ORIGINAL RESEARCH
Resting State Cortical Oscillations of Patients With Parkinson Disease and With and Without Subthalamic Deep Brain Stimulation: A Magnetoencephalography Study Chunyan Cao,* Dianyou Li,* Tianxiao Jiang,† Nuri Firat Ince,† Shikun Zhan,* Jing Zhang,* Zhiyi Sha,‡ and Bomin Sun*
Purpose: In this study, we investigate the modification to cortical oscillations of patients with Parkinson disease (PD) by subthalamic deep brain stimulation (STN-DBS). Methods: Spontaneous cortical oscillations of patients with PD were recorded with magnetoencephalography during on and off subthalamic nucleus deep brain stimulation states. Several features such as average frequency, average power, and relative subband power in regions of interest were extracted in the frequency domain, and these features were correlated with Unified Parkinson Disease Rating Scale III evaluation. The same features were also investigated in patients with PD without surgery and healthy controls. Results: Patients with Parkinson disease without surgery compared with healthy controls had a significantly lower average frequency and an increased average power in 1 to 48 Hz range in whole cortex. Higher relative power in theta and simultaneous decrease in beta and gamma over temporal and occipital were also observed in patients with PD. The Unified Parkinson Disease Rating Scale III rigidity score correlated with the average frequency and with the relative power of beta and gamma in frontal areas. During subthalamic nucleus deep brain stimulation, the average frequency increased significantly when stimulation was on compared with off state. In addition, the relative power dropped in delta, whereas it rose in beta over the whole cortex. Through the course of stimulation, the Unified Parkinson Disease Rating Scale III rigidity and tremor scores correlated with the relative power of alpha over left parietal. Conclusions: Subthalamic nucleus deep brain stimulation improves the symptoms of PD by suppressing the synchronization of alpha rhythm in somatomotor region. Key Words: Magnetoencephalography, Deep brain stimulation, Parkinson disease, Cortical oscillations, Spectra. (J Clin Neurophysiol 2015;32: 109–118)
L
oss of dopaminergic innervations of Parkinson disease (PD) leads to abnormal processing of the striatal inputs from the cortex and thalamus and subsequently changes the neuronal activity of the
From the *Department of Functional Neurosurgery, Affiliated Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China; †Department of Biomedical Engineering, University of Houston, Houston, Texas, U.S.A.; and ‡Department of Neurology, University of Minnesota, Minneapolis, Minnesota, U.S.A. Supported by a grant from the National Natural Science Foundation of China No. 81271518 and in part by the National Science Foundation of USA, award CBET-1067488. C. Cao and D. Li have contributed equally to this study. Address correspondence and reprint requests to Bomin Sun, MD, PhD, Department of Functional Neurosurgery, Affiliated Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200025, China; e-mail address: [email protected]. Copyright 2014 by the American Clinical Neurophysiology Society
ISSN: 0736-0258/14/3202-0109
efferent nucleus of basal ganglia, that is, the globus pallidus internus and the subthalamic nucleus (STN) (Castner et al., 2008). The output of the basal ganglia is sent primarily to the thalamus and from there to the frontal cortex, forming the basal ganglia-thalamocortical loop (Joel and Weiner, 1994). Bilateral deep brain stimulation (DBS) to STN has been proven to be highly effective in alleviating Parkinsonian motor symptoms, but the mechanism is still not elucidated (Benabid et al., 2001; Deuschl et al., 2006; Herzog et al., 2007). One consistent neurophysiological marker of PD is an increase in the spontaneous beta band activity of 11 to 30 Hz in the basal ganglia-thalamocortical networks (Brown, 2003). Suppression of synchrony in the local field potential pattern from the STN in this frequency band through l-dopa and subthalamic deep brain stimulation (STN-DBS) is linked to the improvements in clinical symptoms (Silberstein et al., 2005). In the past few years, there are growing evidences that the hypersynchronizations of beta band in STN are attenuated by high-frequency stimulation (Kuhn et al., 2006, 2008; Ray et al., 2008). Local field potentials recorded with macroelectrodes in STN for DBS show that use of those electrode contacts with maximum power in the 13- to 32-Hz band provided optimal efficacy of stimulation results to patients with PD, evidencing that oscillations assessed from basal ganglia can be strategically used for the improvement of therapy (Ince et al., 2010). It has been observed that local field potentials in the STN are coherent with the scalp EEG, predominantly in the 8 to 13 Hz and 21 to 32 Hz range (Fogelson et al., 2006). Investigation of effects of DBS on the cortical activity provides important information about neural dynamics of the corticobasal ganglia loop. A recent study that measured the local field potential in STN of patients with PD and simultaneously recorded ECoG during STN-DBS treatment has demonstrated an attenuation of beta band power in both STN and cortical signals over sensorimotor cortex during DBS (Whitmer et al., 2012). Magnetoencephalography (MEG) is a noninvasive modality for assessing cortical oscillatory activities with high temporal and spatial resolutions and can be used to extract neurobiomarkers in PD. In an earlier MEG study, Timmermann et al. (2003) reported tremorrelated oscillatory activity within a cerebral network in patients with PD, with abnormal coupling in a cerebello-diencephalic-cortical loop contralateral to the hand tremor, the main frequency of cerebrocerebral coupling corresponds to approximately double the tremor frequency 8 to 12 Hz (Timmermann et al., 2003). Additionally, resting MEG recording of patients with PD has shown increased relative power of theta, low alpha, and decreased relative power of beta band activity (Bosboom et al., 2006). These results evidenced the functional use of MEG to study the cortical oscillations in PD. However, because of the severe electromagnetic artifacts generated by the implanted stimulator, the investigation on the cortical
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oscillations during STN-DBS with MEG is limited. The recently introduced spatiotemporal signal space separation (tSSS) algorithm recognizes the temporally and spatially correlated artificial components and removes them from multichannel neural data (Taulu and Hari, 2009; Taulu and Simola, 2006). The tSSS algorithm was successfully used to suppress the magnetic artifacts caused by DBS (Airaksinen et al., 2011; Park et al., 2009), and in later studies, it was demonstrated that after tSSS processing, somatomotor mu rhythm (9–13 Hz) amplitude correlates with rigidity score in patients with PD with STN-DBS (Airaksinen et al., 2011, 2012). Here, we investigated the effect of STN-DBS on spontaneous cortical oscillations of patients with PD, which were detected noninvasively with a whole head MEG. To deal with stimulation artifacts in MEG, we applied the well-established tSSS algorithm. Using the preprocessed MEG data recorded in DBS on and off states, several features such as the average frequency, the average power, and the relative power of the subbands were computed in the frequency domain as characteristic features to quantify the effect of DBS on the cortex. To clarify the change of cortical oscillation in patients with PD, we studied the same features in patients with PD without surgery by contrasting their data with the recordings obtained from healthy controls. Finally, the correlations between Unified Parkinson Disease Rating Scale (UPDRS) III scores and features extracted from cortical oscillations were addressed.
MATERIALS AND METHODS Subject Characteristics Three groups of subjects participated in this study: 1. Healthy elderly controls (N ¼ 11; 5 men/6 women, 56.6 6 6.5 years) 2. Parkinson disease patients without surgery (N ¼ 16; 9 men/7 women, 55.8 6 11.1 years) 3. Parkinson disease patients with STN-DBS surgery (N ¼ 16; 10 men/6 women, 55.0 6 8.3 years), including short term, 1 week of stimulation (N ¼ 8) and long term, at least 1 year of stimulation (N ¼ 8). All patients were recruited from the Functional Neurosurgery Department of Ruijin Hospital (Tables 1 and 2) and fulfilled the United Kingdom Parkinson Disease Society Brain Bank (UK-PDSBB) clinical diagnostic criteria for probable PD (Gibb, 1988). All patients were free of significant medical, neurologic, and psychiatric diseases except for PD and were not given any drugs during recording. All study subjects provided written consent for participation. The study was approved by the Local Ethics Committee of Affiliated Ruijin Hospital, Shanghai JiaoTong University School of Medicine.
Surgical Procedure Surgery was performed according to established DBS procedures (Cao et al., 2013; Li et al., 2013). Under local anesthesia, implantation of the DBS electrodes (7482; Medtronic, Minneapolis, MN) was performed bilaterally using a Leksell stereotactic frame and magnetic resonance imaging-guided targeting (1.5 T, General Electric). The DBS electrodes were connected to an implantable pulse generator ([IPG], Medtronic Itrel II) placed in the infraclavicular area under general anesthesia. The accuracy of electrode placement was validated with a postoperative magnetic resonance imaging. Postoperative programming began 2 110
days after surgery. The parameters of IPG were 2.0 to 3.6 V in amplitude, 60 to 90 microseconds in pulse width and 130 to 185 Hz in frequency.
Magnetoencephalography Recordings The subjects participated in this study had been off of their Parkinsonism medication for a minimum of 12 hours. The MEG recordings were performed in the morning with a 306-channel whole head MEG system (Elekta Oy, Helsinki, Finland) in a magnetically shielded room (Euroshield, Eura, Finland). The raw MEG data were band pass filtered in 0.03 to 330 Hz range and digitized at a sampling rate of 1011 Hz. The MEG recordings were obtained while patients rested with eyes closed for 3 minutes. The MEG recordings in the DBS off state started 10 minutes after the IPG was turned off.
Artifact of DBS Devices Detection During MEG Recordings The magnetic artifacts caused by DBS device and stimulation were removed by tSSS method using the Neuromag software (Neuromag 3.4; Elekta, Helsinki, Finland). To determine the appropriate tSSS parameters, a study with a phantom and MEG system was executed. Specifically, the IPG, lead, and its extension cable were immersed in an enclosed waterproof container of saline (0.9% NaCl). The 4-contact DBS lead was put inside the container and then placed in the MEG head unit. The IPG was positioned 15 cm outside of the head unit. The MEG recordings were obtained during both on and off states of the IPG. Each trial lasted 3 minutes with the following DBS parameters: contact 0 and 1 cathode, case anode, a stimulation frequency of 145 Hz, a pulse width of 90 microseconds, and an amplitude of 2.5 V. In the next step, we computed the spectra of the MEG data obtained from the phantom and selected 8-second raw data buffer for tSSS parameter according to Airaksinen et al. (2012) and compare between subspace correlation level of 0.95 and 0.8 to determine which one suppress artifact peaks at the stimulation frequency and its harmonics more efficiently.
