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Cortico-cortical coupling in Parkinson's disease and its modulation by therapy

Paul Silberstein , Alek Pogosyan , Andrea A. Kühn , Gary Hotton , Stephen Tisch , Andreas Kupsch , Patricia Dowsey-Limousin , Marwan I. Hariz , Peter Brown
DOI: http://dx.doi.org/10.1093/brain/awh480 1277-1291 First published online: 17 March 2005

Summary

The role of changes in inter-regional cortical synchronization in the pathophysiology of Parkinson's disease and the mechanism of action of dopaminergic therapy and high frequency subthalamic nucleus (STN) stimulation is unclear. We hypothesized that synchronization between distributed cortical areas would correlate with parkinsonism and that changes in synchronization with treatment would correlate with improvements in parkinsonism. To this end, we recorded scalp EEG in parkinsonian patients off treatment (16 patients, 31 sides) and then separately during high frequency stimulation (HFS) of the STN (16 patients, 31 sides) and following drug treatment (12 patients, 24 sides). All recordings were made at rest to avoid the confounding effects of differences in task performance. The motor Unified Parkinson's Disease Rating Scale (UPDRS) score was determined in each state. We found that EEG–EEG coherence over ∼10–35 Hz correlated with the severity of parkinsonism, and reductions in cortical coupling over this frequency range with both l-dopa and STN stimulation correlated with clinical improvement. These results suggest that both dopaminergic therapy and STN stimulation may support the restoration of normal cortico-cortical interactions in the frequency domain. This mechanistic similarity may underscore the strong clinical correlation between the therapeutic effects of these treatment modalities.

  • cortical coupling
  • Parkinson's disease
  • dopamine
  • STN stimulation
  • DBS = deep brain stimulation
  • HFS = high frequency stimulation
  • LFP = local field potential
  • STN = subthalamic nucleus
  • tCoh = transformed coherence
  • UPDRS = Unified Parkinson's Disease Rating Scale

Introduction

Recent theories regarding the pathophysiology of Parkinson's disease have moved away from the neuronal firing rate-based explanations encompassed in the model of Albin and DeLong (Albin et al., 1989; DeLong, 1990) to focus on the importance of alterations in the temporal patterning of neuronal discharge in the development of Parkinsonian symptoms (Marsden and Obeso, 1994; Obeso et al., 1997; Brown and Marsden, 1998). In particular, recordings in patients with Parkinson's disease undergoing functional neurosurgery suggest excessive synchronization of neurons in the subthalamic nucleus (STN) and globus pallidus. Evidence for this comes from microelectrode recordings of pairs of units (Hurtado et al., 1999; Levy et al., 2000, 2002b) and macroelectrode recordings of local field potentials (LFPs), a surrogate marker of local synchronization (Brown, 2003). Synchronization is particularly evident in the beta band from 13 to 30 Hz. This is reduced by treatment with levodopa (Levy et al., 2000, 2001, 2002a, b; Marsden et al., 2000; Brown et al., 2001; Cassidy et al., 2002; Silberstein et al., 2003; Priori et al., 2002, 2004; Williams et al., 2002). Treatment may in turn be associated with synchronization in the gamma band or even higher frequencies (Brown et al., 2001; Cassidy et al., 2002; Williams et al., 2002; Foffani et al., 2003). These spectral changes in oscillatory activity appear at least in part to be network phenomena, as evidenced by the finding of frequency- and dopaminergic state-dependent coherence between population activity in STN, globus pallidus internus and cerebral cortex (Brown et al., 2001; Williams et al., 2002; Cassidy et al., 2002). Thus abnormal synchronized neuronal activity in the basal ganglia is linearly coupled to activity in the cortex. The issue of synchronization within the cerebral cortex in Parkinson's disease is a critical one, as basal ganglia disease can only lead to motor dysfunction through effects on its executive motor projection sites, the motor areas of the cerebral cortex and brainstem. Moreover, oscillatory synchronization within and between cortical areas is increasingly recognized as a key mechanism in motor organization (Leocani et al., 1997; Farmer, 1998; Gerloff et al., 1998; Andres et al., 1999; Marsden et al., 2001; Ohara et al., 2001; Serrien and Brown, 2002, 2003; Serrien et al., 2003).

Changes in synchronization within and between neuronal populations in the cerebral cortex are readily determined through recordings of EEG, and the oscillatory structure of the synchronization evident in the EEG can be characterized through spectral analysis (Pfurtscheller and Lopes da Silva, 1999). Increases in oscillatory synchronization within local cortical neuronal populations are evident as increases in EEG power, while increases in the synchronization between distributed cortical regions are manifest as cortico-cortical coherence. That local cortical oscillatory activity is abnormal in relation to movement in Parkinson's disease is well established. The normal suppression (desynchronization) of mu activity prior to voluntary movement is delayed over the contralateral sensorimotor cortex in Parkinson's disease, a delay that can be partially reversed by acute (Magnani et al., 2002) or chronic (Defebvre et al., 1998; Devos et al., 2004) treatment with levodopa or deep brain stimulation (DBS) (Devos et al., 2004). Similarly, the normal increase (synchronization) of beta activity following voluntary movement is impaired in Parkinson's disease (Pfurtscheller et al., 1998) and restored by levodopa or DBS (Devos et al., 2003b, 2004). Impairments in the degree of suppression of mu power during movement have been shown to correlate with bradykinesia (Brown and Marsden, 1999; Wang et al., 1999). In addition, levodopa-dependent changes in task-related cortico-cortical coherence have been reported in Parkinson's disease (Cassidy and Brown, 2001).

However, the interpretation of the above studies is complicated by differences in task performance during movement between treatment states, so that changes in cortical power and cortico-cortical coherence might relate directly to treatment or indirectly reflect the change in task performance with treatment. Changes in cortical oscillatory activity at rest are less ambiguous, and EEG studies over the last few decades have shown an increased incidence of background and focal intermittent EEG slowing in Parkinson's disease (England et al., 1959; Enge et al., 1966; Yeager et al., 1966; McPherson, 1970; Wiederholt, 1974; Yaar, 1977). The significant correlation between motor disability and slowing of the background EEG suggests that at least part of this effect is related to failure of normal nigrostriatal modulation of basal ganglia input to cortex (Neufeld et al., 1988). Excessive synchronization between neurons in the motor cortex has also been reported at very low frequencies in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of Parkinson's disease (Goldberg et al., 2002).

