Patient Reported Feedback Suggests an Alternative Sweet Spot for DBS Programming in Essential Tremor

doi:10.21203/rs.3.rs-8050162/v1

Background: Deep brain stimulation (DBS) of the ventral intermediate nucleus (VIM) and caudal zona incerta (cZi) is an established therapy for essential tremor (ET). Clinical outcomes depend on precise electrode placement and optimal stimulation parameters. Effective programming must balance tremor suppression with side-effect risk, yet systematic incorporation of patient-reported feedback remains limited.

Objective: To assess whether subjective patient feedback, quantified via a visual analogue scale (VAS), can guide DBS programming for effective tremor control.

Methods: In 15 VIM-DBS patients, 1,253 unique stimulation settings were collected, each rated with a VAS reflecting perceived clinical benefit. Associated volumes of tissue activated (VTA) were mapped and analyzed. VAS-optimized settings were compared to standard-of-care (SoC) programming. Voxel-wise permutation statistics identified stimulation sweet and sour spots, while structural and functional connectivity analyses determined neural correlates of subjective benefit.

Results: VAS-optimized stimulation achieved tremor suppression comparable to SoC settings but with lower energy consumption. Sweet spots correlated with high VAS ratings localized to the dorsal VIM, whereas sour spots were ventral. Connectivity between sweet spots and prefrontal, frontal, and insular regions positively correlated with perceived benefit.

Conclusions: Integrating patient-reported feedback offers a structured, individualized approach to DBS optimization in ET. VAS-guided programming identifies patient-specific sweet spots and delineates connectivity profiles associated with clinical benefit. Notably, VAS-derived sweet spots were more dorsal than previously suggested targets, highlighting the importance of incorporating subjective feedback to refine optimal stimulation regions.

Neurology

DBS

VIM

Tremor

VAS

PROMs

Essential tremor (ET) is a common movement disorder, characterized by progressive, bilateral action and postural tremor predominantly affecting the upper limbs, but also involving the head, voice, trunk, and, less frequently, the lower limbs.¹ For patients with medically refractory symptoms, deep brain stimulation (DBS) has emerged as a safe and effective long-term treatment option, achieving tremor reductions of 66% to 78% at one year following bilateral implantation, with some individuals experiencing up to 90% improvement.^4–6

Optimal DBS outcomes depend on proper patient selection, accurate electrode placement, and systematic postoperative programming.^7,8 Directional DBS electrodes allow finer control of current spread, improving efficacy and side-effect profiles.⁹ However, this advance increases programming complexity due to more possible parameter combinations.¹⁰ Thus, efficient strategies are needed to optimize stimulation parameters and lead configurations.

Selecting the most effective and well-tolerated contact remains central to DBS programming. The “sweet spot” for tremor suppression is still being refined. The ventral intermediate nucleus (VIM) of the thalamus—connecting the primary motor cortex and contralateral dentate nucleus via the dentato-rubro-thalamic tract (DRTT)—has been the main ET-DBS target.^11–13 However, similar benefits have been achieved by stimulating adjacent regions like the posterior subthalamic area (PSA) and caudal zona incerta (cZi). Few studies have directly compared efficacy and side-effect profiles across DBS targets. Evidence indicates that proximity to the DRTT predicts outcomes better than anatomical location, with closer stimulation linked to lower tremor-suppression thresholds.^15,16 This supports the idea that effective sites modulate the shared DRTT pathway spanning the red nucleus, zona incerta, PSA, and VIM.^17–21

Although some studies include patient-reported outcomes via ADL assessments,^13,18,22,23 few examine patients’ subjective perceptions, which are crucial to treatment success. Despite technological advances, programming routines that integrate patient feedback remain scarce.^10,24 Previously, we showed that a VAS-based programming approach in Parkinson’s disease achieved outcomes comparable to standard methods while identifying stimulation networks linked to patient-rated benefit.^25,26 In the present study, we extend this VAS-based approach to VIM-DBS for ET, aiming to investigate its feasibility, its effectiveness in guiding stimulation programming, and its potential to elucidate patient-specific stimulation sweet spots and associated connectivity profiles.

Study Participants: All study procedures were approved by the local ethics committee of Ludwig Maximilian University of Munich, Germany (approval #18-809), and written informed consent was obtained from all participants prior to inclusion according to the Declaration of Helsinki. Patients with ET who had undergone DBS targeting the VIM were recruited between June 2022 and December 2023 during routine visits to our movement disorders outpatient clinic. Participants were eligible if they met the following criteria: (i) a diagnosis of ET according to the German Neurological Society (DGN) guidelines;²⁷ and (ii) treatment with VIM-DBS. Additional inclusion criteria required that patients had been implanted for at least one year and that their DBS settings had remained stable for a minimum of three months prior to study participation. Patients with manifest dementia or uncontrolled psychiatric disorders were excluded.

Study Visit and VAS Rating: Details of the study protocol and programming procedures have been described previously.²⁶ In brief, chronic stimulation parameters were recorded, and tremor severity was assessed using the Fahn-Tolosa-Marín Tremor Rating Scale (FTMTRS) prior to deactivation of bilateral stimulation. Following a 60-second washout period, FTMTRS scores were reassessed in the STIM-OFF condition. Subsequently, patients underwent VAS-based reprogramming of their stimulator, with each hemisphere tested independently while the contralateral side remained switched off. For each hemisphere, different stimulation amplitudes (ranging from 0.5 to 3.0 mA, including a 0mA – DBS OFF – control) were applied individually to each contact. The sequence of contact-amplitude combinations was randomized using a predefined list to minimize habituation effects (Supp. Table 1). Following each adjustment, patients were asked to rate the overall quality of the DBS effect on a scale from 0 to 10, where 0 indicated "very bad" and 10 indicated "very good," without further elaboration. Intermediate scores were left to the patients' interpretation, based purely on their subjective perception of the stimulation effect. Patients were instructed to provide a rating within 10-15 seconds, a time window deemed sufficient for symptom modulation to occur.²⁸ The corresponding VAS score was recorded immediately after each setting. Throughout the procedure, patients were blinded to the specific stimulation settings. If intolerable side effects emerged, the corresponding contact was excluded from further testing at higher amplitudes. After each stimulation setting, a 10-second washout period was maintained before the next parameter combination was applied.^25,26,28 Once one hemisphere was completed, the same procedure was repeated for the contralateral side. The overall VAS-based adjustment process required approximately 60 minutes per electrode per patient. Following completion of testing, the best-rated stimulation setting (combination of contact and amplitude with the highest VAS rating, PW = 60 ms; f = 130 Hz) for each hemisphere was selected. Stimulation was then reinitiated bilaterally using the chosen settings, and after a three-minute habituation period, final FTMTRS assessments were conducted.