MEG Data Analysis Because of the artifact caused by motor symptoms of patients with PD or the artifact of DBS hardware, sometimes we could not fully obtain 3 minutes of MEG recording. Therefore, 100 seconds of MEG data were analyzed. We used the same parameters of an 8-second raw data buffer with a subspace correlation limit of 0.8 during the application of tSSS algorithm to all the subjects in this study. The data were analyzed using a custom in-house developed Matlab software (The Mathworks, Natick, MA). After the application of tSSS algorithm, the raw data were digitally band pass filtered from 1 Hz to 48 Hz and visually inspected to identify potential artifact segments that were removed before the next spectral analysis step. For the ease of analysis, only signals from 102 planar magnetometers of MEG system were used. The MEG channels were grouped into regions of interest corresponding to the major cortical areas (temporal, parietal, occipital, and frontal) on the bilateral hemispheres. Then, the MEG signals from these regions were analyzed in frequency domain by using a fast Fourier transform. For each channel, the power spectrum is estimated using Welch method with a 1-second window and a 50% overlap. Computed spectra were averaged over channels in each regions of Copyright 2014 by the American Clinical Neurophysiology Society
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TABLE 1. Surgery
Case
Clinical Subtypes
Disease Duration, years
Total
T
R
50/m 34/m 56/f 49/f 50/m 43/f 42/m 55/f 64/f 64/f 69/m 51/m 67/f 75/m 60/m 64/m
R/T R/T R R/T T/R T T/R T/R R T/R T T/R R/T R T/R T/R
12 4 12 8 5 10 2 11 30 13 6 10 10 12 10 4
43 32 45 47 30 35 32 69 64 57 33 43 42 50 62 30
15 5 0 4 9 0 6 10 0 12 0 12 8 0 10 5
13 8 18 15 5 10 6 18 20 15 7 10 10 12 13 8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
was computed by dividing its power with the total power in 1 to 48 Hz spectrum and then compared with healthy controls. For the MEG recordings with STN-DBS, after tSSS, the spectrum was scrutinized by two experts to identify the remaining artifact peaks. Only the frequency bands without these artifact peaks were analyzed. The relative power for each subband was computed by dividing its power with the total power of artifact-free bands within 1 to 48 Hz spectrum. Additionally, average frequency and average power over regions of interest were computed. Average frequency is calculated from the power spectrum.
Clinical Characteristics of Patients With PD Without
Age (years)/Sex
UPDRS III
PN i 51 fi · pi fm 5 P ; N i 51 pi Where, pi is the power value of the corresponding frequency and fi is the average power obtained by algorithmic mean of power spectrum. PN pm 5
R, rigidity, T, tremor; UPDRS, Unified Parkinson Disease Rating Scale.
i 51 pi
N
:
UPDRS Motor Scores Evaluation The UPDRS III score was used to assess the motor symptoms of the patients with PD after overnight medication withdrawal. For the patients with PD with DBS treatment, the UPDRS III score was evaluated during stimulation on and 10 minutes after stopping the stimulation. Motor impairments were assessed with subscores for rest tremor (sum of UPDRS III subitem 20 for hand and foot) and rigidity (sum of UPDRS III subitem 22 for upper and lower extremities) particularly.
interest. The following features were extracted from the average MEG spectrum of each regions of interest. 1. Relative power of sub-bands 2. Average frequency 3. Average power. For the MEG recordings of patients with PD without surgery, the relative powers were calculated in the following subbands: 1 to 4 Hz (delta), 4 to 8 Hz (theta), 8 to 10 Hz (low alpha), 10 to 13 Hz (high alpha), 13 to 30 Hz (beta), and 30 to 48 (gamma) (Bosboom et al., 2006). The relative power for each frequency subband above
TABLE 2.
Cortical oscillations of PD with and without DBS
Statistical Analysis Comparisons of average frequency, average power, and relative power between healthy controls and patients with PD were carried out using independent-samples t-test. Comparisons of
Clinical Characteristics of Patients With PD Taking STN-DBS Treatment UPDRS III
Case 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
DBS on DBS off
Age (years)/Sex
Clinical Subtype
Disease Duration, years
Stimulation Duration
Total
T
R
Total
T
R
54/m 59/f 57/m 57/f 58/f 72/m 34/m 50/m 64/m 63/f 55/m 50/m 51/f 50/f 57/m 49/m
T/R T/R T/R R/T T/R R/T R/T T R/T T/R T/R R T/R T/R T/R T/R
4 11 11 7 8 13 4 5 13 4 5 12 10 5 8 8
1 year 6 years 2 years 2 years 4 years 5 years 1 year 1 year 1 week 1 week 1 week 1 week 1 week 1 week 1 week 1 week
12 25 10 15 28 34 3 0 15 10 9 9 8 7 11 9
0 3 0 0 6 5 0 0 0 0 0 0 2 0 0 0
2 10 3 5 8 10 0 0 7 5 3 3 3 0 5 5
34 60 30 39 60 84 10 7 40 20 23 29 19 21 36 29
8 9 6 9 11 16 0 7 8 0 4 6 7 7 9 3
9 15 7 10 15 20 5 0 10 8 2 8 6 2 12 10
DBS, deep brain stimulation; R, rigidity; STN, subthalamic nucleus; T, tremor; UPDRS, Unified Parkinson Disease Rating Scale.
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FIG. 1. The power spectra of phantom at stimulation on and off states after tSSS filtering. The spectra of data filtered with tSSS in a 8-second raw data buffer, subspace correlation limit of 0.95 at DBS on (green line) and DBS off (red line) states and then filtered in a 8-second raw data buffer, subspace correlation limit of 0.8 at DBS on (blue line) and DBS off (black line) states. At 50 Hz, an artifact showed in every signal because of the power line of environment. The artifact peaks presented at DBS on state due to electrical stimulation, the power spectra filtered with tSSS in subspace correlation limit of 0.8 have fewer artifact peaks. DBS, deep brain stimulation; tSSS, spatiotemporal signal space separation. average frequency, average power, relative power, and UPDRS III score between DBS on and off states were performed using a paired t-test. Relative power, average frequency, and average power were also correlated with the UPDRS III scores (using linear regression to determine the significance of the regression coefficient) to evaluate the association of cortical oscillations to disease severity, age, and duration (set as correction variables). All of the data were tested with SPSS (SPSS 13.0 for windows; SPSS, Chicago, IL), and the results were reported as mean 6 SD with significant levels set at 0.05.
RESULTS Removal of Artifact Caused by Deep Brain Stimulation There is a line frequency artifact at 50 Hz in every signal because of the power line of environment. The artifact peaks presented at DBS on state due to electrical stimulation. Spectra of the phantom MEG data filtered by the tSSS algorithm in a 8-second raw data buffer with a subspace correlation limit of 0.95 exhibited greater artifact peaks compared with spectra of the MEG data filtered by the tSSS algorithm in a 8-second raw data buffer with a subspace correlation limit of 0.8 (Fig. 1). We noted that the lower correlation
limit is more sensitive in detecting artifact waveforms and therefore more efficient in suppressing artifacts. Subsequently, MEG data from all subjects were analyzed after filtering them with tSSS algorithm in an 8-second raw data buffer using a subspace correlation limit of 0.8. Additionally, the spectra of patients with PD with STN-DBS treatment were visually inspected to find the unresolved artifacts. The peaks of artifacts in the spectra mainly consisted of frequencies of 19 to 20 Hz, 23 to 25 Hz, 32 to 33 Hz, 39 to 41 Hz, and 46 to 48 Hz (Fig. 2), which were excluded from the analysis. Finally, the relative power of 1 to 4 Hz (delta), 4 to 8 Hz (theta), 8 to 10 Hz (low alpha), 10 to 13 Hz (high alpha), 13 to 18 Hz (beta), and 34 to 38 Hz (gamma) were compared between DBS on and off conditions; therein 13 to 18 Hz represents beta and 34 to 38 Hz represents gamma band to avoid the contamination of the remaining artifacts in the full band. The raw MEG recordings of PD patient before and after the application of tSSS are shown in Fig. 3. The MEG data are heavily corrupted by stimulation artifacts during the DBS on state. As the amplitude of the artifact caused by electrical stimulation is intensively larger than the brain oscillations, it is difficult to visually isolate any oscillatory components. After the application of tSSS, the MEG recording shows clear brain oscillations. As we mentioned earlier, we applied tSSS to the MEG recordings obtained during
FIG. 2. The power spectral lines of four patients with PD with STN stimulation at different frequencies, pulse width, and voltages. The spectra of each patient shows different artifact peak frequencies. The 50 Hz is power line that exists in both DBS on (red line) and off (black line) states. The artifact peak frequencies appeared mainly in 13 to 30 Hz (beta) and 30 to 75 Hz (gamma). DBS, deep brain stimulation; PD, Parkinson disease; STN, subthalamic nucleus. 112
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Cortical oscillations of PD with and without DBS
FIG. 3. The raw data of MEG recording before tSSS and after tSSS of a PD patient with STN-DBS in the on and off conditions. A, The raw data of multiple channels are shown. The amplitude of the raw data at DBS on state before tSSS is much more than the data after tSSS, indicating the artifacts from electrical stimulation cover up real brain oscillation. After tSSS, the brain oscillations appear. Vertical bars represent the amplitude scales at different state. B, The raw recording extracted from one MEG channel is displayed. Recording duration is 10 seconds. MEG, magnetoencephalography; PD, Parkinson disease; STN-DBS, subthalamic deep brain stimulation; tSSS, spatiotemporal signal space separation.
DBS off state. We note that the amplitude of the raw MEG data before tSSS is comparable with the raw data after tSSS (Fig. 3). Substantially, after tSSS, no artifacts caused by electrical stimulation, devices, and different other nature such as electrocardiogram were observed.