The above studies in resting subjects concentrated on local synchronization of cortical activities evident in EEG power and upon synchronization at low frequency. However, important changes may also occur in the pattern of synchronization (cortico-cortical coherence) across distributed areas of cortex. This is likely to be particularly true in the beta and gamma bands, given the importance of cortico-cortical coherence at these frequencies in motor organization and the evidence that basal ganglia activity is preferentially synchronized in these bands. The role of changes in cortical synchronization at higher frequencies in the pathophysiology of Parkinson's disease and the effect of DBS and dopaminergic therapy upon any such changes is hitherto unexplored. We hypothesized that coherent activity between distributed populations of cortical neurons would correlate with parkinsonism and that changes in cortico-cortical coherence with treatment correlate with improvements in parkinsonism.

To this end, we recorded scalp EEG in resting patients with Parkinson's disease with chronically implanted STN electrodes while without treatment, during therapeutic high frequency stimulation (HFS) of the STN and following treatment with their usual oral antiparkinsonian medication. We determined whether oscillatory cortico-cortical coupling correlated with the severity of Parkinsonism in the untreated state and whether STN HFS and dopaminergic medication-induced reductions in cortical coupling correlated with reduction in motor impairment.

Material and methods

Patients and surgery

All patients (n = 16, mean age 56 ± 7 years, range 42–66) participated with informed consent and the permission of the local ethics committees. Their clinical details are summarized in Table 1. None of the patients was demented as determined by pre-surgical neuropsychological assessment. Implantation of bilateral STN macroelectrodes was performed simultaneously in 12 subjects and sequentially (separated by 6 months) in four subjects (cases 2, 3, 5 and 8) for treatment of severe Parkinson's disease. The macroelectrode used was model 3389 (Medtronic Neurological Division, MN) with four platinum–iridium cylindrical surfaces (1.27 mm diameter and 1.5 mm length) and a centre-to-centre separation of 2 mm. Contact 0 was the lowermost and contact 3 was the uppermost. The intended coordinates at the tip of contact 0 were the midpoint of the STN as determined by direct visualization and reference to the anterior border of the red nucleus on preoperative MRI (Bejjani et al., 2000). These coordinates corresponded closely with stereotactic coordinates determined from the Schaltenberg and Wahren atlas (Schaltenbrand and Wahren, 1977): 10–12 mm from the midline, 0–2 mm behind the midcommissural point and 4–5 mm below the AC–PC line. Intraoperative electrode localization was tested by macrostimulation in all patients. No microelectrode recordings were made. Macroelectrodes were connected to a battery-operated programmable pulse generator [Itrel II (4 subjects) or Kinetra 7428 (12 subjects), Medtronic]. All patients received postoperative imaging that consisted of MRI (n = 15) or CT (n = 1, case 3). Postoperative imaging was consistent with the placement of at least one macroelectrode contact in the STN, except for the left side in case 2 which was excluded from further analysis. Patients had an overall improvement of 47 ± 5% in Unified Parkinson's Disease Rating Scale (UPDRS) motor score during continuous HFS off medication rated at least 6 months after surgery, which further supports a satisfactory placement of the macroelectrodes (subjects 1–11 and 13). Comparable postoperative UPDRS scores were not yet available in subjects 12, 14, 15 and 16. In these subjects, however, the mean improvement in hemibody score with contralateral stimulation using the study stimulation parameters (see Table 1) was 46 ± 6%, also supporting satisfactory electrode placement.