Localization of DBS Leads: Preoperative imaging (T1- and T2-weighted MRI) and postoperative computed tomography (CT) scans were processed for electrode localization using the Lead-DBS toolbox (www.lead-dbs.org).²⁹ Postoperative CT images were linearly co-registered to the corresponding preoperative MRI using Advanced Normalization Tools (ANTs).³⁰ Native-space images were subsequently normalized to Montreal Neurological Institute (MNI) space via a three-step affine registration pipeline, with additional manual refinement of the atlas fit performed where necessary. Correction for brain shift and pneumocephalus resulting from surgery was applied using the Schönecker brain shift correction algorithm implemented in Lead-DBS.³¹ All registration steps were carefully inspected and manually adjusted to ensure accurate alignment. Electrode trajectories were initially reconstructed using the PaCER algorithm,³² with manual refinement performed as needed to optimize anatomical fidelity. In patients implanted with directional leads, rotational orientation was assessed and corrected using the DiODe algorithm.³³ Anatomical segmentations were defined using the DISTAL atlas.³⁴

Volume of Tissue Activated (VTA) Estimation: The volume of tissue activated (VTA) was estimated using a finite element method (FEM)-based model implemented in the Lead-DBS toolbox. To accurately model the distribution of the electric field, subcortical gray matter nuclei were defined using the DISTAL atlas. Consistent with established protocols, tissue conductivities were set to 0.33 S/m for gray matter and 0.14 S/m for white matter³⁵. The electric fields (E-fields) were calculated using the SimBio/FieldTrip computational pipeline within Lead-DBS. A threshold of 0.2 V/mm was applied to delineate the VTA boundaries, following similar methodologies used in previous studies³⁵. For each stimulation configuration, the corresponding VTA was generated and then associated with the corresponding VAS rating.

Sweet Spot Analysis: Sweet-spot mapping was performed using Sweetspot Explorer³⁶ to identify neuroanatomical regions within the VIM associated with subjective patient ratings. First, VAS scores were first normalized using z-scores for standardization. VTAs were generated for each stimulation setting (i.e., combinations of contact and amplitude) and thresholded at an electric field magnitude above 0.2 V/mm. To ensure meaningful inclusion of stimulation-affected regions, only voxels that covered by at least 20% with the VTAs were retained for subsequent analyses, minimizing the inclusion of areas with negligible or no stimulation coverage.^29,35 A non-linear transformation was applied to mirror the left hemisphere VTAs onto the right hemisphere. For each voxel (0.22 × 0.22 × 0.22 mm), all stimulation settings whose VTA encompassed that voxel contributed their corresponding VAS scores. These scores were then subjected to a voxel-wise one-sample T-test against zero, yielding a statistical T-map representing regions where stimulation effects significantly differed from zero. Amplitudes were corrected to account for larger VTAs generated during high-amplitude stimulation affect more on stimulation maps as they provide more voxels as described previously.^29,35 The T-values were color coded and the significance was determined at an a-level of 0.05 as described.³⁷ To evaluate effectiveness and robustness of the resulting voxel-wise sweet spot model, we employed a leave-one-cohort-out strategy, treating each patient’s multiple settings (25 to 49) as an individual cohort, and the model was tested by excluding one cohort’s data and training on the remaining cohorts.

Connectivity Estimation: To delineate the network correlates of subjective benefit, we performed whole-brain normative structural and functional connectivity analyses using each individual VTA as a seed region. Functional connectivity was assessed using the resting-state functional group connectome of 1000 GSP healthy subjects,³⁸ while structural connectivity was estimated using a structural group connectome of 985 HCP subjects from the Human Connectome Project.³⁹ For each VTA, an individual whole-brain connectivity profile was computed, representing either the probability of structural connections or the strength of functional coupling to all other voxels in the brain. Three types of group-level connectivity maps were then generated in accordance with the Lead-DBS Network Mapping Explorer framework. First, weighted average maps (A-maps) were calculated by weighting each patient’s connectivity profile by the corresponding VAS score, thereby highlighting regions preferentially connected to VTAs associated with greater subjective improvement. Secondly, voxel-wise correlations between connectivity strength and VAS ratings were computed across patients using Spearman’s rank correlation, yielding correlation maps (R-maps) with associated significance testing (permutation-based correction for multiple comparisons). Finally, a combined map (C-map) was derived by retaining only voxels consistently identified in both the A-map and the R-map, with the sign of the correlation (positive or negative) preserved. These maps delineate the structural and functional networks whose connectivity with the stimulation site was most reliably associated with higher or lower subjective ratings of stimulation.

Fiber Filtering: Structural connectome data from 32 adult Diffusion MGH–USC HCP subjects⁴⁰ were used to assess whole-brain fiber pathways intersecting each VTA. For each stimulation setting, the corresponding VTA was employed to identify fibers traversing the stimulated volume. Fiber filtering was then applied to determine white matter tracts whose modulation best predicted clinical outcomes by comparing fibers stimulated in patients with higher versus lower VAS ratings. Each fiber was assigned a statistical weight (T-value) using a mass-univariate two-sample t-test implemented in the Lead-DBS Fiber Filtering Explorer. Significant tracts (p < 0.05, Bonferroni-corrected) were visualized on the MNI template brain for group-level interpretation.

