plots frequency and the horizontal axis plots time during task performance, relative to the ‘End hold’ task marker. Peaks at around 20 Hz can be seen in the hold period (−1 to 0 s relative to ‘End hold’). C, the coherence between the two signals, illustrated in a similar way. The coherence peaks during the hold period at around 20 Hz. D and E, averages of finger and thumb positions, respectively, compiled with respect to the ‘End hold’ marker and shown on the same horizontal scale as A–C. Same experimental session as shown in Fig. 2. Compiled from 274 trials of the behavioural task.

Panels A and B in Fig. 3 show an increase in power around 20 Hz in the three bins (ca 0.75 s) prior to ‘End hold’, confirming the impression given by Fig. 1. Figure 3C plots the coherence between the two recordings. There is a similar peak in coherence at 20 Hz at the end of the hold period. The peaks in power and coherence at 50 and 100 Hz are less noticeable in Fig. 3 than in Fig. 2. This is because the 20 Hz peaks are much larger when only hold period data are analysed than when all available data are used. These larger 20 Hz peaks therefore overshadow the 50 and 100 Hz peaks on the colour scales used in Fig. 3.

In monkey 29, a similar task dependence of the cortical oscillations and their coherence was seen in eight of the other nine pairs of recordings available for analysis. Between 102 and 323 trials were available for analysis in these recordings. These results were confirmed in simultaneous cortical recordings from two primary motor cortex (M1) sites in a second animal (monkey 30). Cortical slow waves in the 20–30 Hz band were again maximal during the hold phase of the task; an example is shown in Fig. 7A.

Figure 7.

Examples of task-dependent coherence peaks recorded in two different monkeys performing the precision grip task

Display conventions as in Fig. 6I. A, coherence between two cortical slow wave recordings, separated by 600 μm (monkey 30). B, coherence between muscles AdP and AbDM (monkey 29). Averages of lever position compiled relative to the ‘End hold’ marker are shown beneath each plot (finger above thumb). The precise timing and frequency band of the coherence varies between animals, presumably as a result of different motor strategies for performing the task; however, there is broad similarity in the general finding. n= 131 trials in A; n= 133 trials in B.

Coherence between cortical slow wave and EMG activity

If the oscillatory activity recorded at the motor cortex affected cortical output neurones, oscillations in the amount of corticospinal tract activity would occur. This could cause oscillatory bursting of motoneurone firing, potentially detectable in the gross EMG (Murthy & Fetz, 1997a). Figure 4 presents a small section of recording where such bursting of the EMG could clearly be seen in the raw data. Figure 4A and B gives the slow wave recording from the two cortical sites; Fig. 4C presents the rectified, unsmoothed EMG from the AdP muscle. There are synchronous bursts of EMG which coincide with the negative phases of the slow wave oscillations.

Figure 4.

Slow wave recording from two cortical

sites and EMG from AdP muscle Section of raw recording showing two cortical slow waves simultaneously recorded (A and B), together with rectified EMG from the AdP muscle (C). Clear bursting behaviour is visible in the EMG, which appears in phase with the synchronous cortical oscillations.

Figure 5 provides quantitative measurements of this phenomenon for two muscles recorded in the same experiment. Panels on the left (Fig. 5A–C) show data from 1DI and those on the right show data (Fig. 5D–F) from AdP muscles. Figure 5A and D plots the power spectra of the EMG from these two muscles. A 20 Hz peak can be seen for AdP. In contrast to the cortical recordings, there is no evident 50 Hz mains contamination. This is probably explained by the much lower impedance of the EMG electrodes (< 10 kΩ), compared with the cortical electrodes (500 kΩ to 1MΩ), leading to relatively clean EMG recordings. The coherence with the cortical slow wave recordings is illustrated in Fig. 5B and E (slow wave from site 1) and Fig. 5C and F (site 2). A dotted horizontal line marks the 5% significance level in each case. The large separation of the cortical and EMG recording sites eliminates electrical cross-talk, so that most points fail to show significant coherence, in contrast to the plots of Fig. 2. However, there are significant peaks close to 20 Hz for each of the four combinations of EMG with slow wave recording.

Figure 5.

Spectral measures calculated from EMG recordings

A–C relate to the 1DI muscle, D–F to AdP. A and D, power spectra of the EMG. A peak at 20 Hz is visible in D, B and E, coherence with cortical slow wave recording from one site; C and F, coherence with cortical slow wave recording from the other site recorded during this session. Dotted lines mark the theoretical 5% significance level. Peaks at 20 Hz are present in all four recordings. Large values of coherence at 0 Hz (DC) have been excluded from these plots for clarity. The bar at the top of each column indicates the range where the amplifiers had a flat frequency response. Insets show cross-correlations calculated between the signals whose coherence is shown in the main part of the figure. Arrows show time of zero lag; dotted lines, the zero correlation level. Peaks to the right of the arrow indicate EMG lagging cortical recordings. Scale bars are 20 ms, r= 0.01. G, coherence between the 1DI and AdP EMG recordings. All points he above the 5% significance limit (not shown); this is presumably due to electrical cross-talk between the two EMGs. A peak is visible, however, at 20 Hz. Same experimental session as Fig. 3. All available data from this recording session were used in this analysis.

Shown as insets in Fig. 5B, C, E and F are the cross-correlations between the slow wave signal and the EMG, whose coherence is illustrated in the main part of the figure. These all show damped oscillations, with a period consistent with the coherence calculations.

The presence of a common oscillatory drive to muscles from the motor cortex implies that there should be coherence at 20 Hz between EMG recordings from different muscles. This was found to be the case, as illustrated in Fig. 5G. The close proximity of 1DI and AdP means that electrical crosstalk becomes a contaminant again, so that all points in Fig. 5G lie above the theoretical 5% significance level. However, a 20 Hz peak can be seen above the presumed artefactual coherence.

