Brain Advance Access originally published online on August 22, 2003
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Brain, Vol. 126, No. 10, 2153-2163,
October 2003
© 2003 Guarantors of Brain
doi: 10.1093/brain/awg229
300-Hz subthalamic oscillations in Parkinson’s disease
1 Department of Neurological Sciences, Università di Milano, IRCCS Ospedale Maggiore di Milano, 2 Department of Biomedical Engineering, Politecnico di Milano, 3 Department of Clinical Neurology, Ospedale San Paolo, Milano, Italy and 4 School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA *These two authors equally contributed to this work.
Correspondence to: Professor Alberto Priori, Dipartimento di Scienze Neurologiche, Clinica Neurologica, Padiglione Ponti, Ospedale Maggiore Policlinico, Via F. Sforza 35, Milano, 20122, Italy E-mail: alberto.priori{at}unimi.it
Received January 8, 2003. Accepted April 21, 2003.
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Summary |
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Despite several studies and models, much remains unclear about how the human basal ganglia operate. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for complicated Parkinson’s disease, but how DBS acts also remains unknown. The clinical benefit of DBS at frequencies >100 Hz suggests the possible importance of neural rhythms operating at frequencies higher than the range normally considered for basal ganglia processing (<100 Hz). The electrodes implanted for DBS also offer the opportunity to record neural activity from the human basal ganglia. This study aimed to assess whether oscillations at frequencies >100 Hz operate in the human STN. While recording local field potentials from the STN of nine patients with Parkinson’s disease through DBS electrodes, we found a dopamine- and movement-dependent 300-Hz rhythm. At rest, and in the absence of dopaminergic medication, in most cases (eight out of 11 nuclei) the 100–1000 Hz band showed no consistent rhythm. Levodopa administration elicited (or markedly increased) a 300-Hz rhythm at rest [(mean ± SD) central frequency: 319 ± 33 Hz; bandwidth: 72 ± 21 Hz; power increase (after medication – before medication)/before medication: 1.30 ± 1.25; n = 11, P = 0.00098]. The 300-Hz rhythm was also increased by apomorphine, but not by orphenadrine. The 300-Hz rhythm was modulated by voluntary movement. Before levodopa administration, movement-related power increase in the 300-Hz rhythm was variably present in different subjects, whereas after levodopa it became a robust phenomenon [before 0.014 ± 0.014 arbitrary units (AU), after 0.178 ± 0.339 AU; n = 8, P = 0.0078]. The dopamine-dependent 300-Hz rhythm probably reflects a bistable compound nuclear activity and supports high-resolution information processing in the basal ganglia circuit. An absent 300-Hz subthalamic rhythm could be a pathophysiological clue in Parkinson’s disease. The 300-Hz rhythm also provides the rationale for an excitatory—and not only inhibitory—interpretation of DBS mechanism of action in humans.
Keywords: deep brain stimulation; event-related synchronization; high frequency; levodopa; subthalamus
Abbreviations: DBS = deep brain stimulation; ERS = event-related synchronization; LFP = local field potential; SN = substantia nigra; STN = subthalamic nucleus; UPDRS = unified Parkinson’s disease rating scale
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Introduction |
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TopSummaryIntroductionPatients and methodsResultsDiscussionReferences |
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About 80 years ago Kinnear Wilson referred to the basal ganglia as ‘the dark basement of the brain’ (Wilson, 1925
). Despite enormous progress achieved by experimental studies and functional models (Wichmann and DeLong, 1996), still little is known about the signals controlling information processing and integration in the basal ganglia of living humans.