Characteristics of the Cortical Spontaneous Oscillatory Activities of Patients With PD Without Surgery The average frequency over all cortical regions of patients with PD without surgery slowed down remarkably compared with healthy controls (PD 18.76 6 1.33 Hz, n ¼ 16 and healthy control 20.67 6 1.32 Hz, n ¼ 11, P ¼ 0.0013). This change is especially evident in the temporal (PD 16.56 6 1.7 Hz and healthy control 20.64 6 0.9 Hz, P , 0.001) and occipital areas (PD 15.64 6 1.0 Hz Copyright 2014 by the American Clinical Neurophysiology Society
and healthy control 19.94 6 1.9 Hz, P , 0.001) (Figs. 4A and 5A). The average power over all cortical regions of patients with PD without surgery increased (PD 0.048 6 0.021 and healthy control 0.033 6 0.012, P ¼ 0.048). There was also a change in temporal (PD 0.03 6 0.01 and healthy control 0.08 6 0.05, P , 0.001) and occipital areas (PD 0.04 6 0.01 and healthy control 0.08 6 0.05, P , 0.001) but not in parietal and frontal areas (P . 0.05) (Figs. 4B and 5A). In addition, patients with PD showed a rise in the relative power of 4 to 8 Hz (theta band), while showing a drop in the relative power of 13 to 30 Hz (beta) and 30 to 48 Hz (gamma) over temporal and occipital areas (P , 0.05). The parietal and frontal areas did not show any significant changes (P . 0.05) (Fig. 5B). Unified Parkinson Disease Rating Scale III rigidity scores strongly correlated with the average frequency over frontal areas (rs ¼ 0.838, P ¼ 0.002 in left and rs ¼ 0.774, P ¼ 0.003 in right). Meanwhile, UPDRS III rigidity scores were noticeably correlated 113
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FIG. 3.
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Continued.
with the relative power of 13 to 30 Hz (rs ¼ 0.687, P ¼ 0.011 in left and rs ¼ 0.661, P ¼ 0.033 in right) and 30 to 48 Hz (rs ¼ 0.681, P ¼ 0.024 in left and rs ¼ 0.819, P ¼ 0.008 in right) over frontal areas (Table 3) (n ¼ 16).
Modification of Cortical Spontaneous Oscillatory Activities of Patients With PD by Subthalamic Deep Brain Stimulation All of the patients with PD with STN-DBS surgery had clear improvement in motor symptoms. Unified Parkinson Disease Rating Scale III total motor scores were 12.8 6 9.0 at stimulation on and 33.8 6 20.0 at stimulation off states (P , 0.001). Unified Parkinson Disease Rating Scale III tremor scores were 1.0 6 1.9 at stimulation on and 6.9 6 4.0 at stimulation off states (P , 0.001). Unified Parkinson Disease Rating Scale III rigidity scores were 4.3 6 3.2 at 114
stimulation on and 8.7 6 5.2 at stimulation off states (P , 0.001) (Table 2) (n ¼ 16). No variations in cortical oscillation including average power, average frequency, and relative power of subbands were found in patients with PD with 1 week of stimulation in both stimulation on and stimulation off conditions (n ¼ 8). In patients with PD with long-term stimulation, the whole cortical average frequency increased with stimulation compared with stimulation off state (18.85 6 1.53 Hz at DBS off and 19.75 6 1.23 Hz at DBS on, P ¼ 0.007). This increase was especially evident in the right temporal (P ¼ 0.0072), left frontal (P ¼ 0.004), right frontal (P ¼ 0.0009), and right occipital regions (P ¼ 0.027) (Fig. 6A). However, the average power over all cortical regions showed no obvious change relative to stimulation off state (P . 0.05) (Fig. 6B) (n ¼ 8). In detail, during stimulation, the relative power of 1 to 4 Hz attenuated significantly (P , 0.05) and the relative power of 14 to Copyright 2014 by the American Clinical Neurophysiology Society
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FIG. 4. Line plot of average frequency and average power of patients with PD without surgery and healthy controls, patients with PD present slowing of average frequency over diffuse cortical areas (A) and incremental average power over temporal and occipital areas (B). *P , 0.05, ***P , 0.001. PD, Parkinson disease. 18 Hz increased remarkably (P , 0.05) over diffuse cortical areas relative to stimulation off state (Fig. 5C) (n ¼ 8). In all of the patients with PD with STN-DBS treatment, the relative power did not correlate to UPDRS III scores in the stimulation off state. Instead, during the stimulation on state, the relative power of 9 to 13 Hz over the left temporal, left occipital, and bilateral parietal areas was significantly correlated with the UPDRS III total motor scores (Table 4). The relative power of 9 to 13 Hz over the left parietal area noticeably correlated with the UPDRS III tremor scores (rs ¼ 0.758, P ¼ 0.002 in 9–10 Hz; rs ¼ 0.635, P ¼ 0.041 in 11–13 Hz). The relative power of 9 to 10 Hz over left parietal area correlated with UPDRS III rigidity scores (rs ¼ 0.385, P ¼ 0.037) (Table 4) (n ¼ 15).
DISCUSSION Our study demonstrated that patients with PD without surgery showed a prominent slowing of resting oscillatory activities compared with healthy controls, which is consistent with previous studies (Bosboom et al., 2006; Soikkeli et al., 1991). Compared with Copyright 2014 by the American Clinical Neurophysiology Society
Cortical oscillations of PD with and without DBS
healthy controls, the patients with PD had lower average frequency in 1 to 48 Hz range over all cortical areas. The average powers in the 1 to 48 Hz frequency band in temporal and occipital areas were higher than controls. Additionally, in patients with PD, the relative power of the theta band was noticeably larger than controls, whereas the relative powers of the beta and gamma bands were noticeably lower over bilateral temporal and occipital areas. Our results support the thalamocortical dysrhythmia characterized by the lowered thalamocortical oscillation in patients with PD (Jeanmonod et al., 1996; Maggini and White, 2011). The enhancements of theta over temporal and occipital areas in EEG of psychiatric patients were elucidated (Ellingson, 1954; MundyCastle, 1951). In this study, MEG recordings of the patients with PD without surgery were obtained before their STN-DBS treatment and all of the patients with PD are in their midterm or endterm disease period. Generally, depression arrests 35% of patients with PD and anxiety arrests 20 to 49% of patients with PD (Dissanayaka et al., 2010; Hagell et al., 1994; Martinez-Martin and Damian, 2010; Tandberg et al., 1997). A progressive increase of EEG delta and theta spectral power with a parallel widespread decrease of beta power was observed from controls to patients with Alzheimer disease, mainly in the anterior areas (Scrascia et al., 2013). In our study, patients with PD did not show an increase in delta spectral power, but an increase of theta spectral power was observed mainly in posterior areas, with a corresponding decrease in both relative powers of beta and gamma bands. Conclusively, the distinctive slowing of cortical oscillations of patients with PD indicates general neurological and psychiatric dysfunction due to neurodegeneration in the basal ganglia cortical loop. However, our findings demonstrate the average frequency and the relative powers of beta and gamma bands in the frontal area correlated to the UPDRS III rigidity score in patients with PD without surgery. A role of frontal lobe in action has been illuminated, which is associated to many higher level aspects of movement planning and decision making (Miller and Cohen, 2001; Wise, 1985). Our study shows the oversynchronization of beta and gamma band (13–48 Hz) over frontal lobe involved in the symptoms of patients with PD. This provides evidence to the proposition that the prevalence of beta band in the basal ganglia-thalamus-frontal cortical circuits may be responsible for the rigidity symptoms of patients with PD (Gatev et al., 2006). In patients with PD at 1 week of stimulation, no difference was observed in average frequency and average power between DBS on and off states. We believe that the patients at 1 week of stimulation were still within the period of “stun effect,” caused by microlesioning due to the electrode implantation. Therefore, the local edema of the vicinity of STN might restrict the STN efferent impulse and interfere in the transmission of stimulation impulse to cortex. The average frequency of patients with PD at long-term stimulation increased significantly in stimulation on state compared with stimulation off state. These findings complement evidence from previous studies, which demonstrated the cortical activation in patients with PD and STN-DBS animal models (Kuriakose et al., 2010; Li et al., 2012). Our findings demonstrated that the increase in average frequency and the recovery of balanced average power by STN stimulation might be caused by the inhibition of relative power of delta and increment of relative power of beta over diffuse cortical areas. This is because patients with PD at stimulation on showed remarkably lower relative power of delta and higher relative power of beta of diffuse cortex compared with stimulation off states, which could be attributed to nonspecific increase in vigilance and intrinsic alertness during stimulation on (Jech et al., 2006). 115
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FIG. 5. Schematic representation of differences of cortical oscillation between patients with PD without surgery versus healthy controls and patients with PD at DBS on versus off states in different frequency subbands from 1 to 48 Hz. A, Average frequency and average power differences from 1 to 48 Hz between patients with PD without surgery and healthy controls; B, Relative power differences of frequency subbands from 1 to 48 Hz between patients with PD without surgery and healthy controls; C, Relative power differences of artifact-free frequency subbands from 1 to 48 Hz between patients with PD at DBS on versus off states. Red color indicates an increase of variables, and blue color indicates a decrease of variables. Only those areas for which a significant group difference was found in t-test are color-painted. Light color indicates P , 0.05; dark color indicates P , 0.01. The MEG channels were clustered based on the approximate underlying cortical areas. DBS, deep brain stimulation; F, frontal; L or R, left or right side; MEG, magnetoencephalography; O, occipital; P, parietal; PD, Parkinson disease; T, temporal. EEG after STN-DBS on (Jech et al., 2006; Li et al., 2007). Specifically, our study demonstrated the UPDRS III rigidity and tremor scores correlates to the relative power of 9 to 13 Hz over the left parietal area. Our study lacks spatial localization of the MEG recordings, the recording channels were grouped into regions of interest corresponding to the major cortical areas (temporal, parietal, occipital, and frontal) on the bilateral hemispheres. Since the somatomotor region locates in the parietal lobe, our results indicate DBS reduced nonfavorable 9 to 13 Hz activity in the left somatomotor region in the amelioration of rigidity and tremor symptoms of PD. Until a few years ago, it was difficult to review and process MEG data due to the large artifacts generated by vagus nerve stimulation and DBS. The advent of tSSS finally allowed doing this (Song, 2009; Taulu and Hari, 2009). The temporal extension of SSS, the tSSS, removes the contribution of the nearby artifact sources by using time information in addition to Maxwell equation. It was shown
It is well recognized that 9 to 13 Hz band is desynchronized and its power attenuates in the somatomotor cortex when subjects are engaged in motor activity and general attention processing (Chatrian et al., 1959; Pfurtscheller et al., 2000). Different to the report that the somatomotor mu rhythm (9–13 Hz) amplitude correlates with rigidity score in patients with PD with STN-DBS (Airaksinen et al., 2011, 2012), our study shows UPDRS III total motor score correlates to the relative power of 9 to 13 Hz (alpha) over left temporal, left occipital, and bilateral parietal areas; however, UPDRS III tremor and rigidity scores correlate to left parietal areas. Our study suggests the total motor function is related to the synchronization of alpha in widespread cortical areas. However, tremor and rigidity is more related the synchronization of alpha in the left parietal lobe. Therefore, our results are in agreement with the findings that the relative power of the dominant frequency of 9 to 13 Hz decreased in all derivations of the cortical areas recorded by TABLE 3. Surgery
Correlation Strengths Between Cortical Oscillation and UPDRS III Rigidity Subscores of Patients With PD Without Average Frequency
UPDRS III Rigidity
Relative Power
Regions
r
P
Wave Bands, Hz
r
P
lf
0.838
0.002
rf
0.774
0.003
13–30 30–48 13–30 30–48
0.687 0.681 0.661 0.819
0.011 0.024 0.033 0.008
Table shows the correlation coefficients, the t-test scores from the linear regression coefficient r, and probability values P. P , 0.05 indicates significant correlation. f, frontal; l, left; r, right; UPDRS, Unified Parkinson Disease Rating Scale.
