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Neuroscience |
1 Dipartimento di Scienze Neurologiche, Università di Milano, Fondazione IRCCS Ospedale Maggiore Policlinico, Milano, Italy
2 School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA
3 Fundación del Hospital Nacional de Parapléjicos para la Investigación y la Integración, SESCAM, Toledo, Spain
4 Dipartimento di Bioingegneria, Politecnico di Milano, Milano, Italy
5 Dipartimento di Neurologia Clinica, Ospedale San Paolo, Milano, Italy
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Abstract |
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(Received 18 October 2005;
accepted after revision 5 January 2006;
first published online 12 January 2006)
Corresponding author A. Priori: Dipartimento di Scienze Neurologiche, Clinica Neurologica, Padiglione Ponti, Ospedale Maggiore Policlinico, Via F. Sforza 35, Milano, 20122 Italy. Email: alberto.priori{at}unimi.it
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Introduction |
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Recent studies with local field potential (LFP) recordings from electrodes implanted for deep brain stimulation (DBS) in patients with Parkinson's disease are providing a new complementary scenario of information processing in the human basal ganglia (Brown & Williams, 2005). In this scenario, the elementary channels of thalamo-cortico-basal ganglia communication are defined at the network level by LFP rhythms operating at various frequencies (Brown et al. 2001; Marsden et al. 2001; Levy et al. 2002b; Priori et al. 2002, 2004; Cassidy et al. 2002; Williams et al. 2002, 2003, 2005; Silberstein et al. 2003; Kühn et al. 2004, 2005; Foffani et al. 2003, 2004, 2005a,b,c,d; Doyle et al. 2005; Alegre et al. 2005; Fogelson et al. 2005, 2006). LFPs reflect the synchronous presynaptic and postsynaptic activity of large neuronal populations and can detect focal network rhythms that are not necessarily observable in single neurones or neurone pairs (Creutzfeldt et al. 1966; Frost, 1968; Murthy & Fetz, 1992, 1996a,b; Baker et al. 1997; Donoghue et al. 1998; Magill et al. 2004; Goldberg et al. 2004). The presence of multiple LFP rhythms suggests ‘the existence at the network level of multiple functionally independent – but not necessarily spatially separated – subsystems operating at different frequencies’ (Priori et al. 2004). Hence ‘tuning to distinct frequencies may provide a means of marking and segregating related processing, over and above any anatomical segregation of processing streams’ (Fogelson et al. 2006). Little information is available on the segregation between LFP rhythms at different frequencies. This issue is critical for clarifying whether the segregation concept is pathophysiologically relevant for human basal ganglia information processing at the network level.
In the same way as a loss of segregation at the cellular level induces linear correlation between neurones (i.e. linearly correlates neural signals oscillating at the same frequency), a loss of segregation at the network level presumably induces non-linear synchronization between LFP rhythms (i.e. non-linearly correlates neural signals oscillating at different frequencies). We therefore hypothesized that Parkinson's disease produces non-linear correlations between subthalamic LFP rhythms oscillating at different frequencies and that these non-linear correlations are reversed by dopaminergic medication. A useful approach for studying non-linear correlations is bispectral analysis, a procedure widely used in scalp EEG studies (Dumermuth et al. 1971; Barnett et al. 1971; Kearse et al. 1994a,b, 1998; Sebel et al. 1995, 1997; Glass et al. 1997; Pfurtsheller et al. 1997; Pfurtsheller & Lopes da Silva, 1999; Bannister et al. 2001) and already applied to investigate non-linear cortico-cortical correlations between LFP rhythms in mammals (Schanze & Eckhorn, 1997; Villa et al. 2000). We therefore tested our hypothesis by applying bispectral analysis to LFPs recorded from DBS electrodes implanted in the STN of patients with Parkinson's disease, searching for non-linear correlation between subthalamic rhythms before and after administration of levodopa.
