ABSTRACT
Background
Elevated low‐frequency activity (4–12 Hz) within the globus pallidus internus (GPi) has been consistently associated with dystonia. However, the impacts of the genetic etiology of dystonia on low‐frequency GPi activity remain unclear; yet it holds importance for adaptive deep brain stimulation (DBS) treatment.
Methods
We compared the properties of GPi electrophysiology acquired from 70 microelectrode recordings (MER) trajectories of DYT‐GNAL, DYT‐KMT2B, DYT‐SGCE, DYT‐THAP1, DYT‐TOR1A, DYT‐VPS16, and idiopathic dystonia (iDYT) patients who underwent GPi‐DBS surgery across standard frequency bands.
Results
DYT‐SGCE patients exhibited significantly lower alpha band activity (2.97%) compared to iDYT (4.44%, p = 0.006) and DYT‐THAP1 (4.51%, p = 0.011). Additionally, theta band power was also significantly reduced in DYT‐SGCE (4.42%) compared to iDYT and DYT‐THAP1 (7.91% and 7.00%, p < 0.05). Instead, the genetic etiology of dystonia did not affect the spatial characteristics of GPi electrophysiology along MER trajectories.
Conclusion
Considering the genetic etiology of dystonia in closed‐loop DBS treatments and utilizing theta and alpha activity for GPi stimulation may optimize clinical outcomes. MER‐based DBS lead placement can proceed independently of the underlying genetic cause.
Keywords: alpha oscillations, deep brain stimulation, dystonia, electrophysiology, genetics
1. Introduction
Dystonia is a movement disorder causing abnormal, often repetitive movements or postures due to muscle contractions, with genetic forms resulting from pathogenic mutations in causative genes leading to diverse clinical presentations [1, 2]. Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) is an effective therapy for the drug‐resistant form of dystonia [3]. Intraoperative microelectrode recordings (MERs) are routinely used to confirm the DBS lead placements, also reveal the pathophysiology of movement disorders [4, 5].
Elevated low‐frequency (4–12 Hz) activity in local field potentials (LFPs) of the GPi [6, 7] and subthalamic nucleus (STN) [8, 9] has been consistently associated with dystonia. This activity could potentially serve as a biomarker for closed‐loop DBS in dystonia [9, 10]. Therefore, examining the impact of genetic etiology on low‐frequency GPi electrophysiology could enable personalized adaptive DBS (aDBS) treatments.
Herein, we compared the properties of low‐frequency GPi activity extracted from MERs collected during GPi‐DBS surgery from patients with various genetic and idiopathic forms of dystonia. A significant reduction in low‐frequency activity (4–12 Hz) was observed in patients with DYT‐SGCE compared to DYT‐THAP1 and idiopathic dystonia, highlighting the potential for personalized aDBS based on genetic factors in dystonia patients. Finally, our findings indicate that genetic etiology has no significant impact on the spatial characteristics of GPi electrophysiology. Therefore, MER‐based DBS lead placement can be performed independently of the genetic etiology of dystonia.
2. Materials and Methods
The study was conducted with genetic and idiopathic dystonia patients who underwent bilateral GPi‐DBS surgery under propofol and remifentanil anesthesia (Table S1) at Fondazione IRCCS Istituto Neurologico Carlo Besta. Before 2000, only TOR1A was tested in patients. Between 2000 and 2015, individuals who were negative for TOR1A were retested via Sanger sequencing as new genes were identified. Since 2015, an NGS‐customized dystonia gene panel has been employed [11], (Table S1).
The surgeries were conducted bilaterally and under stereotactic conditions with the Leksell (Elekta Inc.) or Maranello (Eidos22) frames. A thorough description of our standard surgical procedure is available elsewhere [12]. Identification of the nuclei borders, the sensorimotor GPi, and MER depths was performed by using the Distal Atlas [13] in Montreal Neurological Institute space (p > 0.5 threshold) with Lead DBS v2.3 [14] and an expert electrophysiologist. At the preoperative and postoperative follow‐up evaluations, the Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS) was employed to assess the motor severity. The demographic and clinical profiles of the patients are presented in Table 1.
TABLE 1.