Changes in the coherent oscillatory activity in the EMG with task performance could also be discerned, as illustrated for the same session in Fig. 6; the design of this figure is intended to mirror that of Fig. 5. Data from 1DI are presented on the left, AdP on the right. Figure 6A and E shows averages of smoothed rectified EMG activity relative to the ‘End hold’ marker. The time scale again runs from 2 s before to 1 s after this marker, and is constant for all plots in Fig. 6. Panels B and F show the power spectra of the EMG from these two muscles, using a similar display to that of Fig. 3. As well as a generalized increase in power coincident with the peaks in EMG activity in each muscle, there is an increase in power in the 20 Hz region during the hold period (−1 to 0 s relative to ‘End hold’).

Figure 6.

Changes in spectral measures derived from EMG with task performance

A–D relate to recordings from 1DI, E–H from AdP. A and E, averages of smoothed, rectified EMG compiled with the ‘End hold’ task marker as trigger (time 0). B and F, change in power of EMG with task performance. Peaks at 20 Hz during the hold period can be seen. C and G, coherence of EMG with slow wave recorded at one site, and D and H at the other site, as a function of task performance. Clear task dependence of the 20 Hz coherence is seen and primarily occurs during the hold period. Colour scales have been adjusted to threshold out all non-significant values of coherence. I, modulation of coherence between 1DI and AdP EMG with task performance. The 20 Hz peak can be seen during the hold period even when calculated between the two EMGs. All measurements were compiled from 274 trials of the behavioural task. The time scale is the same for all panels, running from 2 s before to 1 s after the ‘End hold’ marker.

Figure 6C, D, G and H shows the coherence of the two EMGs with the two cortical slow wave recordings. The colour scale used has been adjusted to threshold out all coherence values below the 5% significance level. The 20 Hz coherence peak during the hold phase is readily apparent.

Figure 6I presents the coherence between the rectified EMG of 1DI and AdP as a function of time during the task. The 20 Hz coherence during the hold period can also be seen here. Similar patterns of coherence and task dependence were found in EMG data recorded from four other animals, although the precise time and frequency of maximum coherence varied between animals. An example is shown in Fig. 7B; there is again increased coherence in the hold period of the task.

In monkey 29, evidence for 20 Hz coherence between cortical slow waves and EMG was obtained in nine out of ten recording sessions. In twenty-nine combinations of slow wave and EMG activity, significant coherence could be seen both when the calculation included all data available (as in Fig. 5) and when the task relationship of the coherence was studied as in Fig. 6. Twenty-four instances were observed where significant coherence was only seen in one or the other of these calculations, of which sixteen showed a significant effect only in the task-dependent data. This is assumed to result from the different sensitivities of each method. Thus, including all the data may decrease the signal-to-noise ratio if long periods in which the monkey is not working are present, when presumably no oscillations occur.

In monkey 29 significant 20 Hz coherence was seen in 37/124 combinations of cortical slow wave recording and rectified EMG when all available data were used. The number of significant calculations for each muscle was: AbPB, 2/18; 1DI, 12/20; AdP, 13/18; AbDM, 4/20; EDC, 1/20; FDP, 1/10; FDS, 4/18. The extent of the peak was defined as the points where it crossed the 5% significance level; in the case where the significance level was crossed several times, the crossings either side of the coherence maximum were used. The mean onset frequency of the coherence peak, measured to the left of the frequency bin, was 20.9 Hz (range, 11.7–30.3 Hz), and the mean offset frequency, measured to the right of the frequency bin, was 32.7 Hz (range, 19.5–43.0 Hz). The mean peak frequency was 25.8 Hz (range, 18.1–34.7 Hz), with mean coherence at the peak of 0.025 (range, 0.00078–0.13).

Cross-correlation analysis on the thirty-seven combinations of slow wave and EMG recordings which showed 20 Hz coherence produced thirty-three cross-correlations in which a clear peak was visible at positive lags (EMG lagging slow waves). The mean lag of this peak was 20.8 ms (range, 4–46 ms). In monkey 30, coherence at 20–30 Hz was also seen between slow wave recordings and EMG, although this was rather less common and weaker than in monkey 29.

Timing of the coherence peak

The timing of the 20 Hz coherence peak relative to the task was determined from the forty-five pairs of EMGs with slow wave recording, where it was seen in the task-related plots. The onset occurred with a mean time of 0.72 s before ‘End hold’ (range, 0.26–1.0 s), the offset with a mean time of 0.065 s after ‘End hold’ (range, 0.51 s before to 0.77 s after). The timing of the peak could be determined in each case with an accuracy of only 0.26 s (the bin width in the task-related frequency plots). Examination of the timing of the coherence separately for each hemisphere showed a small but significant difference in the mean onset time (0.65 s for left hemisphere compared with 0.82 s for right, P < 0.05, Student's t test), but no significant difference in offset time.

Coherence at low threshold intracortical microstimuli (ICMS) sites

If the cortico-motoneuronal (CM) system mediates the coherence between oscillations in cortical slow wave and EMG, a correlation might be expected between the presence of such coherence and evidence that neurones at the recording site project to the motoneurone pool of that muscle. This was tested in monkey 29 as follows. A slow wave recording and EMG were classed as showing 20–30 Hz coherence if they showed it either in overall coherence plots or in those compiled relative to the ‘End hold’ marker. Neurones at the site were assumed to make functional connections with motoneurones supplying the muscle if responses in that EMG to trains of ICMS had been seen with threshold less than 20 μA during the initial electrode placement (see Lemon et al. 1986). Such a classification produced the contingency table shown in Table 1.