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Methods |
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The study sample consisted of nine patients (four men, five women) with idiopathic Parkinson's disease admitted to the Department of Neurological Science, IRCCS Ospedale Maggiore di Milano and fulfilling the specific inclusion criteria for DBS treatment (L.I.P.E., 2003). All of them were studied after informed consent and local ethical committee approval, and the study conformed with the Declaration of Helsinki. The average age was 55 years (range 44–69 years), years of disease history 12.3 (7–20 years), levodopa equivalent therapy presurgery 1535 mg day–1 (800–2800 mg day–1), after surgery 335 mg day–1 (75–800 mg day–1), Unified Parkinson's Disease Rating Scale (UPDRS) III (motor part) presurgery off therapy 43.7 (27–72.5), on therapy 5.2 (1–23.5), UPDRS III 12 months postsurgery off therapy on stimulation 9.0 (1.5–20), UPDRS IV (complications, A + B) presurgery 10.1 (5–14), and UPDRS IV 12 months postsurgery 2.3 (0–6). Patients were predominantly rigid-akinetic, with severe motor fluctuations not responsive to pharmacological treatment. No patient was on long-acting agonists at the time of surgery. One of the main inclusion criteria was a good response to levodopa, as measured by the UDPRS III.
All patients were bilaterally implanted in the STN with macroelectrodes for DBS (model 3389 Medtronic, Minneapolis, MN, USA). The STN was targeted by direct visualization through a CT-MRI fusion-based technique before surgery, as extensively reported elsewhere (Egidi et al. 2002; Rampini et al. 2003). The STN position was estimated by matching the CT-MRI fused images with a digitized stereotactic atlas. During surgery, the implant position was assessed by microrecordings from explorative microelectrodes (Priori et al. 2003) and by clinical assessment of the effects induced by stimulation through the implanted macroelectrodes. The implanted 3389 Medtronic electrode has four cylindrical contacts (1.27 mm in diameter, 1.5 mm in length, placed 2 mm apart, centre-to-centre) denominated 0–1–2–3, beginning from the more caudal contact. According to neuroimaging and intraoperative clinical and neurophysiological tests, for all nuclei studied the position of contact 1 was consistent with placement within the STN. Four subjects were studied bilaterally, five only unilaterally, for a total of 13 nuclei.
Local field potential (LFP) recordings
The LFP recording procedure is described in detail elsewhere (Priori et al. 2004). In brief, LFPs were recorded from the implanted electrodes 2 or 3 days after surgery, before the subcutaneous high-frequency stimulators were connected. LFPs were recorded at rest (60–80 s), 8–12 h after withdrawal of dopaminergic treatment both before (‘off’ therapy condition) and after (‘on’ therapy condition) patients received dopaminergic medication (100–200 mg of oral fast-acting levodopa – Madopar Dispersibile; Roche, Monza, Italy).
After-medication LFPs were recorded at least 20 min after drug administration, when the patient showed clinical changes, as evaluated by self-scoring and by an experienced neurologist. In order to monitor the temporal dynamics of the dopaminergic effects, in three nuclei from three of the above patients, LFPs were recorded at rest (60–80 s) every 2–5 min from the off-state until the on-state, 33–50 min after levodopa administration.
LFPs were bipolarly captured from the 3389 electrode using the closely spaced pair of contacts 1–2, then preamplified, differentially amplified (100 000x) and filtered (band pass 2–1000 Hz) with an analogical amplifier (Signal Conditioner Cambridge 1902, Cambridge Electronic Design, Cambridge, UK). The output signal was digitized (Cambridge Micro 1402, Cambridge Electronic Design), with sampling rate 2500 Hz and 12 bit quantization with 5 V range. All further analysis was conducted off-line with Matlab software (version 6.5, The Mathworks, Natik, MA, USA). As a preprocessing step, each LFP recording was normalized by subtracting the mean and dividing by the standard deviation of the 600–1000 Hz band-pass filtered signal, which is supposed to contain only background noise. This procedure therefore imposes the same background noise on all recordings, thus reducing the variability between subjects and between pharmacological conditions (Foffani et al. 2003, 2005d; Priori et al. 2004).