Neuronal rhythms, i.e. oscillatory activities, play a key role in brain information processing (Gray, 1994) at multiple scales, from subcellular regulation (Goldbeter, 2002) to network integration (Varela et al., 2001). The importance of neuronal rhythms in the basal ganglia circuit has been demonstrated in animal studies in vitro (Bevan and Wilson, 1999; Plenz and Kital, 1999) and in vivo (Bergman et al., 1998). In humans, electrophysiological recordings of local field potentials (LFPs) from patients implanted with electrodes for deep brain stimulation (DBS) have so far revealed the presence of various rhythms in the classic EEG frequency range, from very low frequencies (<5 Hz) up to the gamma range (30–80 Hz) (Brown et al., 2001; Marsden et al., 2001; Pesenti et al., 2003). These studies showed the existence within the basal ganglia circuit of multiple modes of operation, observable as network activities oscillating at different frequencies (Bevan et al., 2002; Levy et al., 2000). The pathophysiological relevance of these network oscillations has been suggested by their dependency on dopaminergic medication and on movement execution (Brown et al., 2001; Cassidy et al., 2002; Foffani et al., 2002; Levy et al., 2002; Priori et al., 2002). We reported that voluntary movement modulates beta (
27 and 17 Hz) oscillations in the human subthalamic nucleus (STN) and globus pallidus internus with a pattern of movement-related desynchronization (i.e. power decrease) similar to the one observed at the scalp over the primary motor area (Foffani et al., 2002; Priori et al., 2002). Similar findings were reported independently by two other groups. Cassidy et al. (2002) studied the movement-related changes in synchronization in the basal ganglia, expanding the movement dependency we observed in the beta range up to gamma frequencies (70–80 Hz); and Levy et al. (2002) described the dependence of STN oscillations on movement and dopamine, also showing that network oscillations in LFPs are not necessarily reflected by the firing rates of the single neurons.
DBS of the STN is an effective treatment for advanced Parkinson’s disease (Limousin et al., 1998; Dowsey-Limousin and Pollak, 2001). Stimulation frequencies typically >100 Hz provide therapeutic efficacy (Moro et al., 2002), similar to the lesion in the STN (Bergman et al., 1990). Even though this parallel represents the rationale for the inhibitory/suppressive interpretation of STN stimulation (Limousin et al., 1995), the mechanism underlying the therapeutic action of DBS remains unclear (Lozano et al., 2002; Vitek, 2002); indeed, excitatory effects of DBS have been proposed recently (Windels et al., 2000; Grill and McIntyre, 2001; Lozano et al., 2002; Vitek, 2002; Hashimoto et al., 2003).
It is reasonable to hypothesize the existence of fast (>100 Hz) neuronal rhythms that would be driven by artificial stimulation at their operating frequency. We therefore recorded LFPs from DBS electrodes placed in the STN of patients affected by Parkinson’s disease searching for oscillatory activities (i.e. spectral peaks using Fourier analysis) in the frequency range of 100–1000 Hz. Two factors were tested in order to assess the possible pathophysiological relevance of these oscillations: (i) their pharmacological modulation by levodopa (dopamine precursor), apomorphine (dopamine agonist) or orphenadrine (anticholinergic); and (ii) their modulation during voluntary movements.
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Patients and methods |
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Patients
Nine patients (five men, four women) with idiopathic Parkinson’s disease were studied after obtaining their informed consent and local ethics committee approval. The average age was 57 years (range 44–69), years of disease history 16 (9–35), levodopa equivalent therapy before surgery 1455 mg (range 915–2000), Unified Parkinson’s Disease Rating Scale (UPDRS) III (motor part) presurgery ‘OFF’ therapy 44.2 (27–63.5), ‘ON’ therapy 6.8 (range 1–23.5), UPDRS III 3 months after surgery OFF therapy ON stimulation 6.6 (range 3–14; seven patients), UPDRS IV (complications) before surgery 11.8 (range 6–17), UPDRS IV 3 months after surgery 4.3 (range 0–12; seven patients).