Table 1. Relationship between slow wave–EMG coherence and ICMS responses
 ICMS responseNo ICMS responseTotal
  1. Contingency table illustrating the relationship between responses in a muscle to stimulation of a cortical site and the presence of significant 20 Hz coherence between the cortical recording and the muscle EMG. Values in parentheses give the expected occurrences on the null hypothesis of no interaction between the presence of ICMS and coherence. A χ2 test shows significant deviation from this null hypothesis (P < 0.01).

Coherence24 (17.1)29 (35.9)53
No coherence16 (22.9)55 (48.1)71

A χ2 test on the data of Table 1 rejected the null hypothesis of no interaction between the two categories at the 1% level. Sites capable of facilitating a muscle when stimulated therefore have a tendency also to show coherence with the EMG of that muscle, although this relationship does not hold invariably.

Spike-triggered averages (STAs) of slow wave recordings

In monkey 29, a total of eighteen pyramidal tract neurones (PTNs) were recorded simultaneously with cortical slow waves, yielding seventeen STAs of the slow wave recorded at the same site and seventeen STAs of the slow wave at the distant site. Figures 8 and 9 illustrate the finding for two cells.

Figure 8.

Example of spike data from two cells recorded simultaneously by the same electrode

A, overlaid spike waveforms showing the difference in amplitude which allowed reliable discrimination. B, collision test. Each sweep shows the cortical spike recording following stimulation through the chronically implanted pyramidal electrodes (dashed line). The stimulus was triggered at a delay after a spike occurred. In the left-hand panels, this delay was just before the critical delay, and an antidromic response can be seen. In the right-hand panels, the delay was 0.1 ms shorter, and the antidromic spike has been collided. C, autocorrelograms of neurone spikes. No periodicity is evident. D, firing modulation of the neurones with task performance. Response histograms are aligned to the ‘End hold’ task marker (time 0 s). n= 274 trials of the task.

Figure 9.

Spike-triggered averages of the cortical slow waves and EMGs

STAs of the slow wave signal extracted from the electrode used to record the spikes (A) and from the electrode 1.5 mm from this site (B). The same neurones as in Fig. 8. The STA is shown by a continuous line; the dotted line marks the best-fit Gabor function used to test significance of oscillatory peaks. In A, an artefact is present at the time of the triggering spike, due to low frequency components of the action potential appearing in the slow wave recording. C and D, spike-triggered averages of rectified EMG from IDI and AdP muscles, respectively. Oscillations can be seen superimposed on slow changes in mean EMG level caused by non-stationarity of spike and EMG data. Dashed lines mark spike times, n gives the number of spikes used to compile the average. All available spikes were used in the compilation of these averages.

Figure 8A and B illustrates the identification of the two PTNs, which were simultaneously recorded on the same electrode and separated on the basis of differences in the size and shape of their action potentials (Fig. 8A). Figure 8C shows the autocorrelograms of the two cells. No periodic features are visible in either of these. As shown in Fig. 8D, the firing rate of the two cells modulated differently during task performance.

Figure 9A illustrates the STAs of the cortical slow wave recorded from the same site as the cell spikes; Fig. 9B shows the STAs of the slow wave recorded from the distant site. In each case, there are oscillations in the averages, indicating that the neurone spike train was partially phase locked to the slow wave oscillations. The dotted lines superimposed on the STAs give the fitted Gabor functions, as described in Methods. These are a relatively good fit to the central portions of the average; the fit becomes less good at the edges. In part this is due to the asymmetric nature of the oscillations. In Fig. 9B, oscillations are stronger to the left of the spike (marked by the dashed line) than to the right. This is probably a result of the non-stationarity of both spike and slow wave data, which vary in a task-dependent fashion.

Since the PTNs illustrated in Figs 8 and 9 are partially phase locked to the slow wave oscillations, and the cortical oscillations show significant coherence with certain EMGs (Figs 5 and 6), it might be expected that STAs of these EMGs would show oscillations. Figure 9C and D shows that this was indeed the case for the 1DI and AdP muscle, for both neurones illustrated. All spikes recorded from these cells were used in the compilation of these averages; the size of the largest peak was 10.5–14.7 % of the background level in these averages. Oscillatory STAs were not always seen, presumably because of their small size, representing the convolution of a partial phase locking of the cell to the cortical oscillations, and a weak coherence between cortical oscillations and EMG. Such small oscillatory peaks can easily disappear into the noise of the STA, particularly since in averages of rectified EMG small signals tend to be underestimated (Baker & Lemon, 1995).

As noted above, the two neurones shown in Figs 8 and 9 showed little evidence of periodic firing in their autocorrelograms. A similar lack of sub-peaks in the auto-correlogram was also seen when the autocorrelograms were compiled from spikes selected from only the hold period of the task. This was not true for all PTNs investigated, many of which had at least one secondary peak in the autocorrelogram. However, the two cells illustrated here show that this is not a necessary condition for a cell to be phase locked, on average, with the local field potential oscillations.

Eleven of the seventeen STAs of slow waves recorded from the same electrode as the triggering spike, and eleven of the seventeen STAs of distant slow waves, showed significant oscillatory activity defined relative to the fitted Gabor function. Out of the eighteen PTNs analysed, fifteen showed oscillations in at least one slow wave STA. The mean number of significant peaks either side of the origin in the twenty-two averages showing oscillations was 1.8 (range, 1–4). This is likely to be an underestimate, however, due to the asymmetric nature of the oscillations, which often resulted in the fitted Gabor function having fewer peaks than were apparent in the raw average. The period of the oscillations had a mean of 45.8 ms (range, 28.0–56.4 ms), corresponding to a frequency of 21.8 Hz. This is within the frequency range of the coherence peaks described above.

