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Corresponding author. Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Universitätsstr. 1, 40225, Düsseldorf, Germany.
]. However, determining the optimal stimulation setting can be a time-consuming trial-and-error process. Thus, there is a need to define non-invasive biomarkers, for instance through the analysis of DBS-evoked cortical responses (CR) [
]. Previous studies indicate that CR with latencies of 2–10 ms – resulting from antidromic hyperdirect pathway activation – are higher for stimulation contacts that elicit a therapeutic effect [
]. Still, neither the direct relationship to objective measures of motor performance nor the precise cortical distribution of CR have been studied so far. Making use of magnetoencephalography (MEG), we aimed to analyze the cortical distribution of stimulation evoked responses and relate them to objective quantitative parameters of motor performance.
22 patients with PD were asked to tap 10 times their right index finger onto their thumb as large, fast and regular as possible with a triaxial accelerometer [
] attached to the tip of their index finger. Patients were in their best medication ON state. Meanwhile, the patient's clinically selected contact within the left STN was stimulated with an omnidirectional monopolar montage (130 Hz, 60 μs). The tapping sequence was performed and recorded for each tested stimulation amplitude (0.5, 1.0, 2.0, 3.0, and 4.0 mA). We determined the average tap frequency and tap variability from the accelerometer data.
Afterwards, the same monopolar stimulation settings as before were applied for 40 seconds each in the MEG scanner with a stimulation frequency of 6 Hz (Elekta Oy, Helsinki, Finland). MEG data analysis was performed with brainstorm [
]. CR for each stimulation amplitude and MEG sensor were obtained by averaging across the corresponding trials. The significant time-windows of interest used for subsequent source analyses were determined through a two-stage, data-driven approach (Fig. 1A–D).
Fig. 1Grand average source time series, cortical pattern of stimulation-evoked responses and their relationship to motor performance. A-D: Source time series across patients and standard errors of the mean at the five different stimulation amplitudes tested. The grey-shaded areas indicate the time windows from which the individual peak amplitudes were extracted based on sensor-level analyses. Time series were extracted from four regions of interest. E-G: Source images for the evoked response peak latencies at 5.6, 11.8, and 21.8 ms. The absolute z-scored amplitudes are depicted on a MNI template (ICBM125 2009c Nonlinear Asymmetric) using a threshold of 5.0 (z-score) for visualisation purposes as indicated by the white line in the colour bar. H–K: Scatter plots depicting finger tap variability and tap frequency as a function of cortical R5, R10, and R20 response amplitudes in the motor cortex and the supplementary motor area with respective regression lines and 90%-confidence intervals. The colour bar indicates the stimulation amplitude. In each panel the coefficients b from the linear mixed effects models in raw units and the respective p-values are provided. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Individual cortical surfaces were co-registered with the MEG sensor locations and the source activity was reconstructed and projected to MNI space. Then, we extracted and averaged the 30 vertex time series around the maximum in each of the following regions in the left hemisphere: motor cortex (M1), supplementary motor area (SMA), middle frontal gyrus (MFG), and inferior frontal gyrus (IFG).
We estimated linear mixed effects models of movement outcomes (e.g., tap frequency, variability) as a function of CR amplitude, DBS stimulation amplitude, and their interaction, with subject included as a random effect. Separate models were run for each response outcome measure, cortical region, and latency. All reported p-values were corrected for multiple comparisons across these dimensions using false discovery rate correction.
The DBS pulse consistently elicited three ipsilateral cortical responses peaking at 5.6 ms (R5), 11.8 ms (R10), and 21.8 ms (R20), see Fig. 1 E-G. Cortical activation of R5 involved M1 and SMA (Fig. 1 E). The R10 was mainly located in M1, and the R20 included the SMA as well as MFG and IFG (Fig. 1 F, G). Both R5 and R20 in M1 and SMA were significant predictors of tap variability (Fig. 1 H, J, K), such that greater R5 and R20 responses predicted greater consistency in finger tapping frequency (R5: M1: b = −8.60 ± 3.12, p = 0.043; SMA: b = −8.60 ± 2.99, p = 0.043; R20: M1: b = −16.10 ± 4.13, p = 0.005; SMA: b = −8.59 ± 3.16, p = 0.043). The R10 response in M1 was a significant predictor of tap frequency (Fig. 1 I), with greater CR related to greater tap frequencies (b = 0.23 ± 0.09, p = 0.043).
In this study we identified distinct CR patterns associated with different latencies. The R5 response involved M1 and SMA, while the R10 response was confined to M1 and the R20 response again included the SMA, MFG and IFG. Interestingly, the R10 response in M1 was a significant predictor of finger tap frequency while the R5 and R20 related to a more regular movement profile. This might indicate a fine-grained discrimination of movement by these responses.
Tap variability reflects the motor task's consistency, the number of hesitations, and errors. This translates to impaired movement initiation – a cardinal element of akinesia. Meanwhile, tap frequency relates to bradykinesia. The relationship between CR and tap variability but not tap frequency might indicate, that CR amplitudes are markers of pathway activation related to movement initiation and inhibition rather than bradykinesia, which is better reflected by local oscillatory beta-band activity within the subthalamic nucleus [
]. Both responses (R5 and R10) involve M1 and could therefore result from recurrent activation of layer V neurons after antidromic cortical activation. Moreover, higher R5 and R10 responses in M1 and SMA were indicative of better finger tapping performance. In animal models of PD antidromic spiking of M1 layer V neurons as well as CR in M1 related to improved motor symptoms [
]. Our identified CR at 21.8 ms localize within MFG, IFG, and SMA. This is consistent with a polysynaptic activation involving various functional areas of the basal ganglia-thalamo-cortical network.
One common limitation of MEG studies with DBS patients is the change from clinically-used monopolar to bipolar DBS to reduce stimulation related artefacts [
]. CR of less than 3 ms might still be contaminated by the monopolar stimulation artefact. Another limitation of our study is the exclusive focus on finger tapping as the behavioural marker of motor performance.
While earlier studies relied on a limited cortical coverage, we reveal a cortical distribution of responses that aligns with the basal ganglia-thalamo-cortical network. Our study identifies a relationship between CR evoked by subthalamic stimulation and motor performance – an important prerequisite to use CR as biomarkers for clinical programming in the future.
Authors’ roles
BHB, RKS, CJH, AS, and EF contributed to the design of the study; BHB, RKS, and EF contributed to the acquisition and analysis of data; BHB and EF contributed to drafting the text and preparing the figures. RKS, CJH, EF, and AS reviewed and revised the manuscript for intellectual content.
Financial disclosures
AS received consultant and speaker fees from Medtronic Inc., Boston Scientific, and Abbott. AS and EF received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation as part of the CRC 295, Project C01, Project-ID 424778381 – TRR 295). EF received funding from the Volkswagen Foundation (Lichtenberg program 89387). CJH received funding and honoraria from Abbott. BHB: none, RKS: none.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: AS received consultant and speaker fees from Medtronic Inc., Boston Scientific and Abbott. CJH received honoraria from Abbott. BHB, RKS and EF declare that they have no known competing interests.
Acknowledgement
We thank Pia Hartmann and Luisa Spallek for their assistance during recordings and Johannes Pfeifer for critically reviewing the manuscript. We thank all of the patients for their participation.
Appendix A. Supplementary data
The following is the Supplementary data to this article.
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