View this table:
Table 1

Patient clinical details and stimulation parameters used for the study

CaseAge (years)/sexDisease duration (years)Predominant symptoms preoperativelySurgical centre/stimulator typeUsual stimulation parameters: contact/voltage/pulse width/frequencyMotor UPDRS part III on/off drugs pre-operativelyMotor UPDRS part III on/off STN stimulation post-operativelyStimulation parameters used for study: contact/voltage/pulse width/frequencyUPDRS hemibody score off-stim off-med: study dateUPDRS hemibody tremor score off-stim off-med: study dateUPDRS hemibody score with study stimulation parametersUPDRS hemibody scores on medication off stimulation on study dateMedications daily dose (mg)
142 F6PD on–off fluctuations; freezing; dyskinesiaNHNN; Kinetra(R) 4-C+/3.6/60/130; (L) 0-C+/1.3/60/13013/6114/61(R) 4–5+/4.6/60/130; (L) 0–1+/1.7/60/130(L) 29.5; (R) 22.5(L) 9.5; (R) 5(L) 7; (R) 13No medication givenNo medication
262 M16PD on–off fluctuations; freezingNHNN; bilateral Itrel II(R) 3-C+/2.6/60/130; (L) 3–2+/3.6/60/14521/3920/35(R) 3–2+/3.0/60/130; (L) electrode misplaced on MRI(L) 19(L) 6(L) 13(L) 12l-dopa 400 mg; entacapone 1600 mg; amantadine 100 mg; benzhexol 4 mg; doxazosin 4 mg
355 F16PD on–off fluctuations; dyskinesiasNHNN; bilateral Itrel II(R) 0–1+/4.5/90/185; (L) 2-C+/2.8/90/1859/4242/79(R) 0–1+/4.5/90/185; (L) 2–1+/3.6/90/185(L) 18.5; (R) 23(L) 5.5; (R) 5.5(L) 8; (R) 9.5(L) 12; (R) 12.5l-dopa 350 mg; ropinirole 15 mg; amantadine 200 mg; amitryptiline 50 mg
460 M15PD on–off fluctuations, dyskinesiasNHNN; Kinetra(R) 5-C+/3.5/60/185; (L) 2-C+/3.0/60/18515/6128/46(R) 5–6+/4.4/60/185; (L) 2–1+/3.7/60/185(L) 23; (R) 25(L) 9; (R) 9(L) 14; (R) 12.5(L) 8; (R) 7l-dopa 200 mg
551 M11PD on–off fluctuations; dyskinesiasNHNN; bilateral Itrel II(R) 3–1+/3.0/60/185; (L) 3-C+ 3.2/60/18523/4921/29(R) 3–1+/3.0/60/185; (L) 3–2+4.0/60/185(L) 19; (R) 17(L) 6; (R) 3(L) 10; (R) 10Dose failurel-dopa 250 mg; pergolide 750 µg; epilim chrono 500 mg BD
666 M16PD on–off fluctuations; freezingSweden*; bilateral Itrel II;(R) 2-C+/2.8/60/185; (L) 0–1+/3.6/60/145Not available38/51(R) 2–0+3.6/60/185; (L) 1–3+4.6/60/145(L) 18; (R) 13(L) 4; (R) 4(L) 15; (R) 9(L) 7; (R) 4l-dopa 400 mg; cabergoline 2 mg BD; amitryptiline 35 mg nocte
764 M20PD on–off fluctuations; dyskinesiasNHNN Kinetra(R) 5-C+/2.3/60/130; (L) 2-C+/1.5/60/1305/415/33(R) 5–6+/2.9/60/130; (L) 2–1+/2.0/60/130(L) 10; (R) 14(L) 2; (R) 2(L) 5.5; (R)10.5(L) 5.5; (R) 10l-dopa 250 mg; apomorphine 2.5–7.5 mg prn
861 M12PD on–off fluctuations; dyskinesiasKing's College Kinetra(R) 1–2+4.0/60/130; (L) 5–6+4.0/60/13024/4026/42(R) 1–2+4.0/60/130; (L) 5–6+4.0/60/130(L) 20; (R) 15(L) 7; (R) 2(L) 10.5; (R) 9(L) 8.5; (R) 8.5l-dopa 400 mg
959 M10PD on–off fluctuations; tremorBerlin; Kinetra(R) 1-C+4.3/60/130; (L) 5-C+4.4/60/13036/6325/46(R) 1–3+6/60/130; (L) 5–6+6/60/130(L) 25.5; (R) 20(L) 5.5; (R) 3(L) 17.5; (R) 14Dose failurel-dopa 800 mg
1051 M18PD on–off fluctuationsBerlin; Kinetra(R) 1-C+3.2/60/180; (L) 5-C+3.2/60/18013/3025/51(R) 1–3+4.0/60/180; (L) 5–4+4.5/60/180(L) 18.5; (R) 10.5(L) 5; (R) 3(L) 12; (R) 4.5(L) 6.5; (R) 4l-dopa 800 mg; pergolide
1151 M8PD on–off fluctuationsBerlin; Kinetra(R) 1-C+2.8/60/130; (L) 5-C+3.0/60/13014/4715/30(R) 1–3+3.6/60/130; (L) 5–7+4.0/60/130(L) 11.5; (R) 13.5(L) 1; (R) 1(L) 8; (R) 9(L) 8; (R) 6l-dopa 300 mg
1253 F9PD on–off fluctuationsBerlin; Kinetra(R) 1-C+1.7/60/130; (L) 6-C+2.2/60/13027/58Not available(R) 1–0+3.0/60/130; (L) 6–5+4.1/60/130(L) 22; (R) 17(L) 2.5; (R) 1.5(L) 15.5; (R) 9.5(L) 12.5; (R) 9.5l-dopa 700 mg; entacapone 1200 mg; pergolide 9 mg; amantadine 300 mg
1363 M18PD on–off fluctuationsBerlin; Kinetra(R) 0-C+2.5/90/130; (L) 5-C+2.0/90/1309/2517/26(R) 0–1+3.2/90/130; (L) 5–6+2.7/90/130(L) 17; (R) 13.5(L) 1; (R) 0.5(L) 12; (R) 8(L) 7.5 (R) 5.5l-dopa 1650 mg; entacapone 1600 mg
1459 F21PD on–off fluctuations; dyskinesiasNHNN; Kinetra(R) 7-C+3.2V/60/130; (L) 3-C+4.0V/60/13015/52Not available(R) 7–5+4.2/60/130; (L) 3–1+5.3/60/130(L) 16; (R) 20(L) 5.5; (R) 8(L) 9.5; (R) 7Dose Failurel-dopa 125 mg; cabergoline 3 mg; amantadine 200 mg
1546 M13PD on–off fluctuations; dyskinesiasNHNN; Kinetra(R) 5-C+3.8/60/130; (L) 1-C+3.9/60/1308/63Not available(R) 5–7+5.0/60/130; (L) 1–0+5.0/60/130(L) 24.5; (R) 19(L) 3; (R) 3(L) 17; (R) 10.5(L) 3.5; (R) 1.5l-dopa 500 mg; cabergoline 3 mg
1656 M20PD on–off fluctuations; dyskinesiasNHNN Kinetra(R) 6-C+3.0/60/130; (L) 1-C+2.8/60/1302/45Not available(R) 6–4+4.3/60/130; (L) 1–3+4.0/60/130(L) 15.5; (R) 22(L) 5; (R) 10.5(L) 6.5; (R) 10(L) 7.5; (R) 8.5l-dopa 600 mg; cabergoline 4 mg; propranolol 80 mg; disopyramide 200 mg; oxybutinin 7.5 mg; simvastatin 10 mg
  • NHNN = National Hospital for Neurology and Neurosurgery, London; PD = Parkinson's disease.

  • * The same surgeon as NHNN patients.

Study protocol

Patients were studied after overnight withdrawal of medications at least 2 months (range 2–56 months) postoperatively. Subjects were seated and recorded at rest. They were asked to maintain visual fixation on a coloured dot on a computer monitor. Scalp EEG at rest and hemibody UPDRS motor assessments were recorded under the following conditions: (i) off medication off stimulation (off-med off-stim); (ii) off medication on left STN stimulation; (iii) off medication on right STN stimulation; and (iv) on medication off stimulation (on-med off-stim). Half points were used to increase the sensitivity of the UPDRS score. This method has been used previously when assessing the efficacy of STN DBS (Limousin et al., 1995).

The off-med off-stim condition was always recorded first. Both STN stimulators were turned off for a minimum of 10 min prior to this recording. Subsequent recordings in the off medication state were performed in randomized order. Recordings lasted 120 s and were performed twice in each condition, separated by ∼5 min rest. We waited a minimum of 5 min between each stimulator change. STN stimulation was always performed bipolarly as monopolar STN stimulation led to significant artefact in the scalp EEG. In patients who were usually stimulated bipolarly, usual stimulation parameters were used. In patients who were stimulated monopolarly, the maximally effective bipolar pair was determined on clinical grounds, and this contact pair was used for subsequent EEG and UPDRS recordings. In the latter case, stimulation amplitude had to be increased by 25–30% in order to achieve a similar clinical effect with bipolar stimulation. Usual stimulation pulse width and frequency were not altered in any subjects for the recordings (see Table 1).

Patients were then instructed to take their usual morning dopaminergic medications (l-dopa ± dopamine agonists). Patients were examined at intervals and asked to report when they felt the medications were acting. After 45–60 min, EEG was recorded (120 s) in the on medication off stimulation state on two occasions separated by at least 10 min. Hemibody UPDRS part III scores were also determined in this state. Three out of the 16 subjects (cases 5, 9 and 14) experienced medication dose failure during the study. One subject (case 1) had stopped all medications postoperatively and was therefore not given l-dopa during the study. Consequently, on medication recordings could only be performed in 12 subjects.

EEG recordings and data analysis

Scalp EEG was recorded according to the 10 : 20 international system and referenced to linked ears (Fig. 1A). Signals were amplified, pass band filtered between 0.25 and 90 Hz and sampled at 184 Hz (Biopotential Analyzer Diana, St Petersburg, Russia). An example of the raw EEG at rest and during left sided HFS is shown for case 8 in Fig. 1B. Note that no stimulus artefact is seen during stimulation.