The present data confirm the presence of slow wave oscillations in primary motor cortex of the monkey and the coherence of signals recorded at remote sites in the 20–30 Hz bandwidth. We demonstrate two additional important features: first we have shown, by spike-triggered averaging, that pyramidal tract neurones contribute to the oscillations. Secondly, that the coherent oscillations in the 20–30 Hz region between cortical slow wave activity and EMG in contralateral hand muscles undergo very marked modulation during the different phases of the precision grip task.

Nature of the oscillations and coherence

The cortical oscillations that we have recorded have a similar bandwidth to those reported previously (Murthy & Fetz, 1992, 1997a; Sanes & Donoghue, 1993). The striking coherence at 20 Hz between the oscillations recorded simultaneously at different sites within M1 seems unlikely to have resulted from the activity of an oscillating generator distant from the electrodes at these sites. Clearly the electrodes, separated by only 1.5 mm, did record common signals, as shown by the significant coherence at all frequencies (see Fig. 2B). However, the coherence plots showed peaks around 20 Hz, which rose above the coherence at both lower and higher frequencies. A monotonic relationship of coherence with frequency would be expected if it were due solely to spread of potentials. Steriade, Amzica & Contreras (1996) estimated the spread of cortical potentials by showing that slow wave oscillations recorded in the grey matter were strongly attenuated when the electrode moved into the white matter, a distance of 1.6 mm. Murthy & Fetz (1997a) have demonstrated phase reversal of oscillatory local field potentials within the cortical grey matter, suggesting an intracortical origin. Secondly, coherence in the 20 Hz region was unambiguously seen between cortical recordings and EMG, such that it rose above the theoretical significance level only in this frequency band. Pairs of cortical sites both showed this coherence with the same EMG (see Fig. 5). Finally, the cortical coherence peaks modulated in a task-dependent manner, which would not be the case with a purely electrical phenomenon.

Most of the evidence for coherence at 20–30 Hz between cortical oscillations and EMG activity was obtained from one monkey, in which oscillatory activity was particularly marked. However the same general results were confirmed in recordings from other animals, although the strength of coherence varied from one to another. This may represent a genuine difference between individuals in the extent to which oscillatory activity is generated by the motor cortex; it could also reflect the particular pattern of muscle activity adopted by each monkey in order to perform the task. Coherence can only be seen in muscles which show activity coincident with the presence of cortical oscillations, i.e. in the hold period.

The presence of coherence provides evidence of synchrony between remote cortical sites. It is in marked contrast to the failure of several authors to show significant cross-correlation peaks between single unit spikes recorded at such large inter-electrode spacings within motor cortex (Kwan, Murphy & Wong, 1987; Smith, 1989; Baker, Olivier & Lemon, 1994). The higher sensitivity of the slow wave coherence compared with single unit cross-correlation probably results from two factors. Firstly, the slow wave is most directly related to synaptic potentials, not cell firing. It therefore will detect effects even when they are subthreshold. Secondly, the slow wave is the average of the synaptic input to a large number of cells close to the recording electrode, such that the signal-to-noise ratio is improved by spatial averaging as well. Thus, coherence between the slow wave recordings is a highly sensitive measure of synchrony between two sites. However, it cannot measure the size of the synchronous input to the two sites as a proportion of the total input, as is possible with single unit cross-correlograms. The slow wave potential is averaged over all cells at the site, and so necessarily represents only the synchronous synaptic input.

It is important to distinguish between the finding of coherence between distant cortical sites and the presence of oscillations. Power in the 20–30 Hz region must be present in order to measure coherence within this bandwidth. However, this does not indicate that the connections mediating coherence are only functionally important when oscillations appear. It is quite possible that the connections which serve to synchronize cell assemblies during oscillatory bursts could act in an asynchronous mode at other times. This would have no effect on slow wave coherence, but could be of great importance in patterning the discharge of individual neurones.

Coherent activity in the cortical output and its impact on target muscles

The presence of coherence between the cortical recordings and the EMG suggests that cortical output neurones receive the common input represented by the slow wave coherence. This is supported by the finding that spike-triggered averages of the slow wave compiled from the discharge of identified PTNs show clear oscillations (see Fig. 8). Thus, these oscillations could act as a component of the descending command to recruit motoneurones (Murthy & Fetz, 1997a). This command is mediated, in part, by changes in the firing rate of CM cells. However, the presence of synchronous oscillations in the CM outflow would act as a stronger input to recruit motoneurones than an asynchronous command having the same overall firing rate (Murthy & Fetz, 1994). The appearance of the 20–30 Hz oscillations in motor unit firing would be expected to have little consequence for motor performance, since they would be largely smoothed out by the twitch time of single motor units and the biomechanics of the limb. In this case, the oscillations might represent an efficient means of maintaining a tonic drive to motoneurones with as little CM activity as possible (see below). A characteristic feature of many identified CM neurones is their relatively slow firing rate during the hold period when compared with other phases of the task (Bennett & Lemon, 1996).

The presence of oscillations at 20–30 Hz could relate to an observation of Lemon & Mantel (1989). In that study, spike-triggered averaging of rectified EMG was performed using CM cell spikes which were selected according to the preceding and succeeding interspike interval. Unsurprisingly, short intervals gave a large postspike facilitation (PSF), probably due to summation and facilitation at the CM synapse (Porter, 1970). However, spikes with a preceding interval of 30–70 ms produced a larger PSF than those with an interval of around 20–30 ms. It could be that the longer intervals often represented spikes where the CM cell discharged in phase with cortical oscillatory activity. More synchronous discharges from other CM cells would then be expected than when the cell fired at random, which could produce the increased PSF seen. It is interesting to note that the PSFs compiled with long preceding intervals were broader than those in STAs compiled with short interval spikes, in agreement with the suggestion that synchrony may be contributing more to these than to the short interval PSFs.