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Cortical oscillations of PD with and without DBS
TABLE 4. Correlation Strengths Between Cortical Oscillation at STN Stimulation On State and UPDRS III Motor of Patients With PD Taking More Than 1 Year of Stimulation Relative Power UPDRS III Total
Regions
Wave Bands, Hz
r
P
lt
9–10 11–13 9–10 11–13 9–10 11–13 9–10 11–13 9–10 11–13 9–10
0.566 0.478 0.520 0.453 0.666 0.592 0.467 0.548 0.758 0.635 0.386
0.006 0.029 0.015 0.040 0.001 0.008 0.029 0.021 0.002 0.041 0.037
lo lp
Tremor
rp lp
Rigidity
lp
Table shows the correlation coefficients, the t-test scores from the linear regression coefficient r, and probability values P. P , 0.05 indicates significant correlation. l, left; o, occipital; r, right; p, parietal; t, temporal; UPDRS, Unified Parkinson Disease Rating Scale.
In conclusion, the rigidity of patients with PD without surgery correlates to the relative spectral power of beta and gamma in bilateral frontal area. MEG detection with the tSSS algorithm provides an efficient method in investigating the modulation to basal gangliathalamocortical oscillations of patients with PD by STN-DBS. The amelioration of tremor and rigidity by STN-DBS might be associated to the dysynchronization of alpha band oscillation over left somatomotor region. For a further detailed understanding of the modification on cortical oscillation by STN-DBS to patients with PD, a blinded long-term prospective study on a large number of subjects who enroll in the STN-DBS treatment is required. FIG. 6. Line plot of average frequency and average power of Parkinson disease patients at stimulation on and off states. Stimulation on increases the average frequency of right temporal, right occipital, and bilateral frontal areas compared with stimulation off state (A). No difference in average power (B). *P , 0.05. that, using tSSS, not only the artifacts caused by electrical stimulation but also the contribution of other sources, such as eye movements and/ or cardiac signals (Kakisaka et al., 2013; Song et al., 2008), are efficiently removed. Our results strongly support the feasibility of the tSSS method in removing, with minimal distortion, external magnetic artifacts from MEG signals. However, the remaining artifact peaks in the MEG spectrum after tSSS indicate that further improvements in signal processing and antialiasing filtering are necessary. There are several limitations in this study. First, the work lacks in spatial specificity, which localizes the variation of frequency bands in cortical areas. Second, the intolerability in the reappearance of motor symptoms in advanced PD when the stimulation was terminated, especially for the patients with long-term stimulation who were adapted to the continuous stimulation, the MEG recordings were operated at 10 minutes after the IPGs were powered off. This is far from 2 hours, the recognized washout time of STN stimulation (Temperli et al., 2003). Third, the intricate correlations of individual subband oscillations within the basal ganglia-thalamocortical nets with specific motor functions are far away from being clarified with the limited analytical strength. Copyright 2014 by the American Clinical Neurophysiology Society
ACKNOWLEDGMENTS The authors would like to express their sincere appreciations to the patients who joined in this study. They also would like to thank Tao Song, Yongxia Qiao for the advices in data analysis, and also Jyrki P. Makela and Ji-Hee Kim for their helpful suggestions in preparing for the manuscript. REFERENCES Airaksinen K, Makela JP, Taulu S, et al. Effects of DBS on auditory and somatosensory processing in Parkinson disease. Hum Brain Mapp 2011;32:1091–1099. Airaksinen K, Butorina A, Pekkonen E, et al. Somatomotor mu rhythm amplitude correlates with rigidity during deep brain stimulation in Parkinsonian patients. Clin Neurophysiol 2012;123:2010–2017. Benabid AL, Koudsie A, Benazzouz A, et al. Deep brain stimulation of the corpus luysi (subthalamic nucleus) and other targets in Parkinson disease. Extension to new indications such as dystonia and epilepsy. J Neurol 2001;248(suppl3): III37–III47. Bosboom JL, Stoffers D, Stam CJ, et al. Resting state oscillatory brain dynamics in Parkinson disease: an MEG study. Clin Neurophysiol 2006;117:2521–2531. Brown P. Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson disease. Mov Disord 2003;18:357–363. Cao C, Pan Y, Li D, et al. Subthalamus deep brain stimulation for primary dystonia patients: a long-term follow-up study. Mov Disord 2013;28:1877–1882. Castner JE, Chenery HJ, Silburn PA, et al. Effects of subthalamic deep brain stimulation on noun/verb generation and selection from competing alternatives in Parkinson disease. J Neurol Neurosurg Psychiatry 2008;79:700–705. Chatrian GE, Petersen MC, Lazarte JA. The blocking of the rolandic wicket rhythm and some central changes related to movement. Electroencephalogr Clin Neurophysiol 1959;11:497–510.
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Deuschl G, Schade-Brittinger C, Krack P, et al. A randomized trial of deep-brain stimulation for Parkinson disease. N Engl J Med 2006;355:896–908. Dissanayaka NN, Sellbach A, Matheson S, et al. Anxiety disorders in Parkinson disease: prevalence and risk factors. Mov Disord 2010;25:838–845. Ellingson RJ. The incidence of EEG abnormality among patients with mental disorders of apparently nonorganic origin: a critical review. Am J Psychiatry 1954;111:263–275. Fogelson N, Williams D, Tijssen M, et al. Different functional loops between cerebral cortex and the subthalmic area in Parkinson disease. Cereb Cortex 2006;16:64–75. Gatev P, Darbin O, Wichmann T. Oscillations in the basal ganglia under normal conditions and in movement disorders. Mov Disord 2006;21:1566–1577. Gibb WR. Accuracy in the clinical diagnosis of Parkinsonian syndromes. Postgrad Med J 1988;64:345–351. Hagell P, Odin P, Freccero-Rosman K, et al. Depression is common in Parkinson disease. Limited studies on symptoms and treatment [in Swedish]. Lakartidningen 1994;91:4270, 4275–4276. Herzog J, Hamel W, Wenzelburger R, et al. Kinematic analysis of thalamic versus subthalamic neurostimulation in postural and intention tremor. Brain 2007;130:1608–1625. Ince NF, Gupte A, Wichmann T, et al. Selection of optimal programming contacts based on local field potential recordings from subthalamic nucleus in patients with Parkinson disease. Neurosurgery 2010;67:390–397. Jeanmonod D, Magnin M, Morel A. Low-threshold calcium spike bursts in the human thalamus. Common physiopathology for sensory, motor and limbic positive symptoms. Brain 1996;119(pt 2):363–375. Jech R, Ruzicka E, Urgosik D, et al. Deep brain stimulation of the subthalamic nucleus affects resting EEG and visual evoked potentials in Parkinson disease. Clin Neurophysiol 2006;117:1017–1028. Joel D, Weiner I. The organization of the basal ganglia-thalamocortical circuits: open interconnected rather than closed segregated. Neuroscience 1994;63:363–379. Kakisaka Y, Mosher JC, Wang ZI, et al. Utility of temporally-extended signal space separation algorithm for magnetic noise from vagal nerve stimulators. Clin Neurophysiol 2013;124:1277–1282. Kuhn AA, Kupsch A, Schneider GH, et al. Reduction in subthalamic 8-35 Hz oscillatory activity correlates with clinical improvement in Parkinson disease. Eur J Neurosci 2006;23:1956–1960. Kuhn AA, Kempf F, Brucke C, et al. High-frequency stimulation of the subthalamic nucleus suppresses oscillatory beta activity in patients with Parkinson disease in parallel with improvement in motor performance. J Neurosci 2008;28:6165–6173. Kuriakose R, Saha U, Castillo G, et al. The nature and time course of cortical activation following subthalamic stimulation in Parkinson disease. Cereb Cortex 2010;20:1926–1936. Li S, Arbuthnott GW, Jutras MJ, et al. Resonant antidromic cortical circuit activation as a consequence of high-frequency subthalamic deep-brain stimulation. J Neurophysiol 2007;98:3525–3537. Li Q, Ke Y, Chan DC, et al. Therapeutic deep brain stimulation in Parkinsonian rats directly influences motor cortex. Neuron 2012;76:1030–1041.