Neuroimaging and stereotactic targeting of the STN
The STN was localized by direct visualization through an MRI–CT fusion-based technique (Fig. 1) reported in detail elsewhere (Egidi et al., 2002). The target position of the recording electrodes was further assessed with intraoperative microrecordings and stimulation through the probe microelectrodes. The 3389 electrode (Medtronic, Minneapolis, MN, USA) was ultimately implanted for DBS. The electrode had four cylindrical contacts (diameter 1.27 mm). Each of them was 1.5 mm long, and was spaced 0.5 mm away from the others (i.e. 2 mm centre to centre). Contacts were denominated 0, 1, 2 and 3 beginning from the more caudal placement. The objective was to place contact 1 into the target position. Double-blind assessment revealed marked clinical effects during intra-operative monopolar macrostimulation through contact 1. The final location of the DBS electrode within the STN was verified on the postoperative T2-weighted MRI–CT fused scan (Fig. 1). In all the patients included in this study for STN recordings (14 sides from nine patients), contact 1 (and consistently less contact 2) induced a remarkably greater clinical effect (scored postoperatively by two independent blind observers) than contacts 0 and 3. All of these procedures taken together suggest that contact 1 was within or close to the STN. We also included two further sides from two of the above patients where contact 1 and 2 had little or no clinical effectiveness and postoperative scans showed that the electrodes were caudally shifted and their final position was in the substantia nigra (SN).
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Postoperative recordings
The postoperative recording sessions took place 2–3 days after electrode implantation. The patients were comfortably seated on an armchair. LFPs were recorded at rest and during voluntary movements, 8–12 h after withdrawal of dopaminergic medication, before and after administration of 100– 200 mg oral fast-acting levodopa (Madopar Dispersibile; Roche, Monza (MI), Italy) (11 STN and two SN at rest, eight STN during movement execution) or 6 mg subcutaneous apomorphine (Apofin Stylo; Chiesi Farmaceutici, Parma, Italy) [one STN at rest: AN R (AN is the patient and R denotes right)] or 40 mg intramuscular orphenadrine (Disipal; Yamanouchi Pharma, Carugate (MI), Italy) [two STN at rest: PI R and PI L (PI is the patient, R denotes right and L denotes left)]. After-medication recordings started 30–40 min after drug administration, when the patient showed clinical improvement. The latter was evaluated by an experienced neurologist: levodopa and apomorphine reduced rigidity, tremor and bradykinesia; orphenadrine improved mainly rigidity and tremor. Most patients had some dyskinesias after levodopa, but none before. Patients were instructed to extend the second finger at the metacarpophalangeal joint from an initial relaxed semi-flexed position every 8–10 s as quickly as possible [mean total movement time (± SD) before levodopa 1.07 ± 0.42 s, after levodopa 0.91 ± 0.28 s]. Movements were self-paced. One extension corresponded to one trial; the mean (± SD) number of artefact-free trials was 113 ± 39 (range 67–165) before levodopa and 77 ± 39 (range 15–133) after. The task execution was interrupted either by the patient, if tired, or after a block of 50 trials. Multiple blocks of trials were usually recorded from each nucleus (range 2–3 before levodopa, and range 1–6 after levodopa) separated by rest periods to avoid fatigue. Dyskinesias, when present, did not interfere with task execution. The EMG signal from the extensor indicis muscle was recorded through a pair of surface non-polarizable Ag/AgCl electrodes with a belly-tendon montage. LFPs were captured from the contralateral 3389 electrode using two pairs of contact: closely spaced (1–2) and widely spaced (0–3). Signals were pre-amplified, differentially amplified and filtered (EMG, 20–1000 Hz; STN, 2–1000 Hz) through a Cambridge 1902 (Cambridge Electronic Design, Cambridge, UK), analogue/digital converted (sampling rate 2500 Hz) through a Cambridge 1401 (Cambridge Electronic Design), on-line analysed on a personal computer and stored by Signal software, version 1.80 (Cambridge Electronic Design). All further analysis was conducted off-line with the MATLAB software, version 5.3 (The Mathworks, Natick, MA, USA) with custom-written programs described below unless otherwise specified.
Rest spectral analysis
The spectra of the STN LFPs were calculated for 1 min at rest. The Welch’s averaged, modified periodogram method was used (Welch, 1967): the signal was divided into 150 sections of 1024 samples, with no overlap; each section was detrended and windowed by a Hanning window; the magnitude squared of the discrete Fourier transforms of the sections was averaged to estimate the power spectral density of the signal. Only spectral frequencies ranging from 100 to 1000 Hz were considered. To compare data before and after medication, LFPs were normalized by subtracting the mean and dividing by the SD of the 600–1000 Hz band-pass filtered signals.