This report is the first to describe oscillatory STAs of rectified EMG activity triggered by PTNs. These features are obviously due to synchrony effects, and should not be confused with the postspike facilitations seen in such STAs and usually taken as evidence for CM connections (Fetz & Cheney, 1980; Lemon et al. 1986). The latter are much sharper, and have latencies consistent with conduction delays in the CM pathway (Porter & Lemon, 1993). STAs are usually shown on a faster time scale than those in Fig. 9C and D, so that oscillatory sub-peaks will not always be visible. However, the averages of Fig. 9C and D would still be classified as a synchrony PSF, due to their slow rise time, broad peak and early onset (Fetz & Cheney, 1980).

The finding of cortical and muscle coherent activity confirms that of Conway et al. (1995), who found coherence in the 14.5–28 Hz bandwidth between MEG recordings and hand muscle EMG in human subjects maintaining a steady contraction of the 1DI muscle. This coherence was present only between MEG recordings directly over the primary motor cortex, and with contralateral but not ipsilateral 1DI EMG.

Functional significance of the cortical oscillations

With a mean of 23.4 Hz, the oscillations reported here are similar to those at 25–35 Hz found by Murthy & Fetz (1992, 1997a, b) in monkey motor cortex, although there are differences in frequency and timing of coherence between animals (see Fig. 7), and even in the same animal between data recorded from left and right hemispheres. It is possible that the lower frequency of oscillations in the motor cortex, compared with the 40 Hz oscillations seen in the visual cortex (Engel et al. 1990; Eckhorn, 1994) reflects differences in their functional significance in motor versus visual cortical areas. It is indeed rather unlikely that oscillations subserve a single function within the cerebral cortex.

Our results show that coherent oscillations are particularly pronounced during the hold period of the precision grip task; Murthy & Fetz (1997a) also reported oscillations to be present during the hold period of a wrist extension–flexion task, although they were far less common during this highly overtrained task than when the monkey used a precision grip to retrieve small food rewards. Sanes & Donoghue (1993) reported oscillations to be most common while the monkey held the wrist or digits in a precued position, whilst awaiting the instruction to move to a new position; oscillations disappeared at the onset of movement. This may be comparable to our task, in that the monkey must maintain the hold period positions until a tone indicates completion of a successful trial and cues release of the levers.

The simplest explanation for the appearance of oscillatory bursts is that they represent an epiphenomenon. Murthy & Fetz (1997a) concluded that oscillations ‘have no reliable relation to particular components of the movement and therefore seem unlikely to be involved directly in movement execution’. In our experience, the hardest part of training an animal is teaching it to squeeze the manipulandum levers gently into the target zone, rather than pressing them up against the end stops. Once this is learnt, sudden increases in the length of the hold period are well tolerated. This suggests that oscillations may occur when the motor cortex is ‘resting’ between the demanding tasks of positioning the digits within the target zone and removal of the hand at the end of the trial. If this is the case, oscillations could be considered as a stable dynamic state which can be held without excessive computational effort, and this might be advantageous for the maintenance of relatively stable motor states, such as a steady grip force to hold an object. Murthy & Fetz (1992) also noted the strong link between finger movements and oscillatory bursts (cf. Fig. 1). The presence of oscillations during precision grip but not during the wrist flexion–extension (Murthy & Fetz, 1992) may reflect the demands of the more demanding precision grip task and increased level of attention that is required (Murthy & Fetz, 1997a).

An alternative proposal, suggested by Engel, Konig, Kreiter, Schillen & Singer (1992), is that oscillatory activity could be used as a ‘carrier wave’ capable of transmitting information reliably via weak cortical connections. However, this proposal implies that neurones can only respond effectively to inputs at quantized time intervals every 50 ms or so, which would seem to prevent rapid processing of information. If this were true, it is also hard to understand why the oscillations occur during the hold period, when probably the most demanding parts of the task are just before and just after it.

The appearance of oscillations preferentially during the hold period of the task may relate to the results of our recent study using transcranial magnetic stimulation (TMS; Baker et al. 1995). Using the same precision grip task, it was reported that the descending corticospinal volley following TMS modulated during task performance and was largest during the hold phase. Previous studies on this task have shown that corticospinal cell firing rate is greatest in the pre-hold movement phase (Lemon et al. 1986; Bennett & Lemon, 1996). It is possible that the oscillations in the membrane potential of corticospinal cells interact with current induced by TMS to produce a mean increase in the size of the descending volley, compared with the situation when no oscillations are present. This would explain why the period of maximal corticospinal excitability does not coincide with the greatest corticospinal activity.

Wessberg & Vallbo (1995) have also recently provided evidence for a ‘pulsatile motor command’. Their work concerns oscillations at 8–10 Hz, not the higher 20–30 Hz found here, and so may involve a different system. However, 8–10 Hz is the firing rate of motoneurones during weak contractions (Milner-Brown, Stein & Yemm, 1973), and it is possible that in some circumstances motoneurone discharge could be phase locked by a higher frequency input from the cortex by, for example, responding every second or third cycle. In this way, cortical oscillations at 20–30 Hz could produce the type of pulsatile motor output seen by Wessberg & Vallbo (1995).

The frequency of oscillations reported here agrees well with estimates of the frequency of the ‘temporal oscillator’ proposed by Treisman, Faulkner, Naish & Brogan (1990) to underlie time perception, and by Treisman, Faulkner & Naish (1992) to play a role in the timing of motor outflow. These experiments studied the effect of giving auditory clicks at different frequencies on time estimation or movement times, and used a model of the expected interference between auditory input and the central oscillator to predict the oscillator frequency. The close agreement between the results of these psychological studies and the current data supports the idea that these oscillations are a pervasive feature of the nervous system.