Fig. 1

(A) EEG channels used for analysis. Electrodes within the dotted line are ‘central’ channels and electrodes outside the dotted line are ‘peripheral’ channels. (B) Examples of EEG data: raw EEG data recorded from C3 and C4 at rest and during high frequency stimulation (HFS) (referenced to linked ears). Note that there is no stimulus artefact during HFS of the STN region. (C) Coherence spectrum (C3-C4) at rest (grey line) and during HFS (black line). Coherence between 45 and 55 Hz is unreliable due to mains artefact and has been omitted. Note that coherence is generally reduced during HFS. (D) Power spectrum at C3 off-med off-stim (grey line) and off-med on left-stim (black line). Power between 45 and 55 Hz is unreliable due to mains noise and has been omitted. Power spectra at C4 were similar (data not shown). Note that power is also reduced at these electrodes, and therefore reductions in coherence are related to an absolute reduction in coupling related to stimulation between these electrodes.

EEG was examined off-line, and eye movement-, blink- and EMG-contaminated sections removed as far as possible by visual inspection before frequency analysis was performed. The two records performed in any given state were concatenated. After artefact rejection, the average total length of EEG records was 185 ± 8 s. Nineteen electrodes were chosen for further analysis to facilitate a comparison of the topography of spectral changes with stimulation and l-dopa therapy (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, CZ, C4, T4, T5, P3, PZ, P4, T6, O1 and O2; see Fig. 1A). Spectral analysis was performed using the MATLAB function ‘fft’. This function employs a high speed radix-2 fast Fourier transform algorithm if the data length is a power of two, otherwise a slower mixed radix algorithm if it is not. Coherence is a measure of the linear association (correlation) between two signals across frequencies. It is a bounded measure taking values from 0 to 1, where 0 indicates that there is no linear association (i.e. that one process is of no use in linearly predicting another process) and 1 indicates a perfect linear association. The coherence |Rab(λ)|2 was calculated as described previously (Halliday et al., 1995) by using the formula: |Rab(λ)|2 = |fab(λ)|2/aa(λ) fbb(λ). In this equation, f characterizes the spectral estimate of two EEG signals a and b for a given frequency (λ). The numerator includes the cross-spectrum for a and b (fab), whereas the denominator includes the autospectra for a (faa) and b (fbb). Spectra were estimated by dividing the data epochs into a number of disjoint sections of 1 s duration. Frequency resolution was 1 Hz. Data were Hanning-windowed to control spectral leakage. Figure 1C shows the coherence between C3–C4 at rest and during HFS in case 8. The square root of the coherence was normalized using a Fisher transform, and the variance of spectral power estimates stabilized by logarithmic transformation (Halliday et al., 1995).

For each subject, data were analysed in four conditions: (i) off medication off stimulation; (ii) off medication left STN stimulation; (iii) off medication right STN stimulation; and (iv) on medication off stimulation. Transformed coherence (tCoh) and log power changes were examined across five frequency bands: 3–7, 8–12, 13–24, 25–45 and 60–80 Hz. Synchronization in the first four frequency bands is evident in basal ganglia LFP recordings made in the untreated state in patients with Parkinson's disease (Brown et al., 2001; Cassidy et al., 2002; Levy et al., 2002a; Priori et al., 2002; Williams et al., 2002). The 60–80 Hz band was chosen to match the frequency of oscillatory synchronization sometimes seen in the basal ganglia following l-dopa (Brown et al., 2001; Cassidy et al., 2002; Williams et al., 2002).

For each subject, the stimulation or drug-related changes in tCoh or log power in each frequency band for each electrode pair or single electrode were calculated by subtracting the tCoh or log power in the off-med off-stim condition from the tCoh or log power in the stimulation or drug condition. To determine the clinical significance of the frequency- and topography-dependent changes in scalp EEG coupling, three correlations were made with the UPDRS hemibody scores. (i) Correlation between off-med off-stim tCoh and off-med-off stim UPDRS scores: for each electrode coupling, the tCoh in the off-med off-stim state was correlated with the contralateral hemibody UPDRS scores (UPDRS part III items 18–26) in this condition. (ii) Correlation of change in tCoh off-med on-stim and difference in hemibody UPDRS: for each channel pair, the change in tCoh with STN stimulation (off-med on-stim–off-med off-stim) was correlated with the difference in contralateral hemibody UPDRS score with STN stimulation (off-med off-stim–off-med on-stim). (iii) Correlation of change in tCoh on med off-stim and difference in UPDRS: for each channel pair, the change in tCoh with medication (on-med off-stim–off-med off-stim) was correlated with the difference in hemibody scores on medication (off-med off-stim–on-med off-stim).

To increase the statistical power of our study to detect correlations, EEG coherence values were correlated with both hemibody scores, but only after inverting right intra-hemispheric coherences about the midline so that F4–P4 became F3–P3 etc., while inverting the corresponding left hemibody score. Thus left and inverted right intra-hemispheric coherences were correlated with right and inverted left hemibody scores to give EEG correlations with ‘contralateral’ scores. The remaining midline and interhemispheric coherence values were also correlated with both hemibody scores. Thus for a significant correlation between an interhemispheric coherence, such as F4–F3, and hemibody score to occur, the coherence values were likely to have a similar proportional relationship with the left and right hemibody scores across patients. Equally, for a significant correlation between an intrahemispheric coherence, such as F4/F3–C4/C3 (e.g. contralateral fronto-central coherence), and hemibody score to occur, then F3–C3 would tend to share the same proportional relationship with right hemibody scores across patients as (inverted) F4–C4 would have with (inverted) left hemibody scores. Note that this approach neglects any effects of hemispheric dominance.

To determine whether there was a topographic distribution of correlations by frequency band, we performed similar correlations to those outlined above, but only for nine ‘central’ EEG channels lying over frontal and parietal regions (F3, FZ, F4, C3, CZ, C4, P3, PZ and P4), and compared these results with the frequency-dependent changes in tCoh and log power in 10 ‘peripheral channels’ (Fp1, Fp2, F8, T4, T6, O1, O2, T3, T5 and F7) (see Fig. 1A). In those frequency bands with at least five significant correlations, central and peripheral correlations were Fisher transformed and separately averaged in each subject. These were then analysed using two-tailed Student's t tests for populations of unequal variance. Significance levels of t tests were Bonferroni corrected for multiple comparisons.

All correlations were performed by frequency band using Spearman's correlation test (SPSS for Windows version 11, SSPS Inc, Chicago, IL) and Fisher transformed prior to statistical evaluation. A P value ≤0.005 was considered significant for band-wise correlations and is the threshold used in the topographic maps of coherence. An asymmetry index was also estimated in those frequency bands with at least five significant correlations to determine whether correlations were lateralized. The mean Fisher-transformed correlation coefficients for each connection involving F3, C3 and P3 with themselves or with Fz, Cz and Pz (total possible 12) were compared with the mean transformed correlation coefficients for all connections involving F4, C4 and P4 with themselves or with Fz, Cz and Pz (total possible 12) and an unpaired t test performed across subjects in each frequency band. Significance levels were Bonferroni corrected for multiple comparisons.