Parameter extraction
In order to evaluate the effect of drugs on the STN rest spectrum, a relative spectrum was calculated for every STN by dividing the spectrum after medication with the spectrum before medication. To quantify the spectral properties of the 300-Hz rhythm (central frequency, area, height and width of the peak), a curve was fitted to the 100–1000 Hz relative spectrum using the equation
y = y0 + [ A/(w · (
/2)1/2)] · exp(–2 · (x – x0)2/w2),
where y0 is the baseline offset, A the total area under the curve from the baseline, x0 the centre of the peak, and w approximately 0.849 the width of the peak at half height. The equation represents a Gaussian curve, like the normal probability distribution function, where x0 would be the mean and w/2 the SD. Non-linear least squares fitting (Dennis, 1977) was performed with the Levenberg–Marquardt algorithm (Levenberg, 1944; Marquardt, 1963) implemented in Origin software, version 6.0 (Microcal Software Inc., Northampton, MA, USA). The goodness of fit was evaluated by two statistics: 
2 and R2. 2 is defined as the sum of the squares of the residuals, i.e. of the deviations of the theoretical curve (the Gaussian) from the experimental points (the relative spectrum), and it is minimized by the fitting procedure. R2 measures how successful the fit is in explaining the variation of the data, and it can be interpreted as the square of the correlation between the experimental curve values and the theoretical curve values. Therefore, good fitting is indicated by a 2 value that approaches zero and an R2 value that approaches one. The values obtained for the two statistics (average ± SD: R2 = 0.75 ± 0.15, 2 = 0.017 ± 0.010; n = 11; see Table 1) guaranteed that a Gaussian curve was an accurate analytical description of the relative spectrum (after/before medication) between 100–1000 Hz. The central frequency (x0) and bandwidth (w) of the 300-Hz rhythm were then calculated from the fitted Gaussian curve. The effect of levodopa on the 300-Hz rhythm was evaluated by testing the null hypotheses that the median height (h = y evaluated in x0) or area (A) of the Gaussian was equal to 0 using the Wilcoxon signed rank test. This non-parametric test was used because of the small sample size (n = 11). The central frequency and the bandwidth were used to define the band-pass filter for the movement-related analysis (see below).
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Movement onset–offset detection
The movement onsets, which triggered the analysis, and movement offsets, which allowed the estimate of movement duration, were evaluated on the EMG signal as follows: (i) the EMG signal was squared to obtain power; (ii) the mean of the resulting signal was subtracted point-by-point; (iii) the cumulative sum of the resulting signal was calculated and then filtered using a moving average forward–backward (zero phase) filter; (iv) the movement onsets were defined as the local minima (the signal at the previous and at the next instants is greater than the signal at the current instant) and the offsets as the local maxima (the signal at the previous and at the next instants is lower than the signal at the current instant) in the filtered cumulative sum; and (v) signals were visually inspected, and only epochs free from artefacts were used for the analysis.
Movement-related analysis
LFPs from the STN were first normalized to their high-frequency content (as described in ‘Rest spectral analysis’) and then filtered with a zero-phase finite impulse response (forward–backward) band-pass filter. The band was defined as the central frequency plus and minus half the bandwidth calculated with the Gaussian fitting of the relative spectra, as described above. The filtered signals were then squared to obtain power and averaged over trials. The average signals were smoothed by subsampling to a final 10-Hz sampling rate after anti-aliasing zero-phase low-pass filtering. Changes in the resulting signals were then detected by change-point analysis (Taylor, 2000; Cassidy et al., 2002) using commercial software (Change-Point Analyser 2.0 shareware program; Taylor Enterprises, IL, USA). Only changes with a probability of change >99% and 95% confidence interval <800 ms were considered, performing 10 000 bootstraps without replacement. The average power before the onset change was considered as ‘baseline power’, and the average power after the onset change less the baseline power was considered as event-related synchronization (ERS) (Pfurtscheller and Lopes da Silva, 1999) or ‘ERS power’. The significance of levodopa effect on baseline power, ERS power and on the ratio between ERS and baseline powers was separately tested using the Wilcoxon matched-pairs signed rank test. This non-parametric test was used because of the small sample size (n = 8).
