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NeuroImage : Clinical logoLink to NeuroImage : Clinical
. 2023 May 10;38:103431. doi: 10.1016/j.nicl.2023.103431

Magnetoencephalography to measure the effect of contact point-specific deep brain stimulation in Parkinson’s disease: A proof of concept study

Lennard I Boon a,b,c,e,⁎, Wouter V Potters c, Arjan Hillebrand b,d,e, Rob MA de Bie c, Maarten Bot f, P Richard Schuurman f, Pepijn van den Munckhof f, Jos W Twisk g, Cornelis J Stam b,e, Henk W Berendse a, Anne-Fleur van Rootselaar c,d,h
PMCID: PMC10197095  PMID: 37187041

Highlights

  • •

    Optimal contact point selection is important for the outcome of deep brain stimulation (DBS) in Parkinson’s disease.

  • •

    Exploration whether functional effects (MEG) may facilitate contact point selection.

  • •

    At group level, contact point position was associated with local and whole-brain activity.

  • •

    At the individual level, switching the active contact point led to highly variable results.

  • •

    It is not yet clear whether MEG has the potential to facilitate the optimization of stimulation settings in individual patients.

Keywords: Parkinson’s disease, Deep brain stimulation, Magnetoencephalography, Contact point-specific, Spectral power, Functional connectivity

Abstract

Background

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for disabling fluctuations in motor symptoms in Parkinson’s disease (PD) patients. However, iterative exploration of all individual contact points (four in each STN) by the clinician for optimal clinical effects may take months.

Objective

In this proof of concept study we explored whether magnetoencephalography (MEG) has the potential to noninvasively measure the effects of changing the active contact point of STN-DBS on spectral power and functional connectivity in PD patients, with the ultimate aim to aid in the process of selecting the optimal contact point, and perhaps reduce the time to achieve optimal stimulation settings.

Methods

The study included 30 PD patients who had undergone bilateral DBS of the STN. MEG was recorded during stimulation of each of the eight contact points separately (four on each side). Each stimulation position was projected on a vector running through the longitudinal axis of the STN, leading to one scalar value indicating a more dorsolateral or ventromedial contact point position. Using linear mixed models, the stimulation positions were correlated with band-specific absolute spectral power and functional connectivity of i) the motor cortex ipsilateral tot the stimulated side, ii) the whole brain.

Results

At group level, more dorsolateral stimulation was associated with lower low-beta absolute band power in the ipsilateral motor cortex (p = .019). More ventromedial stimulation was associated with higher whole-brain absolute delta (p = .001) and theta (p = .005) power, as well as higher whole-brain theta band functional connectivity (p = .040). At the level of the individual patient, switching the active contact point caused significant changes in spectral power, but the results were highly variable.

Conclusions

We demonstrate for the first time that stimulation of the dorsolateral (motor) STN in PD patients is associated with lower low-beta power values in the motor cortex. Furthermore, our group-level data show that the location of the active contact point correlates with whole-brain brain activity and connectivity. As results in individual patients were quite variable, it remains unclear if MEG is useful in the selection of the optimal DBS contact point.

1. Introduction

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment in case of disabling fluctuations in motor symptoms of Parkinson’s disease (PD) patients (Benabid et al., 2001, Deuschl et al., 2006, Odekerken et al., 2013). In addition to the excellent effects on motor symptoms, DBS may positively or negatively affect non-motor symptoms, such as neuropsychological and neuropsychiatric functioning (Ulla et al., 2011, Hatz, 2018, Castrioto et al., 2014). DBS electrodes are implanted bilaterally and each DBS electrode generally contains multiple contact points. After DBS placement, the stimulation settings have to be iteratively explored by the clinician during a rather long period, often spanning months, to find the optimal stimulation settings. Optimal contact point selection is important, because motor and non-motor outcomes seem to depend on the location of the active contact point in the STN and its surrounding white matter (Bot et al., 2019, Dafsari et al., 2018, Petry-Schmelzer et al., 2019).

STN target identification is based on T2 magnetic resonance imaging (MRI) coordinates (Aviles-Olmos et al., 2014). Post-operative refinements of stimulation parameters are mainly based on the clinical effects of the stimulation. In theory, the expected clinical effects of stimulation could be based on the anatomical position of contact points in relation to their surrounding structural networks, obtained using coregistered pre- and post-operative images (Garcia-Garcia et al., 2016). However, this does not offer insight into the effects on functional brain networks, which have gained increasing attention over the years. A better understanding of the effects of DBS on functional brain networks may provide insight in the underlying mechanisms and can help to optimize treatment effects (Boutet et al., 2021).