Bispectral analysis
Previous studies defined the LFP rhythms in the human basal ganglia by means of power spectral analysis. The power spectrum transforms second-order statistics (i.e. the variance) from the temporal to the frequency domain, assuming that spectral rhythms at different frequencies are statistically independent (i.e. not correlated). Non-linear correlation between spectral rhythms can be detected by means of bispectral analysis (Kim & Powers, 1979; Nikias & Mendel, 1993). The bispectrum (third-order spectrum) transforms third-order statistics (i.e. the skewness) from the temporal to the frequency domain, representing non-linear correlations between spectral components in a two-dimensional (2-D) frequency map, according to the following equation:
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| (1) |
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| (2) |
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| (3) |
The bicoherence can be estimated by applying eqn (2) with the spectrum and bispectrum estimated with eqn (3). Two main quantitative measures were used in our study: the modulus of the bispectrum, |B(f1, f2)|, and the squared modulus of the bicoherence, |Bic(f1, f2)|2, which throughout this paper will be consistently called bispectrum and bicoherence for simplicity. The bicoherence has three important properties: (i) if the generating process is Gaussian, then the bicoherence is constant, which makes it a useful detector for the gaussianity of a given signal (Godfrey, 1965; Hinich, 1982); (ii) because the bicoherence is normalized by the power spectrum, it provides a measure of non-linear correlation between rhythms that is independent of the peaks in the power spectrum (Godfrey, 1965); (iii) because the asymptotic distribution of the estimated bicoherence is known, its statistical significance is easy to evaluate on a single signal. However, the estimate of the bicoherence is highly dependent on the number of samples per segment and on the total number of samples used in the analysis. We therefore used the bicoherence for quantifying the significance of the results at the single-nucleus level, and used the bispectrum to confirm the results at the population level.
Parameter extraction and statistical analysis
The data were analysed in the classic EEG range of frequencies (2–40 Hz), consistent with our previous work (Priori et al. 2004). Signals were digitally low-pass filtered (cutoff at 40 Hz) and down-sampled at 125 Hz. To facilitate statistical analyses within and between nuclei, frequencies below 40 Hz were divided into four bands, as previously described (Priori et al. 2004): very-low frequencies (2–7 Hz), alpha (8–12 Hz), low-beta (13–20 Hz) and high-beta (20–35 Hz). Accordingly, we defined six regions of interest (ROIs) in the bicoherence/bispectrum frequency map that represent all the possible intersections of the spectral bands (Fig. 1C): ROI 1 =[2–7 Hz, 2–7 Hz], ROI 2 =[8–12 Hz, 8–12 Hz], ROI 3 =[13–20 Hz, 13–20 Hz], ROI 4 =[2–7 Hz, 8–12 Hz], ROI 5 =[2–7 Hz, 13–20 Hz], and ROI 6 =[8–12 Hz, 13–20 Hz]. Intersections corresponding to the high-beta band were not considered, because the generated harmonics would be within the range of the line noise (50 Hz). The direct non-parametric method (eqn (3)) was applied to estimate both spectra and bispectra, to be consistent with the methodology used in previous studies (Priori et al. 2004). For each nucleus, the LFP signal was divided into segments of 128 samples with no overlap and no tapering; each segment was detrended (i.e. the mean was subtracted); the spectrum and bispectrum were estimated in each segment and then averaged to obtain the spectrum and bispectrum of the whole signal (Nikias & Raghuveer, 1987; Mendel, 1991; Nikias & Mendel, 1993). The frequency resolution is 0.98 Hz.