Throughout the text, values are means ± 1 SD. A P-value <0.05 was considered to indicate statistical significance.
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Results |
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TopSummaryIntroductionPatients and methodsResultsDiscussionReferences |
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Searching for neural rhythms at frequencies >100 Hz, we recorded LFPs from DBS electrodes in the human STN at rest and during voluntary movement.
Rest spectral analysis
At rest, in the absence of dopaminergic medication, no consistent rhythmic activity (i.e. spectral peak) was observed between 100 and 1000 Hz (Fig. 2C and F). As well as inducing a prompt clinical motor improvement comparable to that before surgery (see UPDRS scores in the section ‘Patients’ and in Table 1), levodopa administration elicited (in eight out of 11 nuclei, from seven patients), or markedly increased (in three out of 11 nuclei) a subthalamic 300-Hz rhythm at rest (Fig. 2C, G and H). Hence, in three out of 11 nuclei the 300-Hz rhythm was already present before levodopa administration, and in the absence of dyskinesias. The 300-Hz rhythm had a central frequency of 319 ± 33 Hz and a bandwidth of 72 ± 21 Hz. Overall, the boosting effect of levodopa on the 300-Hz rhythm in the human STN at rest was highly significant. Two parameters were used to support this assertion: (i) the fitted Gaussian height, which corresponds to maximal percent increase spectral peak after medication compared with before medication; and (ii) the fitted Gaussian area, which corresponds to the integral of the relative spectrum over the entire 100–1000 Hz spectrum, i.e. a sort of total power increase of the 300-Hz rhythm in arbitrary units. The average height was 1.30 ± 1.25, where 0 represents the value before medication and 1 corresponds to 100% increase; the increase was significantly different from zero (Wilcoxon test; n = 11, P = 0.00098). The average area was 99.96 ± 58.73 arbitrary units (AU) and significantly differed from zero (Wilcoxon test; n = 11, P = 0.00098). The area and the height in one nucleus were markedly higher than the others (PA R; see Table 1). The average relative spectrum [(after levodopa – before levodopa)/before levodopa] is shown in Fig. 2H. The results are detailed in Table 1. The magnitude of the 300-Hz rhythm was compared with frequencies <100 Hz, namely the 15–40 and 70–80 Hz frequency bands. The peak power of the 300-Hz rhythm was smaller than the 15–40 Hz peak power (average 300 Hz/15–40 Hz power ratio = 0.23 ± 0.19) and greater than the 70–80 Hz peak power (average 300 Hz/70–80 Hz power ratio = 1.85 ± 0.66).
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To test the hypothesis that the 300-Hz rhythm was dopamine dependent, and not exclusively levodopa dependent, we studied the effect of subcutaneous apomorphine administration (one nucleus). The drug reduced rigidity, tremor and bradykinesia. Before apomorphine the 300-Hz rhythm was barely detectable, after drug injection it markedly increased: the fitted Gaussian height was 0.53 (i.e. 53% increase), the area was equal to 47 arbitrary units. The central frequency of the peak was 298 Hz with a bandwidth of 71 Hz (Fig. 2D).
To confirm further the specific dopaminergic dependency of the 300-Hz rhythm at rest, we studied changes in the oscillatory activity induced by intramuscular administration of orphenadrine, an anticholinergic drug (two nuclei). Although orphenadrine improved rigidity and tremor, it did not enhance the 300-Hz rhythm (Fig. 2E). Hence, despite the possible variability of the responses to apomorphine and orphenadrine and the small number of nuclei tested, the findings with these two drugs support a dopaminergic dependence of the 300-Hz rhythm.