Characterization of the study’s participants. In total, data from 15 study participants was analyzed (Table 1), as one out of 16 enrolled patients had to be excluded at a later time-point due to severe symptoms when the stimulation was turned off. Five were female, with a mean age of 71.0 ± 8.1 years. The average disease duration for the entire group was 30.8 ± 16.9 years, and the average duration of DBS treatment was 7.4 ± 5.4 years. The FTMTRS (Part A+B) scores prior to the study visit were 44.5 ± 4.7, and 24.0 ± 4.7 (mean ± SEM) in the Off-setting and VAS conditions, respectively. Four patients had unsegmented electrodes, and eleven had directional electrodes. These patients were exposed to varying amplitudes ranging from 0.5 to 3.0 mA in 0.5 mA increments at each ring level (additionally the stimulation was switched off in between for each side once), while each contact was tested separately for patients with segmented electrodes. This resulted in 48 different combinations per side for each patient with 8-contact leads, and 24 combinations per side for those with 4-contact leads. Each set of combinations is referred to as a cohort. The dataset included 15 patients in total: 4 with 4-contact electrodes and 11 with 8-contact electrodes. One patient had a single electrode implanted, while all others had bilateral (left and right) electrode placements. This configuration resulted in a total of 204 individual contacts tested. Across these contacts, 1,253 unique stimulation settings were applied. The average VAS score recorded for the dataset was 4.46 ± 2.83 (mean ± SEM).

VAS-based programming results in similar acute effects. We first compared FTMTRS scores under standard of care (SoC-DBS), STIM-OFF (Off), and in response to the best rated VAS-based setting (VAS-DBS). When the stimulator was switched off, tremor reoccurred within seconds, with statistically significant differences in Part A, and Part A+B of the FTMTRS (Fig. 1a). Despite individual differences we found no significant difference when we compared the FTMTRS scores of SoC-DBS and VAS-DBS on a group level (Fig. 1a,b). When comparing the stimulation amplitude under SoC-DBS and VAS-DBS, we found significant differences on both the left and right electrodes, with lower amplitudes in VAS-based stimulation (Fig. 1c,d), with no significant interhemispheric differences. Correlating VAS scores across all contacts revealed a peak at 1 mA and 0.5 mA for the left and right hemispheres. At higher amplitudes, VAS scores significantly decreased with increasing DBS amplitude (F(6,162) = 9.65, P < 0.0001) with no significant difference between left and right stimulation (F(1,27) = 0.06, P = 0.81) (Fig. 1e). Conversely, while the height of ring levels (#1–4) varied across cases, no significant group-level differences were found between SoC-DBS and VAS-DBS (Fig. 1f).

Optimal stimulation regions predict subjective patient feedback. To identify brain regions associated with subjective sweet and sour spots, DBS electrode reconstructions were analyzed across all participants (Fig. 2a). VTAs were correlated with individual VAS scores to map regions linked to favorable and unfavorable subjective ratings. Voxels corresponding to higher VAS ratings clustered within the dorsal (cranial) portion of the VIM, although this putative sweet spot did not reach statistical significance (P > 0.05). In contrast, a significant sour spot (P < 0.05) was identified in the posteroventral (caudal) VIM and adjacent subthalamic region (Fig. 2b, Supp. Fig. 1). The center of mass for the sweet spots in both hemispheres combined was at MNI coordinates (x, y, z) + 15.86, - 14.60, 4.04 mm, and the sour spot was at + 15.64, - 18.12, - 2.78 mm. Using a leave-one-cohort-out approach, data from 11 cohorts (patients) were used to predict the outcomes for the first cohort (patient), testing whether VAS scores could help predict stimulation effectiveness. The results showed a positive correlation between VAS scores and the degree of overlap between the VTA and the sweet spot map (R = 0.29, p < 1^-16) (Fig. 2c). The VAS sweet spot was near the DRTT in the dorsomedial region of the VIM whereas the VAS sour spot deviated ventral and posterior from the DRTT (Fig. 2d).

Effect of ring level and amplitude in the subjective patient’s rating. We hypothesized that electrodes positioned more precisely would yield more favorable VAS ratings. To test this, VAS scores were analyzed separately for each electrode and subject. As expected, significant differences emerged in the overall distribution of VAS scores, with some electrodes displaying a bimodal pattern (Fig. 3a). To further examine the relationship between subjective ratings and VTA localization, we compared VAS scores across ring levels. Ratings were significantly higher at the 2nd and 3rd ring levels compared to the 1st (ventral) level (Fig. 3b). Statistically significant “sweet spots” were identified at the 3rd and 4th ring levels, whereas no significant effect was observed at the 1st and 2nd levels (Fig. 3c). Consistent with these findings, average VAS ratings peaked at 0.5–1 mA, but declined at higher amplitudes (Fig. 3d). Accordingly, significant sweet spots were detected at 0.5, 1, and 1.5 mA, while statistical significance disappeared at higher amplitudes (Fig. 3e).

Beneficial brain networks associate with positive patient-reported feedback. Stimulation sweet spots are believed to connect to various remote brain networks that patients may perceive as either favorable or unfavorable.⁴¹ To explore this, we examined the connectome profiles of our subjective sweet spots using whole-brain structural and functional connectivity seeding from bilateral VTAs. Structural connectivity demonstrated positive association between VTAs with a high VAS score in the prefrontal and frontal lobe, mainly the superior and inferior frontal gyri (Fig. 4a). Additionally, fiber filtering confirmed structural substrates that connect the VAS sweet spot with prefrontal cortical areas (Supp. Fig. 2). As for functional connectivity, it exhibited beneficial connectivity profile that was largely similar as those of structural connectivity with additional connectivity to the insular cortex (Fig. 4b). These models were further validated by employing a leave-one-cohort-out strategy (functional: R = 0.18, p < 1^-16; structural: R = 0.16, p = 0.001).

