TY - JOUR
AU - Sauerbier, Anna
AB - Abstract Deep brain stimulation of the subthalamic nucleus is an effective and established therapy for patients with advanced Parkinson’s disease improving quality of life, motor symptoms and non-motor symptoms. However, there is a considerable degree of interindividual variability for these outcomes, likely due to variability in electrode placement and stimulation settings. Here, we present probabilistic mapping data from a prospective, open-label, multicentre, international study to investigate the influence of the location of subthalamic nucleus deep brain stimulation on non-motor symptoms in patients with Parkinson’s disease. A total of 91 Parkinson’s disease patients undergoing bilateral deep brain stimulation of the subthalamic nucleus were included, and we investigated NMSScale, NMSQuestionnaire, Scales for Outcomes in Parkinson’s disease-motor examination, -activities of daily living, and -motor complications, and Parkinson’s disease Questionnaire-8 preoperatively and at 6-month follow-up after surgery. Leads were localized in standard space using the Lead-DBS toolbox and individual volumes of tissue activated were calculated based on clinical stimulation settings. Probabilistic stimulation maps and non-parametric permutation statistics were applied to identify voxels with significant above or below average improvement for each scale and analysed using the DISTAL atlas. All outcomes improved significantly at follow-up. Significant spatial distribution patterns of neurostimulation were observed for NMSScale total score and its mood/apathy and attention/memory domains. For both domains, voxels associated with below average improvement were mainly located dorsal to the subthalamic nucleus. In contrast, above average improvement for mood/apathy was observed in the ventral border region of the subthalamic nucleus and in its sensorimotor subregion and for attention/memory in the associative subregion. A trend was observed for NMSScale sleep domain showing voxels with above average improvement located ventral to the subthalamic nucleus. Our study provides evidence that the interindividual variability of mood/apathy, attention/memory, and sleep outcomes after subthalamic nucleus deep brain stimulation depends on the location of neurostimulation. This study highlights the importance of holistic assessments of motor and non-motor aspects of Parkinson’s disease to tailor surgical targeting and stimulation parameter settings to patients’ personal profiles. non-motor symptoms, deep brain stimulation, subthalamic nucleus, volume of activated tissue, volume of tissue activated Introduction Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective treatment option for patients with advanced Parkinson’s disease improving quality of life, motor symptoms, and non-motor symptoms (Krack et al., 2003; Deuschl et al., 2006; Dafsari et al., 2018d). However, a considerable degree of interindividual variability has been reported for these outcomes (Floden et al., 2014; Dafsari et al., 2018e). A previous study by our group provided evidence that the position of active DBS contacts significantly contributed to the interindividual variability of quality of life, motor and non-motor outcomes after STN-DBS in Parkinson’s disease (Dafsari et al., 2018a). However, this approach neglected differences in stimulation parameters and the spatial distribution of the volume of tissue activated (VTA). Studies investigating motor, neuro-psychological and neuro-psychiatric DBS outcomes took this limitation into account by creating probabilistic stimulation maps (Butson et al., 2011; Eisenstein et al., 2014; Akram et al., 2017; Gourisankar et al., 2018) or considering the overlap of patient-specific VTAs with either a predefined target volume (Frankemolle et al., 2010) or the sensorimotor, associative, and limbic subregions of the STN (Mosley et al., 2018). Some studies provided evidence for a greater motor improvement in the dorsolateral STN (Eisenstein et al., 2014; Akram et al., 2017; Gourisankar et al., 2018) or zona incerta (Butson et al., 2011; Eisenstein et al., 2014), whereas neuropsychological impairment and the likelihood of neuropsychiatric side-effects could be reduced by focusing the stimulation towards the dorsal border (Frankemolle et al., 2010) or the motor subregion of the STN (Mosley et al., 2018). In contrast, other studies found no distinctive spatial patterns influencing motor (Eisenstein et al., 2014) or neuropsychiatric outcomes (Gourisankar et al., 2018). The aim of this study was to investigate whether the location of electrical stimulation influences the outcome on a wide range of non-motor symptoms in a prospective, multicentre cohort. Materials and methods Design and ethical approval We prospectively recruited patients in an international, multicentre, open-label study (German Clinical Trials Register 0006375, DBS arm of the NILS study) and retrospectively analysed imaging data from the centres Cologne, London and Manchester. The study was carried out in accordance with the Declaration of Helsinki and approved by the local ethics committees (Cologne, 0012-145; UK: NRES South East London REC3, 10/H0808/141, #10084) (Dafsari et al., 2019a). All patients gave written informed consent prior to study participation. Patients and clinical assessment Parkinson’s disease diagnosis was based on the UK Brain Bank criteria and patients were selected for bilateral STN-DBS according to guidelines of the International Parkinson and Movement Disorders Society (Lang et al., 2006). Patients were excluded from the analysis when 6-month follow-up or postoperative imaging were missing. Clinical assessments were performed at preoperative baseline in the ON medication state and at 6-month follow-up after surgery in ON medication and on stimulation states, using the following scales: Non-motor symptoms were assessed with two scales: (a) The clinician-rated Non-motor Symptom Scale (NMSS) was used to investigate non-motor symptoms in nine