In conclusion, the results reported here have shown the presence of oscillations which are synchronized across different sites in the motor cortex. These encompass the pyramidal tract neurones and even propagate to the EMG of some muscles active at the time of the cortical oscillatory bursts. Oscillations show a clear tendency to appear preferentially during the hold period of the precision grip task used here, but their function remains a matter of speculation.

The authors wish to thank Miss Nora Philbin for technical assistance, Dr Peter Kirkwood for useful discussions and Miss Elizabeth Pinches for assisting in gathering the data from monkey 30. This work was funded by The Wellcome Trust, Action Research, the MRC and the Brain Research Trust.


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1.  Physiological control of NKT cell-dependent hepatitis induction by extracellular adenosine
European journal of immunology  2014;44(4):1119-1129.
Extracellular adenosine regulates inflammatory responses via A2A adenosine receptor (A2AR). A2AR-deficiency results in much exaggerated acute hepatitis, indicating non-redundancy of adenosine-A2AR pathway in inhibitory mechanisms of immune activation. To identify a critical target of immunoregulatory effect of extracellular adenosine, we focused on NKT cells, which play an indispensable role in hepatitis. A2AR agonist abolished NKT cell-dependent induction of acute hepatitis by Con A or α-galactosylceramide (α-GalCer), corresponding to down-regulation of activation markers and cytokines in NKT cells and of NK cell co-activation. These results show that A2AR signaling can down-regulate NKT cell activation and suppress NKT cell-triggered inflammatory responses. Next, we hypothesized that NKT cells might be under physiological control of the adenosine-A2AR pathway. Indeed, both Con A and α-GalCer induced more severe hepatitis in A2AR−/− mice than in wild-type controls. Transfer of A2AR−/− NKT cells into A2AR-expressing recipients resulted in exaggeration of Con A-induced liver damage, suggesting that NKT cell activation is controlled by endogenous adenosine via A2AR, and this physiological regulatory mechanism of NKT cells is critical in the control of tissue-damaging inflammation. The current study suggests the possibility to manipulate NKT cell activity in inflammatory disorders through intervention to the adenosine-A2AR pathway.
PMCID: PMC4482763  PMID: 24448964
NKT cell; adenosine; A2A adenosine receptor; hepatitis; immunoregulation
2.  The development and immunosuppressive functions of CD4+ CD25+ FoxP3+ regulatory T cells are under influence of the adenosine-A2A adenosine receptor pathway
The A2A adenosine receptor (A2AR)-mediated immunosuppression is firmly implicated in the life-saving down-regulation of collateral tissue damage during the anti-pathogen immune response and in highly undesirable protection of cancerous tissues during anti-tumor immune response. Therefore, depending on specific clinical situation there is a need to either weaken or strengthen the intensity of A2AR signal. While the A2AR-mediated immunosuppression was shown to be T cell autonomous in studies of effector T cells, it was not clear how A2AR stimulation affects regulatory T cells (Treg). Here we show in parallel assays that while A2AR stimulation on T cells directly inhibits their activation, there is also indirect and longer-lasting T cell inhibitory effect through modulation of Treg. A2AR stimulation expanded CD4+ CD25hi FoxP3+ cells, which also express CD39, CD73, and CTLA-4. Treg cultured with A2AR agonist showed increased expression of CTLA-4 and stronger immunosuppressive activity. There was a significant increase of Treg cell number after A2AR stimulation. The CD4+ FoxP3+ population contained those induced from CD4+ CD25− cells, but CD4+ FoxP3+ cells predominantly derived from CD4+ CD25+ natural Treg. Thus, A2AR stimulation numerically and functionally enhanced Treg-mediated immunosuppressive mechanism. These data suggest that the A2AR-mediated stimulation of lymphocytes using A2AR agonists should be considered in protocols for ex vivo expansion of Treg before the transfer to patients in different medical applications.
PMCID: PMC3389649  PMID: 22783261
regulatory T cells; adenosine; immunosuppression; A2A adenosine receptor; cancer; autoimmune; transplantation
3.  In vivo T Cell Activation in Lymphoid Tissues is Inhibited in the Oxygen-Poor Microenvironment
Activation of immune cells is under control of immunological and physiological regulatory mechanisms to ensure adequate destruction of pathogens with the minimum collateral damage to “innocent” bystander cells. The concept of physiological negative regulation of immune response has been advocated based on the finding of the critical immunoregulatory role of extracellular adenosine. Local tissue oxygen tension was proposed to function as one of such physiological regulatory mechanisms of immune responses. In the current study, we utilized in vivo marker of local tissue hypoxia pimonidazole hydrochloride (Hypoxyprobe-1) in the flowcytometric analysis of oxygen levels to which lymphocytes are exposed in vivo. The level of exposure to hypoxia in vivo was low in B cells and the levels increased in the following order: T cells < NKT cells < NK cells. The thymus was more hypoxic than the spleen and lymph nodes, suggesting the variation in the degree of oxygenation among lymphoid organs and cell types in normal mice. Based on in vitro studies, tissue hypoxia has been assumed to be suppressive to T cell activation in vivo, but there was no direct evidence demonstrating that T cells exposed to hypoxic environment in vivo are less activated. We tested whether the state of activation of T cells in vivo changes due to their exposure to hypoxic tissue microenvironments. The parallel analysis of more hypoxic and less hypoxic T cells in the same mouse revealed that the degree of T cell activation was significantly stronger in better-oxygenated T cells. These observations suggest that the extent of T cell activation in vivo is dependent on their localization and is decreased in environment with low oxygen tension.
PMCID: PMC3342240  PMID: 22566817
T cell; oxygen; hypoxia; hyperoxia; Hypoxyprobe-1; cytometry; tumor
4.  In vitro induction of T cells that are resistant to A2 adenosine receptor-mediated immunosuppression
British Journal of Pharmacology  2008;156(2):297-306.
Background and purpose:
The increased levels of extracellular adenosine in inflamed tissues down-regulate activated immune cells via the A2A adenosine receptor. This A2A adenosine receptor-mediated immunosuppression is a disqualifying obstacle in cancer immunotherapy as it protects cancerous tissues from adoptively transferred anti-tumour T cells. The aim of this study was to test whether the negative selection of T cells will produce T cells that are resistant to inhibition by extracellular adenosine.
Experimental approach:
Cytotoxic T lymphocytes (CTL) were developed by mixed lymphocyte culture in the presence or absence of the adenosine receptor agonist 5′-N-ethylcarboxamidoadenosine (NECA). The sensitivity of CTL to adenosine analogues was characterized by cAMP induction, interferon-γ production and cytotoxicity.
Key results:
CTL that could proliferate even in the presence of NECA were less susceptible to inhibition by A2A adenosine receptor agonists, as shown by a much smaller accumulation of cAMP and less inhibition of interferon-γ production compared with control CTL. The successful protocol to produce CTL that are both resistant to adenosine-mediated immunosuppression and maintain strong cytotoxicity and interferon-γ secretion required NECA to be added only during the expansion stage after the establishment of CTL. In contrast, the priming of resting T cells in the presence of NECA resulted in T cells with impaired effector functions.
Conclusions and implications:
Adenosine-resistant effector T cells were successfully obtained by exposure of activated T cells to NECA. These in vitro studies form the basis for future attempts to produce anti-tumour T cells that are more effective in adoptive immunotherapy.
PMCID: PMC2697837  PMID: 19076726
Cytotoxic T lymphocytes; cancer; adenosine; A2A adenosine receptor; adoptive immunotherapy; mouse