Interestingly, the 300-Hz rhythm was always maximal in the more closely spaced pair of contacts (1–2), but absent or reduced in the wider pair (0–3) (Fig. 2I). On average, after levodopa administration the ratio between the 300-Hz power in the closely spaced pair and the 300-Hz power in the wider spaced pair (1–2/0–3) was 1.96 ± 0.62 (Wilcoxon test; n = 7, P = 0.0156). In addition, when the closely spaced pairs of contacts (1–2) were supposedly in the SN (n = 2: CR L and CM R), and hence outside the STN, we failed to detect the 300-Hz rhythm (Fig. 2J).
Movement-related power modulation of the 300-Hz oscillations
To test whether voluntary movement modulated the 300-Hz rhythm, we asked our patients to execute self-paced contralateral second finger extensions during LFP recordings from the STN (eight nuclei from six patients). When EMG activity began, power in the 300-Hz rhythm increased; when EMG activity ended, power returned to baseline values (Fig. 3). The results are detailed in Table 2. The responses of PA R are much higher than those of the other nuclei.
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Before levodopa administration, the movement-related power increase in the 300-Hz rhythm was variably present in different subjects (significant changes detected in four out of eight nuclei); after levodopa it became a robust phenomenon (significant changes in eight out of eight nuclei). As shown in Table 2, levodopa administration significantly increased the baseline power of the 300-Hz rhythm (Wilcoxon test; P = 0.0078), as well as the ERS power (Wilcoxon test; P = 0.0078) and the ERS/baseline ratio (Wilcoxon test; P = 0.0391). When the movement-related power increase of the 300-Hz rhythm was already significant before levodopa administration, the phenomenon was enhanced by the drug.
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Discussion |
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The spectrum of oscillatory activities in the human basal ganglia is hence wider than known to date, spreading also to unusually high frequencies, around 300 Hz. The 300-Hz subthalamic oscillations in Parkinson’s disease appear after dopaminergic medication and their power increases during voluntary movement.
300-Hz oscillations at rest
At rest, the 300-Hz rhythm appears only when the patient is in ‘ON’ condition after levodopa or apomorphine, but not after the anticholinergic drug. Hence dopaminergic medication in parkinsonian patients specifically shifts the system to a new dynamic equilibrium in which the human STN oscillates at a characteristic frequency around 300 Hz. Three pieces of evidence: (i) the fact that 15–40 Hz activity is decreased (and not increased) by levodopa (Brown et al., 2001); (ii) the greater peak power of the 300-Hz rhythm compared with 70–80 Hz; and (iii) the absence of spectral peaks in harmonic relationship around 100 or 150 Hz, argue against, though do not exclude, the possibility of the 300-Hz rhythm being a harmonic of lower frequencies.
Even though, for ethical and methodological reasons, we did not systematically map the spatial distribution of the 300-Hz rhythm, the fact that 300-Hz oscillations are reduced in the wider spaced pair of DBS contacts (0–3) compared with the closely spaced ones (1–2) suggests that the 300-Hz rhythm is probably located within or close to the STN. The localization of the finding is also confirmed by the fact that 300-Hz oscillations are not observable ‘out of target’, outside the STN (two SN). Nevertheless, the presence of 300-Hz oscillations in other basal ganglia nuclei is possible.
Although in this paper 300 Hz is used as the reference frequency, the range of the central frequencies of the 300-Hz rhythm actually encompasses more than 100 Hz on the frequency axis (from 232 to 354 Hz). This variability is not as large as it might seem, considering that the bandwidth of the 300-Hz rhythm can be >100 Hz. The broad bandwidth of high-frequency oscillations is in accordance with the uncertainty principle expressed by the Heisenberg inequality (
f · t 
1/4, where f is frequency resolution and t time resolution), which states that resolution in time and frequency can not be arbitrarily small, because their product is lower bounded (Gabor, 1946; Heisenberg, 1949). In other words, higher frequencies guarantee better time resolution (i.e. faster processing), but such ‘speed’ is paid for in term of frequency resolution, which necessarily decreases. This simply means that higher is the frequency of neural oscillations, wider is their bandwidth. An example of this can be seen in the classical EEG frequency range in the STN (Levy et al., 2002) or, more traditionally, at the scalp: the alpha or slower rhythms are well localized in frequency, whereas the relatively fast beta activities are more spread out on the frequency axis.