Short-term clinical efficacy of VAS-based programming. The advent of multi-segmented electrodes has greatly increased DBS programming complexity due to numerous parameter combinations. Recent advances in aDBS for PD use electrophysiological biomarkers like local field potentials (LFPs) for dynamic parameter adjustment,⁴² yet structured integration of patient-reported feedback remains lacking in electrophysiology- or image-based approaches for ET and other conditions. Here, we evaluated the short-term clinical efficacy of VAS-based programming in ET. Consistent with previous findings,²⁶ no significant differences were observed between VAS-based and standard programming (Fig. 1a,b), supporting subjective patient feedback as a valid DBS programming signal. Notably, VAS-based settings achieved similar outcomes with lower stimulation amplitudes (Fig. 1c), suggesting reduced energy use and battery drain without loss of efficacy—an important factor given that battery replacements are a leading cause of DBS-related infections.⁴³ These results underscore the need to reconsider high-amplitude reliance and to integrate patient experience into DBS parameter adjustments (Fig. 1e).

Subjective sweet and sour spots: Treatment success inET-DBS is largely determined by choosing the most effective target structure. Whereas the VIM has traditionally been regarded as an effective target for tremor control,⁴⁴ more recent results suggest stimulation sites caudal to the VIM to be most effective.^{13,20,45–47} Some studies imply that the proximity of the VTA to the DRTT was associated with greater tremor suppression efficiency^15,16 and that the distance to the DRTT is more critical for clinical efficacy than specific coordinates,^17–19,48 suggesting that different target regions may represent a common anatomical fibre tract – the DRTT.^21,49 In accord, the PSA, including the cZi, Forel field H, and the prelemniscal radiation, was proposed as an effective and alternative stimulation target for ET. In fact, several studies proposed that PSA-DBS might have better efficacy in controlling tremor symptoms and cause fewer stimulation related side effects.^15,50–54 Other studies have also postulated the existence of stimulation sweet spot more anteriorly in the region of the ventralis oralis posteriornucleus (VOp) or along the VIM/VOp border.^46,55,56 By pairing VAS scores with VTAs, we identified regions with the highest and lowest subjective ratings—termed the “subjective sweet” and “sour” spots. The sweet spot localized dorsally within the VIM, while the sour spot lay posteroventrally, below the VIM (Fig. 2b). This contrasts with reports placing the tremor sweet spot more ventrally, possibly due to side effects or negative subjective sensations not evident in clinical exams. The VAS sweet spot was near the DRTT, whereas the sour spot was farther from its ventral entry into the VIM, consistent with effective tremor control involving DRTT engagement (Fig. 2d).Overall, our findings emphasize incorporating patient feedback when selecting ventral contacts in VIM-DBS and support the concept of outcome-specific sweet spots, as recently described in PD.⁵⁷ Future prospective studies should compare these distinct sweet spots in terms of clinical efficacy and patient satisfaction in chronic ET-DBS.

Connectivity of subjective sweet spots in ET: Recent DTI-based connectivity studies on thalamic DBS suggest that the cerebello-thalamo-cortical network plays a key role in tremor modulation ⁵⁸. Strong connectivity between active DBS contacts and network nodes is assumed to be linked to therapeutic effects. Some studies focused on specific network nodes ⁵⁹, while others analysed whole-brain connectivity using patient-specific or normative connectome data ^20,45. For instance, Akram et al. used probabilistic tractography to show high structural connectivity between the VIM and M1, SMA, S1, and contralateral dentate nucleus ⁴⁵. Grimm et al. examined 20 ET patients who had undergone bilateral DBS using patient-specific probabilistic diffusion tensor imaging, identifying that the connectivity of the M1, and somatosensory cortex, was most closely related to complete and incomplete tremor suppression, with the anterior lobe of the cerebellum and SMA also involved ⁶⁰. Similarly, Al-Fatly et al. identified the patterns of effective VIM-DBS connectivity by normative brain connectomes, found that there was positive connectivity in multiple regions, mainly in the paracentral gyrus, visual cortex, and superior and inferior cerebellar lobules ^13,20. Our study investigated the structural and functional connectivity associated with the VTA and its correlation with subjective patient feedback. We identified significant positive structural connectivity between the subjective sweet spot and brain regions including the prefrontal and frontal lobe, and the insular cortex (Fig. 4). On the one hand, our sweet spot is indeed located close to the DRTT aligning closely with these above-mentioned studies^58,61. However, our results also suggest additional connectivity patterns. The involvement of the frontal and prefrontal regions, as well as the insular cortex, in our connectivity analysis is particularly noteworthy. The prefrontal and superior frontal cortices are central to executive control, decision-making, and cognitive flexibility, while the inferior frontal gyrus is implicated in inhibitory control and motor planning.^62,63 Notably, the prefrontal cortex has also been implicated in affective responses to DBS.⁶⁴ The insula, in turn, serves as a hub integrating interoceptive awareness, affective processing, and salience detection.⁶⁵ Taken together, these regions contribute to the cognitive and emotional dimensions of symptom perception. In the context of our results, connectivity of the subjective sweet spot to these cortical areas may reflect the integration of motor improvement with higher-order evaluative and affective processes that shape patients’ VAS ratings. This suggests that subjective feedback during DBS titration may not solely depend on sensorimotor tremor suppression but also on cognitive and affective appraisal mediated by prefrontal and insular circuits.

LIMITATIONS

This study has several limitations. First, data were collected in an acute setting, capturing only short-term effects; thus, long-term outcomes of VAS-based programming remain unknown. However, as tremor typically responds to DBS within seconds,¹⁰ our conclusions likely extend to longer periods. Future studies should include long-term follow-up to confirm this. Second, self-reported measures may be influenced by mood or cognitive bias; combining subjective ratings with objective clinical and biomarker data would yield a more comprehensive assessment. Third, the small, single-center sample limits generalizability and may explain the low statistical significance of the identified sweet spot. Larger, multicenter studies with diverse populations are needed to validate these findings and allow subgroup analyses across DBS targets or conditions.