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Li D, Cao C, Zhang J, et al. Subthalamic nucleus deep brain stimulation for Parkinson disease: 8 years of follow-up. Transl Neurodegener 2013;2:11. Maggini S, White PS. [m-1,3-Dioxo-1,3-bis(pyridin-2-yl)propane-2,2-diido-kappaN, C:kappaC,N’]bis[(1,3- diphenylpropane-1,3-dionato-kappaO,O’)palladium(II)] (Pd-Pd). Acta Crystallogr Sect E Struct Rep Online 2011;67:m749–m750. Martinez-Martin P, Damian J. Parkinson disease: depression and anxiety in Parkinson disease. Nat Rev Neurol 2010;6:243–245. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci 2001;24:167–202. Mundy-Castle AC. Theta and beta rhythm in the electroencephalograms of normal adults. Electroencephalogr Clin Neurophysiol 1951;3:477–486. Park H, Kim JS, Paek SH, et al. Cortico-muscular coherence increases with tremor improvement after deep brain stimulation in Parkinson disease. Neuroreport 2009;20:1444–1449. Pfurtscheller G, Neuper C, Krausz G. Functional dissociation of lower and upper frequency mu rhythms in relation to voluntary limb movement. Clin Neurophysiol 2000;111:1873–1879. Ray NJ, Jenkinson N, Wang S, et al. Local field potential beta activity in the subthalamic nucleus of patients with Parkinson disease is associated with improvements in bradykinesia after dopamine and deep brain stimulation. Exp Neurol 2008;213:108–113. Scrascia FCG, Ursini F, Trotta L, et al. Relationship among diffusion tensor imaging, EEG activity, and cognitive status in mild cognitive impairment and Alzheimer disease patients. J Alzheimers Dis 2013;38:939–950. Silberstein P, Pogosyan A, Kuhn AA, et al. Cortico-cortical coupling in Parkinson disease and its modulation by therapy. Brain 2005;128:1277–1291. Soikkeli R, Partanen J, Soininen H, et al. Slowing of EEG in Parkinson disease. Electroencephalogr Clin Neurophysiol 1991;79:159–165. Song T, Cui L, Gaa K, et al. Signal space separation algorithm and its application on suppressing artifacts caused by vagus nerve stimulation for magnetoencephalography recordings. J Clin Neurophysiol 2009;26:392–400. Song T, Gaa K, Cui L, et al. Evaluation of signal space separation via simulation. Med Biol Eng Comput 2008;46:923–932. Tandberg E, Larsen JP, Aarsland D, et al. Risk factors for depression in Parkinson disease. Arch Neurol 1997;54:625–630. Taulu S, Hari R. Removal of magnetoencephalographic artifacts with temporal signal-space separation: demonstration with single-trial auditory-evoked responses. Hum Brain Mapp 2009;30:1524–1534. Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys Med Biol 2006;51:1759–1768. Temperli PGJ, Villemure JG, Burkhard PR, et al. How do Parkinsonian signs return after discontinuation of subthalamic DBS? Neurology 2003;60:78–81. Timmermann L, Gross J, Dirks M, et al. The cerebral oscillatory network of Parkinsonian resting tremor. Brain 2003;126:199–212. Whitmer D, de Solages C, Hill B, et al. High frequency deep brain stimulation attenuates subthalamic and cortical rhythms in Parkinson disease. Front Hum Neurosci 2012;6:155. Wise SP. The primate premotor cortex: past, present, and preparatory. Annu Rev Neurosci 1985;8:1–19.
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Resting State Cortical Oscillations of Patients With Parkinson Disease and With and Without Subthalamic Deep Brain Stimulation: A Magnetoencephalography Study Chunyan Cao,* Dianyou Li,* Tianxiao Jiang,† Nuri Firat Ince,† Shikun Zhan,* Jing Zhang,* Zhiyi Sha,‡ and Bomin Sun*
Purpose: In this study, we investigate the modification to cortical oscillations of patients with Parkinson disease (PD) by subthalamic deep brain stimulation (STN-DBS). Methods: Spontaneous cortical oscillations of patients with PD were recorded with magnetoencephalography during on and off subthalamic nucleus deep brain stimulation states. Several features such as average frequency, average power, and relative subband power in regions of interest were extracted in the frequency domain, and these features were correlated with Unified Parkinson Disease Rating Scale III evaluation. The same features were also investigated in patients with PD without surgery and healthy controls. Results: Patients with Parkinson disease without surgery compared with healthy controls had a significantly lower average frequency and an increased average power in 1 to 48 Hz range in whole cortex. Higher relative power in theta and simultaneous decrease in beta and gamma over temporal and occipital were also observed in patients with PD. The Unified Parkinson Disease Rating Scale III rigidity score correlated with the average frequency and with the relative power of beta and gamma in frontal areas. During subthalamic nucleus deep brain stimulation, the average frequency increased significantly when stimulation was on compared with off state. In addition, the relative power dropped in delta, whereas it rose in beta over the whole cortex. Through the course of stimulation, the Unified Parkinson Disease Rating Scale III rigidity and tremor scores correlated with the relative power of alpha over left parietal. Conclusions: Subthalamic nucleus deep brain stimulation improves the symptoms of PD by suppressing the synchronization of alpha rhythm in somatomotor region. Key Words: Magnetoencephalography, Deep brain stimulation, Parkinson disease, Cortical oscillations, Spectra. (J Clin Neurophysiol 2015;32: 109–118)
L
oss of dopaminergic innervations of Parkinson disease (PD) leads to abnormal processing of the striatal inputs from the cortex and thalamus and subsequently changes the neuronal activity of the
From the *Department of Functional Neurosurgery, Affiliated Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China; †Department of Biomedical Engineering, University of Houston, Houston, Texas, U.S.A.; and ‡Department of Neurology, University of Minnesota, Minneapolis, Minnesota, U.S.A. Supported by a grant from the National Natural Science Foundation of China No. 81271518 and in part by the National Science Foundation of USA, award CBET-1067488. C. Cao and D. Li have contributed equally to this study. Address correspondence and reprint requests to Bomin Sun, MD, PhD, Department of Functional Neurosurgery, Affiliated Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200025, China; e-mail address: [email protected]. Copyright 2014 by the American Clinical Neurophysiology Society
ISSN: 0736-0258/14/3202-0109
efferent nucleus of basal ganglia, that is, the globus pallidus internus and the subthalamic nucleus (STN) (Castner et al., 2008). The output of the basal ganglia is sent primarily to the thalamus and from there to the frontal cortex, forming the basal ganglia-thalamocortical loop (Joel and Weiner, 1994). Bilateral deep brain stimulation (DBS) to STN has been proven to be highly effective in alleviating Parkinsonian motor symptoms, but the mechanism is still not elucidated (Benabid et al., 2001; Deuschl et al., 2006; Herzog et al., 2007). One consistent neurophysiological marker of PD is an increase in the spontaneous beta band activity of 11 to 30 Hz in the basal ganglia-thalamocortical networks (Brown, 2003). Suppression of synchrony in the local field potential pattern from the STN in this frequency band through l-dopa and subthalamic deep brain stimulation (STN-DBS) is linked to the improvements in clinical symptoms (Silberstein et al., 2005). In the past few years, there are growing evidences that the hypersynchronizations of beta band in STN are attenuated by high-frequency stimulation (Kuhn et al., 2006, 2008; Ray et al., 2008). Local field potentials recorded with macroelectrodes in STN for DBS show that use of those electrode contacts with maximum power in the 13- to 32-Hz band provided optimal efficacy of stimulation results to patients with PD, evidencing that oscillations assessed from basal ganglia can be strategically used for the improvement of therapy (Ince et al., 2010). It has been observed that local field potentials in the STN are coherent with the scalp EEG, predominantly in the 8 to 13 Hz and 21 to 32 Hz range (Fogelson et al., 2006). Investigation of effects of DBS on the cortical activity provides important information about neural dynamics of the corticobasal ganglia loop. A recent study that measured the local field potential in STN of patients with PD and simultaneously recorded ECoG during STN-DBS treatment has demonstrated an attenuation of beta band power in both STN and cortical signals over sensorimotor cortex during DBS (Whitmer et al., 2012). Magnetoencephalography (MEG) is a noninvasive modality for assessing cortical oscillatory activities with high temporal and spatial resolutions and can be used to extract neurobiomarkers in PD. In an earlier MEG study, Timmermann et al. (2003) reported tremorrelated oscillatory activity within a cerebral network in patients with PD, with abnormal coupling in a cerebello-diencephalic-cortical loop contralateral to the hand tremor, the main frequency of cerebrocerebral coupling corresponds to approximately double the tremor frequency 8 to 12 Hz (Timmermann et al., 2003). Additionally, resting MEG recording of patients with PD has shown increased relative power of theta, low alpha, and decreased relative power of beta band activity (Bosboom et al., 2006). These results evidenced the functional use of MEG to study the cortical oscillations in PD. However, because of the severe electromagnetic artifacts generated by the implanted stimulator, the investigation on the cortical
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oscillations during STN-DBS with MEG is limited. The recently introduced spatiotemporal signal space separation (tSSS) algorithm recognizes the temporally and spatially correlated artificial components and removes them from multichannel neural data (Taulu and Hari, 2009; Taulu and Simola, 2006). The tSSS algorithm was successfully used to suppress the magnetic artifacts caused by DBS (Airaksinen et al., 2011; Park et al., 2009), and in later studies, it was demonstrated that after tSSS processing, somatomotor mu rhythm (9–13 Hz) amplitude correlates with rigidity score in patients with PD with STN-DBS (Airaksinen et al., 2011, 2012). Here, we investigated the effect of STN-DBS on spontaneous cortical oscillations of patients with PD, which were detected noninvasively with a whole head MEG. To deal with stimulation artifacts in MEG, we applied the well-established tSSS algorithm. Using the preprocessed MEG data recorded in DBS on and off states, several features such as the average frequency, the average power, and the relative power of the subbands were computed in the frequency domain as characteristic features to quantify the effect of DBS on the cortex. To clarify the change of cortical oscillation in patients with PD, we studied the same features in patients with PD without surgery by contrasting their data with the recordings obtained from healthy controls. Finally, the correlations between Unified Parkinson Disease Rating Scale (UPDRS) III scores and features extracted from cortical oscillations were addressed.