different domains (cardiovascular, sleep/fatigue, mood/apathy, perceptual problems/hallucinations, attention/memory, gastrointestinal, urinary, sexual function, and miscellaneous symptoms including unexplained pain, olfaction, weight changes, and excessive sweating) over the last four weeks (Chaudhuri et al., 2007); and (b) The patient-based Non-motor Symptoms Questionnaire (NMSQ) (Chaudhuri et al., 2006). Quality of life was assessed with the 8-item Parkinson’s disease Questionnaire (PDQ-8), reported as Summary Index (SI) (Martinez-Martin et al., 2011; Dafsari et al., 2018c). The PDQ-8 is an instrument recommended by the Movement Disorders Society Scales Committee for quality of life assessments in Parkinson’s disease (Martinez-Martin et al., 2011) and has been used in DBS studies before (Storch et al., 2013; Dafsari et al., 2018b, c). Motor aspects, such as motor examination, activities of daily living and motor complications were surveyed with corresponding parts of the Scales for Outcomes in Parkinson’s disease (SCOPA-A, -B, -C). The SCOPA is a well-established, validated and time-efficient abbreviated version of the Unified Parkinson’s Disease Rating Scale from which it was derived and the two scales highly correlate (Marinus et al., 2004; Martinez-Martin et al., 2005; Dafsari et al., 2018a). The levodopa equivalent daily dose (LEDD) was calculated following Tomlinson et al. (2010). Statistical analysis of clinical outcomes Scores are reported as mean ± standard deviation (SD). To detect significant changes from baseline, either a paired t-test or a Wilcoxon signed-rank test was used, depending on the normality of data which was examined using the Shapiro-Wilk test. P-values were Bonferroni corrected for multiple comparisons (seven scales) and reported as P-values corrected to the significance threshold P < 0.05. Additionally, effect sizes (Martinez-Martin and Kurtis, 2012), change scores computed as Testbaseline − Testfollow-up and relative change scores were calculated. Furthermore, we explored the relationship between outcome parameters with Spearman correlations or Pearson’s correlations, if parametric test criteria were fulfilled. To investigate the relationship between changes of quality of life and other clinical outcome parameters, correlations were computed between change scores for PDQ-8 SI and respective scales. To test for confounding effects of medication requirements or motor impairment on non-motor outcomes, correlations were calculated between SCOPA-A or relative LEDD changes and non-motor symptoms (NMSQ, NMSS total and domain scores). DBS lead localization DBS leads were localized with the Lead-DBS toolbox (www.lead-dbs.org) (Horn et al., 2019). The detailed processing pipeline has been described elsewhere (Horn et al., 2017). In brief, postoperative images were linearly coregistered to preoperative MRI (Cologne: 3 T Philips Ingenia MRI system, Philips Medical Systems; London: 1.5 T General Electric Signa HDx MRI system, General Electric Healthcare; Manchester: 1.5 T Philips Intera MRI system) using ANTs (advanced normalization tools, http://stnava.github.io/ANTs/, n = 83) (Avants et al., 2008) or BRAINSFit (https://www.nitrc.org/projects/multimodereg/, n = 8) if results of ANTs coregistration were unsatisfactory. Then images were nonlinearly normalized into standard space (ICBM 2009b NLIN, Asym) using advanced normalization tools based on the preoperative MRI and the ‘effective (low variance)’ strategy as implemented in Lead-DBS. This procedure used preoperative MRI (T1 and T2) and was recently evaluated to register the STN to a template atlas as precisely as human experts (Ewert et al., 2019). DBS leads were automatically pre-reconstructed using the PaCER-algorithm (Husch et al., 2018) (postoperative CT, n = 83) or TRAC/CORE algorithm (Horn et al., 2019) (postoperative MRI, n = 8) and manually refined and corrected for brain shift as implemented in Lead-DBS. Orientation of directional leads was determined using the DiODe algorithm (Sitz et al., 2017; Horn et al., 2019). Volume of tissue activated estimation Volume of tissue activated estimation was conducted using a finite element method as introduced by Horn et al. (2017) based on clinically optimized stimulation parameters. The spread of the electric field was estimated for homogenous tissue with a conductivity of σ = 0.1 S/m (Astrom et al., 2015). The VTA was thresholded at the electrical field isolevel of 0.19 V/mm (Astrom et al., 2015) to reflect clinical stimulation results of Mädler and Coenen (2012), adapted depending on the respective pulse width (Dembek et al., 2017). Probabilistic stimulation maps A probabilistic stimulation map was generated for each investigated clinical scale. First, all VTAs were pooled to the right hemisphere by non-linearly flipping left hemispheric VTAs as implemented in Lead-DBS (Horn et al., 2019). The creation of probabilistic stimulation maps followed Dembek et al. (2017) and Eisenstein et al. (2014). First, the number of VTAs overlapping in each voxel was counted to create the N-image. Voxels in which fewer than 10 VTAs overlapped were discarded to ensure the validity of the subsequent voxel-wise statistical testing. To create a mean effect image, each individual change score was assigned to each voxel of its respective VTA. Subsequently, a mean effect image was generated by calculating the mean change score for each voxel. To increase visual comparability between different scales, the mean effect image was z-transformed. Voxel-wise statistical testing Voxel-wise statistical tests were performed for each voxel of the respective mean effect image to create the respective p-image. Two-sided Wilcoxon signed-rank tests were used to detect significant (P < 0.05) deviations of each voxel’s mean change score from average, calculated as the mean of the respective change scores of all patients. Voxels with a mean change score significantly below average were reported as negative voxels, whereas those above average were reported as positive