Magnetoencephalography (MEG) has been used for the in-vivo assessment of the modulatory effect of stimulation of the STN on neural networks involving both the cerebral cortex and subcortical brain regions. To date, this has only been studied in a DBS ON versus OFF design in which only one contact point (with clinically optimal effect) per hemisphere was stimulated (Boon, 2020, Abbasi et al., 2018, Luoma et al., 2018, Litvak, 2021, Cao et al., 2015, Boon et al., 2021). In our own studies, we have found that DBS, using the optimal contact point, led to a whole-brain acceleration of neuronal oscillations and to a suppression of absolute band power (delta to low-beta power) in the sensorimotor cortices (Boon, 2020). Furthermore, changes in functional connectivity correlated with improvement in motor function (Boon, 2020) and with the occurrence of the non-motor side effect apathy (Boon et al., 2021). Two studies from other research groups found a lowering effect of STN-DBS on alpha and low-beta band power in the sensorimotor cortices (Abbasi et al., 2018, Luoma et al., 2018).

In this proof of concept study, we tested for the first time the functional effects of stimulating different DBS contact points in the STN within individual patients using MEG. If differential effects would prove to be observable, MEG recordings could potentially aid in the process of selecting the optimal contact point, and perhaps reduce the time to achieve optimal stimulation settings. We first analysed the effect of stimulation on the ipsilateral motor cortex. Based on previous studies (Boon, 2020, Abbasi et al., 2018, Luoma et al., 2018), we expected that stimulation in the dorsolateral STN would lead to a suppression of absolute alpha2 and low-beta band power and functional connectivity. This suppression may occur via stimulation of the hyperdirect pathway that structurally connects the dorsolateral STN with the motor cortex, which may lead to desynchronization of cortical neurons (Brunenberg et al., 2012, Li et al., 2007). Next, we analysed the effect of stimulating different contact points on whole-brain spectral power and functional connectivity. The ventral STN has recently been demonstrated to have functional loops with the cerebral cortex via theta and alpha oscillations (van Wijk et al., 2022, Rappel et al., 2020), so we expected ventral stimulation to affect these oscillations. Finally, we set out to analyse whether an acute change in stimulation position would lead to consistent changes in brain activity and connectivity in individual patients.

2. Materials and methods

2.1. Patients

A total of 30 PD patients participated in this study after we consecutively approached eligible PD patients who had undergone bilateral DBS implantation between 2016 and 2018 at Amsterdam UMC, location AMC. All patients were implanted with a bilateral Boston Scientific Vercise directional stimulation system (Valencia, CA, USA). Inclusion and exclusion criteria were previously described (Boon, 2020). In all patients, monopolar stimulation in so-called ring mode at one of four available depths was used, with the implantable pulse generator set positive and (deepest; generally ventromedial) contact point 1, contact points 2–4, contact points 5–7 or (upper; generally dorsolateral) contact point 8 set negative (Supplementary Fig. 1).

The study protocol was approved by the medical ethical committee of the Amsterdam UMC, location VUmc (2017.306). All patients gave written informed consent before participation.

2.2. Data acquisition

Patients underwent MEG recordings at least 6 months after DBS placement (range 6–17 months; median 7 months), after an overnight withdrawal of dopaminergic medication (practically defined OFF-medication state). MEG data were recorded using a 306-channel whole-head system (Elekta Neuromag Oy, Helsinki, Finland) in an eyes-closed resting-state condition, with a sample rate of 1250 Hz and online anti-aliasing (410 Hz) and high-pass (0.1 Hz) filters. The head position relative to the MEG sensors was recorded continuously using the signals from five head position indicator (HPI) coils. The HPI positions were digitized before each recording, as well as the outline of the patient’s scalp (∼500 points), using a 3D digitizer (Fastrak, Polhemus, Colchester, VT, USA).

For each patient, the total MEG recording time was 55 min, consisting of 11 trials of 5 min. A different DBS stimulation setting was used for each trial with a ‘wash-out’ period of approximately one minute. The first and eleventh (last) recording were performed during bilateral stimulation using the standard DBS-settings of the individual patient (DBS-ON; results presented previously (Boon, 2020, Boon et al., 2021). In between, nine recordings took place in randomized order, eight of which consisted of unilateral stimulation using a single contact point, and one recording during DBS OFF (results previously presented (Boon, 2020, Boon et al., 2021). For each individual participant, unilateral stimulation was performed within the limits of the results of the initial threshold screening for effects and adverse effects of stimulating each contact point. This threshold screening was obtained in the context of standard care, two to four weeks after DBS placement, and indicated the maximally tolerable stimulation strength for each contact point. In the first 13 patients of the study, stimulation strength differed between recordings, but in patients that were recorded later in the project we aimed to keep the stimulation strength more equal between contact points. The stimulation frequency and pulse width we used were the same as during bilateral stimulation (standard settings). After each individual recording, we asked whether patients experienced discomfort. If so, then we limited the number of recordings. Further details on the experimental set-up can be found in our previous publications on this study cohort (Boon, 2020, Boon et al., 2021).