MATERIALS AND METHODS Subject Characteristics Three groups of subjects participated in this study: 1. Healthy elderly controls (N ¼ 11; 5 men/6 women, 56.6 6 6.5 years) 2. Parkinson disease patients without surgery (N ¼ 16; 9 men/7 women, 55.8 6 11.1 years) 3. Parkinson disease patients with STN-DBS surgery (N ¼ 16; 10 men/6 women, 55.0 6 8.3 years), including short term, 1 week of stimulation (N ¼ 8) and long term, at least 1 year of stimulation (N ¼ 8). All patients were recruited from the Functional Neurosurgery Department of Ruijin Hospital (Tables 1 and 2) and fulfilled the United Kingdom Parkinson Disease Society Brain Bank (UK-PDSBB) clinical diagnostic criteria for probable PD (Gibb, 1988). All patients were free of significant medical, neurologic, and psychiatric diseases except for PD and were not given any drugs during recording. All study subjects provided written consent for participation. The study was approved by the Local Ethics Committee of Affiliated Ruijin Hospital, Shanghai JiaoTong University School of Medicine.
Surgical Procedure Surgery was performed according to established DBS procedures (Cao et al., 2013; Li et al., 2013). Under local anesthesia, implantation of the DBS electrodes (7482; Medtronic, Minneapolis, MN) was performed bilaterally using a Leksell stereotactic frame and magnetic resonance imaging-guided targeting (1.5 T, General Electric). The DBS electrodes were connected to an implantable pulse generator ([IPG], Medtronic Itrel II) placed in the infraclavicular area under general anesthesia. The accuracy of electrode placement was validated with a postoperative magnetic resonance imaging. Postoperative programming began 2 110
days after surgery. The parameters of IPG were 2.0 to 3.6 V in amplitude, 60 to 90 microseconds in pulse width and 130 to 185 Hz in frequency.
Magnetoencephalography Recordings The subjects participated in this study had been off of their Parkinsonism medication for a minimum of 12 hours. The MEG recordings were performed in the morning with a 306-channel whole head MEG system (Elekta Oy, Helsinki, Finland) in a magnetically shielded room (Euroshield, Eura, Finland). The raw MEG data were band pass filtered in 0.03 to 330 Hz range and digitized at a sampling rate of 1011 Hz. The MEG recordings were obtained while patients rested with eyes closed for 3 minutes. The MEG recordings in the DBS off state started 10 minutes after the IPG was turned off.
Artifact of DBS Devices Detection During MEG Recordings The magnetic artifacts caused by DBS device and stimulation were removed by tSSS method using the Neuromag software (Neuromag 3.4; Elekta, Helsinki, Finland). To determine the appropriate tSSS parameters, a study with a phantom and MEG system was executed. Specifically, the IPG, lead, and its extension cable were immersed in an enclosed waterproof container of saline (0.9% NaCl). The 4-contact DBS lead was put inside the container and then placed in the MEG head unit. The IPG was positioned 15 cm outside of the head unit. The MEG recordings were obtained during both on and off states of the IPG. Each trial lasted 3 minutes with the following DBS parameters: contact 0 and 1 cathode, case anode, a stimulation frequency of 145 Hz, a pulse width of 90 microseconds, and an amplitude of 2.5 V. In the next step, we computed the spectra of the MEG data obtained from the phantom and selected 8-second raw data buffer for tSSS parameter according to Airaksinen et al. (2012) and compare between subspace correlation level of 0.95 and 0.8 to determine which one suppress artifact peaks at the stimulation frequency and its harmonics more efficiently.
MEG Data Analysis Because of the artifact caused by motor symptoms of patients with PD or the artifact of DBS hardware, sometimes we could not fully obtain 3 minutes of MEG recording. Therefore, 100 seconds of MEG data were analyzed. We used the same parameters of an 8-second raw data buffer with a subspace correlation limit of 0.8 during the application of tSSS algorithm to all the subjects in this study. The data were analyzed using a custom in-house developed Matlab software (The Mathworks, Natick, MA). After the application of tSSS algorithm, the raw data were digitally band pass filtered from 1 Hz to 48 Hz and visually inspected to identify potential artifact segments that were removed before the next spectral analysis step. For the ease of analysis, only signals from 102 planar magnetometers of MEG system were used. The MEG channels were grouped into regions of interest corresponding to the major cortical areas (temporal, parietal, occipital, and frontal) on the bilateral hemispheres. Then, the MEG signals from these regions were analyzed in frequency domain by using a fast Fourier transform. For each channel, the power spectrum is estimated using Welch method with a 1-second window and a 50% overlap. Computed spectra were averaged over channels in each regions of Copyright 2014 by the American Clinical Neurophysiology Society
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TABLE 1. Surgery
Case
Clinical Subtypes
Disease Duration, years
Total
T
R
50/m 34/m 56/f 49/f 50/m 43/f 42/m 55/f 64/f 64/f 69/m 51/m 67/f 75/m 60/m 64/m
R/T R/T R R/T T/R T T/R T/R R T/R T T/R R/T R T/R T/R
12 4 12 8 5 10 2 11 30 13 6 10 10 12 10 4
43 32 45 47 30 35 32 69 64 57 33 43 42 50 62 30
15 5 0 4 9 0 6 10 0 12 0 12 8 0 10 5
13 8 18 15 5 10 6 18 20 15 7 10 10 12 13 8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
was computed by dividing its power with the total power in 1 to 48 Hz spectrum and then compared with healthy controls. For the MEG recordings with STN-DBS, after tSSS, the spectrum was scrutinized by two experts to identify the remaining artifact peaks. Only the frequency bands without these artifact peaks were analyzed. The relative power for each subband was computed by dividing its power with the total power of artifact-free bands within 1 to 48 Hz spectrum. Additionally, average frequency and average power over regions of interest were computed. Average frequency is calculated from the power spectrum.
Clinical Characteristics of Patients With PD Without
Age (years)/Sex
UPDRS III
PN i 51 fi · pi fm 5 P ; N i 51 pi Where, pi is the power value of the corresponding frequency and fi is the average power obtained by algorithmic mean of power spectrum. PN pm 5
R, rigidity, T, tremor; UPDRS, Unified Parkinson Disease Rating Scale.
i 51 pi
N
:
UPDRS Motor Scores Evaluation The UPDRS III score was used to assess the motor symptoms of the patients with PD after overnight medication withdrawal. For the patients with PD with DBS treatment, the UPDRS III score was evaluated during stimulation on and 10 minutes after stopping the stimulation. Motor impairments were assessed with subscores for rest tremor (sum of UPDRS III subitem 20 for hand and foot) and rigidity (sum of UPDRS III subitem 22 for upper and lower extremities) particularly.
interest. The following features were extracted from the average MEG spectrum of each regions of interest. 1. Relative power of sub-bands 2. Average frequency 3. Average power. For the MEG recordings of patients with PD without surgery, the relative powers were calculated in the following subbands: 1 to 4 Hz (delta), 4 to 8 Hz (theta), 8 to 10 Hz (low alpha), 10 to 13 Hz (high alpha), 13 to 30 Hz (beta), and 30 to 48 (gamma) (Bosboom et al., 2006). The relative power for each frequency subband above
TABLE 2.
Cortical oscillations of PD with and without DBS
Statistical Analysis Comparisons of average frequency, average power, and relative power between healthy controls and patients with PD were carried out using independent-samples t-test. Comparisons of
Clinical Characteristics of Patients With PD Taking STN-DBS Treatment UPDRS III
Case 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
DBS on DBS off
Age (years)/Sex
Clinical Subtype
Disease Duration, years
Stimulation Duration
Total
T
R
Total
T
R
54/m 59/f 57/m 57/f 58/f 72/m 34/m 50/m 64/m 63/f 55/m 50/m 51/f 50/f 57/m 49/m
T/R T/R T/R R/T T/R R/T R/T T R/T T/R T/R R T/R T/R T/R T/R
4 11 11 7 8 13 4 5 13 4 5 12 10 5 8 8
1 year 6 years 2 years 2 years 4 years 5 years 1 year 1 year 1 week 1 week 1 week 1 week 1 week 1 week 1 week 1 week
12 25 10 15 28 34 3 0 15 10 9 9 8 7 11 9
0 3 0 0 6 5 0 0 0 0 0 0 2 0 0 0
2 10 3 5 8 10 0 0 7 5 3 3 3 0 5 5
34 60 30 39 60 84 10 7 40 20 23 29 19 21 36 29
8 9 6 9 11 16 0 7 8 0 4 6 7 7 9 3
9 15 7 10 15 20 5 0 10 8 2 8 6 2 12 10
DBS, deep brain stimulation; R, rigidity; STN, subthalamic nucleus; T, tremor; UPDRS, Unified Parkinson Disease Rating Scale.
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FIG. 1. The power spectra of phantom at stimulation on and off states after tSSS filtering. The spectra of data filtered with tSSS in a 8-second raw data buffer, subspace correlation limit of 0.95 at DBS on (green line) and DBS off (red line) states and then filtered in a 8-second raw data buffer, subspace correlation limit of 0.8 at DBS on (blue line) and DBS off (black line) states. At 50 Hz, an artifact showed in every signal because of the power line of environment. The artifact peaks presented at DBS on state due to electrical stimulation, the power spectra filtered with tSSS in subspace correlation limit of 0.8 have fewer artifact peaks. DBS, deep brain stimulation; tSSS, spatiotemporal signal space separation. average frequency, average power, relative power, and UPDRS III score between DBS on and off states were performed using a paired t-test. Relative power, average frequency, and average power were also correlated with the UPDRS III scores (using linear regression to determine the significance of the regression coefficient) to evaluate the association of cortical oscillations to disease severity, age, and duration (set as correction variables). All of the data were tested with SPSS (SPSS 13.0 for windows; SPSS, Chicago, IL), and the results were reported as mean 6 SD with significant levels set at 0.05.