Our findings indicate that VAS-guided patient feedback is a valuable tool for optimizing and personalizing DBS programming in ET, linking perceived benefit to sensorimotor network connectivity and warranting further evaluation in larger, diverse cohorts.

DATA AVAILABILITY:

The data supporting the findings of this study are available from the authors upon reasonable request

ACKNOWLEDGMENTS:

T.K. serves as the vice president of the German DBS Society.

FUNDING & FINANCIAL DISCLOSURES:

Autor	Financial Disclosures
Jing Dong	None
Sophia Peschke	None
Angelina Kirschner	None
Maximilian Scherer	M.S. was supported by a Feodor-Lynen Return-Fellowship
Carla Palleis	C.P. was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198), the Thiemann Stiftung and Else-Kröner-Fresenius Stiftung.
Jan H. Mehrkens	None
Johannes Off	None
Juhi Shaik	None
Elisabeth Kaufmann	E.K. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation); Medical & Clinician Scientist Program (MCSP) E.K. received speaker honoraria and financial compensation for travel expenses from Medtronic, UCB, Livanova, Desitin, Precisis, UNEEG, and Eisai and has participated in clinical trials for Medtronic, UCB, Ergomed, and Precisis, all unrelated to the submitted work.
Dr. Thomas Koeglsperger	T.K. was funded by Parkinson Fonds Deutschland; Stichting ParkinsonFonds; Fritz Thyssen Stiftung; FRAXA, VDI, Medical & Clinician Scientist Program (MCSP) T.K. received industry funding from Abbott Medical Inc., Medtronic & AbbVie T.K. served on advisory boards for Mitsubishi. T.K. received speaker honoraria and travel support for scientific presentations from Abbott Medical Inc. & AbbVie

COMPETING INTERESTS:

The authors declare no competing interests related to this manuscript.

AUTHOR’S ROLES

J.D., S.P., C.P., E.K. & T.K. designed the experiments; S.P., A.K., J.O., J.H.M., J.S. & J.D. executed the experiments; S.P., J.D., M.S. & T.K. analysed the data; J.D., S.P. & T.K. wrote the manuscript; E.K., J.D., M.S., C.P. & T.K. edited the final version of the manuscript.