Anatomical images of the head were obtained in the context of standard pre-operative imaging up to 6 months before surgery using a 3T MRI scanner (Philips Ingenia, Best, the Netherlands) and a 16-channel receiver coil. Further details on the MRI parameters have been described previously (Boon, 2020, Boon et al., 2021). Up to 1 day after surgery, a CT scan of the head was acquired (slice thickness 1–2 mm; FOV 512×512 mm; number of slices 56–169). For 25 patients, on the postoperative day, a multidetector CT-scan of the head was acquired (Philips Medical System, Best, The Netherlands; slice thickness 1–2 mm; FOV 512×512 mm; 56–169 slices). For the five remaining participants, an intra-operative CT-scan was acquired (O-arm O2, Medtronic Inc., Minneapolis, MN, USA) with a 20 cm FOV (high definition mode; 192 slices; 120 kV; 150 mAs).

2.3. Data processing

2.3.1. MEG data

MEG channels that were malfunctioning or noisy were ignored after visual inspection of the data. Thereafter, the temporal extension of Signal Space Separation (tSSS (Taulu and Hari, 2009, Taulu and Simola, 2006) in MaxFilter software (Elekta Neuromag Oy, version 2.2.15) was applied with a subspace correlation-limit of 0.8 to suppress the strong magnetic artefacts; see also (Boon, 2020) for an example of the effect of tSSS on MEG data recorded during DBS). MEG data of each patient were co-registered to their structural MRI using a surface-matching procedure, with an estimated accuracy of 4 mm (Whalen et al., 2008). A single sphere was fitted to the outline of the scalp as obtained from the co-registered MRI, which was used as a volume conductor model for the beamformer approach described below.

The automated anatomical labelling (AAL) atlas was used to label the voxels in 78 cortical and 12 subcortical regions of interest (ROIs) (Gong et al., 2009, Tzourio-Mazoyer et al., 2002). We used each ROI’s centroid as representative for that ROI (Hillebrand et al., 2016). Subsequently, an atlas-based beamforming approach (Hillebrand et al., 2012) was used to project broad-band (0.5–48 Hz) filtered sensor signals to these centroid voxels, resulting in broad-band time-series for each of the 90 ROIs (see (Hillebrand et al., 2016) for details). The source-reconstructed MEG data were downsampled from 1250 Hz to 312.5 Hz (4x) and cut into epochs containing 4096 samples (13.11 s). The epochs were visually inspected (by LIB) for tremor-, motion- and stimulation-related artefacts and drowsiness. In addition, for each recording, the 50% epochs with the lowest peak frequency (frequency with maximum power within the 4–13 Hz frequency range) were discarded in order to make the occurrence of drowsiness in the selected data even more unlikely. Finally, the 10 epochs with the best quality were selected for further analysis. Spectral and functional connectivity analyses were performed using in-house software (BrainWave, version 0.9.152.12.26; CJS, available from https://home.kpn.nl/stam7883/brainwave.html). For frequency band-specific analyses, epochs were filtered in five frequency bands (delta (0.5–4 Hz), theta (4–8 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), and low-beta (13–22 Hz), using a Fast Fourier Transform. The high-beta and gamma bands were not analysed as we observed stimulation-related artefact peaks in this frequency range (22–48 Hz) in our previous study (Boon, 2020). For each epoch, frequency band-specific functional connectivity was estimated using the corrected Amplitude Envelope Correlation (AEC-c), an implementation of the AEC (Brookes et al., 2012, Bruns et al., 2000) corrected for volume conduction/field spread, using a symmetric (pairwise) orthogonalisation procedure applied to each epoch (Brookes et al., 2012, Hipp et al., 2012). To adjust for any negative correlations, 1 was added to the raw AEC values and this sum was subsequently divided by 2, leading to values between 0 and 1, with 0.5 indicating absence of functional connectivity. The AEC-c was calculated for all possible pairs of ROIs, leading to a 90x90 adjacency matrix for each frequency band. We obtained region-specific functional connectivity of one ROI with the rest of the brain by taking the average of each of the 90 columns of the matrix.