Demographic, genetic, and clinical data of the cohort patients. The table reports the demographic and genetic data of the enrolled patients. In addition, data on the motor component of the Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS) at preoperative and 1‐year follow‐up following the deep brain stimulation (DBS) surgery are reported. Abbreviations: F, female; M, male.
| Id | Gene | Sex | Age onset | Age at surgery | Main body part | Pattern | Preoperative BFMDRS‐M | Postoperative 1Y‐FU BFMDRS‐M | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total | Arms | Cranial | Neck | Legs | Trunk | Total | Arms | Cranial | Neck | Legs | Trunk | |||||||
| P1 | GNAL | F | 37 | 42 | Cervical | Phasic | 26 | 8 | 0 | 6 | 0 | 12 | 22 | 8 | 0 | 6 | 0 | 8 |
| P2 | GNAL | F | 42 | 63 | Cervical | Phasic | 24 | 10 | 6 | 6 | 0 | 2 | 14 | 5 | 3 | 4 | 0 | 2 |
| P3 | KMT2B | M | 28 | 45 | Trunk | Tonic | 27 | 4 | 3 | 8 | 0 | 12 | 10 | 1 | 0 | 3 | 0 | 6 |
| P4 | KMT2B | M | 8 | 16 | Upper | Phasic | 28 | 13 | 0 | 6 | 9 | 0 | 14 | 5 | 0 | 3 | 6 | 0 |
| P5 | SGCE | M | 2 | 48 | Upper | Phasic | 11.5 | 6 | 1.5 | 1 | 2 | 1 | 10.5 | 5 | 1.5 | 1 | 2 | 1 |
| P6 | SGCE | M | 3 | 17 | Upper | Phasic | 16 | 7 | 0 | 4 | 4 | 1 | 2.5 | 1 | 0 | 0.5 | 1 | 0 |
| P7 | THAP1 | F | 40 | 14 | Cervical | Tonic | 17 | 4 | 3 | 8 | 1 | 1 | 20 | 2 | 8 | 8 | 1 | 1 |
| P8 | THAP1 | F | 7 | 32 | Trunk | Tonic | 32.5 | 4 | 4 | 0.5 | 12 | 12 | 5 | 0 | 0 | 0 | 3 | 2 |
| P9 | THAP1 | M | 6 | 46 | Cervical | Tonic | 45 | 4 | 6 | 8 | 15 | 12 | 20 | 2 | 1.5 | 4.5 | 3 | 9 |
| P10 | THAP1 | M | 14 | 43 | Cervical | Tonic | 60.5 | 13 | 12.5 | 8 | 15 | 12 | 31 | 11 | 4.5 | 4.5 | 2 | 9 |
| P11 | TOR1A | M | 14 | 49 | Upper | Phasic | 30.5 | 20 | 0.5 | 4 | 5 | 1 | 24.5 | 16 | 0.5 | 4 | 3 | 1 |
| P12 | TOR1A | M | 13 | 9 | Lower | Tonic | 29.5 | 0 | 0 | 0.5 | 20 | 9 | 4 | 0 | 0 | 0 | 2 | 2 |
| P13 | TOR1A | M | 10 | 33 | Upper | Tonic | 25 | 13 | 1 | 0 | 9 | 2 | 18 | 10 | 1 | 0 | 5 | 2 |
| P14 | TOR1A | F | 7 | 47 | Lower | Tonic | 43 | 14 | 0 | 0 | 25 | 4 | 4 | 2 | 0 | 0 | 2 | 0 |
| P15 | TOR1A | M | 12 | 44 | Cervical | Tonic | 36 | 16 | 0 | 6 | 6 | 8 | 19 | 7 | 0 | 6 | 2 | 4 |
| P16 | TOR1A | M | 10 | 9 | Lower | Tonic | 23.5 | 7 | 0 | 0.5 | 13 | 3 | 10 | 4 | 1.5 | 0.5 | 2 | 2 |
| P17 | TOR1A | F | 11 | 23 | Upper | Tonic | 59 | 24 | 2.5 | 0.5 | 20 | 12 | 17.5 | 2 | 1 | 0.5 | 6 | 8 |
| P18 | TOR1A | M | 8 | 43 | Trunk | Tonic | 34 | 0 | 0 | 0 | 18 | 16 | 3 | 0 | 0 | 0 | 2 | 1 |
| P19 | VPS16 | F | 12 | 49 | Cranial | Tonic | 23 | 7 | 11 | 2 | 2 | 1 | 13.5 | 7 | 4 | 0.5 | 0 | 2 |
| P20 | VPS16 | M | 15 | 45 | Cervical | Phasic | 32 | 12 | 6 | 6 | 2 | 6 | 15.5 | 3 | 5 | 4.5 | 1 | 2 |
| P21 | VPS16 | M | 14 | 24 | Trunk | Tonic | 52.5 | 20 | 0.5 | 6 | 14 | 12 | 25.5 | 12 | 0.5 | 6 | 5 | 2 |
| P22 | IDIOPATHIC | F | 42 | 38 | Cervical | Phasic | 31 | 10 | 5 | 12 | 2 | 2 | 17 | 6 | 2 | 6 | 2 | 1 |
| P23 | IDIOPATHIC | M | 3 | 57 | Cranial | Tonic | 45 | 2 | 28 | 3 | 0 | 12 | 15 | 3 | 4 | 6 | 0 | 2 |
| P24 | IDIOPATHIC | F | 61 | 49 | Cranial | Tonic | 45 | 2 | 28 | 3 | 0 | 12 | 15 | 3 | 4 | 6 | 0 | 2 |
| P25 | IDIOPATHIC | F | 35 | 30 | Cervical | Phasic | 32.5 | 2 | 8.5 | 8 | 6 | 8 | 5 | 1 | 0 | 3 | 0 | 1 |
| P26 | IDIOPATHIC | M | 39 | 60 | Cervical | Tonic | 50 | 28 | 2 | 8 | 0 | 12 | 9 | 5 | 0 | 4 | 0 | 0 |
| P27 | IDIOPATHIC | M | 35 | 31 | Cervical | Tonic | 40.5 | 18 | 10.5 | 6 | 2 | 4 | 19 | 10 | 6.5 | 1.5 | 0 | 1 |
| P28 | IDIOPATHIC | M | 19 | 42 | Cervical | Phasic | 35.5 | 4 | 8.5 | 8 | 3 | 12 | 19 | 0 | 2 | 6 | 3 | 8 |
| P29 | IDIOPATHIC | F | 51 | 63 | Cervical | Tonic | 26 | 2 | 0 | 6 | 6 | 12 | 21 | 2 | 0 | 4 | 6 | 9 |
| P30 | IDIOPATHIC | M | 3 | 45 | Trunk | Tonic | 43 | 10 | 14 | 6 | 4 | 9 | 33 | 8 | 11 | 4 | 4 | 6 |