Results 1-4 (4)


Kendrick Lamar stars in the

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VIZ Media Presents Exciting Properties at 2007 MIPTV

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Shane Black To Direct ‘Death Note’

Death NoteShane Black — the dude who pretty much redefined the action drama by writing films like Lethal Weapon and The Last Boy Scout — has been hired by Warner Bros. to direct an adaptation of the Japanese magna series, Death Note. Currently, Anthony Bagarozzi and Charles Mondry are writing the script with Black overseeing. This will be his first directorial venture since his relaunching Robert Downey Jr.‘s career back in 2005 with Kiss Kiss Bang Bang.

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Menopause sometimes requires a survival guide

Go for new cancer treatments

WHEN it comes to cancer, many healthcare professionals advocate early detection to increase the chances of successful treatment. In reality, this is hardly the case. Although there are no Malaysian-centric statistics, research has shown that almost 50% of cancer patients in Britain are diagnosed late, making treatment less likely to succeed and reducing their chances of survival.

What this means is we need to ensure that patients with late diagnosis are able to access treatment without compromising their quality of life.

Renal cell carcinoma (RCC) or kidney cancer is often diagnosed late. This is because the symptoms for RCC are similar to those of other diseases and may only surface in the late stages. In fact, 49% of patients in Malaysia are diagnosed with RCC when the cancer is in the final stage (Stage IV). A study showed that the five-year survival rate of patients with Stage IV RCC was only 13%.

Kidney cancer is among the top 10 cancers in Western communities. According to the 2007 Malaysia National Cancer Registry Report, RCC accounts for 43.8% of new kidney cancers. However, these statistics are quite dated as it has been nine years since the data was collected.

Advances in medical research have led to new treatment modules. A revised healthcare policy should ideally be aligned with innovation in cancer treatments. Despite new targeted therapies being approved for use in the US and Europe, these therapies are still limited in most parts of South-East Asia, including Malaysia. And even if they are available in the market, patients have to purchase the drugs from private medical facilities, excluding the majority of Malaysians (75%) who seek treatment at government hospitals.

In the treatment for RCC, there is only one drug approved in the government formulary. More options are needed because a single drug may not be right for every patient. For those who are not able to respond to this particular treatment, access to an alternative drug is often a lengthy and uncertain process. For some patients, the options available to them are so dismal, there is almost a case of no option at all.

In developed countries, drug choices are fully funded by the government, leading to patients having equal access to various drugs of treatment that best suit them. In Malaysia, drug choices are limited. Patients may have to pay out-of-pocket to access these treatments, putting them in a financial dilemma of cost versus survival.

In fact, a recent study by Universiti Malaya showed that 5% of cancer patients and their families were pushed into poverty, and that cancer resulted in “financial catastrophe” for almost half of the patients who suffered from economic hardship.

The policy of approving new drugs is based on an analysis of the quality of life years patients gain versus the cost of the drug. Unfortunately, drug affordability is determined by pharmaceutical companies based on the affordability of developed countries. This leads to a mismatch in drug affordability in a country like Malaysia, where Malaysians have a diverse range of economic situations. Furthermore, no matter how clinically effective a drug is touted to be, no drug has been approved in the government formulary in recent years.

Cancer is set to be a major burden of disease worldwide and the leading cause of morbidity and mortality. It is imperative for policy makers to review and update the targeted cancer therapy treatments currently available in the national formulary so that efficacious medicines are accessible to the majority of the population in public hospitals.

We hope increased funding will be made available to assist patients in their treatment, allowing them to live longer with a better quality of life and without putting them at risk of financial catastrophe.

While Malaysia’s public healthcare system continues to evolve to meet the needs of a growing and aging population as well as alarming rate of non-communicable diseases (NCD), let us be aware of the imperative need for this country to also keep abreast of breakthrough therapies available for patients and to champion for these therapies to be accessible at our public hospitals.