RESULTS Removal of Artifact Caused by Deep Brain Stimulation There is a line frequency artifact at 50 Hz in every signal because of the power line of environment. The artifact peaks presented at DBS on state due to electrical stimulation. Spectra of the phantom MEG data filtered by the tSSS algorithm in a 8-second raw data buffer with a subspace correlation limit of 0.95 exhibited greater artifact peaks compared with spectra of the MEG data filtered by the tSSS algorithm in a 8-second raw data buffer with a subspace correlation limit of 0.8 (Fig. 1). We noted that the lower correlation
limit is more sensitive in detecting artifact waveforms and therefore more efficient in suppressing artifacts. Subsequently, MEG data from all subjects were analyzed after filtering them with tSSS algorithm in an 8-second raw data buffer using a subspace correlation limit of 0.8. Additionally, the spectra of patients with PD with STN-DBS treatment were visually inspected to find the unresolved artifacts. The peaks of artifacts in the spectra mainly consisted of frequencies of 19 to 20 Hz, 23 to 25 Hz, 32 to 33 Hz, 39 to 41 Hz, and 46 to 48 Hz (Fig. 2), which were excluded from the analysis. Finally, the relative power of 1 to 4 Hz (delta), 4 to 8 Hz (theta), 8 to 10 Hz (low alpha), 10 to 13 Hz (high alpha), 13 to 18 Hz (beta), and 34 to 38 Hz (gamma) were compared between DBS on and off conditions; therein 13 to 18 Hz represents beta and 34 to 38 Hz represents gamma band to avoid the contamination of the remaining artifacts in the full band. The raw MEG recordings of PD patient before and after the application of tSSS are shown in Fig. 3. The MEG data are heavily corrupted by stimulation artifacts during the DBS on state. As the amplitude of the artifact caused by electrical stimulation is intensively larger than the brain oscillations, it is difficult to visually isolate any oscillatory components. After the application of tSSS, the MEG recording shows clear brain oscillations. As we mentioned earlier, we applied tSSS to the MEG recordings obtained during
FIG. 2. The power spectral lines of four patients with PD with STN stimulation at different frequencies, pulse width, and voltages. The spectra of each patient shows different artifact peak frequencies. The 50 Hz is power line that exists in both DBS on (red line) and off (black line) states. The artifact peak frequencies appeared mainly in 13 to 30 Hz (beta) and 30 to 75 Hz (gamma). DBS, deep brain stimulation; PD, Parkinson disease; STN, subthalamic nucleus. 112
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Journal of Clinical Neurophysiology Volume 32, Number 2, April 2015
Cortical oscillations of PD with and without DBS
FIG. 3. The raw data of MEG recording before tSSS and after tSSS of a PD patient with STN-DBS in the on and off conditions. A, The raw data of multiple channels are shown. The amplitude of the raw data at DBS on state before tSSS is much more than the data after tSSS, indicating the artifacts from electrical stimulation cover up real brain oscillation. After tSSS, the brain oscillations appear. Vertical bars represent the amplitude scales at different state. B, The raw recording extracted from one MEG channel is displayed. Recording duration is 10 seconds. MEG, magnetoencephalography; PD, Parkinson disease; STN-DBS, subthalamic deep brain stimulation; tSSS, spatiotemporal signal space separation.
DBS off state. We note that the amplitude of the raw MEG data before tSSS is comparable with the raw data after tSSS (Fig. 3). Substantially, after tSSS, no artifacts caused by electrical stimulation, devices, and different other nature such as electrocardiogram were observed.
Characteristics of the Cortical Spontaneous Oscillatory Activities of Patients With PD Without Surgery The average frequency over all cortical regions of patients with PD without surgery slowed down remarkably compared with healthy controls (PD 18.76 6 1.33 Hz, n ¼ 16 and healthy control 20.67 6 1.32 Hz, n ¼ 11, P ¼ 0.0013). This change is especially evident in the temporal (PD 16.56 6 1.7 Hz and healthy control 20.64 6 0.9 Hz, P , 0.001) and occipital areas (PD 15.64 6 1.0 Hz Copyright 2014 by the American Clinical Neurophysiology Society
and healthy control 19.94 6 1.9 Hz, P , 0.001) (Figs. 4A and 5A). The average power over all cortical regions of patients with PD without surgery increased (PD 0.048 6 0.021 and healthy control 0.033 6 0.012, P ¼ 0.048). There was also a change in temporal (PD 0.03 6 0.01 and healthy control 0.08 6 0.05, P , 0.001) and occipital areas (PD 0.04 6 0.01 and healthy control 0.08 6 0.05, P , 0.001) but not in parietal and frontal areas (P . 0.05) (Figs. 4B and 5A). In addition, patients with PD showed a rise in the relative power of 4 to 8 Hz (theta band), while showing a drop in the relative power of 13 to 30 Hz (beta) and 30 to 48 Hz (gamma) over temporal and occipital areas (P , 0.05). The parietal and frontal areas did not show any significant changes (P . 0.05) (Fig. 5B). Unified Parkinson Disease Rating Scale III rigidity scores strongly correlated with the average frequency over frontal areas (rs ¼ 0.838, P ¼ 0.002 in left and rs ¼ 0.774, P ¼ 0.003 in right). Meanwhile, UPDRS III rigidity scores were noticeably correlated 113
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FIG. 3.
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Continued.
with the relative power of 13 to 30 Hz (rs ¼ 0.687, P ¼ 0.011 in left and rs ¼ 0.661, P ¼ 0.033 in right) and 30 to 48 Hz (rs ¼ 0.681, P ¼ 0.024 in left and rs ¼ 0.819, P ¼ 0.008 in right) over frontal areas (Table 3) (n ¼ 16).
Modification of Cortical Spontaneous Oscillatory Activities of Patients With PD by Subthalamic Deep Brain Stimulation All of the patients with PD with STN-DBS surgery had clear improvement in motor symptoms. Unified Parkinson Disease Rating Scale III total motor scores were 12.8 6 9.0 at stimulation on and 33.8 6 20.0 at stimulation off states (P , 0.001). Unified Parkinson Disease Rating Scale III tremor scores were 1.0 6 1.9 at stimulation on and 6.9 6 4.0 at stimulation off states (P , 0.001). Unified Parkinson Disease Rating Scale III rigidity scores were 4.3 6 3.2 at 114
stimulation on and 8.7 6 5.2 at stimulation off states (P , 0.001) (Table 2) (n ¼ 16). No variations in cortical oscillation including average power, average frequency, and relative power of subbands were found in patients with PD with 1 week of stimulation in both stimulation on and stimulation off conditions (n ¼ 8). In patients with PD with long-term stimulation, the whole cortical average frequency increased with stimulation compared with stimulation off state (18.85 6 1.53 Hz at DBS off and 19.75 6 1.23 Hz at DBS on, P ¼ 0.007). This increase was especially evident in the right temporal (P ¼ 0.0072), left frontal (P ¼ 0.004), right frontal (P ¼ 0.0009), and right occipital regions (P ¼ 0.027) (Fig. 6A). However, the average power over all cortical regions showed no obvious change relative to stimulation off state (P . 0.05) (Fig. 6B) (n ¼ 8). In detail, during stimulation, the relative power of 1 to 4 Hz attenuated significantly (P , 0.05) and the relative power of 14 to Copyright 2014 by the American Clinical Neurophysiology Society
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FIG. 4. Line plot of average frequency and average power of patients with PD without surgery and healthy controls, patients with PD present slowing of average frequency over diffuse cortical areas (A) and incremental average power over temporal and occipital areas (B). *P , 0.05, ***P , 0.001. PD, Parkinson disease. 18 Hz increased remarkably (P , 0.05) over diffuse cortical areas relative to stimulation off state (Fig. 5C) (n ¼ 8). In all of the patients with PD with STN-DBS treatment, the relative power did not correlate to UPDRS III scores in the stimulation off state. Instead, during the stimulation on state, the relative power of 9 to 13 Hz over the left temporal, left occipital, and bilateral parietal areas was significantly correlated with the UPDRS III total motor scores (Table 4). The relative power of 9 to 13 Hz over the left parietal area noticeably correlated with the UPDRS III tremor scores (rs ¼ 0.758, P ¼ 0.002 in 9–10 Hz; rs ¼ 0.635, P ¼ 0.041 in 11–13 Hz). The relative power of 9 to 10 Hz over left parietal area correlated with UPDRS III rigidity scores (rs ¼ 0.385, P ¼ 0.037) (Table 4) (n ¼ 15).