2.3.2. Imaging data

To determine the stimulation locations after placement of the DBS system, the electrode trajectories were reconstructed. To this end, the post-operative CT-scan was co-registered to the pre-operative MR image using a two-stage (rigid and affine) registration as implemented in Advanced Normalization Tools (ANT) (Avants et al., 2008). For the five patients of whom only an intra-operative CT-scan was available, the co-registration failed using ANT. In these cases, co-registration was successfully performed using FSL FLIRT. The co-registration was followed by a semiautomatic localization of the electrode positions in the CT data in patient space (Lead-DBS, version 2.2; https://www.lead-dbs.org (Horn and Kuhn, 2015).

The electrode stimulation positions were then transformed from patient space to Montreal Neurological Institute space (MNI ICBM 2009b NLIN ASYM space) to facilitate group-level analyses. We used the DISTAL Minimal atlas (Ewert et al., 2018) as an outline of the STN. Next, in line with our previous analysis on stimulation positions in this study cohort (Boon et al., 2021), stimulation positions were projected on a vector running through the longitudinal axis of the STN (from ventromedial to dorsolateral), leading to one scalar value to indicate each stimulation position, where negative values indicated more ventromedial stimulation positions (see Fig. 1A for a schematic display, in which the 25% most dorsolateral and 25% most ventrolateral contact points have been indicated).

Fig. 1.

Fig. 1

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Visualization of ventromedial versus dorsolateral stimulation and their effects on neurophysiological parameters. A Stimulation locations in MNI-space, viewed from lateral left, ventral-posterior and lateral right, and projected on a standard subthalamic nucleus. Parallel to the left STN (middle panel), the longitudinal axis containing scalarized values is indicated. The 25% most dorsolateral contact points (positive values) are depicted in red, the 25% most ventromedial contact points are depicted in blue (negative values). The black dots indicate the remaining 50% of the contact points. MNI, Montreal Neurological Institute; R, right. B Topographic distribution of absolute spectral power and functional connectivity values for; Left panel, the 25% most ventromedial contact points; Middle panel, the 25% most dorsolateral contact points; Right panel, difference between both conditions. We present neurophysiological measures that had a significant association on a whole-brain scale. Logarithmic absolute power/difference values are visualized as a color-coded map on a parcellated template brain viewed from, in clockwise order, the left, top, right, right-midline and left-midline.

2.4. Statistical analysis

2.4.1. Group analyses

For each stimulation condition and frequency band separately, neurophysiological results were averaged over 10 epochs. Next, we obtained i) alpha2 and low-beta band absolute band power and functional connectivity of the motor cortex ipsilateral to the stimulated hemisphere and ii) absolute band power and functional connectivity per frequency band, averaged for the brain as a whole (all 90 AAL regions).

We used linear mixed models to evaluate the association between the (scalarized) stimulation locations and neurophysiological measures. Linear mixed models can account for the dependency of the observations within the patient (by adding a random intercept) and the fact that not all patients had complete data. The neurophysiological measures were used as dependent variables and stimulation locations as independent variables. The side of stimulation, age, gender, post-operative use of dopaminergic medication (expressed as levodopa equivalent dose; LEDD (Olde Dubbelink et al., 2013), disease duration, and stimulation strength (mA) were included in the model as covariates. In the models that focused on the motor cortex, we added the Unified Parkinson’s disease Rating Scale (UPDRS-III) motor score (during DBS and levodopa OFF) as additional covariate.

All analyses were performed using the SPSS Statistics 20.0 software package (IBM Corporation, New York, USA), and a significance level of 0.05 (two-tailed). Results of linear mixed models are expressed as standardized effect sizes.

2.4.2. Post-hoc visualizations of group analysis

We visualized the distribution of the neurophysiological measures that were significantly associated with stimulation location in the linear mixed model analyses and compared dorsolateral with ventromedial stimulation. For this, we compared the results of stimulation of the 25% most dorsolateral contact points with stimulation of the 25% most ventrolateral contact points of each hemisphere. We also compared the results of these ‘extreme’ contact point locations (25% most dorsolateral and ventromedial) with the results of the DBS OFF condition using analysis of variance (ANOVA) to study the direction of change that caused the observed associations.

2.4.3. Individual subjects

We explored visually within one randomly selected patient how the measures of absolute spectral power evolved during the recording session, with the aim to observe distinct changes in neurophysiological patterns upon change of the stimulation position.

2.5. Data availability statement

The data used in this study are available under certain conditions. These conditions include i) a formal project outline, ii) approval from our local ethics committee, iii) a formal data sharing agreement. The codes of our main analysis are available upon reasonable request.