Data were analysed statistically at two levels, as previously introduced: at the single-nucleus level using the bicoherence, and at the population level using the bispectrum. At the single nucleus level, significant non-linear correlations between LFP rhythms were identified by the presence of significant bicoherence within the aforementioned ROIs. The bicoherence estimate has an asymptotical chi-squared distribution with 1/
2 degrees of freedom, where 
, N0 is the number of samples per segment (i.e. 128), fs is the sampling frequency (i.e. 125 Hz), N is the total number of samples (about 60–80 s with 125 Hz of sampling frequency), when no tapering or overlapping procedures are applied to the data (Brillinger, 1965; Brillinger & Rosenblatt, 1967; Huber et al. 1971; Kim & Powers, 1979; Elgar & Guza, 1988; Nikias & Mendel, 1993). We therefore used the 95% confidence interval of the chi-squared distribution as the threshold level for the significance of the bicoherence (Huber et al. 1971; Kim & Powers, 1979; Elgar & Guza, 1988; Nikias & Mendel, 1993), given by:
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| (4) |
The significant threshold used was calculated from eqn (4) applied to our data for each recording, namely 0.12 ± 0.05 for STN LFPs recorded before levodopa and 0.10 ± 0.03 for STN LFPs recorded after levodopa. At the population level, the effect of levodopa on the non-linear synchronizations between LFP rhythms was quantified by submitting the maximum value of the bispectrum (in arbitrary units, AU) in each ROI and each clinical condition to a two-way repeated measures analysis of variance (ANOVA). Each nucleus was considered as a different sample and logarithmic transformation was applied to reduce the variance. The first main factor of the ANOVA was therefore the ROI, with six levels: ROIs 1–6. The second main factor was the clinical condition, with two levels: off (i.e. before levodopa) and on (i.e. after levodopa). Tukey's honestly significant difference test was used for post hoc comparisons. Results were considered significant at P < 0.05. Throughout the text values are means ± standard deviation.
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Results |
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In the off clinical state, after overnight withdrawal of dopaminergic therapy, the power spectrum evidenced activity in the four frequency bands previously described (Priori et al. 2004), namely very-low frequencies (2–7 Hz), alpha band (8–12 Hz), low-beta band (13–20 Hz) and high-beta band (20–35 Hz) (Fig. 2A and Fig. 3A, left and lower plots). In the analysis seeking non-linear correlations between LFP rhythms, all nuclei and ROIs displayed significant bicoherence (ROI 1 =[2–7 Hz, 2–7 Hz]; ROI 2 =[8–12 Hz, 8–12 Hz]; ROI 3 =[13–20 Hz, 13–20 Hz]; ROI 4 =[2–7 Hz, 8–12 Hz]; ROI 5 =[2–7 Hz, 13–20 Hz]; ROI 6 =[8–12 Hz, 13–20 Hz]; Fig. 1C), suggesting that the off condition led to numerous non-linear correlations between LFP rhythms. More specifically, 11 nuclei (of 13) displayed significant bicoherence in ROI 1 (max bicoherence = 0.26 ± 0.17 at [3.46 ± 0.8 Hz, 3.46 ± 0.8 Hz]), five nuclei in ROI 2 (0.31 ± 0.23 at [9.2 ± 1.3 Hz, 9.2 ± 1.3 Hz]), 12 nuclei in ROI 3 (0.36 ± 0.19 at [14.9 ± 2.0 Hz, 14.9 ± 2.0 Hz]), two nuclei in ROI 4 (0.23 at [6.8 Hz, 10.7 Hz] and 0.31 at [4.9 Hz, 8.8 Hz]), two nuclei in ROI 5 (0.12 at [5.8 Hz, 17.6 Hz] and 0.28 at [5.8 Hz, 14.6 Hz]) and three nuclei in ROI 6 (0.11 ± 0.01 at [10.4 ± 1.1 Hz, 15.6 ± 1.7 Hz]). Of 13 nuclei, 11 displayed significant bicoherence in more than one ROI, and seven nuclei in three or more ROIs (see Fig. 2 for a representative example). Despite the frequency variability between nuclei, the mean bispectrum clearly showed the most consistent peaks of non-linear correlation (i.e. in ROIs 1, 2, 3) thus indicating exactly how bicoherence behaved in the population as a whole (Fig. 3A). The mean bispectral peak in ROI 1 suggests that the broad peak in the low-frequency band (2–7 Hz) of the power spectrum reflects complex activity resulting from non-linear correlations within this frequency band. The mean bispectral peak in ROI 2 suggests that alpha LFP rhythms (8–12 Hz) can produce harmonic non-linear correlation with the low-beta range (13–20 Hz) or even with the high-beta range (20–35 Hz). The mean bispectral peak in ROI 3 was remarkably consistent, suggesting that in the off state the LFP activity in the high-beta range (20–35 Hz) is, partially, a harmonic of the LFP activity in the low-beta range (13–20 Hz).