Bhatia KP, Bain P, Bajaj N, et al. Consensus Statement on the classification of tremors. from the task force on tremor of the International Parkinson and Movement Disorder Society. Mov Disord. 2018;33(1):75-87. doi:10.1002/mds.27121
Findley LJ, Cleeves L, Calzetti S. Primidone in essential tremor of the hands and head: a double blind controlled clinical study. J Neurol, Neurosurg Psychiatry. 1985;48(9):911. doi:10.1136/jnnp.48.9.911
Koller WC, Biary N. Metoprolol Compared With Propranolol in the Treatment of Essential Tremor. Arch Neurol. 1984;41(2):171-172. doi:10.1001/archneur.1984.04050140069026
Baizabal-Carvallo JF, Kagnoff MN, Jimenez-Shahed J, Fekete R, Jankovic J. The safety and efficacy of thalamic deep brain stimulation in essential tremor: 10 years and beyond. Journal of neurology, neurosurgery, and psychiatry. 2014;85(5):567-572. doi:10.1136/jnnp-2013-304943
Pahwa R, Lyons KE, Wilkinson SB, et al. Long-term evaluation of deep brain stimulation of the thalamus. Journal of Neurosurgery. 2006;104(4):506-512. doi:10.3171/jns.2006.104.4.506
Dallapiazza RF, Lee DJ, Vloo PD, et al. Outcomes from stereotactic surgery for essential tremor. Journal of neurology, neurosurgery, and psychiatry. 2019;90(4):474-482. doi:10.1136/jnnp-2018-318240
Volkmann J, Moro E, Pahwa R. Basic algorithms for the programming of deep brain stimulation in Parkinson’s disease. Movement disorders : official journal of the Movement Disorder Society. 2006;21 Suppl 14(S14):S284-9. doi:10.1002/mds.20961
Machado A, Rezai AR, Kopell BH, Gross RE, Sharan AD, Benabid AL. Deep brain stimulation for Parkinson’s disease: Surgical technique and perioperative management. Movement disorders : official journal of the Movement Disorder Society. 2006;21(S14):S247-S258. doi:10.1002/mds.20959
Rammo RA, Ozinga SJ, White A, et al. Directional Stimulation in Parkinson’s Disease and Essential Tremor: The Cleveland Clinic Experience. Neuromodulation: Technol Neural Interface. 2021;25(6):829-835. doi:10.1111/ner.13374
Koeglsperger T, Palleis C, Hell F, Mehrkens JH, Bötzel K. Deep Brain Stimulation Programming for Movement Disorders: Current Concepts and Evidence-Based Strategies. Front Neurol. 2019;10:410. doi:10.3389/fneur.2019.00410
Dum RP, Strick PL. An Unfolded Map of the Cerebellar Dentate Nucleus and its Projections to the Cerebral Cortex. J Neurophysiol. 2003;89(1):634-639. doi:10.1152/jn.00626.2002
Benabid AL, Pollak P, Hoffmann D, et al. Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus. Lancet. 1991;337(8738):403-406. doi:10.1016/0140-6736(91)91175-t
Kübler D, Kroneberg D, Al-Fatly B, et al. Determining an efficient deep brain stimulation target in essential tremor - Cohort study and review of the literature. Park Relat Disord. 2021;89:54-62. doi:10.1016/j.parkreldis.2021.06.019
Paschen S, Forstenpointner J, Becktepe J, et al. Long-term efficacy of deep brain stimulation for essential tremor: An observer-blinded study. Neurology. 2019;92(12):e1378-e1386. doi:10.1212/wnl.0000000000007134
Barbe MT, Reker P, Hamacher S, et al. DBS of the PSA and the VIM in essential tremor. Neurology. 2018;91(6):e543-e550. doi:10.1212/wnl.0000000000005956
Dembek TA, Petry-Schmelzer JN, Reker P, et al. PSA and VIM DBS efficiency in essential tremor depends on distance to the dentatorubrothalamic tract. Neuroimage Clin. 2020;26:102235. doi:10.1016/j.nicl.2020.102235
Coenen VA, Allert N, Mädler B. A role of diffusion tensor imaging fiber tracking in deep brain stimulation surgery: DBS of the dentato-rubro-thalamic tract (drt) for the treatment of therapy-refractory tremor. Acta Neurochir. 2011;153(8):1579-1585. doi:10.1007/s00701-011-1036-z
Low HL, Ismail MohdN bin M, Taqvi A, Deeb J, Fuller C, Misbahuddin A. Comparison of posterior subthalamic area deep brain stimulation for tremor using conventional landmarks versus directly targeting the dentatorubrothalamic tract with tractography. Clin Neurol Neurosurg. 2019;185:105466. doi:10.1016/j.clineuro.2019.105466
Fenoy AJ, Schiess MC. Comparison of tractography‐assisted to atlas‐based targeting for deep brain stimulation in essential tremor. Mov Disord. 2018;33(12):1895-1901. doi:10.1002/mds.27463
Al-Fatly B, Ewert S, Kübler D, Kroneberg D, Horn A, Kühn AA. Connectivity profile of thalamic deep brain stimulation to effectively treat essential tremor. Brain : a journal of neurology. 2019;142(10):3086-3098. doi:10.1093/brain/awz236
Fiechter M, Nowacki A, Oertel MF, et al. Deep Brain Stimulation for Tremor: Is There a Common Structure? Stereotact Funct Neurosurg. 2017;95(4):243-250. doi:10.1159/000478270
Tsuboi T, Jabarkheel Z, Zeilman PR, et al. Longitudinal follow-up with VIM thalamic deep brain stimulation for dystonic or essential tremor. Neurology. 2020;94(10):e1073-e1084. doi:10.1212/wnl.0000000000008875
Plaha P, Javed S, Agombar D, et al. Bilateral caudal zona incerta nucleus stimulation for essential tremor: outcome and quality of life. Journal of neurology, neurosurgery, and psychiatry. 2011;82(8):899-904. doi:10.1136/jnnp.2010.222992
Hell F, Palleis C, Mehrkens JH, Koeglsperger T, Bötzel K. Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation. Front Neurol. 2019;10:314. doi:10.3389/fneur.2019.00314
Dong J, Peschke S, Kirchner A, et al. Subjective Patient Rating as a Novel Feedback Signal for DBS Programming in Parkinson’s Disease. Brain Stimul. Published online 2025. doi:10.1016/j.brs.2025.03.008
Palleis C, Gehmeyr M, Mehrkens JH, Bötzel K, Koeglsperger T. Establishment of a Visual Analog Scale for DBS Programming (VISUAL-STIM Trial). Front Neurol. 2020;11:561323. doi:10.3389/fneur.2020.561323
Deuschl G, Schwingenschuh P. Tremor, S2k-Leitlinie, 2022. Deutsche Gesellschaft für Neurologie (Hrsg.), Leitlinien für Diagnostik und Therapie in der Neurologie. www.dgn.org/leitlinien
McIntyre CC, Anderson RW. Deep brain stimulation mechanisms: the control of network activity via neurochemistry modulation. Journal of Neurochemistry. 2016;139(Suppl 3):338-345. doi:10.1111/jnc.13649
Horn A, Li N, Dembek TA, et al. Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. NeuroImage. 2019;184:293-316. doi:10.1016/j.neuroimage.2018.08.068
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage. 2011;54(3):2033-2044. doi:10.1016/j.neuroimage.2010.09.025
Schönecker T, Kupsch A, Kühn AA, Schneider GH, Hoffmann KT. Automated Optimization of Subcortical Cerebral MR Imaging−Atlas Coregistration for Improved Postoperative Electrode Localization in Deep Brain Stimulation. Am J Neuroradiol. 2009;30(10):1914-1921. doi:10.3174/ajnr.a1741