3. Results

3.1. Patients

30 DBS-treated PD patients, whose characteristics are summarized in Table 1, were included in this study. The median number of MEG-recordings performed for each subject was 8 (range 3–8) out of 8 possible recordings. 9% of these recordings could not be used for further analysis due to insufficient quality, leading to a median number of 7 recordings per patient (range 3–8) and a total of 199 recordings (27.1% (deepest) contact point 1, 23.6% contact points 2–4, 25.1% contact points 5–7, 24.1% (upper) contact point 8)). Of the data that had sufficient quality, the median number of excluded MEG channels before running tSSS was 9 (range 3–12).

Table 1.

Patient characteristics.

Patient Age Sex Disease duration (years) Range of stimulation strengths (mA) Pulse width and frequency of stimulation LEDD post-DBS (mg/day) #datasets available/ #recordings
1 38 M 8 L; 2.5–3.5 R; 1.5–3.5 60 µs; 179 Hz 996 8/8
2 70 F 25 L;1.5–3.5 R; 2.0 60 µs; 130 Hz 567 8/8
3 66 M 10 L; 2.5–3.5 R; 1.5–3.0 60 µs; 149 Hz 575 8/8
4 55 M 8 L; 2.0–2.5 R; 2.0–3.0 60 µs; 130 Hz 775 7/8
5 57 M 11 L; 3.0 R; 1.0–2.0 60 µs; 130 Hz 606 8/8
6 61 M 7 L; 1.5–3.5 R; 1.5–3.0 60 µs; 130 Hz 375 8/8
7 60 F 10 L; 2.5 R; 3.0 60 µs; 179 Hz 350 6/7
8 60 M 14 L; 1.5–3.0 R; 1.0–2.0 60 µs; 130 Hz 425 5/8
9 63 F 5 L; 2.0 R; 2.0 60 µs; 130 Hz 567 8/8
10 65 F 27 L; 2.5–3.0 R; 2.0–2.5 60 µs; 130 Hz 400 8/8
11 49 F 10 L; 1.5–2.0 R; 1.5–2.0 60 µs; 130 Hz 536 4/5
12 57 M 12 L; 2.5 R; 2.0–2.5 60 µs; 130 Hz 720 5/5
13 69 M 12 L; 2.0 R; 1.0–1.5 60 µs; 130 Hz 150 7/8
14 61 M 8 L; 2.0 R; 1.5 60 µs; 130 Hz 946 6/8
15 60 M 8 L; 1.5 R; 1.5 60 µs; 179 Hz 300 7/8
16 56 M 12 L; 2.5 R; 2.5 60 µs; 130 Hz 1245 7/8
17 56 M 14 L; 2.5 R; 2.5 60 µs; 130 Hz 783 3/3
18 53 M 11 L; 2.0 R; 2.0 60 µs; 130 Hz 1043 6/7
19 66 F 8 L; 2.0–2.5 R; 2.5 60 µs; 130 Hz 753 7/8
20 45 M 5 L; 2.5 R; 2.5 60 µs; 130 Hz 283 7/8
21 58 M 16 L; 3.0 R; 3.0 60 µs; 130 Hz 613 6/6
22 55 F 20 L; 2.0 R; 1.3 60 µs; 130 Hz 558 8/8
23 57 M 12 L; 3.5 R; 3.5 60 µs; 130 Hz 533 4/4
24 65 F 18 L; 1.5 R; 1.5 60 µs; 130 Hz 500 8/8
25 57 F 14 L; 2.5 R; 2.5 60 µs; 130 Hz 660 8/8
26 63 F 11 L; 2.0 R; 2.0 60 µs; 130 Hz 887 6/8
27 53 F 5 L; - R; 3.0 60 µs; 130 Hz 883 3/4
28 55 M 12 L; 1.5 R; 1.5 60 µs; 130 Hz 679 8/8
29 64 M 22 L; 3.0 R; 3.0 60 µs; 130 Hz 780 7/8
30 48 M 6 L; 1.0–1.5 R; 1.5–2.0 60 µs; 130 Hz 110 7/8
Mean (SD) 58.1 (6.9) M, n = 19; F, n = 11 12.0 (5.6) 620 (2 6 0)
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mA, milliampère; µs, microseconds; LEDD, Levodopa equivalent daily dose; mg, milligrams, M/F, male/female, L/R left/right; SD, standard deviation; Hz, Hertz.

3.2. Associations between stimulation location and neurophysiological measures

The stimulation positions are depicted in Fig. 1A, in standard MNI space relative to an atlas representing the STN. Using linear mixed models, we analysed the associations between the (scalarized) stimulation locations and absolute spectral power as well as functional connectivity values. An overview of the results can be found in Table 2.

Table 2.

Associations between location of stimulated contact point and brain activity/connectivity.