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As previously described (Priori et al. 2004), after patients received levodopa the frequency patterns in the power spectrum changed (Fig. 3B, left and lower plots): the spectral power at very-low frequencies dramatically increased, while the spectral power in the low-beta band decreased; oscillations in the alpha range remained almost unchanged; spectral power in the high-beta range tended to decrease, but a low-amplitude peak persisted. Bispectral analysis showed that levodopa administration also altered the non-linear correlation between rhythms: significant bicoherence remained in ROI 1 (11 of 13 nuclei; max bicoherence = 0.53 ± 0.50 at [4.7 ± 1.0 Hz, 4.7 ± 1.0 Hz]) and in ROI 2 (6 nuclei; 0.16 ± 0.08 at [11.1 ± 1.2 Hz, 11.1 ± 1.2 Hz]), but only few nuclei exhibited significant bicoherence in ROI 3 (2 nuclei; 0.46 at [17.6 Hz, 17.6 Hz] and 0.36 at [17.6, 17.6]) and in ROI 4 (1 nucleus; 0.20 at [3.9 Hz, 8.8 Hz]) and no significant bicoherence was found in ROI 5 and 6. These dopamine-dependent changes were clearly detectable in the mean bispectrum (Fig. 3B). The two-way ANOVA (Fig. 4) showed a significant interaction factor (P < 0.000001), between ROIs (6 levels: 1, 2, 3, 4, 5, 6) and the clinical condition (2 levels: off, on). The post hoc test (Tukey's HSD) revealed that after levodopa administration the max bispectrum significantly increased in ROI 1 (2–7 Hz, 2–7 Hz; P= 0.0114) and significantly decreased in ROI 3 (13–20 Hz, 13–20 Hz; P= 0.0001) and in ROI 6 (8–12 Hz, 13–20 Hz; P= 0.0003). The results obtained with the analysis at the population level using the bispectrum therefore confirmed the results obtained with the analysis at the single-nucleus level using the bicoherence. In three nuclei from three patients, we also studied the temporal dynamics of the dopaminergic effects. The non-linear correlation between the low-beta rhythm and the high-beta rhythm was very stable in the off state and it abruptly disappeared with the on state (Fig. 5), paralleling the abrupt changes observed in the power spectrum (Priori et al. 2004). In synthesis, levodopa increased non-linear correlations within the low-frequency band and decreased (or left unchanged) all the other non-linear correlations, thereby increasing segregation between LFP rhythms operating at different frequencies.
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Discussion |
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Methodological considerations
The concept of non-linear correlation as used in this paper refers to non-linear phase coupling between LFP rhythms operating at different frequencies, as detected by applying higher-order spectral analysis – bicoherence and bispectrum – to single LFP signals. In parallel, we have employed the expression linear correlation to refer to the linear coupling between neural rhythms operating at the same frequency, frequently described in the literature by applying second-order spectral analysis – cross-correlation function and coherence – to pairs of neural signals. It is worth mentioning that the coupling between neural rhythms, either linear or non-linear, is often described in terms of synchronization (Varela et al. 2001; Gross et al. 2004; Schnitzler & Gross, 2005; Foffani et al. 2005b). However, the word synchronization is used in neuroscience literature with a variety of slightly different meanings (Salinas & Sejnowski, 2001). We have therefore preferred the term ‘correlation’, interpreted in its more rigorous and general sense, as absence of statistical independence between communication channels (Schneidman et al. 2003; Latham & Nirenberg, 2005; Narayanan et al. 2005).