voxels. Type 1 errors were corrected by using a non-parametric permutation algorithm and summary statistic as proposed by Eisenstein et al. with n = 1000 permutations of the original dataset (Eisenstein et al., 2014; Dembek et al., 2017). A probabilistic stimulation map was accepted as valid if the sum of the negative decadic logarithm of the significant P-values ranked higher than the sum in 950 of the 1000 permuted datasets (Dembek et al., 2017) (equals a significance threshold of P < 0.05). Quantitative neuroanatomical analysis All significant probabilistic stimulation maps were superimposed onto the DISTAL atlas (Ewert et al., 2018) which features a tripartite STN model (Accolla et al., 2014). Subsequently, the local distribution of the significant voxels of each probabilistic stimulation map in relation to the atlas STN was determined. Figure 1 shows a schematic overview of the analysis. Figure 1 Open in new tabDownload slide Analysis workflow. Postoperative imaging was co-registered to preoperative MRI (A) and normalized to standard space following lead localization (B). VTAs were calculated (C) and assigned to the respective clinical outcome to create mean effect images (D). Finally, voxel-wise statistical testing was applied to create P-images, revealing voxels deviating significantly from average (E). Technical realization All computational work was carried out with MATLAB 2016b (The MathWorks Inc., Natick, Massachusetts, USA) on a DELL Precision T7910 Workstation with two Intel Xeon E5-2670 CPUs and 64 GB RAM (Dell Inc.). Data availability The data included in this study are available on request to the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of the participants. Results Patient characteristics and clinical outcomes We included 91 patients (53 male) from our database (Fig. 2) aged 62.7 (±7.9) years at intervention with 9.9 (±4.6) years disease duration at intervention and a median Hoehn and Yahr score of 2.5 (interquartile range: 2.0–3.0). PDQ-8 SI, SCOPA-A, -B, -C, NMSS total score (NMSS-T), and NMSQ, improved significantly and LEDD was significantly reduced at follow-up. An explorative analysis of NMSS domains showed a statistically significant improvement of sleep/fatigue, perceptual problems/hallucinations, urinary, and miscellaneous symptoms (Table 1). Effect sizes were moderate (0.50–0.79) for PDQ-8 SI, SCOPA-A, -B, NMSQ, and NMSS-T and large (>0.80) for SCOPA-C, and LEDD. NMSS domain effect sizes were small (0.20–0.49) for cardiovascular symptoms, perceptual problems/hallucinations, gastrointestinal, and urinary symptoms, and moderate (0.50–0.79) for miscellaneous symptoms. Table 1 Clinical outcomes Scale [range]a . n . Baseline . Follow-up . Relative change, % . Effect sizeb . P . Mean . SD . Mean . SD . PDQ-8 SI* [0–100] 83 32.94 15.46 24.29 14.97 26.26 0.62 <0.001 SCOPA-A* [0–42] 85 11.26 5.78 7.90 4.37 29.82 0.70 <0.001 SCOPA-B* [0–21] 90 7.53 3.25 5.80 3.34 23.01 0.51 <0.001 SCOPA-C* [0–12] 90 5.21 2.98 2.80 2.64 46.27 0.92 <0.001 LEDD* [mg] 86 1060.97 489.26 611.07 354.37 42.40 0.85 <0.001 NMSQ* [0–30] 82 10.39 4.61 8.00 3.75 23.00 0.54 <0.001 NMSS-T* [0–360] 90 63.59 34.44 42.73 28.76 32.80 0.62 <0.001     1 Cardiovascular [0–24] 2.16 3.44 1.27 2.51 41.24 0.23 0.075     2 Sleep/fatigue* [0–48] 15.91 9.94 9.58 7.50 39.80 0.63 <0.001     3 Mood/apathy [0–72] 6.53 9.16 6.06 10.82 7.31 0.04 0.298     4 Perceptual problems/hallucinations* [0–36] 1.33 3.47 0.43 1.27 67.50 0.30 0.005     5 Attention/memory [0–36] 4.76 5.90 4.50 5.78 5.37 0.04 0.692     6 Gastrointestinal tract [0–36] 6.62 7.10 5.10 6.47 22.99 0.23 0.060     7 Urinary* [0–36] 10.72 9.53 6.84 7.06 36.17 0.42 <0.001     8 Sexual Function [0–24] 2.86 4.95 2.67 5.02 6.61 0.05 0.486     9 Miscellaneous* [0–48] 12.70 9.44 6.67 6.35 47.51 0.66 <0.001 Scale [range]a . n . Baseline . Follow-up . Relative change, % . Effect sizeb . P . Mean . SD . Mean . SD . PDQ-8 SI* [0–100] 83 32.94 15.46 24.29 14.97 26.26 0.62 <0.001 SCOPA-A* [0–42] 85 11.26 5.78 7.90 4.37 29.82 0.70 <0.001 SCOPA-B* [0–21] 90 7.53 3.25 5.80 3.34 23.01 0.51 <0.001 SCOPA-C* [0–12] 90 5.21 2.98 2.80 2.64 46.27 0.92 <0.001 LEDD* [mg] 86 1060.97 489.26 611.07 354.37 42.40 0.85 <0.001 NMSQ* [0–30] 82 10.39 4.61 8.00 3.75 23.00 0.54 <0.001 NMSS-T* [0–360] 90 63.59 34.44 42.73 28.76 32.80 0.62 <0.001     1 Cardiovascular [0–24] 2.16 3.44 1.27 2.51 41.24 0.23 0.075     2 Sleep/fatigue* [0–48] 15.91 9.94 9.58 7.50 39.80 0.63 <0.001     3 Mood/apathy [0–72] 6.53 9.16 6.06 10.82 7.31 0.04 0.298     4 Perceptual problems/hallucinations* [0–36] 1.33 3.47 0.43 1.27 67.50 0.30 0.005     5 Attention/memory [0–36] 4.76 5.90 4.50 5.78 5.37 0.04 0.692     6 Gastrointestinal tract [0–36] 6.62 7.10 5.10 6.47 22.99 0.23 0.060     7 Urinary* [0–36] 10.72 9.53 6.84 7.06 36.17 0.42 <0.001     8 Sexual Function [0–24] 2.86 4.95 2.67 5.02 6.61 0.05 0.486     9 Miscellaneous* [0–48] 12.70 9.44 6.67 6.35 47.51 0.66 <0.001 * Significant improvement at follow-up. a Higher scores reflect more impairment for all scales. b Effect size: small (0.20–0.49), moderate (0.50–0.79), large (>0.80). Open in new tab Table 1 Clinical outcomes Scale [range]a . n . Baseline . Follow-up . Relative change, % . Effect sizeb . P . Mean . SD . Mean . SD . PDQ-8 SI* [0–100] 83 32.94 15.46 24.29 14.97 26.26 0.62 <0.001 SCOPA-A* [0–42] 85 11.26 5.78 7.90 4.37 29.82 0.70 <0.001 SCOPA-B* [0–21] 90 7.53 3.25 5.80 3.34 23.01 0.51 <0.001 SCOPA-C* [0–12] 90 5.21 2.98 2.80 2.64 46.27 0.92 <0.001 LEDD* [mg] 86 1060.97 489.26 611.07 354.37 42.40 0.85 <0.001 NMSQ* [0–30] 82 10.39 4.61 8.00 3.75 23.00 0.54 <0.001 NMSS-T* [0–360] 90 63.59 34.44 42.73 28.76 32.80 0.62 <0.001     1 Cardiovascular [0–24] 2.16 3.44 1.27 2.51 41.24 0.23 0.075     2 Sleep/fatigue* [0–48] 15.91 9.94 9.58 7.50 39.80 0.63 <0.001     3 Mood/apathy [0–72] 6.53 9.16 6.06 10.82 7.31 0.04 0.298     4 Perceptual problems/hallucinations* [0–36] 1.33 3.47 0.43 1.27 67.50 0.30 0.005     5 Attention/memory [0–36] 4.76 5.90 4.50 5.78 5.37 0.04 0.692     6 Gastrointestinal tract [0–36] 6.62 