Absolute power Standardized effect size 95% CI (standardized) p value
Motor cortex
Alpha2 −0.045 −0.146 to 0.057 0.421
Low-beta −0.112 −0.206 to −0.018 0.019



Whole brain
Delta −0.154 −0.247 to −0.061 0.001
Theta −0.059 −0.100 to −0.018 0.005
Alpha1 0.015 −0.043 to 0.072 0.614
Alpha2 −0.01275 −0.054 to 0.028 0.540
Low-beta −0.014 −0.061 to 0.032 0.548



Functional connectivity Standardized effect size 95% CI (standardized) p value
Motor cortex
Alpha2 −0.0432 −0.155 to 0.068 0.483
Low-beta −0.0139 −0.126 to 0.099 0.870



Whole brain
Delta −0.101 −0.234 to 0.033 0.140
Theta −0.125 −0.243 to −0.006 0.040
Alpha1 −0.011 −0.109 to 0.087 0.820
Alpha2 −0.019 −0.106 to 0.068 0.669
Low-beta −0.054 −0.147 to 0.038 0.249
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The location of the stimulated contact point was obtained by projecting the stimulation position on a vector following the longitudinal axis of the STN, leading to one scalar value for each stimulation position. The following variables were included in the model as covariates: Side of stimulation, age, gender, levodopa equivalent daily dose (LEDD), disease duration, and stimulation strength (mA). In the models regarding the motor cortex, we added the Unified Parkinson’s disease Rating Scale (UPDRS-III) motor score (during DBS and levodopa OFF) as additional covariate.

Significant associations are indicated in bold.

CI, 95% confidence interval.

The analysis of the motor cortex ipsilateral to the stimulated STN demonstrated a significant association between more dorsolateral stimulation and lower (absolute) low-beta band power (standardized effect size −0.112; p = .019). There were no significant associations for absolute alpha2 power or alpha2 and low-beta functional connectivity.

The whole-brain analysis showed a significant association between more ventromedial stimulation and higher (absolute) delta and theta power (standardized effect sizes −0.154; p = .001 and −0.059; p = .005 respectively). In addition, more ventromedial stimulation was associated with higher whole-brain theta functional connectivity (standardized effect size −0.125; p = .040). Scatter plots of the neurophysiological measures that had a significant association with stimulation positions are shown in Supplementary Fig. 2.

When we added tremor severity during DBS OFF as a covariate in the mixed models, the model still led to significant associations between stimulation location and whole-brain neurophysiological measures (absolute delta power p = .001; standardized effect size −0.149; absolute theta power p = .005 (−0.059); theta functional connectivity p = .033 (−0.135)).

3.3. Post-hoc visualizations

We selected the data obtained by stimulation of the 25% most dorsolateral and 25% most ventromedial contact points. We visualized the distribution of absolute delta power, theta power and theta band functional connectivity (as these measures showed a significant association with contact point position in the linear mixed model analyses), for both selections separately. Furthermore, we visualized the difference between both selections (Fig. 1B). As can be appreciated from Fig. 1B, when comparing ventromedial stimulation positions with dorsolateral stimulation positions, higher absolute delta power values were mainly present in frontal brain regions, higher absolute theta power values in occipitotemporal brain regions and higher theta band functional connectivity in frontotemporal brain regions.

Next, we compared the values obtained by stimulating the 25% most dorsolateral and ventromedial contact points with the values in the DBS OFF condition, to assess the direction of change of the neurophysiological measures following stimulation (e.g.: was there an increase induced by ventromedial contact points or a decrease induced by dorsolateral contact points?). The absolute low-beta band power in the motor cortex was higher compared to the DBS OFF condition with a (log) low-beta power in the ipsilateral motor cortices of 4.19 for dorsolateral stimulation and 4.20 for ventromedial stimulation versus 4.15 in the bilateral motor cortices for DBS OFF (p = .472). Average whole-brain absolute delta power was comparable for the three conditions ((log) delta power ∼4.22; p = .858). For absolute whole-brain theta power, values during DBS OFF were in between those obtained during the 25% most dorsolateral and ventromedial stimulation (dorsolateral whole-brain absolute (log) theta power 4.09; DBS OFF 4.12; ventromedial 4.19; p = .036). This was also the case for whole-brain theta band functional connectivity (dorsolateral whole-brain theta band functional connectivity 0.513; DBS OFF 0.516; ventromedial 0.524; p = .001).

3.4. Acute switch in individual patients

We visualized the evolution of absolute spectral power over epochs and conditions (in chronological order) in a single randomly selected patient. Although significant changes in absolute spectral power were present between different stimulation conditions (DBS-OFF condition was not included in the statistical analyses), we could not distinguish a clear pattern in the evolution of neurophysiological measures during the recording session (Supplementary Fig. 3). In Supplementary Fig. 4 we demonstrate that, by visual comparison between the left- and right hemisphere, there are no large outliers in spectral power upon switching between stimulation conditions.