Patterns of non-linear correlation between LFP rhythms in our patients showed variability. Possible reasons for variability within and between studies using LFP recordings in humans include the time elapsed from electrode implant, the clinical features of the patients and possible biases in the localization of electrodes within the STN (Foffani et al. 2003, 2005c; Priori et al. 2004). In relation to the clinical conditions of the patients, even though the Parkinsonism substantially worsened after overnight withdrawal of levodopa and no patient was on long-acting agonists at the time of surgery, our experimental design leaves open the possibility that long-term drug effects influenced LFP activity. Nevertheless if they did we would probably have underestimated the non-linear correlations between LFP rhythms in the off state and the segregating effects of levodopa, variables that both yielded statistically significant results. More important, studies on polarity reversals, amplitude gradients between adjacent electrode contacts and topographical synchronization between LFP oscillations and single-neurone activity supported the hypothesis that LFP oscillations in the alpha and beta range originate in the subthalamic nucleus (Brown et al. 2001; Levy et al. 2002a; Kühn et al. 2004; Doyle et al. 2005; Fogelson et al. 2006), whereas the origin of LFP oscillations at very-low frequencies is still not well defined. The non-linear correlations we observed between the alpha, low-beta and high-beta LFP rhythms probably therefore reflect non-linear interactions within the subthalamic nucleus, whereas the strong correlations we observed at low frequencies might have been more broadly distributed in nearby brainstem regions. Another possibility we cannot exclude is that the correlation between LFP rhythms at least in part reflected the non-linear behaviour of a single oscillator. Nevertheless, the presence of non-linear correlation between LFP rhythms should be carefully considered to interpret correctly the results obtained with traditional spectral analysis. Finally, even though our results strongly suggest that independence between LFP rhythms is physiologically relevant, studies based on LFP recordings from DBS electrodes have been conducted exclusively in patients with movement disorders. Caution is therefore needed in extending these findings to physiological conditions.
Multiple rhythms in the human subthalamic nucleus
In this study we show that in the parkinsonian off state the low-beta rhythm in the human STN harmonically distorts the high-beta rhythm; the distortion disappears after dopaminergic medication. This distortion is probably responsible for the small power decreases already observed in the high-beta rhythm after dopaminergic medication (Priori et al. 2004), for the correlation between the two beta rhythms in their dopamine-dependent power-changes (Priori et al. 2004) and for at least part of the subthalamo-pallido-cortical coherence overlapping from the low-beta band to the high-beta band (Brown et al. 2001; Williams et al. 2002; Foffani et al. 2005b; Fogelson et al. 2006). Previous studies of human subthalamic activity showed that LFP beta oscillations (frequency range 13–35 Hz) are tightly related to local single-unit activity (Levy et al. 2002a; Kühn et al. 2005), are coherent across multiple structures in the cortico-basal ganglia loop (Brown et al. 2001; Cassidy et al. 2002; Williams et al. 2002; Foffani et al. 2005b; Fogelson et al. 2006), and are typically decreased by dopaminergic medication (Brown et al. 2001; Levy et al. 2002a; Priori et al. 2004; Foffani et al. 2005a) and movement execution (Priori et al. 2002; Cassidy et al. 2002; Levy et al. 2002a; Foffani et al. 2002, 2004, 2005d; Kühn et al. 2004; Williams et al. 2005; Doyle et al. 2005). These observations implied that beta oscillations could be essentially ‘antikinetic’, directly contributing to the bradykinetic parkinsonian symptomatology (Brown, 2003). This pathological interpretation nevertheless seemingly contrasts with the widespread modulation of beta activity observed in the striatum of normal primates (Courtemanche et al. 2003), with the remarkably small differences observed in the movement-related amplitude modulation of beta activity in the human STN during the off and on states (Priori et al. 2002; Doyle et al. 2005; Foffani et al. 2005d; Alegre et al. 2005), and with the presence of (probably physiological) movement-related beta modulations in the external pallidum of patients with epilepsy (Sochurkova & Rektor, 2003). By separating the subthalamic beta activity into two rhythms – low-beta and high-beta (Priori et al. 2002, 2004; Foffani et al. 2004, 2005a,d; Fogelson et al. 2006) – our study may help to solve the discrepancy. Overall, our results suggest that the previously postulated pathological ‘antikinetic’ role of beta activity in the human STN (Brown, 2003) could be more specifically played by the low-beta rhythm, whereas the high-beta rhythm could be essentially physiological. The significant decrease in non-linear correlation between the high-beta rhythm and rhythms at lower frequencies in the on state supports the idea that under physiological conditions multiple rhythms operate independently in the human STN (Priori et al. 2004; Foffani et al. 2005d; Fogelson et al. 2006).