7.10 5.10 6.47 22.99 0.23 0.060     7 Urinary* [0–36] 10.72 9.53 6.84 7.06 36.17 0.42 <0.001     8 Sexual Function [0–24] 2.86 4.95 2.67 5.02 6.61 0.05 0.486     9 Miscellaneous* [0–48] 12.70 9.44 6.67 6.35 47.51 0.66 <0.001 Scale [range]a . n . Baseline . Follow-up . Relative change, % . Effect sizeb . P . Mean . SD . Mean . SD . PDQ-8 SI* [0–100] 83 32.94 15.46 24.29 14.97 26.26 0.62 <0.001 SCOPA-A* [0–42] 85 11.26 5.78 7.90 4.37 29.82 0.70 <0.001 SCOPA-B* [0–21] 90 7.53 3.25 5.80 3.34 23.01 0.51 <0.001 SCOPA-C* [0–12] 90 5.21 2.98 2.80 2.64 46.27 0.92 <0.001 LEDD* [mg] 86 1060.97 489.26 611.07 354.37 42.40 0.85 <0.001 NMSQ* [0–30] 82 10.39 4.61 8.00 3.75 23.00 0.54 <0.001 NMSS-T* [0–360] 90 63.59 34.44 42.73 28.76 32.80 0.62 <0.001     1 Cardiovascular [0–24] 2.16 3.44 1.27 2.51 41.24 0.23 0.075     2 Sleep/fatigue* [0–48] 15.91 9.94 9.58 7.50 39.80 0.63 <0.001     3 Mood/apathy [0–72] 6.53 9.16 6.06 10.82 7.31 0.04 0.298     4 Perceptual problems/hallucinations* [0–36] 1.33 3.47 0.43 1.27 67.50 0.30 0.005     5 Attention/memory [0–36] 4.76 5.90 4.50 5.78 5.37 0.04 0.692     6 Gastrointestinal tract [0–36] 6.62 7.10 5.10 6.47 22.99 0.23 0.060     7 Urinary* [0–36] 10.72 9.53 6.84 7.06 36.17 0.42 <0.001     8 Sexual Function [0–24] 2.86 4.95 2.67 5.02 6.61 0.05 0.486     9 Miscellaneous* [0–48] 12.70 9.44 6.67 6.35 47.51 0.66 <0.001 * Significant improvement at follow-up. a Higher scores reflect more impairment for all scales. b Effect size: small (0.20–0.49), moderate (0.50–0.79), large (>0.80). Open in new tab Figure 2 Open in new tabDownload slide Patient selection. A total number of 145 patient undergoing STN-DBS were screened for the present study. In the final analysis, 91 patients were included. Explorative correlation analysis between PDQ-8 SI and clinical outcome parameter change scores were significant for SCOPA-B [r(81) = 0.38; P < 0.001], NMSQ [r(77) = 0.31; P < 0.001], NMSS-T [r(81) = 0.29; P = 0.008], NMSS sleep/fatigue [r(81) = 0.22; P = 0.043], NMSS mood/apathy [r(81) = 0.36; P < 0.001] and NMSS attention/memory [r(81) = 0.29; P = 0.007]. Other correlations for quality of life were not significant (P > 0.05). Correlations between relative LEDD or SCOPA-A changes and non-motor symptoms (NMSQ, NMSS total and domain scores) were not significant (P > 0.05). DBS localization and N-image The lead location of all patients (148 Medtronic 3389, Medtronic; six VerciseTM, 28 CartesiaTM, Boston Scientific) and the mean position of all leads pooled on the right hemisphere are illustrated in Fig. 3. Mean position of ventral contacts in relation to the mid commissural point was x = 11.67 mm (±1.47 mm), y = −3.60 mm (±2.35 mm), z = −4.75mm (±1.41 mm). The N-image revealed that most VTAs overlapped in the dorsolateral STN (Fig. 3B and C). The overall volume included in our voxel-based statistical analysis covered 81.3% of the STN (sensorimotor: 100%; associative: 90.7%, limbic: 45.3%). Figure 3 Open in new tabDownload slide Lead location and N-image. (A) shows the frontal view of bilateral lead positions in standard stereotactic space projected on the STN as implemented in the DISTAL atlas (Ewert et al., 2018). The lower row illustrates the N-map in frontal (B) and sagittal view (C). The number of VTAs overlapping in each voxel is colour-coded by grey (n < 10), green (n < 50), yellow (n < 90) and red (n ≥ 90). The lead’s outline illustrates the pooled mean lead position of all patients. Image orientation is indicated by 3D head model. Specific functional subregions of the STN are highlighted (sensorimotor STN in copper, associative STN in blue and limbic STN in yellow). 3D headmodel (‘Head’; https://www.thingiverse.com/thing:979818) by figure (https://www.thingiverse.com/figure/about) is licensed under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/); taken on 07/15/2018; increased transparency from the original in anterior view on the right side. Voxel-wise statistics and stimulation location Probabilistic stimulation maps for NMSS-T (rank: 991/1000; P = 0.009), NMSS mood/apathy (rank: 996/1000; P = 0.004), attention/memory (rank: 967/ 1000; P = 0.043) and relative LEDD reduction (rank: 983/ 1000, P = 0.017) were statistically valid after permutation analysis. A trend was observed for NMSS sleep/fatigue (rank: 904/1000; P = 0.091), mainly driven by NMSS item 3 ‘unintentional sleep’ (rank: 982/1000; P = 0.018) and NMSS item 5 ‘difficulties falling or staying asleep’ (rank: 1000/1000; P < 0.001), whereas NMSS item 4 ‘fatigue’ (rank: 204/1000; P = 0.769) and NMSS item 6 ‘restless legs syndrome’ (rank: 890/1000; P = 0.11) were not significant. Probabilistic stimulation maps for all other investigated scales and other NMSS domains were not significant. Valid probabilistic stimulation maps showed a dorsoventral gradient in the respective mean effect image with more improvement towards the ventral border of the STN for NMSS and more improvement towards the dorsolateral border of the STN for LEDD reduction (Fig. 4A). Voxel-wise statistical testing revealed the local distribution of voxels significantly deviating from the respective mean change score (Fig. 4B). For all significant P-images, >90% of the VTAs contributed data to the significant voxels (NMSS-T: 172/180; NMSS sleep/fatigue: 168/180; NMSS mood/apathy 177/180; NMSS attention/memory: 176/180; LEDD: 166/172). The quantitative analysis of the local distribution of significant voxels is shown in Table 2. For NMSS-T and NMSS mood/apathy most of the positive voxels were in the ventral border region of the STN. Positive voxels inside the STN were mainly located in the sensorimotor subregion. For NMSS attention/memory most positive voxels were located within the STN, mainly in the associative subregion. In contrast to the location of positive voxels, nearly all negative voxels of these probabilistic stimulation maps were located outside, dorsal to the STN. Compared to these subdomains NMSS sleep/fatigue showed a different distribution. Inside the STN were mainly negative voxels, whereas close to all positive voxels were located outside, ventral to the STN. For the relative LEDD reduction positive voxels were in the dorsolateral border region of the STN. Positive voxels inside the STN were only in the sensorimotor subregion. Negative voxels were located more anterior and outside, dorsal to the STN. Table 2 Local distribution of significant voxels Region . NMSS-T* . Mood/apathy* . Attention/memory* . Sleep/fatigue† . LEDD* . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Voxels, n 242 505 602 243 53 441 68 418 184 724 Inside STN, % 15.7 3.4 28.6 1.2 64.5 0.5 2.9 24.4 14.1 1.7     Sensorimotor, % 14.1 0.4 23.1 0.0 0.0 0.5 2.9 9.3 14.1 0.3     Associative, % 1.2 3.0 1.2 1.2 52.8 0.0 0.0 0.5 0.0 0.0     Limbic, % 0.4 0.0 4.3 0.0 11.3 0.0 0.0 14.6 0.0 1.4 Outside STN, % 84.3 96.6 71.4 98.8 35.9 99.6 97.1 75.6 85.9 98.3 Region . NMSS-T* . Mood/apathy* . Attention/memory* . Sleep/fatigue† . LEDD* . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Voxels, n 242 505 602 243 53 441 68 418 184 724 Inside STN, % 15.7 3.4 28.6 1.2 64.5 0.5 2.9 24.4 14.1 1.7     Sensorimotor, % 14.1 0.4 23.1 0.0 0.0 0.5 2.9 9.3 14.1 0.3     Associative, % 1.2 3.0 1.2 1.2 52.8 0.0 0.0 0.5 0.0 0.0     Limbic, % 0.4 0.0 4.3 0.0 11.3 0.0 0.0 14.6 0.0 1.4 Outside STN, % 84.3 96.6 71.4 98.8 35.9 99.6 97.1 75.6 85.9 98.3 Values indicate the percentage of voxels with a mean change score significantly above (positive) or below (negative) average in relation to the STN. Neg = negative voxels; Pos = positive voxels. * Significant after non-parametric permutation analysis. † Trend after non-parametric permutation analysis. Open in new tab Table 2 Local distribution of significant voxels Region . NMSS-T* . Mood/apathy* . Attention/memory* . Sleep/fatigue† . LEDD* . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Voxels, n 242 505 602 243 53 441 68 418 184 724 Inside STN, % 15.7 3.4 28.6 1.2 64.5 0.5 2.9 24.4 14.1 1.7     Sensorimotor, % 14.1 0.4 23.1 0.0 0.0 0.5 2.9 9.3 14.1 0.3     Associative, % 1.2 3.0 1.2 1.2 52.8 0.0 0.0 0.5 0.0 0.0     Limbic, % 0.4 0.0 4.3 0.0 11.3 0.0 0.0 14.6 0.0 1.4 Outside STN, % 84.3 96.6 71.4 98.8 35.9 99.6 97.1 75.6 85.9 98.3 Region . NMSS-T* . Mood/apathy* . Attention/memory* . Sleep/fatigue† . LEDD* . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Pos . Neg . Voxels, n 242 505 602 243 53 441 68 418 184 724 Inside STN, % 15.7 3.4 28.6 1.2 64.5 0.5 2.9 24.4 14.1 1.7     Sensorimotor, % 14.1 0.4 23.1 0.0 0.0 0.5 2.9 9.3 14.1 0.3     Associative, % 1.2 3.0 1.2 1.2 52.8 0.0 0.0 0.5 0.0 0.0     Limbic, % 0.4 0.0 4.3 0.0 11.3 0.0 0.0 14.6 0.0 1.4 Outside STN, % 84.3 96.6 71.4 98.8 35.9 99.6 97.1 75.6 85.9 98.3 Values indicate the percentage of voxels with a mean change score significantly above (positive) or below (negative) average in relation to the STN. Neg = negative voxels; Pos = positive voxels. * Significant after non-parametric permutation analysis. † Trend after non-parametric permutation analysis. Open in new tab Figure 4 Open in new tabDownload slide Mean effect and P-images. (A) Coronal slices of the respective mean effect image, colour-coded by the degree of deviation from the z-transformed mean change score (red = 1 SD below average; green = 1 SD above average) at 10 mm, 13 mm and 16 mm posterior to the anterior commissure (AC; slice position in relation to STN indicated in the first column). These are superimposed by the T1 standard space template together with the DISTAL atlas (Ewert et al., 2018). (B) The respective P-image. Voxels with a total change score significantly above (green) or below (red) average are shown in relation to the STN in sagittal and frontal view (top and bottom row). The image orientation is indicated by 3D head model. Specific functional subregions of the STN are highlighted (sensorimotor STN in copper, associative STN in blue and limbic STN in yellow). AC = anterior commissure; D = dorsal; GPe = globus pallidus externus; GPi = globus pallidus internus; L = left; R = right; RN = red nucleus; Th = thalamus; V = ventral. *Significant after non-parametric permutation analysis. †Trend after non-parametric permutation analysis. 3D headmodel (‘Head’; https://www.thingiverse.com/thing:979818) by figure (https://www.thingiverse.com/figure/about) is licensed under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/); taken on 07/15/2018; increased transparency from the original in anterior view on the right side. Discussion This study demonstrates in a large multicentre cohort that, while most non-motor symptoms generally improve after subthalamic DBS, the improvement in mood/apathy, attention/memory and sleep is dependent on the exact location of electrical stimulation. Moreover, our results provide evidence that there is no general non-motor hotspot but a specific location dependency for specific non-motor symptoms. While mood/apathy improved more with more ventral neurostimulation and in the sensorimotor subregion, attention/memory improved more in the associative subregion, and beneficial effects on sleep were observed for neurostimulation ventral to the STN. The importance of these findings is underlined by the fact that only these three non-motor subdomains were significantly correlated with quality of life outcome after subthalamic DBS. Influence of the stimulation location on non-motor outcomes In line with previous studies, relative reductions and effect sizes indicated considerable improvements of quality of life, motor and non-motor outcomes, and daily medication requirements after STN-DBS (Krack et al., 2003; Deuschl et al., 2006, 2018d, 2019b; Schuepbach et al., 2019). However, only the changes in the NMSS subdomains mood/apathy, attention/memory and sleep/fatigue were found to depend on the location of stimulation in STN subregions and in the subthalamic area. Regarding mood/apathy, the finding that a more ventral stimulation might be associated with beneficial effects is in accordance with previous studies only investigating the coordinates of the electrode position (York et al., 