Cancer does not discriminate. Every patient, regardless of their economic status or cancer stage, deserves access to treatment.

Medical Director, Beacon International Medical Centre

Vice President of College of Radiology (COR) Malaysia

Some info from the Mayo Clinic

Is blood in your urine cause for concern?

The News Item that inspired this post:

Causes of blood in the urine:

Vigorous or strenuous exercise
Sexual activity
Urinary tract infection
Kidney infection
Kidney or bladder stones
Family history of kidney disease

Kidney or bladder cancer
Polycystic kidney disease
Blood clots
Sickle cell disease
Enlarged prostate

Diagnosing hematuria

Treating hematuria

Are there any advantages to human growth hormone?

Harvard Men’s Health Watch

Ask the doctor

Q. I’ve heard about the benefits of human growth hormone (HGH) for older individuals. Is this something I should try?

A. The benefits of HGH supplementation for older adults are unproven, and perhaps most telling is that these products have a negligible effect on HGH levels. In addition, there are concerns about potential side effects.

HGH comes in two forms: injections and pills. Since HGH injections are difficult to administer, pills are often preferred. Yet, these supplements do not actually contain HGH like injections do, because the hormone would quickly break down in the digestive tract. Instead, they contain amino acids that are absorbed by the body, which raises HGH levels. (They are also more expensive and can cost $100-plus for a month’s supply.)

HGH levels naturally decline as people age, which makes sense since our bodies stop growing during the late teenage years. So why would you need higher HGH levels later in life? The hype around HGH comes from a few studies that showed HGH injections can increase lean body mass and shrink body fat, which led to claims of HGH as an “anti-aging” hormone. However, the effects on strength and body weight are quite minimal. In addition, HGH can increase the amount of soft tissues in the body, which can lead to swelling, joint pain, carpal tunnel syndrome, and breast tenderness in men.

There is also a concern that HGH might promote cancer growth. (MaryO’Note:  I always mentioned this to doctors when I was diagnosed with kidney cancer.  Even though I couldn’t take HGH for the first 5 years after diagnosis, none of my doctors would confirm a connection between HGH and my cancer)

If you want to improve your strength, forget about HGH and increase your exercise. Some studies suggest this alone may be more effective than HGH supplementation for raising growth hormone levels in the body.

—William Kormos, MD
Editor in Chief, Harvard Men’s Health Watch

Originally published: July 2016

Adapted from

Simple Kidney Cysts vs Polycystic Kidney Disease

A kidney cyst is a swollen, round, fluid-filled sac that develops on one or both of the kidneys. These cysts can be associated with serious conditions that affect the kidney’s ability to function, but usually they do not tend to cause complications and are referred to as simple kidney cysts.

Simple kidney cysts differ from the ones that develop when a person has the genetic condition polycystic kidney disease (PKD). Simple cysts do not enlarge the kidneys, change their structure or reduce their function, as is the case in people with PKD.

Simple Kidney Cysts

It is not fully understood what causes simple kidney cysts, although it is thought that obstruction of the tiny tubules within kidneys may be the cause and a deficient supply of blood to the kidney is suspected to play a role. Also, sacs that form on the tubules, referred to as diverticula, may detach from the tubule and become simple cysts.

Whether or not genetics plays a role in the formation of these cysts has not yet been studied.

Simple kidney cysts do not usually cause symptoms, damage to the kidney, or impair its function. However, if a cyst becomes large enough, it may cause pain between the ribs and hips and press on other organs. Sometimes, the cyst can become infected, in which case a person may develop a fever, as well as pain.

A simple cyst that is causing pain or obstructing urine or blood flow through the kidney may need to be treated. A procedure called sclerotherapy is used, where the cyst is punctured with a long, thin needle and drained.

For cysts that have become very large, a procedure called laparoscopy may be performed, where a surgeon drains and removes the cyst under the guidance of a fibre-optic instrument that helps view the cyst.

PKD is a genetic condition characterized by the formation of numerous cysts on the kidneys. These cysts can cause significant enlargement of the kidneys and distort their normal structure, giving rise to chronic kidney disease and impaired kidney function. This condition can even lead to kidney failure.

The cysts seen in PKD can also cause high blood pressure, vascular problems in the brain and heart, and liver cysts.

PKD is caused by a gene mutation, which is usually inherited. The remaining cases are caused by a spontaneous gene mutation, where neither parent is a carrier of the mutation. Three different mutations have been discovered that are associated with PKD. These mutations affect proteins that are required for normal kidney development.

There are two forms of PKD: autosomal dominant PKD (ADPKD) and autosomal recessive (ARPKD). These are described in more detail below.

This is the most common type of inherited kidney disease, affecting about 12.5 million individuals worldwide. Cysts filled with a watery fluid form, proliferate and grow in both of the kidneys and often also in the liver and pancreas. Over time, the cysts eventually replace healthy tissue and cause loss of kidney function. Common symptoms include pain in the back and/or abdomen, hematuria, recurrent urinary tract infection, kidney stones, and kidney failure.

This condition is inherited in a “dominant” fashion, which means that there is a 50% chance that an affected parent will pass the condition on to each child they have.

This is a rare condition that occurs in around 1 in every 20,000 newborns. Around one in three babies born with this condition die within the first four weeks of their lives. Fluid-filled sacs form in the tubules of both kidneys and in the liver. Eventually, the cysts cause scarring or fibrosis that replaces the healthy tissue in these organs. If the fibrosis is severe enough, it can lead to kidney and liver failure. ARPKD is inherited in a “recessive” manner, that is, if both parents are carriers of this condition, there is a 25% chance that each of their children will be affected.

Reviewed by Susha Cheriyedath, MSc

Further Reading