Pathophysiological implications
The presence of multiple physiological LFP rhythms is in agreement with the idea that LFP rhythms not only reflect pathological linear correlation between single neurone pairs but also represent independent physiological channels of information. This duality in the pathophysiological interpretation of LFP rhythms (i.e. pathological or physiological) could shed new light on an old debate about information processing in the basal ganglia circuit: information sharing versus segregated parallel processing (Alexander & Crutcher, 1990; Percheron & Filion, 1991; Parent & Hazrati, 1993; Joel & Weiner, 1994; Bergman et al. 1998). The information sharing view (i.e. great overlap in incoming information to different cells at the basal ganglia output) is supported by the bulk of anatomical studies describing the convergence/divergence from the input to the output of the basal ganglia (Yelnik et al. 1984; Percheron et al. 1984; Parent & Hazrati, 1993; Kita & Kitai, 1994). The segregated parallel processing view is supported by neurophysiological studies showing the absence of linear correlation between neurones at the basal ganglia output in normal animals (DeLong et al. 1985; Hoover & Strick, 1993; Yoshida et al. 1993; Bergman et al. 1994; Nini et al. 1995; Bar-Gad et al. 2003). How can our results reconcile these conflicting views? The wide convergence/divergence in the basal ganglia anatomical pathways could be necessary to sustain ‘normal’ LFP rhythms distributed in large ensembles of neurones (e.g. the ‘physiological’ high-beta rhythm), but not sufficient to induce correlated firing between pairs of neurones. The loss of dopamine in Parkinson's disease alters this equilibrium, generating pathological linear correlation between pairs of neurones (Bergman et al. 1994; Nini et al. 1995; Levy et al. 2000, 2002a; Raz et al. 2000, 2001; Heimer et al. 2002), inducing important alterations in the patterns of LFP rhythms (e.g. the pathological increase in the low-beta rhythm) and provoking the appearance of non-linear correlations between different LFP rhythms (e.g. the harmonic distortion of the low-beta rhythm onto the high-beta rhythm). In other words, our results suggest that Parkinson's disease determines a loss of segregation not only between different neurones, but also between different LFP rhythms. The present study therefore confirms and extends to the LFP level the idea that ‘the normal dopaminergic system supports segregation of the functional subcircuits of the basal ganglia, and that a breakdown of this independent processing is a hallmark of Parkinson's disease’ (Bergman et al. 1998). Intriguingly, the disruption of non-linear correlations between different LFP rhythms in the human STN could be critical not only for improving the clinical efficacy of dopaminergic medication, but also for understanding the mechanisms responsible for the action of deep brain stimulation. In conclusion, the dopamine-dependent non-linear correlations between LFP rhythms in the human STN open a non-linear dimension for rhythm-based pathophysiological models of basal ganglia processing, pointing out the importance of interactions between rhythms.
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Footnotes |
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