Husch A, Petersen MV, Gemmar P, Goncalves J, Hertel F. PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation. NeuroImage: Clinical. 2018;17:80-89. doi:10.1016/j.nicl.2017.10.004
Hellerbach A, Dembek TA, Hoevels M, et al. DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging. Stereotact Funct Neurosurg. 2018;96(5):335-341. doi:10.1159/000494738
Ewert S, Plettig P, Li N, et al. Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity. NeuroImage. 2017;170:271-282. doi:10.1016/j.neuroimage.2017.05.015
Astrom M, Diczfalusy E, Martens H, Wardell K. Relationship between Neural Activation and Electric Field Distribution during Deep Brain Stimulation. IEEE Trans Biomed Eng. 2014;62(2):664-672. doi:10.1109/tbme.2014.2363494
Neudorfer C, Butenko K, Oxenford S, et al. Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks. NeuroImage. 2023;268:119862. doi:10.1016/j.neuroimage.2023.119862
Dembek TA, Roediger J, Horn A, et al. Probabilistic sweet spots predict motor outcome for deep brain stimulation in Parkinson disease. Ann Neurol. 2019;86(4):527-538. doi:10.1002/ana.25567
doi:10.7910/dvn/kktjqc
Elias GJB, Germann J, Joel SE, et al. A large normative connectome for exploring the tractographic correlates of focal brain interventions. Sci Data. 2024;11(1):353. doi:10.1038/s41597-024-03197-0
Horn A, Kühn AA, Merkl A, Shih L, Alterman R, Fox M. Probabilistic conversion of neurosurgical DBS electrode coordinates into MNI space. NeuroImage. 2017;150:395-404. doi:10.1016/j.neuroimage.2017.02.004
Horn A, Fox MD. Opportunities of Connectomic Neuromodulation. NeuroImage. Published online 2020. doi:10.1016/j.neuroimage.2020.117180
Bronte-Stewart HM, Beudel M, Ostrem JL, et al. Long-Term Personalized Adaptive Deep Brain Stimulation in Parkinson Disease. JAMA Neurol. 2025;82(11). doi:10.1001/jamaneurol.2025.2781
Pepper J, Zrinzo L, Mirza B, Foltynie T, Limousin P, Hariz M. The Risk of Hardware Infection in Deep Brain Stimulation Surgery Is Greater at Impulse Generator Replacement than at the Primary Procedure. Stereotact Funct Neurosurg. 2013;91(1):56-65. doi:10.1159/000343202
Benabid AL, Pollak P, Gao D, et al. Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders. Journal of Neurosurgery. 1996;84(2):203-214. doi:10.3171/jns.1996.84.2.0203
Akram H, Dayal V, Mahlknecht P, et al. Connectivity derived thalamic segmentation in deep brain stimulation for tremor. NeuroImage: Clin. 2018;18:130-142. doi:10.1016/j.nicl.2018.01.008
Middlebrooks EH, Okromelidze L, Wong JK, et al. Connectivity correlates to predict essential tremor deep brain stimulation outcome: Evidence for a common treatment pathway. NeuroImage: Clin. 2021;32:102846. doi:10.1016/j.nicl.2021.102846
Papavassiliou E, Rau G, Heath S, et al. Thalamic Deep Brain Stimulation for Essential Tremor: Relation of Lead Location to Outcome. Neurosurgery. 2004;54(5):1120-1130. doi:10.1227/01.neu.0000119329.66931.9e
Sandoe C, Krishna V, Basha D, et al. Predictors of deep brain stimulation outcome in tremor patients. Brain Stimul. 2018;11(3):592-599. doi:10.1016/j.brs.2017.12.014
Al-Fatly B, Ewert S, Kübler D, Kroneberg D, Horn A, Kühn AA. Connectivity profile of thalamic deep brain stimulation to effectively treat essential tremor. bioRxiv. Published online 2019:575209. doi:10.1101/575209
Holslag JAH, Neef N, Beudel M, et al. Deep Brain Stimulation for Essential Tremor: A Comparison of Targets. World Neurosurg. 2018;110:e580-e584. doi:10.1016/j.wneu.2017.11.064
Fytagoridis A, Sandvik U, Åström M, Bergenheim T, Blomstedt P. Long term follow-up of deep brain stimulation of the caudal zona incerta for essential tremor. Journal of neurology, neurosurgery, and psychiatry. 2012;83(3):258-262. doi:10.1136/jnnp-2011-300765
Eisinger RS, Wong J, Almeida L, et al. Ventral Intermediate Nucleus Versus Zona Incerta Region Deep Brain Stimulation in Essential Tremor. Movement Disorders Clinical Practice. 2018;5(1):75-82. doi:10.1002/mdc3.12565
Blomstedt P, Sandvik U, Tisch S. Deep brain stimulation in the posterior subthalamic area in the treatment of essential tremor. Movement disorders : official journal of the Movement Disorder Society. 2010;25(10):1350-1356. doi:10.1002/mds.22758
Fan H, Bai Y, Yin Z, et al. Which one is the superior target? A comparison and pooled analysis between posterior subthalamic area and ventral intermediate nucleus deep brain stimulation for essential tremor. CNS Neurosci Ther. 2022;28(9):1380-1392. doi:10.1111/cns.13878
Tsuboi T, Wong JK, Eisinger RS, et al. Comparative connectivity correlates of dystonic and essential tremor deep brain stimulation. Brain. 2021;144(6):1774-1786. doi:10.1093/brain/awab074
Elias GJB, Boutet A, Joel SE, et al. Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy. Ann Neurol. 2021;89(3):426-443. doi:10.1002/ana.25975
Tödt I, Al‐Fatly B, Granert O, et al. The Contribution of Subthalamic Nucleus Deep Brain Stimulation to the Improvement in Motor Functions and Quality of Life. Mov Disord. 2022;37(2):291-301. doi:10.1002/mds.28952
Younger E, Ellis EG, Parsons N, et al. Mapping Essential Tremor to a Common Brain Network Using Functional Connectivity Analysis. Neurology. 2023;101(15):e1483-e1494. doi:10.1212/wnl.0000000000207701
Anthofer J, Steib K, Lange M, et al. Distance between Active Electrode Contacts and Dentatorubrothalamic Tract in Patients with Habituation of Stimulation Effect of Deep Brain Stimulation in Essential Tremor. J Neurol Surg Part A: Cent Eur Neurosurg. 2017;78(04):350-357. doi:10.1055/s-0036-1597894
Grimm F, Walcker M, Milosevic L, et al. Strong connectivity to the sensorimotor cortex predicts clinical effectiveness of thalamic deep brain stimulation in essential tremor. NeuroImage: Clin. 2025;45:103709. doi:10.1016/j.nicl.2024.103709
Hellwig B, Häußler S, Schelter B, et al. Tremor-correlated cortical activity in essential tremor. Lancet. 2001;357(9255):519-523. doi:10.1016/s0140-6736(00)04044-7
Friedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47(1):72-89. doi:10.1038/s41386-021-01132-0
Schaum M, Pinzuti E, Sebastian A, et al. Right inferior frontal gyrus implements motor inhibitory control via beta-band oscillations in humans. eLife. 2021;10:e61679. doi:10.7554/elife.61679
Ulla M, Thobois S, Llorca PM, et al. Contact dependent reproducible hypomania induced by deep brain stimulation in Parkinson’s disease: clinical, anatomical and functional imaging study. Journal of neurology, neurosurgery, and psychiatry. 2011;82(6):607-614. doi:10.1136/jnnp.2009.199323
Wang X, Wu Q, Egan L, et al. Anterior insular cortex plays a critical role in interoceptive attention. eLife. 2019;8:e42265. doi:10.7554/elife.42265