The division of coherent frequencies into specific frequency bands, although generally supported in the literature, inevitably uses somewhat arbitrary definitions. Accordingly, we also examined the correlations detailed above per 1 Hz frequency bin, rather than within frequency bands. To this end, we plotted significantly coherent electrode connections as a percentage of the total number of possible connections, with significance defined here as correlations with P < 0.005. Thus only 0.25% positive and 0.25% negative correlations were to be expected to arise by chance at a given frequency.

Correlation between spectral effects of STN stimulation and dopaminergic medication

In addition, we determined if there was any correlation between the effects of STN stimulation and dopaminergic medications on scalp EEG coherence in terms of the topography in the different frequency bands. Only subjects with satisfactory bilateral STN macroelectrodes who also experienced an l-dopa response on the date of the study were included for this analysis (n = 11; patients 3, 4, 6, 7, 8, 10, 11, 12, 13, 15 and 16). Here, we determined the average change in coherence with left and right STN stimulation in the off-med off-stim condition at each EEG channel pair in each frequency band: (left STN stim − off-med off-stim)+(right STN stim − off-med off-stim)/2. This value was then averaged across subjects and correlated with the average change in coherence with medication at each contact pair (on-med off-stim–off-med off-stim) for the corresponding frequency band.

Results

Correlations between transformed coherence and motor state off medication off stimulation

In each frequency band, the tCoh off-med off-stim for each electrode pair was correlated with the off treatment contralateral hemibody UPDRS scores across subjects. Here we found multiple positive correlations, which almost exclusively occurred within the 13–24 Hz band (Fig. 2A). Significant positive correlations were seen within and between hemispheres and tended to cluster around the central region, with the exception of some correlations that bridged central (including frontal electrodes) and occipital electrodes. Positive correlations indicated that motor difficulties were greater the higher the cortico-cortical coherence at rest. The average Fisher-transformed ρ was significantly higher in correlations involving the central than peripheral electrodes in the 13–24 Hz band (P < 0.005). No significant lateralization was present as determined by the asymmetry index.

Fig. 2

Off medication off stimulation correlations between EEG and motor state. (A) Maps denote tCoh in EEG channel pairs that correlated significantly (P < 0.005) with contralateral hemibody UPDRS motor scores at rest. Most correlations are positive (red in A) and occur in the 13–24 Hz band. (B) Each line graph refers to the percentage of significant correlations between tCoh and contralateral hemibody UPDRS motor scores at rest (P < 0.005) across all (total possible 171), central (total possible 36) or peripheral channel pairs (total possible 45 electrodes) at 1 Hz frequency intervals. Most correlations are preferentially centrally distributed and occur from 10 to 32 Hz.

The percentages of significant correlations between tCoh off-med off-stim at each individual frequency and off treatment contralateral hemibody UPDRS scores are shown in Fig. 2B. This confirmed that virtually all correlations were positive and most occurred over 10–36 Hz. In addition, note that the percentage of significant correlations within the nine central electrodes exceeded that within the 10 peripheral electrodes at almost all individual frequencies (Fig. 2B). The central predominance of the positive correlations with UPDRS motor scores argues against the spurious correlation of EEG parameters and motor state through tremor- (and its harmonics) or rigidity-related EMG contamination of EEG. Such contamination would be expected to have been much more prominent in the more peripheral EEG electrodes. The number of significant correlations within each hemisphere at each individual frequency did not show any asymmetry (data not shown).

Note that in the above, tCoh off-med off-stim for each electrode pair was correlated with the off treatment contralateral hemibody UPDRS. As tCoh was absolute and not the result of a subtraction between two states (see below), it will include the influence of both physiological cortico-cortical coupling and volume conduction. There is, however, no plausible reason for expecting volume conduction to differ in proportion to the contralateral hemibody UPDRS. Nevertheless, we repeated the above analysis for the tCoh off-med off-stim between all possible combinations of central bipolar electrode pairs rather than between all possible combinations of central monopolar electrodes. Correlating the tCohbipolar at each individual frequency with off treatment contralateral hemibody UPDRS, we again found that virtually all correlations (thresholded at P < 0.005, as before) were positive and occurred over 10–30 Hz (95 positive correlations and 18 negative correlations over this band, χ2 test P < 0.0001).

Correlations between coherence and motor state during STN HFS

In each frequency band, the change in tCoh related to STN stimulation (off-med on-stim–off-med off-stim) in each electrode pair was correlated with the difference in hemibody UPDRS score (off-med off-stim–off-med on-stim) across subjects. Here we found only significant negative correlations so that the greater the reduction in tCoh, the greater was the difference in UPDRS hemibody score and hence improvement in parkinsonism. Multiple correlations were found in all but the highest frequency band (Fig. 3A). In the 3–7 Hz band, significant correlations involved predominantly peripheral or peripheral to central EEG channel pairs, so that it is possible that the loss of rest tremor contributed to the correlation through volume conduction of EMG to the EEG electrodes or the induction of movement artefact. In contrast, significant correlations at higher frequencies were preferentially distributed over central EEG channel pairs, and are therefore unlikely to reflect the effects of tremor or its harmonics. This distribution was confirmed by a comparison of the average Fisher-transformed Spearman's ρ for all connections between the nine central electrodes with that for all connections involving the 10 peripheral electrodes. The average Fisher-transformed ρ was higher in correlations involving the central electrodes in 8–12 (P < 0.001), 13–24 (P < 0.001) and 25–45 Hz (P < 0.001) frequency bands. The one exception to this general rule was the presence of some correlations involving connections bridging fronto-central and occipital electrodes.

Fig. 3

Off medication on stimulation correlations between EEG coherence and contralateral summed UPDRS part III scores normalized to left stimulation and right limbs. (A) Maps denote tCoh changes in EEG channel pairs that correlated significantly (P < 0.005) with contralateral hemibody UPDRS motor scores at rest. Most correlations are negative (blue in A) and are concentrated in the 13–24 Hz band. (B) Each line graph refers to the percentage of significant correlations between tCoh changes and contralateral hemibody UPDRS motor scores at rest (P < 0.005) across all (total possible 171), central (total possible 36) or peripheral channel pairs (total possible 45 electrodes) at 1 Hz frequency intervals. Correlations are preferentially centrally distributed and concentrated from 10 to 32 Hz.