4. Discussion

In this proof of concept MEG study in 30 PD patients with DBS of the STN we analyzed the effect of different stimulation locations on neurophysiological patterns recorded using MEG. We explored whether MEG recordings might aid in the selection of the optimal contact point for stimulation. In our group analysis, we confirmed the hypothesis that more dorsolateral STN stimulation is associated with lower absolute low-beta band power in the ipsilateral motor cortex compared to more ventromedial stimulation. In addition, more ventromedial stimulation was associated with higher whole-brain absolute delta and theta power, as well as higher whole-brain functional connectivity in the theta band. At the level of the individual patient, we observed significant changes in absolute spectral power when we switched the active contact point, but the results seem too variable to draw conclusions on.

We found an association between more dorsolateral stimulation positions and lower absolute low-beta band power in the motor cortex ipsilateral to stimulation. Based on previous MEG studies (Boon, 2020) (Abbasi et al., 2018, Luoma et al., 2018) we hypothesized that stimulation of the motor part of the STN (in comparison to other parts of the STN) would lead to stronger suppression of beta band power in the motor cortex via the hyperdirect pathway. We could partly confirm this hypothesis, but in the post-hoc analysis we found stimulation of the (25%) most ventromedial and dorsolateral contact points to both lead to higher absolute low-beta band power values in the motor cortex compared to DBS OFF. The net result of dorsolateral stimulation was not so much a ‘suppression’, but rather ‘less activation’ of the motor cortex. As discussed in our previous work (Boon, 2020), the general increase in high-frequency band power could be an effect of a stimulation-related “release” of the thalamus (DeLong and Wichmann, 2007), increased intrinsic alertness upon stimulation (Fimm et al., 2009), and/or an increase in background noise from the stimulator. This general increase may have been superimposed by a local suppression of absolute low-beta band power in the motor cortex.

We have concluded from our post-hoc analyses that ventromedial stimulation led to higher whole-brain theta power and functional connectivity, for the delta band the effect leading to the significant correlation (increase in delta band power on ventromedial stimulation or decrease upon dorsolateral stimulation) was not clear. Increases in absolute theta power upon ventromedial stimulation would be in line with the literature, as theta/alpha band interactions between the STN and cortex are more frequently located ventrally in the STN than beta band interaction, which is located in the dorsolateral STN (van Wijk et al., 2022, Rappel et al., 2020). The function of theta/alpha band interactions with the STN is not well understood, but these interactions may be involved in emotional and cognitive processes (Rappel et al., 2020). Another possible explanation for the increases in absolute delta and theta power is a reduced effect on intrinsic alertness when stimulating ventromedial versus a stronger level of arousal when stimulating in dorsolateral STN regions (Serranová et al., 2019).

In an exemplary case (Supplementary Fig. 3), we observed significant differences in absolute spectral power upon switching stimulation locations, but the amount of variation within conditions was rather high and we could not observe a consistent pattern of stimulation effects in this case. In Supplementary Table S1 we have explored intra-individual correlation values for all subjects. There was no consistent direction of correlations, perhaps because the correlation values are unstable due to low number of values per subject (four to eight contact points per subjects), We assumed a linear relationship between stimulation location and functional effects of stimulation in our analyses. However, the implantation trajectory of a DBS electrode usually does not follow the dorsolateral to ventromedial axis of the STN. Ideally, the dorsolateral sensorimotor part of the STN is targeted, resulting in two (middle) contact points within, the upper contact just above and the deepest contact just below the dorsolateral STN. In less ideally implanted electrodes, all contact points end up more medially or even ventromedially in/around the STN. Switching of the active contact points may then have no effect or a different (non-linear) effect for each individual, which may be a factor contributing to the inconsistent effects in individuals. In the group level analysis using linear mixed models, the sum of these individual effect still led to significant observations. We expect that the within-subject analysis can be improved by including individual high-resolution anatomical information, which was lost in the group analysis that we performed in MNI space.

We believe that the data acquisition protocol can be further optimized to reveal the full potential of MEG for the assessment of the effects of changes in stimulation location in individual DBS-patients. I) We used a ‘wash-out’ period for stimulation effects of approximately one minute, which is rather short. The wash-out period for motor effects is highly variable between patients (Cooper et al., 2013), but five minutes on average (Little et al., 2013). Furthermore, stimulation effects on brain networks may take time to stabilize. By mainly selecting data from the last part of the recordings, we think we have partly accounted for this, but longer wash-out periods and longer recordings (using a lower number of stimulation settings) may be necessary to both obtain stronger group-level results and to be able to draw conclusions regarding the effects of stimulation in individual patients. II) Optimization of artefact rejection and/or artefact correction may improve the signal-to-noise ratio and hence the study results (Litvak, 2021). III) The addition of a measure of (acute) stimulation-related clinical changes is vital to prove or disprove the clinical utility of MEG in the selection of the optimal contact point.