DISCUSSION Our study demonstrated that patients with PD without surgery showed a prominent slowing of resting oscillatory activities compared with healthy controls, which is consistent with previous studies (Bosboom et al., 2006; Soikkeli et al., 1991). Compared with Copyright 2014 by the American Clinical Neurophysiology Society
Cortical oscillations of PD with and without DBS
healthy controls, the patients with PD had lower average frequency in 1 to 48 Hz range over all cortical areas. The average powers in the 1 to 48 Hz frequency band in temporal and occipital areas were higher than controls. Additionally, in patients with PD, the relative power of the theta band was noticeably larger than controls, whereas the relative powers of the beta and gamma bands were noticeably lower over bilateral temporal and occipital areas. Our results support the thalamocortical dysrhythmia characterized by the lowered thalamocortical oscillation in patients with PD (Jeanmonod et al., 1996; Maggini and White, 2011). The enhancements of theta over temporal and occipital areas in EEG of psychiatric patients were elucidated (Ellingson, 1954; MundyCastle, 1951). In this study, MEG recordings of the patients with PD without surgery were obtained before their STN-DBS treatment and all of the patients with PD are in their midterm or endterm disease period. Generally, depression arrests 35% of patients with PD and anxiety arrests 20 to 49% of patients with PD (Dissanayaka et al., 2010; Hagell et al., 1994; Martinez-Martin and Damian, 2010; Tandberg et al., 1997). A progressive increase of EEG delta and theta spectral power with a parallel widespread decrease of beta power was observed from controls to patients with Alzheimer disease, mainly in the anterior areas (Scrascia et al., 2013). In our study, patients with PD did not show an increase in delta spectral power, but an increase of theta spectral power was observed mainly in posterior areas, with a corresponding decrease in both relative powers of beta and gamma bands. Conclusively, the distinctive slowing of cortical oscillations of patients with PD indicates general neurological and psychiatric dysfunction due to neurodegeneration in the basal ganglia cortical loop. However, our findings demonstrate the average frequency and the relative powers of beta and gamma bands in the frontal area correlated to the UPDRS III rigidity score in patients with PD without surgery. A role of frontal lobe in action has been illuminated, which is associated to many higher level aspects of movement planning and decision making (Miller and Cohen, 2001; Wise, 1985). Our study shows the oversynchronization of beta and gamma band (13–48 Hz) over frontal lobe involved in the symptoms of patients with PD. This provides evidence to the proposition that the prevalence of beta band in the basal ganglia-thalamus-frontal cortical circuits may be responsible for the rigidity symptoms of patients with PD (Gatev et al., 2006). In patients with PD at 1 week of stimulation, no difference was observed in average frequency and average power between DBS on and off states. We believe that the patients at 1 week of stimulation were still within the period of “stun effect,” caused by microlesioning due to the electrode implantation. Therefore, the local edema of the vicinity of STN might restrict the STN efferent impulse and interfere in the transmission of stimulation impulse to cortex. The average frequency of patients with PD at long-term stimulation increased significantly in stimulation on state compared with stimulation off state. These findings complement evidence from previous studies, which demonstrated the cortical activation in patients with PD and STN-DBS animal models (Kuriakose et al., 2010; Li et al., 2012). Our findings demonstrated that the increase in average frequency and the recovery of balanced average power by STN stimulation might be caused by the inhibition of relative power of delta and increment of relative power of beta over diffuse cortical areas. This is because patients with PD at stimulation on showed remarkably lower relative power of delta and higher relative power of beta of diffuse cortex compared with stimulation off states, which could be attributed to nonspecific increase in vigilance and intrinsic alertness during stimulation on (Jech et al., 2006). 115
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FIG. 5. Schematic representation of differences of cortical oscillation between patients with PD without surgery versus healthy controls and patients with PD at DBS on versus off states in different frequency subbands from 1 to 48 Hz. A, Average frequency and average power differences from 1 to 48 Hz between patients with PD without surgery and healthy controls; B, Relative power differences of frequency subbands from 1 to 48 Hz between patients with PD without surgery and healthy controls; C, Relative power differences of artifact-free frequency subbands from 1 to 48 Hz between patients with PD at DBS on versus off states. Red color indicates an increase of variables, and blue color indicates a decrease of variables. Only those areas for which a significant group difference was found in t-test are color-painted. Light color indicates P , 0.05; dark color indicates P , 0.01. The MEG channels were clustered based on the approximate underlying cortical areas. DBS, deep brain stimulation; F, frontal; L or R, left or right side; MEG, magnetoencephalography; O, occipital; P, parietal; PD, Parkinson disease; T, temporal. EEG after STN-DBS on (Jech et al., 2006; Li et al., 2007). Specifically, our study demonstrated the UPDRS III rigidity and tremor scores correlates to the relative power of 9 to 13 Hz over the left parietal area. Our study lacks spatial localization of the MEG recordings, the recording channels were grouped into regions of interest corresponding to the major cortical areas (temporal, parietal, occipital, and frontal) on the bilateral hemispheres. Since the somatomotor region locates in the parietal lobe, our results indicate DBS reduced nonfavorable 9 to 13 Hz activity in the left somatomotor region in the amelioration of rigidity and tremor symptoms of PD. Until a few years ago, it was difficult to review and process MEG data due to the large artifacts generated by vagus nerve stimulation and DBS. The advent of tSSS finally allowed doing this (Song, 2009; Taulu and Hari, 2009). The temporal extension of SSS, the tSSS, removes the contribution of the nearby artifact sources by using time information in addition to Maxwell equation. It was shown
It is well recognized that 9 to 13 Hz band is desynchronized and its power attenuates in the somatomotor cortex when subjects are engaged in motor activity and general attention processing (Chatrian et al., 1959; Pfurtscheller et al., 2000). Different to the report that the somatomotor mu rhythm (9–13 Hz) amplitude correlates with rigidity score in patients with PD with STN-DBS (Airaksinen et al., 2011, 2012), our study shows UPDRS III total motor score correlates to the relative power of 9 to 13 Hz (alpha) over left temporal, left occipital, and bilateral parietal areas; however, UPDRS III tremor and rigidity scores correlate to left parietal areas. Our study suggests the total motor function is related to the synchronization of alpha in widespread cortical areas. However, tremor and rigidity is more related the synchronization of alpha in the left parietal lobe. Therefore, our results are in agreement with the findings that the relative power of the dominant frequency of 9 to 13 Hz decreased in all derivations of the cortical areas recorded by TABLE 3. Surgery
Correlation Strengths Between Cortical Oscillation and UPDRS III Rigidity Subscores of Patients With PD Without Average Frequency
UPDRS III Rigidity
Relative Power
Regions
r
P
Wave Bands, Hz
r
P
lf
0.838
0.002
rf
0.774
0.003
13–30 30–48 13–30 30–48
0.687 0.681 0.661 0.819
0.011 0.024 0.033 0.008
Table shows the correlation coefficients, the t-test scores from the linear regression coefficient r, and probability values P. P , 0.05 indicates significant correlation. f, frontal; l, left; r, right; UPDRS, Unified Parkinson Disease Rating Scale.
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Cortical oscillations of PD with and without DBS
TABLE 4. Correlation Strengths Between Cortical Oscillation at STN Stimulation On State and UPDRS III Motor of Patients With PD Taking More Than 1 Year of Stimulation Relative Power UPDRS III Total
Regions
Wave Bands, Hz
r
P
lt
9–10 11–13 9–10 11–13 9–10 11–13 9–10 11–13 9–10 11–13 9–10
0.566 0.478 0.520 0.453 0.666 0.592 0.467 0.548 0.758 0.635 0.386
0.006 0.029 0.015 0.040 0.001 0.008 0.029 0.021 0.002 0.041 0.037
lo lp
Tremor
rp lp
Rigidity
lp
Table shows the correlation coefficients, the t-test scores from the linear regression coefficient r, and probability values P. P , 0.05 indicates significant correlation. l, left; o, occipital; r, right; p, parietal; t, temporal; UPDRS, Unified Parkinson Disease Rating Scale.
In conclusion, the rigidity of patients with PD without surgery correlates to the relative spectral power of beta and gamma in bilateral frontal area. MEG detection with the tSSS algorithm provides an efficient method in investigating the modulation to basal gangliathalamocortical oscillations of patients with PD by STN-DBS. The amelioration of tremor and rigidity by STN-DBS might be associated to the dysynchronization of alpha band oscillation over left somatomotor region. For a further detailed understanding of the modification on cortical oscillation by STN-DBS to patients with PD, a blinded long-term prospective study on a large number of subjects who enroll in the STN-DBS treatment is required. FIG. 6. Line plot of average frequency and average power of Parkinson disease patients at stimulation on and off states. Stimulation on increases the average frequency of right temporal, right occipital, and bilateral frontal areas compared with stimulation off state (A). No difference in average power (B). *P , 0.05. that, using tSSS, not only the artifacts caused by electrical stimulation but also the contribution of other sources, such as eye movements and/ or cardiac signals (Kakisaka et al., 2013; Song et al., 2008), are efficiently removed. Our results strongly support the feasibility of the tSSS method in removing, with minimal distortion, external magnetic artifacts from MEG signals. However, the remaining artifact peaks in the MEG spectrum after tSSS indicate that further improvements in signal processing and antialiasing filtering are necessary. There are several limitations in this study. First, the work lacks in spatial specificity, which localizes the variation of frequency bands in cortical areas. Second, the intolerability in the reappearance of motor symptoms in advanced PD when the stimulation was terminated, especially for the patients with long-term stimulation who were adapted to the continuous stimulation, the MEG recordings were operated at 10 minutes after the IPGs were powered off. This is far from 2 hours, the recognized washout time of STN stimulation (Temperli et al., 2003). Third, the intricate correlations of individual subband oscillations within the basal ganglia-thalamocortical nets with specific motor functions are far away from being clarified with the limited analytical strength. Copyright 2014 by the American Clinical Neurophysiology Society
ACKNOWLEDGMENTS The authors would like to express their sincere appreciations to the patients who joined in this study. They also would like to thank Tao Song, Yongxia Qiao for the advices in data analysis, and also Jyrki P. Makela and Ji-Hee Kim for their helpful suggestions in preparing for the manuscript. REFERENCES Airaksinen K, Makela JP, Taulu S, et al. Effects of DBS on auditory and somatosensory processing in Parkinson disease. Hum Brain Mapp 2011;32:1091–1099. Airaksinen K, Butorina A, Pekkonen E, et al. Somatomotor mu rhythm amplitude correlates with rigidity during deep brain stimulation in Parkinsonian patients. Clin Neurophysiol 2012;123:2010–2017. Benabid AL, Koudsie A, Benazzouz A, et al. Deep brain stimulation of the corpus luysi (subthalamic nucleus) and other targets in Parkinson disease. Extension to new indications such as dystonia and epilepsy. J Neurol 2001;248(suppl3): III37–III47. Bosboom JL, Stoffers D, Stam CJ, et al. Resting state oscillatory brain dynamics in Parkinson disease: an MEG study. Clin Neurophysiol 2006;117:2521–2531. Brown P. Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson disease. Mov Disord 2003;18:357–363. Cao C, Pan Y, Li D, et al. Subthalamus deep brain stimulation for primary dystonia patients: a long-term follow-up study. Mov Disord 2013;28:1877–1882. Castner JE, Chenery HJ, Silburn PA, et al. Effects of subthalamic deep brain stimulation on noun/verb generation and selection from competing alternatives in Parkinson disease. J Neurol Neurosurg Psychiatry 2008;79:700–705. Chatrian GE, Petersen MC, Lazarte JA. The blocking of the rolandic wicket rhythm and some central changes related to movement. Electroencephalogr Clin Neurophysiol 1959;11:497–510.
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