The cellular mechanisms responsible for the 300-Hz oscillations are unclear. Until now, no stable neural activity oscillating as a network at such high frequencies has, to our knowledge, been reported in any brain structure involved in human motor control. Correlated neuronal rhythms at frequencies >100 Hz have been observed in vivo in the hippocampus (Buzsáki et al., 1992; Hirai et al., 1999). The cellular origin of these high-frequency oscillations has been related to electrical coupling (Draguhn et al., 1998) and axo-axonal connections (Schmitz et al., 2001).
A synchronized, phase-shifted recruitment of STN cells, reverberating activity of STN units due to intrinsic membrane properties, subthreshold fluctuations determined by coordinated pre-synaptic input and high-frequency dendrosomatic potential uncoupling represent other possible and not mutually exclusive mechanisms for the origin of 300-Hz oscillations. Hence, the presence of a 300-Hz activity does not necessarily imply either a single presynaptic 300-Hz drive or a postsynaptic unitary activity at such high frequencies.
Movement-related modulation of the 300-Hz oscillations
The relatively slow alpha and beta EEG rhythms are commonly interpreted as ‘idling’ rhythms and, accordingly, they are lost or reduced during active processing (e.g. eye opening, movement execution, etc.); movement-related power decreases are often referred as event-related desynchronization (Pfurtscheller and Lopes da Silva, 1999). On the other hand, gamma oscillations (40–80 Hz) typically increase their power in correspondence of movements or other cognitive actions; movement-related power increases are often referred to as ERS (Pfurtscheller and Lopes da Silva, 1999); for their characteristic ERS, gamma rhythms have been proposed to directly reflect active processing (Pfurtscheller and Lopes da Silva, 1999) and they have been functionally related to the need for ‘binding’ information from different brain structures (Von der Malsburg, 1995) to produce fast integration for higher-level processing (Singer and Gray, 1995). Indeed, the 300-Hz rhythm we observed in the human STN presents pharmacological and movement-related dynamics that are very similar to those previously reported in the same STN, coherently with the globus pallidus internus, in the high-gamma range at 70–80 Hz (Brown et al., 2001; Cassidy et al., 2002): they closely follow the EMG burst, with the power rapidly increasing and decreasing back to baseline values.
During movement execution, levodopa significantly increased the 300-Hz baseline power, the ERS power and the ERS/baseline ratio. The fact that levodopa effect is significant on the 300-Hz baseline power is obvious: the baseline power corresponds to a ‘dynamic rest’ between two movements, therefore the levodopa dependent increase confirms the results obtained at rest. On the other hand, the fact that levodopa effect is significant on the 300-Hz ERS power means that greater the dopamine -dependent 300-Hz activity at rest, the greater its movement-related modulation. Finally, the fact that effect of levodopa on the ERS/baseline ratio is significant is subtler, and it indicates that the relationship qualitatively stated at the previous sentence is non-linear. Intuitively the concept is that the ‘amount’ of oscillation at rest not only allows a certain ‘amount’ of movement-related modulation, but also increases the ‘efficiency’ of the modulation itself.
Pathophysiological implications
One might wonder whether high-frequency oscillations in the STN, either at 70–80 Hz (Brown et al., 2001; Cassidy et al., 2002) or at 300 Hz, are not causally responsible for the levodopa induced improvement but only its epiphenomenon. Also, these oscillations might be, at least in part, related to drug-induced involuntary movements. However, Cassidy et al. (2002) reported that more than half of the patients had no clinical evidences of dyskinesia or tremor during the recordings and changes at 70–80 Hz started after a warning stimulus, during the reaction time and before movement onset. In addition, 300-Hz activity can be observed even before levodopa administration (30% of the nuclei at rest and 50% movement related), in absence of any involuntary movement. Finally, these high-frequency subthalamic oscillations are modulated by both dopaminergic stimulation and voluntary movement. Therefore, at least in Parkinson’s disease, the 300-Hz rhythm appears to be a determinant of voluntary motor behaviour.