There are several other methodological factors that deserve attention. Firstly, in the initial 13 patients the stimulation strength differed between contact points, as we stimulated at the maximally tolerable strength based on the threshold screening. In patients we included later in the study, the stimulation strength used was more comparable between contact points, in order to reduce the possibility of bias, given that stimulation strength itself may influence the neurophysiological results. However, when repeating our main analysis with the 17 remaining patients in whom the stimulation strength had not changed, we found largely the same results (although the two results that had the weakest significance in the main analysis were no longer significant; Supplementary Table S1). Secondly, we did not perform a correction for multiple comparisons in this explorative study. We aimed to reduce the number of statistical tests by preselecting frequency bands (alpha2 and beta band analyses involving the motor cortex) and analysing global spectral power and functional connectivity (whole-brain analyses). In addition, we consider different frequency bands as different neurophysiological phenomena. When we perform an FDR-correction for the 14 linear mixed models that were performed, only the two results that were most significant (whole-brain delta and theta power) remained statistically significant (Supplementary Table S3).

The results of our study may be a stepping-stone for future studies with the aim to better understand disease and therapy mechanisms (e.g. how whole-brain phenomena may find their origin in a subcortical brain region as small as the STN). MEG recordings can also be combined with DBS of other targets for other indications such as obsessive compulsive disorders (van Westen et al., 2021), depression (van der Wal et al., 2020), and disorders of consciousness (Vanhoecke and Hariz, 2017). Although DBS is increasingly used for these indications, the underlying mechanisms of its effect are only poorly understood. MEG may be combined with local field potential recordings of the target that is being stimulated to characterize the functional loops that are influenced by the stimulation. In conclusion, in this proof of concept study, we explored whether MEG could be used to measure the effects of DBS on brain activity and connectivity in PD patients, which could be a stepping-stone towards the use of MEG recordings in the process of selecting the optimal contact point for stimulation. The group-level data showed that a change in stimulation location produces measurable changes in brain activity and connectivity. More dorsolateral stimulation led to lower absolute low-beta band power in the ipsilateral motor cortex, whereas ventromedial stimulation led to higher whole-brain absolute delta and theta band power, as well as higher whole-brain theta band functional connectivity. The clinical implications of these findings are currently unknown, but our results suggest that functional mapping of the STN using MEG is feasible at a group level. However, as we did not find consistent intra-individual patterns, it remains to be determined whether MEG can be useful in the selection of the optimal DBS contact point. Optimizations in the acquisition (both MRI and MEG) and analysis protocols (including acute changes in clinical measures) may improve the potential of this approach in individual patients.

CRediT authorship contribution statement

Lennard I. Boon: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Wouter V. Potters: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization, Project administration. Arjan Hillebrand: Conceptualization, Methodology, Software, Validation, Investigation, Writing – review & editing, Visualization. Rob M.A. de Bie: Conceptualization, Investigation, Writing – review & editing. Maarten Bot: Investigation, Writing – review & editing. P. Richard Schuurman: . Pepijn van den Munckhof: Conceptualization, Validation, Investigation, Writing – review & editing. Jos W. Twisk: . Cornelis J. Stam: Conceptualization, Methodology, Software, Investigation, Resources, Writing – review & editing. Henk W. Berendse: Conceptualization, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition. Anne-Fleur van Rootselaar: Conceptualization, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: RDB received unrestricted research grants from Medtronic paid to the institution. PRS is a consultant on educational activities for Medtronic, Boston Scientific and Elekta. All other authors report no declarations of interest.

Acknowledgments

Acknowledgements

We thank all patients for their participation. We also thank Karin Plugge, Nico Akemann and Marieke Alting Siberg for the MEG acquisitions.

Study funding

This study was supported by Amsterdam Neuroscience; 05 Amsterdam Neuroscience Alliantieproject – ND 2016. The funding source had no involvement in the study design, collection, analysis and interpretation of the data, writing of the report, and in the decision to submit the article for publication.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2023.103431.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.docx (3.7MB, docx)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data 1
mmc1.docx (3.7MB, docx)

Data Availability Statement

The data used in this study are available under certain conditions. These conditions include i) a formal project outline, ii) approval from our local ethics committee, iii) a formal data sharing agreement. The codes of our main analysis are available upon reasonable request.

Data will be made available on request.

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