2009; Dafsari et al., 2018a). Additionally, there are reports of hypomania/mania associated with a stimulation in or near the ventromedial STN (Chopra et al., 2012), which might be interpreted as side-effect, due to overstimulation in this subregion. Eisenstein et al. (2014) reported mood and anxiety improvements to be strongly related to DBS sites within the left dorsal STN. Following the authors, the anteroventral and ventromedial STN were not represented in their sample of stimulation settings. Therefore, our study adds the finding that most of the more beneficial voxels for mood/apathy inside the STN were located in the sensorimotor STN. This subregion is also part of the posterior dorsal STN and, following the DISTAL atlas, extends to the posterior-ventral STN border (Ewert et al., 2018). In contrast, another study by the same group reported no alteration of the short-term effects on mood and cognition through the electrode site, although ventral STN stimulation showed a better outcome for anxiety (Gourisankar et al., 2018). As discussed by the authors, this might be due to the fact that (i) patients were tested without optimized DBS settings; and (ii) long-term effects on mood and cognitive function were not taken into account in the study design. While a stimulation of ventral non-motor STN subregions may result in beneficial effects on mood, early case reports provided evidence for an induction of acute severe depression by substantia nigra stimulation (Bejjani et al., 1999; Blomstedt et al., 2008). Regarding attention/memory, our study provides evidence for beneficial effects of stimulation within the STN, especially the associative subregion. This finding is in line with previous studies, reporting a beneficial effect on cognition when focusing the stimulation to the dorsal part of the STN (Frankemolle et al., 2010; Eisenstein et al., 2014), overlapping the associative STN as defined by the DISTAL atlas (Ewert et al., 2018). In accordance with a recent study by our group, showing a more ventral active contact’s location to be associated with improvement of sleep (Dafsari et al., 2018a), a stimulation location ventral to the STN was beneficial for the sleep items of the NMSS sleep/fatigue domain. In contrast a study by Baumann-Vogel et al. (2017), including 42 patients, observed more improvement of sleep after STN-DBS in patients with Parkinson’s disease for a bigger distance of the left hemispheric lowest active contact to the ventral STN margin. However, in this study differences in stimulation parameters were not taken in account. Neurophysiological considerations Non-motor symptoms are an agglomeration of symptoms defined by exclusion and result from a wide-range of pathomechanisms (Kurtis et al., 2017; Dafsari et al., 2018d). Neurodegenerative processes linked to specific neurotransmitter systems have been associated with non-motor symptoms, for example the dopaminergic system with apathy and depression, the cholinergic system with cognitive decline, and serotonergic system with anxiety (Qamar et al., 2017). Furthermore, neurodegenerative grey and white matter changes have been identified in Parkinson’s disease patients with depressive symptoms (van Mierlo et al., 2015), cognitive impairment (Hall and Lewis, 2019), and visual hallucinations (Lenka et al., 2015). If neurodegeneration causes non-motor symptoms, however, the question arises, which mechanisms of action (location-specific and general) may mediate the observed beneficial non-motor effects of STN-DBS. Location-specific mechanisms Direct effects of neurostimulation in the target region Recent electrophysiological studies provide evidence that a direct stimulation of the STN has profound effects on its function (Alkemade et al., 2015). For example, the observed beneficial effects of neurostimulation in ventral parts of the STN on mood are in line, e.g. with a study by Buot et al. (2103) who reported that the ventral part of the STN processes the emotional valence of stimuli independently of the motor context, a study by Gourisankar et al. (2018) who reported that neurostimulation in the ventral STN results in better outcome of anxiety than in the dorsal STN (Gourisankar et al., 2018), and with the concept of the functional tripartition of the STN with a limbic part located in ventral subregions (Krack et al., 2010). Spread of current to neighbouring structures When stimulating in the STN, there may occur spread of current to anatomical structures in proximity to the STN, which could be beneficial for several non-motor symptoms. For example, the observed beneficial effects of neurostimulation ventral to the STN on sleep has previously been associated with a stimulation of the pedunculopontine nucleus (Romigi et al., 2008; Stefani et al., 2013), which is located ventral to the STN within ∼5 mm and with even closer projections (Moreau et al., 2008). Furthermore, regarding mood, the spatial distribution of more beneficial effects observed in our study is supported by previous reports of mood improvements facilitated by a spread of neurostimulation to the medial forebrain bundle, located near the medial STN (Coenen et al., 2009, 2018). Network effects As the STN forms part of the basal ganglia and integrates projections from motor, associative and limbic basal ganglia-thalamo-cortical loops (Krack et al., 2010; Castrioto et al., 2014; Accolla et al., 2016; Eisinger et al., 2018), also network effects of STN-DBS have to be considered. For example, regarding cognition, Campbell et al. (2008) showed that variability in cognitive performance after STN-DBS is correlated to STN-DBS induced cortical blood flow changes in the dorsolateral prefrontal cortex and the anterior cingulate gyrus. These cortical regions are connected to the associative part of the STN and support the observed cognitive effects in the present study (Krack et al., 2010; Accolla et al., 2016). General mechanisms Sensory gating Several studies reported an improvement of multimodal sensory information processing