We also examined the percentage of significant correlations between tCoh related to STN stimulation (off-med on-stim–off-med off-stim) at each individual frequency with the difference in hemibody UPDRS (off-med off-stim–off-med on-stim) (Fig. 3B). This confirmed that virtually all correlations were negative and most occurred over 10–32 Hz. The percentage of significant correlations within the nine central electrodes exceeded that within the 10 peripheral electrodes except at tremor-related frequencies (Fig. 3B). Lateralization was only found in the upper beta band (P = 0.02). Here the asymmetry index indicated that there was a greater (negative) correlation between tCoh over the hemisphere ipsilateral to stimulation with the contralateral hemibody UPDRS than correlation between tCoh over the hemisphere contralateral to STN stimulation and contralateral (to STN stimulation) hemibody UPDRS.

Correlations between transformed coherence and motor state after levodopa

In each frequency band, the change in coherence with medication (on-med off-stim–off-med off-stim) in each electrode pair was correlated with the averaged right and left hemibody UPDRS scores (off-med off-stim–on-med off-stim) across subjects. Again we found only significant negative correlations so that the greater the reduction in tCoh, the greater was the difference in UPDRS hemibody score and hence improvement in parkinsonism. Multiple correlations were found, and, unlike STN HFS or off-med off-stim correlations, included the highest frequency bands (Fig. 4A). In the 3–7 Hz band, significant correlations involved predominantly peripheral or peripheral to central EEG channel pairs, so, as for STN stimulation, it is possible that the loss of rest tremor contributed to the correlation through volume conduction of EMG to the EEG electrodes or the induction of movement artefact. In contrast, significant correlations at higher frequencies were preferentially distributed over central EEG channel pairs (with the exception of some correlations involving connections between occipital and fronto-central electrodes). This was confirmed by a comparison of the average Fisher-transformed Spearman's ρ for all connections between the nine central electrodes with that for all connections involving the 10 peripheral electrodes. The average Fisher-transformed ρ was higher in correlations involving the central electrodes in the 8–12 (P < 0.001), 13–24 (P < 0.001), 25–45 (P < 0.001) and 60–80 Hz (P < 0.001) frequency bands. No significant lateralization was determined by the asymmetry index.

Fig. 4

On medication off stimulation correlations between EEG coherence and contralateral summed UPDRS part III scores. (A) Maps denote tCoh changes in EEG channel pairs that correlated significantly (P < 0.005) with contralateral hemibody UPDRS motor scores at rest. Most correlations are negative (blue) and are concentrated in the lower and upper beta frequency bands. (B) Each line graph refers to the percentage of significant correlations between tCoh changes and contralateral hemibody UPDRS motor scores (P < 0.005) across all (total possible 171), central (total possible 36) or peripheral channel pairs (total possible 45 electrodes) at 1 Hz frequency intervals. Most correlations are preferentially centrally distributed but involve more frequencies than STN HFS (see B).

We also examined the percentage of significant correlations with medication (on-med off-stim–off-med off-stim) at each individual frequency with the difference in hemibody UPDRS (off-med off-stim–on-med off-stim) (Fig. 4B). This confirmed that virtually all correlations were negative. Although correlations predominated over a similar band as with correlations in the untreated and HFS states (i.e. ∼10–35 Hz), less frequent correlations were seen additionally at higher frequencies following levodopa. The percentage of significant correlations within the nine central electrodes exceeded those within the 10 peripheral electrodes except at tremor-related frequencies (Fig. 4B). The presence of correlations at higher frequencies than with STN HFS raises the question of whether these correlations might be spurious and arise through dyskinesia-related EMG contamination of EEG following drug treatment. This seems unlikely as this would have caused positive rather than negative correlations and would have failed to be centrally predominating.

Correlation of tCoh and power changes

Power was assessed primarily so as to ensure that changes in coherence were not the result of modulations of non-linearly related frequency components (Florian et al., 1998). Coherence denotes that proportion of a pair of signals that covaries with respect to phase and amplitude at a given frequency. Thus, increases in activities that do not covary may lead to reductions in coherence, even though the absolute degree of coupling between areas may not have changed. Such a relationship can be suspected when coherence and power changes occur in opposite directions, i.e. are negatively correlated. Figure 1C and D shows that this was not the case in example raw spectra: power and coherence both dropped with stimulation. In addition, across patients, therapy-induced changes in the log power averaged over all electrodes failed to correlate or showed a positive correlation with therapy-induced changes in tCoh averaged over all connections (Table 2).

View this table:
Table 2

Correlation of therapy-induced changes in log power versus therapy induced changes in tCoh by frequency band

Spearman's ρP-value
STN stimulation correlations (n = 30)
    3–7 Hz0.650<0.005
    8–12 Hz0.732<0.005
    13–24 Hz0.5300.015
    25–45 Hz0.121NS
    60–80 Hz−0.269NS
Medication correlations (n = 12)
    3–7 Hz0.7340.035
    8–12 Hz0.704NS
    13–24 Hz0.685NS
    25–45 Hz0.692NS
    60–80 Hz0.210NS

Correlation of stimulation- versus medication-related change in tCoh

In each frequency band, we correlated the change in tCoh by EEG electrode pairs in the STN stimulation condition with the change in tCoh in the medication condition by electrode pair (see Material and methods for details). Here we found positive correlations in all five frequency bands: 3–7 (r = 0.629, P < 0.001), 8–12 (r = 0.694, P < 0.001), 13–24 (r = 0.714, P < 0.001), 24–45 (r = 0.729, P < 0.001) and 60–80 Hz (r = 0.552, P < 0.001). This confirmed a strong correlation in the frequency band-dependent topographic effects of both therapeutic modalities on cortical coupling (Fig. 5).

Fig. 5

Correlation of change in tCoh by electrode pair in the off medication on stimulation condition with change in tCoh by electrode pair in the on medication off stimulation condition in the 13–24 Hz band (r2 = 0.714, P < 0.001).

Discussion

We have demonstrated that EEG–EEG coherence over ∼10–35 Hz correlates with severity of parkinsonism in untreated Parkinson's disease. The STN stimulation-induced reductions in EEG–EEG coherence correlated with clinical improvement at similar frequencies. Collectively these observations suggest that elevated cortico-cortical coupling in this band may be an important feature of parkinsonism, that reverses with STN HFS. Dopaminergic therapy also induced reductions in EEG–EEG coherence correlated with clinical improvement, again predominantly in the 10–35 Hz band, although less marked correlations extended to higher frequencies. This suggests that the two treatments may achieve some of their beneficial effects through common actions at the cortical level. However, before examining our findings in detail, we should bear in mind some of the potential limitations of our study.