The finding of the 300-Hz rhythm in the human STN has several possible implications. First, because signals at higher frequencies can process larger amounts of information than rhythms in the beta or even gamma range, faster rhythms accord well with the operational complexity of the basal ganglia circuit. Using an analogy with the classic communication theory, the STN processes phasic movement information in amplitude modulation mode, on the 300-Hz carrier frequency. The maximum theoretical bandwidth of the transmission (i.e. the amount of information per time unit) is directly proportional to the carrier frequency (Haykin, 1994). Although the information currency of the nervous system is the action potential rather than the LFP, 300-Hz oscillations may provide a coordinated ‘clock’ that paces the neurons excitability at a resolution of 3 ms, acting somehow similarly to the clock of a microprocessor in a personal computer. Therefore, the 300-Hz subthalamic rhythm might not be directly involved in individual movements or actions, but it could be a non-specific regulator of neural synchrony in order to guarantee specific modulation of individual movements, that probably are more directly determined by lower frequencies. The 300-Hz power increase during movement execution would further enhance the clock precision when fast and robust processing is needed. Hence, the loss of 300-Hz oscillations can contribute to movement abnormalities in Parkinson’s disease.
Secondly, from a pathophysiological point of view, the appearance of the 300-Hz rhythm after dopaminergic medication in subjects at rest may reflect the shift of a bistable neural subsystem from quiescence to an operational state that allows the optimal information processing required for normal motor control. Neural bistability has recently been suggested as a key element for robust neural integration (Koulakov et al., 2002). At a cellular level, bistable behaviour has been experimentally observed in neurons from many brain structures, including the striatum (Nicola et al., 2000), depending on specific neurotransmitters, including dopamine (Nicola et al., 2000; Lavin and Grace, 2001). At the network level, the sudden appearance of synchronized neural rhythms in correspondence with pathological or physiological events, as, for example, in epilepsy (Manuca et al., 1998) or simply in attention (Steinmetz et al., 2000; Salinas and Sejnowski, 2001), may be interpreted as a state transition of a complex neural system. In Parkinson’s disease, the loss of nigrostriatal dopaminergic projections might determine an opposite transition to a desynchronized state, electrophysiologically reflected by absence of the 300-Hz subthalamic rhythm and, eventually, reversed by dopaminergic medication.
Finally, the 300-Hz rhythm could also help to explain how STN DBS induces its beneficial effects in Parkinson’s disease. The clinical efficacy of DBS at frequencies >100 Hz could reflect its role as artificial (subharmonic) drive for the physiological neural oscillations at 300 Hz required for normal basal ganglia function. If we interpret the basal ganglia as a complex dynamic system, dopamine may be viewed as an external input that sets some internal variables at the working oscillatory equilibrium required for a correct output. When the dopaminergic system is defective as it is in Parkinson’s disease, DBS would directly drive those internal variables into or closer to their physiological oscillatory equilibrium. This excitatory interpretation of DBS at a network level is in agreement with recent findings on the MPTP primate model of Parkinson’s disease (Hashimoto et al., 2003), and it does not contrast with inhibitory effects at the cellular level that have been originally suggested for the similar effect of STN stimulation and its lesion. Indeed, DBS and lesions can elicit a similar (but probably not the same) clinical improvement through different mechanisms of actions. As shown by intracerebral microdialysis studies in rats (Windels et al., 2000, 2002), excitatory and inhibitory effects might coexist and both contribute to the therapeutic effect of DBS. Hence, DBS could act in a far more complex way than was once thought.
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Acknowledgements |
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The authors wish to thank Dr G. Ardolino, Professor G. Baselli, Ing. A. Bianchi, Dr V. Chiesa, Dr M. Locatelli and Dr A. Pesenti, for their helpful cooperation, and S. Croci, S. Garlaschi, G. Gherardi, A. Marsilio, B. Meda, M. Pastori and M. Pellegrini for their technical assistance. The study was partially supported by Associazione Amici del Centro Dino Ferrari for Neurodegenerative Disorders, by a Health Italian Ministry Collaborative grant with IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy and by MURST.
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References |
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TopSummaryIntroductionPatients and methodsResultsDiscussionReferences |
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