via STN-DBS, resulting, e.g. in an improvement of bladder control (Herzog et al., 2008) and the processing of auditory stimuli (Gulberti et al., 2015). Importantly, this improvement of sensory gating of specific sensory information, such as auditory stimuli, was not observed for dopaminergic medication and, therefore, is specific to neurostimulation (Gulberti et al., 2015). Impaired sensory gating has also been observed in patients with depression (Wang et al., 2009) and there is evidence for a correlation between sensory gating and cognitive functions in patients with Parkinson’s disease. Further studies are needed to investigate the role of sensory gating in non-motor symptoms improvement after STN-DBS. Effects of neurostimulation on multiple neurotransmitter systems Non-motor symptoms are influenced by multi-neurotransmitter systems including central dopaminergic, cholinergic, noradrenergic, and serotonergic systems (Qamar et al., 2017). A modulation of these neurotransmitter systems by STN-DBS seems feasible considering, e.g. the indirect connection of the STN to the serotonergic dorsal raphe nucleus via the lateral habenula, and the projections to the STN originating from the noradrenergic locus coeruleus (Jakobs et al., 2019). Animal studies provided evidence for these hypothesized serotonin and noradrenalin-modulating effects of DBS, which might be related to depressive symptoms in patients with Parkinson’s disease (Tan et al., 2011; Navailles and De Deurwaerdere, 2012; Torres-Sanchez et al., 2018). Reduction of dopaminergic medication Closely related to the previous point, the postoperative reduction in dopaminergic medication below patient-specific thresholds may influence non-motor outcomes, such as apathy (Lhommee et al., 2012), and side-effects of dopaminergic treatment, e.g. gastrointestinal symptoms, daytime sleepiness, and hallucinations (Chaudhuri and Schapira, 2009). However, no significant correlation between LEDD and non-motor symptoms improvement was found, which is in line with previous studies (Dafsari et al., 2018a,d), and, importantly, no spatial overlap was observed between significant voxels for LEDD reduction and non-motor symptoms improvement in the present study. LEDD reduction is closely connected to motor outcomes, as it is usually higher for patients with particularly good effects on motor symptoms. In patients with less beneficial effects of STN-DBS on motor symptoms, in real-life scenarios, LEDD is typically maintained at rather high levels to achieve the same level of motor improvement. Therefore, SCOPA as outcome parameter for motor improvement may be influenced by the confounding effect of LEDD, which in turn may explain why LEDD was a more sensitive parameter for interindividual variability than SCOPA in the present study. Taking together the above hypothesized mechanisms of action of STN-DBS on non-motor symptoms and our finding that specific non-motor outcomes can be influenced by the location of neurostimulation, we postulate that not just neurodegenerative processes underlie non-motor symptoms in Parkinson’s disease, but also symptom-specific neural network changes, which are amenable to STN-DBS. Methodological considerations and limitations This study expands the findings of previous studies by investigating real-life 6-month non-motor effects of STN-DBS instead of immediate, task-specific outcomes (Frankemolle et al., 2010; Eisenstein et al., 2014; Gourisankar et al., 2018). The importance of long-term non-motor outcomes regarding quality of life after STN-DBS has recently been highlighted (Dafsari et al., 2018d) and longer follow-up periods beyond the immediate and short-term phase are needed. One should consider the role of possible confounding factors, such as placebo effects and an improvement of motor impairment, on postoperative non-motor outcome changes. However, relative contribution of these factors was not significant in the present study as (i) no significant correlations were observed between changes of non-motor symptoms and motor outcomes/medication requirements; and (ii) the spatial distributions of significant voxels differed for non-motor symptoms and motor outcomes/medication requirements. To the best of our knowledge this cohort including 91 patients was one of the largest in mapping studies. In contrast to previous studies, which only investigated electrode locations (York et al., 2009; Eisenstein et al., 2014; Dafsari et al., 2018a; Gourisankar et al., 2018), the present study included patient-specific stimulation parameters and a well-established VTA estimation approach (Madler and Coenen, 2012; Astrom et al., 2015; Dembek et al., 2017; Horn et al., 2017), resulting in models reflecting the individual spread of neurostimulation within the surgical target region. However, the theoretical concept of VTA modelling is still a simplistic approach and only approximates reality. Several factors beyond the state-of-the-art approach implemented here, such as fibre orientation or differences in stimulation around cathodal and anodal electrodes in bipolar settings have to be considered in future studies (Anderson et al., 2019). There have been several previous approaches to create probabilistic stimulation maps, e.g. to investigate effects of STN-DBS in Parkinson’s disease (Butson et al., 2011; Eisenstein et al., 2014; Gourisankar et al., 2018; Nguyen et al., 2019), antidystonic effects in dystonia (Cheung et al., 2014; Reich et al., 2019), and tremor control in essential tremor patients (Dembek et al., 2017). While some of the early approaches did not include any voxel-based statistical testing (Butson et al., 2011; Cheung et al., 2014), more recent works all used voxel-wise statistical testing to identify voxels which significantly improve or worsen respective symptoms. As in the present work, most studies then used a correction for type I errors to increase the statistical validity of the respective maps (Eisenstein et al., 2014; Dembek et al., 2017; Gourisankar et al., 2018; Nguyen et