Experimental limitations

Surgical placement of stimulating macroelectrode

To determine accurately the physiological effects of STN stimulation in humans, it is first important to be sure of correct electrode placement. Examination of the postoperative images in our patients was consistent with placement of at least one of the electrode contacts in the STN; however, it is important to keep in mind the limitations of image interpretation in reaching this conclusion. Whilst the borders of the STN may be depicted on preoperative, thin slice T2-weighted MRI (Hariz et al., 2003), the borders of the STN are not always clearly defined on conventional postoperative images, due to artefact arising from the macroelectrode, making any estimation as to contact position presumptive, based on relationships to clearly defined surrounding anatomic structures (Bejjani et al., 2000). Further, tissue compression, inevitable in even thin MRI slices, may overestimate the proximity of electrode contacts to the STN. Support for correct electrode placement may, however, also be provided by the clinical effects of stimulation, and the reduction in the postoperative UPDRS score of almost 50% in our patients is consistent with satisfactory electrode placement. We also limited the effects of variance in electrode positioning on STN stimulation-induced changes in cortical coupling by recording as many patients and sides as possible. To achieve this, we collaborated across several surgical centres (see Table 1), but this itself may have introduced systemic bias, albeit non-intended, in target localization between centres. Here again, MRI findings in individual cases along with UPDRS improvement with stimulation would argue against this as a significant confound.

Experimental protocol

Our protocol required assessment of patients under the different treatment conditions in a single sitting. Randomization of recordings between subjects and separate recordings of left and right sides necessitated switching on and off each stimulator side on a number of occasions. In order that the study was not excessively long and uncomfortable for our subjects, we limited the off medication–no stimulation period to 10 min and the period between stimulation changes to 5 min. The recurrence of Parkinsonian signs after stimulation is switched off increases with time (Temperli et al., 2003), making it possible that the limited time interval between recordings resulted in less clinical difference in each stimulation state than might be seen with longer intervals. On the other hand, clinical improvement related to stimulation, as determined by changes in UPDRS hemibody score of at least 30%, was determined in all cases, so any underestimate of effects was probably limited. Furthermore, the intervals used were also sufficient to allow appreciable changes in EEG dynamics to be detected, and both changes in EEG coupling and improvements in clinical score correlated. In conclusion, timing limitations could have only served to underestimate the relationships demonstrated. The use of standard dosages of medications after overnight withdrawal may have also contributed to a possible underestimation of the effects of l-dopa on cortical coupling.

EEG recordings and analysis

We recorded scalp EEG referenced to linked ears. This introduced the possibility of volume conduction between electrode sites, leading to overestimates of coherence and blurring of any topographic differences. We limited these effects by using a subtractive approach, with respect to the consequences of stimulation and l-dopa on power and coherence change. Furthermore, correlations with clinical state were unlikely to have highlighted coupling that related to volume conduction or common reference, rather than that associated with functional change. The central predominance of the correlations at individual frequencies and in frequency bands higher than those associated with tremor, although expected from proximity to mesial and lateral motor areas, raises the possibility of another confound. Given that all of our patients had frontal burr holes, our EEG recordings necessarily included a partial breach in the skull and associated tissues. Whilst the position of the burr hole varied between subjects, it tended to be located between F3, FZ, C3 and CZ on the left and corresponding electrodes on the right hand side. The burr holes may have improved the signal to noise ratio of the cortically derived EEG signal, but could not have caused the correlations between coherence changes and improvements in parkinsonism. On the other hand, burr holes may have contributed to the central predominance of these correlations, although any effect was insufficient to obscure the peripheral predominance of correlations at those frequencies under 10 Hz associated with parkinsonian rest and action tremor. The latter finding means that correlations at such frequencies may have been spurious and related to decreases in tremor contamination of peripheral scalp recordings with treatment.

Statistical considerations

Our primary approach was correlation to demonstrate an association between changes in spectral differences in the EEG and changes in UPDRS motor scores upon treatment across patients, and thereafter to show a similarity in effects between treatment types. Correlation does not necessarily imply causation, and, moreover, there were a number of confounding variables, such as the potential variability in precise electrode positioning between patients, disparate stimulation parameters and the variable timing of recordings following surgery. However, factors such as these would have acted to bias against the finding of significant correlations and led to an underestimation of effects rather than generate spurious effects.

We assumed an empirical significance level of 0.005 in correlations by frequency band. Thus one in 200 of the correlations tested may have been spuriously significant. The contribution of these spurious results to the overall picture seems likely to have been insignificant, given that correlations occurred with a much higher incidence than expected by chance, were relatively frequency selective and were of uniform sign. Spurious correlations would have been equally represented across all frequency bands and would have been just as likely to be positive as negative. Similar arguments apply to the correlations at individual frequencies, where we applied the same significance level of 0.005. The proportion of coherent connections in the 10–35 Hz band far exceeded the 0.5% expected by chance and correlations were almost entirely of consistent sign. Nevertheless, the qualitative similarity between correlations of STN stimulation and levodopa-induced EEG–EEG coherence changes with clinical improvement should be stressed rather than any quantative differences or similarities. The most obvious factor militating against a direct comparison of these correlations was the lower number of observations and hence reduced statistical power of the levodopa correlations.

In order to investigate similarities in the frequency-dependent topography of STN stimulation and l-dopa effects, we correlated frequency-dependent changes in coupling by electrode pair between these therapeutic modalities, finding strong correlations in each frequency band. As we did not record subjects during simultaneous left and right STN stimulation, we determined a surrogate measure of bilateral stimulation effects by averaging the coupling change from separate left and right stimulation recordings in each subject. These were then compared with the frequency-dependent changes in coupling after l-dopa. Data sets were not paired given that one of the subjects that achieved a satisfactory medication response was excluded from the off-med on-stim group due to unilateral macroelectrode misplacement, and three subjects in the off-med on-stim group were not included in the average calculations in the on-med off-stim group due to medication dose failure. One could argue that since only a surrogate measure of the off-med on-stim state and non-paired data was used, our findings may be unreliable. Against this, correlation analysis depends on similarity of effects, and both of these factors would be likely to introduce greater variance to the difference between the treatment states. This suggests that a direct comparison of bilateral STN stimulation with levodopa effects may have yielded even stronger correlations.

Cortico-cortical coherence varies with clinical state

There are three possible, non-mutually exclusive, explanations for the correlation between untreated cortico-cortical coupling and clinical state and between treatment-induced changes in coupling and improvements in clinical state. First, correlations may have been related, in part, to decreased movement artefact or volume conduction related to tremor upon treatment. This may have been a confound in the 4–7 Hz band, where correlations were not central in their distribution, but is unlikely to have been a significant factor at frequencies higher than those represented in tremor, where correlations were maximal centrally.