Pat. no.	Age range (yrs)	Gender	Disease onset (yr)	DBS duration (yrs)	Device	Frequency Pre-VAS (Hz)	Frequency Post-VAS (Hz)	Pulse width Pre-VAS (μs)	Pulse width Post-VAS (μs)	Amplitude Pre-VAS (mA)	Amplitude Post-VAS (mA)	Contact Pre-VAS	Contact Post-VAS	Ringlevel Pre-VAS	Ringlevel Post-VAS
LEFT ELECTRODE
1	60-65	m	2000	5	Boston	185	130	60	60	3.2	2.5	C (+); 4/7 (-)	C (+); 4 (-)	2/3	2
2	65-70	w	1999	11	Medtronic	130	-	60	60	5.5	-	10 (+); 9 (-)	-	2	-
3	70-75	m	2008	6	Boston	185	OFF	30	60	3.3	OFF	C (+); 6 (-)	OFF	3	OFF
4	75-80	m	1965	2	Abbott	180	130	30	60	4.0	1.0	C (+); 2 A/B (-)	C (+); 1 (-)	2	1
5	65-70	w	1995	11	Medtronic	180	130	60	60	7.0	1.5	C (+); 0 (-)	C (+); 0 (-)	1	1
6	60-65	w	2018	2	Boston	185	130	30	60	4.9	2.5	C (+); 1 (-)	C (+); 5 (-)	1	3
7	60-65	m	2003	1	Boston	159	130	40	60	6.5	1.0	5 (+); 4 (-)	C (+); 5 (-)	2	3
8	75-80	m	2008	5	Boston	149	130	30	60	5.0	2.0	C (+); 4/7 (-)	C (+); 4 (-)	2/3	2
9	80-85	m	1970	20	Medtronic	130	130	90	60	4.0	3.0	C (+); 0 (-)	C (+); 0 (-)	1	1
10	80-85	m	1995	6	Boston	198	130	30	60	4.4	2.0	C (+); 1/2 (-)	C (+); 2 (-)	1/2	2
11	75-80	m	2005	10	Boston	119	130	60	60	7.0	3.0	1 (+); 2/3 (-)	C (+); 8 (-)	2	4
12	70-75	w	1985	6	Boston	185	130	60	60	0.1	1.0	8 (+); 1 (-)	C (+); 7 (-)	1	3
13	65-70	w	1968	2	Boston	130	130	60	60	2.5	1.0	C (+); 2/3/4 (-)	C (+); 8 (-)	2	4
14	70-75	m	2003	15	Medtronic	130	130	60	60	2.9	3.0	C (+); 1 (-)	C (+); 1 (-)	2	2
15	80-85	w	1989	15	Medtronic	180	130	60	60	2.2	1.0	C (+); 2 (-)	C (+); 3 (-)	3	4
16	55-60	m	1970	5	Boston	130	130	60	60	3.8	1.5	C (+); 2/3/4 (-)	C (+); 3 (-)	2	2
RIGHT ELECTRODE
1	60-65	m	2000	5	Boston	185	130	50	60	10.0	3.0	6 (+); 3 (-)	C (+); 3 (-)	2	2
2	65-70	w	1999	11	Medtronic	130	-	60	-	7.5	-	1 (+); 2 (-)	-	3	-
3	70-75	m	2008	6	Boston	185	130	30	60	2.5	1.0	C (+); 8 (-)	C (+); 8 (-)	4	4
4	75-80	m	1965	2	Abbott	180	130	30	60	4.5	2.0	C (+); 11 A/B/C (-)	C (+); 11 C (-)	3	3
5	65-70	w	1995	11	Medtronic	180	130	60	60	6.6	3.0	C (+); 9 (-)	C (+); 9 (-)	2	2
6	60-65	w	2018	2	Boston	185	130	30	60	7.5	1.0	5 (+); 2 (-)	C (+); 5 (-)	2	3
7	60-65	m	2003	1	Boston	159	130	40	60	12.0	0.5	8/1 (+); 6 (-)	C (+); 4 (-)	2	2
8	75-80	m	2008	5	Boston	149	130	30	60	2.6	1.0	C (+); 8 (-)	C (+); 2 (-)	4	2
9	80-85	m	1970	20	Medtronic
10	80-85	m	1995	6	Boston	198	130	40	60	5.2	3.0	C (+); 1 (-)	C (+); 4 (-)	1	2
11	75-80	m	2005	10	Boston	119	130	60	60	8.5	3.0	1 (+); 2 (-)	C (+); 8 (-)	2	4
12	70-75	w	1985	6	Boston	185	130	30	60	5.0	2.0	C (+); 2/5 (-)	C (+); 8 (-)	2/3	4
13	65-70	w	1968	2	Boston	130	130	60	60	1.7	0.5	C (+); 2/3/4 (-)	C (+); 1 (-)	2	1
14	70-75	m	2003	15	Medtronic	130	130	60	60	3.3	3.0	C (+); 9 (-)	C (+); 9 (-)	2	2
15	80-85	w	1989	15	Medtronic	180	130	60	60	0.8	0.5	C (+); 9 (-)	C (+); 10 (-)	2	3
16	55-60	m	1970	5	Boston	130	130	60	60	3.7	3.0	C (+); 2/3/4 (-)	C (+); 4 (-)	2	2

The authors declare no competing interests.

GraphAbstract.jpeg
Graphical Abstract: To sample subjective patient rating by VAS, random stimulation parameters were presented to the study subjects (1). Next, corresponding VTAs were generated and paired with VAS values (2). These paired VAS/VTA data were aggregated voxel-wise to create a heatmap representing the “subjective sweet and sour spot” (3). Finally, connectomes were seeded from the identified sweet spots (4).

Patient Reported Feedback Suggests an Alternative Sweet Spot for DBS Programming in Essential Tremor

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Abstract

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INTRODUCTION

METHODS & MATERIALS

RESULTS

DISCUSSION

CONCLUSION

Declarations

References

Tables

Additional Declarations

Supplementary Files

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Version 1