al., 2019). Reich et al. recently chose a t-test-based analysis with a slightly different approach. Type I error correction was omitted and instead a leave-one-out design as well as an independent test dataset were used to demonstrate that the voxel overlap of individual VTAs with the stimulation map could predict clinical improvement (Reich et al., 2019). For the present study, different factors were considered to increase statistical validity: (i) only including voxels with data from more than 10 overlapping VTAs; (ii) using voxel-wise two-sided Wilcoxon signed-rank tests to account for the data distribution; and (iii) using a non-parametric correction for multiple comparisons to correct for type 1 errors (Eisenstein et al., 2014; Dembek et al., 2017). Using the maps provided by this study to predict non-motor outcomes in future independent cohorts of DBS patients may provide a path towards prospectively optimizing not only motor, but also non-motor DBS outcomes. Some methodological limitations should be considered when interpreting the results of this study. First, we pooled the VTAs on the right hemisphere for statistical analysis as a previous study by our group did not find a dependency of non-motor outcomes on the hemisphere stimulated in linear mixed-effect models including ‘hemisphere’ as random effect (Dafsari et al., 2018a). Second, an atlas-based approach neglects a certain degree of interindividual heterogeneity. However, the state-of-the-art multispectral co-registration approach used in this study has proved to be accurate (Ewert et al., 2018, 2019). The algorithm used here performed best in comparison to multiple other deformation algorithms in a large study that involved manual segmentations of >100 brains and >11 000 non-linear warps. Lastly, using probabilistic stimulation maps as an analytic approach results in some general limitations. First, only areas stimulated in patients of the study cohort can be analysed and possible DBS effects on non-stimulated areas cannot be explored. Second, probabilistic stimulation maps can overemphasize effects at the outer edges of the analysed volume (‘shell-effect’), which is why we excluded voxels with <10 VTAs overlapping. We also showed that over 90% of the investigated VTAs contributed to our results. Therefore, it seems unlikely that only few outliers led to our results. Third, probabilistic stimulation maps only investigate local effects of DBS and do not account for activation or inhibition of larger networks. To take these limitations into account, further studies using more sophisticated imaging methods, such as (i) diffusion tensor imaging to investigate the individual connectivity of the stimulated STN subregions and its adjacent fibre connections; or (ii) functional MRI studies to reveal changes in functional connectivity patterns associated with non-motor symptoms are needed. Conclusion The results presented here provide evidence that 6-month outcomes of attention/memory, mood/apathy and sleep symptoms in patients with Parkinson’s disease undergoing STN-DBS depend on the specific location of neurostimulation in the surgical target area. Marked improvements of mood/apathy were observed for neurostimulation in the ventral border region of the STN, mainly in its sensorimotor subregion, for attention/memory in the associative subregion, and for sleep symptoms ventral to the STN. This finding provides a rationale for preoperative holistic assessments of non-motor and motor symptoms to determine potential neurostimulation regions of interest for patients’ individual clinical profiles. The long-term aim of this line of research is to identify symptom-specific brain networks and to utilize this knowledge to address each patient’s individual clinical profile in concurrence with the concept of precision medicine. Funding This paper is independent research funded by the German Research Foundation (Grant KFO 219). J.N.P-S., M.K. W.S. and C.S. were funded by the Koln Fortune Program. P.A.L. was funded by the Koln Fortune Program and the Konrad-Adenauer Foundation. H.S.D. was funded by the Koln Fortune Program, the Prof. Klaus Thiemann Foundation and the Felgenhauer Foundation. Competing interests The authors report no competing interests. Supplementary material Supplementary material is available at Brain online. Appendix 1 Full details of the members of the Non-motor Parkinson's Disease Study Group of the International Parkinson's Disease and Movement Disorders Society (IPMDS) can be found in the Supplementary material. Kallol Ray-Chaudhuri, UK; Angelo Antonini, Italy; Pablo Martinez-Martin, Spain; Per Odin, Sweden; Anette Schrag, UK; Daniel Weintraub, USA; Paolo Barone, Italy; David J Brooks, UK; Richard G Brown, UK; Peter Jenner, UK; B Jeon, Korea; Kelly Lyons, USA; Nicola Pavese, UK; Marios Politis, UK; Ronald B. Postuma, Canada; Anthony Schapira, UK; Fabrizio Stocchi, Italy; Lars Timmermann, Germany; Yoshio Tsuboi, Japan; Alexandra Rizos, UK; Anna Sauerbier, UK. 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Google Scholar Crossref Search ADS PubMed WorldCat Abbreviations Abbreviations DBS = deep brain stimulation LEDD = levodopa equivalent daily dose NMSQ = Non-motor Symptoms Questionnaire NMSS-T = Non-motor Symptom Scale total score PDQ-8 SI = 8-item Parkinson’s disease Questionnaire Summary Index SCOPA-A, -B, -C = Scales for Outcomes in Parkinson’s Disease-motor examination, -activities of daily living and -motor complications STN = subthalamic nucleus VTA = volume of tissue activated Author notes Jan Niklas Petry-Schmelzer and Max Krause authors contributed equally to this work. Appendix 1. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com
TI - Non-motor outcomes depend on location of neurostimulation in Parkinson’s disease
JO - Brain
DO - 10.1093/brain/awz285
DA - 2019-11-01
UR - https://www.deepdyve.com/lp/oxford-university-press/non-motor-outcomes-depend-on-location-of-neurostimulation-in-parkinson-HYhn3cVi2l
SP - 3592
EP - 3604
VL - 142
IS - 11
DP - DeepDyve
ER -