Secondly, the relationship between cortico-cortical coherence and clinical state might represent a direct effect of the impairment of dopaminergic tone. In this regard, it is notable that the dopaminergic system has considerable and widespread modulatory cortical influences (Steiner and Kitai, 2001). Furthermore, LFP recordings in STN and its major target, globus pallidus internus, are characterized by a preponderance of activity in the 10–30 Hz range in the untreated parkinsonian state (Brown et al., 2001; Marsden et al., 2002; Priori et al., 2002; Silberstein et al., 2003). This activity is coupled between these nuclei and cerebral cortex (Marsden et al., 2001; Williams et al., 2002). The direction of this coupling at rest, however, suggests net cortical driving of synchronization in the basal ganglia, rather than the converse (Marsden et al., 2001; Williams et al., 2002).

Thirdly, the relationship between cortico-cortical coherence and clinical state might reflect the existence of compensatory cortical mechanisms in the off state. The possibility of compensatory mechanisms in Parkinson's disease has been raised by imaging, transcranial magnetic stimulation (TMS) and previous EEG studies. Imaging studies demonstrate task-related hyperactivity in untreated Parkinson's disease in the lateral motor system and caudal supplementary motor area, although hypoactivity is present in the rostral supplementary motor area and prefrontal cortex (Samuel et al., 1997). This apparent upregulation of the lateral motor system has been linked to a greater utilization of and attention to visual cues as a compensatory motor strategy, and it is interesting to note that we found a number of connections between the occipital electrodes and more frontal ones that correlated with severity of parkinsonism in untreated Parkinson's disease and negatively correlated with clinical improvement. TMS studies have shown some inconsistencies, but in general suggest a reduction in intracortical inhibition in Parkinsonian patients (Ridding et al., 1995; Cantello et al., 2002), ostensibly inconsistent with the Albin and DeLong model of basal ganglia function (Alexander et al., 1986; Albin et al., 1989). Consequently, Cunic et al. (2002) have reasoned that the reduction in intracortical inhibition may be a compensation for akinesia/bradykinesia. In EEG studies, movement-induced desynchronization of the mu rhythm in untreated de novo patients or subjects withdrawn from medication was delayed over the contralateral central area, but occurred earlier over frontocentral regions in comparison with treated subjects or normal controls performing the same self-paced task (Defebvre et al., 1996; Devos et al., 2004). These alterations in the timing of the mu desynchronization have been interpreted as showing that other cortical areas, especially the ipsilateral primary sensorimotor cortex, are activated to compensate for deficiencies in cortical motor preparation (Devos et al., 2004).

If the relationship between cortico-cortical coherence and clinical state were to be partly due to cortical compensation for the motor dysfunction accrued through the basal ganglia defect in Parkinson's disease, then one might predict corresponding changes in patients with motor dysfunction due to different pathological mechanisms. This seems to be the case in patients recovering from motor stroke. In such patients, there is increased coherence in the 13–24 Hz band over mesial areas during gripping with the affected relative to the unaffected hand compared with the intermanual difference in healthy controls. This increased hand-related asymmetry is negatively correlated with recovery, consistent with a compensatory role for these cortical changes (Strens et al., 2004).

In the above, we have considered the possibility of compensatory changes in cortical function in Parkinson's disease and stroke as adaptive and beneficial, making up for the core dysfunction related to the underlying pathology. However, we cannot discount the alternative hypothesis that any secondary increase in cortical coupling is maladaptive and actually compounds the pathological dysfunction. For example, a net cortical drive to the basal ganglia in the beta band might increase subcortical synchronization in this frequency range, thereby exacerbating an oscillatory activity that has been considered essentially antikinetic (Brown, 2003).

STN HFS and dopaminergic drugs have similar effects on cortical oscillatory activity

We found strong frequency-dependent correlations in the topographic effects of HFS STN and dopaminergic medications on cortical coupling, suggesting that, at rest, dopaminergic medications and l-dopa affect cortical oscillatory coupling in a similar manner. This finding is in keeping with other lines of evidence suggesting that the cortical effects of these therapies, both at rest and with movement, are similar, as determined by TMS (Ridding et al., 1995; Pierantozzi et al., 2001, 2002; Cunic et al., 2002), imaging (Jenkins et al., 1992; Limousin et al., 1997) and EEG studies. In the latter case, Devos et al. (2004) showed that a similar improvement in contralateral pre-movement- and bilateral movement-related mu desynchronization was achieved by STN stimulation as with l-dopa. In an earlier study, the same group (Devos et al., 2003a) demonstrated that attenuated beta Event related synchronisation (ERS) over the central region was significantly increased with STN stimulation and l-dopa.

Overall, these findings are consistent with the notion that STN stimulation and l-dopa alter the pattern and extent of cortical activity in a similar manner. Nevertheless, our data revealed one aspect in which correlations between EEG coherence and clinical state differed between STN HFS and oral dopaminergic therapy. Correlations following drug therapy, although concentrated over ∼10–35 Hz, did extend over higher frequencies and, as indicated in the Results, this could not be explained by dyskinesia-related EMG contamination of EEG following drug treatment. This additional effect of dopaminergic therapy may relate, in part, to a more complex effect of dopamine on the patterning of striatal output, or have involved extra-striatal effects, such as direct dopaminergic effects on the cerebral cortex acting to suppress the synchronization between cortical regions at high frequency. Note that the correlation between changes in coherence at frequencies >35 Hz and difference in hemibody scores on medication was also negative. Specifically, inter-regional synchronization in the 60–80 Hz band dropped as the clinical state improved. This result was unexpected, given that dopaminergic medication induces increases in cortico-subcortical coupling in this frequency band (Williams et al., 2003). There may be several explanations for this. Cortico-subcortical coupling in the 60–80 Hz band may only promote local (intra-regional) rather than the distributed inter-regional cortical synchronization examined here. In addition, not all treated Parkinson's disease patients show an increase in 60–80 Hz cortico-subcortical coupling (Brown, 2003) and, when present, the effect is most marked during task performance (Cassidy et al., 2003), whereas our recordings were made at rest.

In conclusion, our results have shown that cortico-cortical coupling in Parkinson's disease relates to clinical state, and decrements in cortico-cortical coupling with STN stimulation or levodopa positively correlate with clinical improvement. The changes in cortical dynamics responsible for these correlations may be partially compensatory or maladaptive, while the similarity between the effects of STN stimulation and levodopa suggests that the two treatments may achieve some of their beneficial effects through common effects on the pattern of interaction between cortical areas.

Acknowledgments

P.B. is supported by the Medical Research Council of Great Britain, P.S. by a fellowship from the Parkinson's disease Society UK, A.K. by a fellowship from the German Academic Exchange Service (DAAD), S.T. by the Brain Research Trust, UK, and P.D.-L. by the Medical Research Council of Great Britain and Parkinson's appeal.

References

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