We adopted the methodology used to compare STN electrophysiology in monogenetic and idiopathic forms of Parkinson's disease (PD) [15]. Briefly, we divided MERs into 50 ms segments, computed the root mean square (RMS), and labeled segments stable if their RMS values were within three standard deviations of the median RMS. The longest stable section of each recording was selected for further analysis [16]. To estimate the power spectral density (PSD), we rectified the stable raw signal and subtracted the mean to reveal the low‐frequency envelope. The PSD was estimated with a resolution of 1/3 Hz and normalized to the total power within the analysis range (2–200 Hz) to mitigate the influence of varying RMS values across patients [16]. The length of the detected GPi region was normalized to 1, where 0 represents the GPi entry [15]. Trajectories were included in analyses if they had a minimum GPi length of 2 mm and recordings from at least four distinct depths (Figure S1). Theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (30–100), band activity was extracted using a four‐pole Butterworth band‐pass filter. The fraction of power and the ratio between these bands were measured as electrophysiological features.
Kruskal–Wallis and post hoc Dunn's test with Holm–Bonferroni correction were used for group comparison. Spearman's correlation with permutation testing was used to assess the significance of linear relationships between variables, with Benjamini/Hochberg (FDR) correction applied to the p values for measured correlations. All tests were two‐tailed, and statistical significance was defined as p ≤ 0.05. We employed a linear mixed model (LMM) with the Wald test to investigate the interaction between normalized depths and spectral properties within the GPi by considering genetic factors as random effects. This approach allowed us to assess the influence of GPi depth on spectral properties while accounting for random variation from genetic factors and individual differences.
3. Results
In total, 597 MERs were collected from 70 trajectories across 30 patients in the cohort (Table S2). All analyzed MER trajectories passed through the sensorimotor portion of the GPi. Therefore, we do not anticipate spatial confounders affecting the electrophysiology (Figure 1A).
FIGURE 1.
Electrophysiological characterization of power spectral properties of GPi for genetic and idiopathic dystonia syndromes. (A) Reconstruction of MER trajectories in MNI space, color‐coded according to genetic etiology. (B) Statistical comparison of power spectral features was performed with Dunn's test with Holm–Bonferroni correction; significant results are indicated by color coding. (C) Box plots displaying the distributions of theta and alpha power fraction across dystonia groups. (D) Correlation matrix depicting linear relationship between spectral and clinical features, as determined by Spearman's correlation coefficient. The magnitude of each correlation score is indicated by the size and color of the marker. Regression plots highlight significant correlations between selected feature pairs, with scatter color‐coded based on the genetic etiology. (E) Heatmap showing the correlation between normalized depth levels and electrophysiological features across patient groups. Regression plots depict the behavior of selected electrophysiological features along normalized depth levels, with the degree of correlation indicated by Spearman's correlation coefficient.
Elevated relative power in the theta and alpha bands compared to the baseline (median power of the whole spectrum up to 100 Hz) was observed across dystonia syndromes (Figures S2 and S3). We detected statistically significant differences between patient groups in all spectral features except for the alpha/theta ratio (p ≤ 0.05, Kruskal–Wallis test with Holm–Bonferroni correction) (Table S4). The fraction of alpha band activity remained significantly lower for DYT‐SGCE (2.97%) compared to iDYT (4.44%, p = 0.006, Dunn's test with Holm–Bonferroni correction) and DYT‐THAP1 (4.51%, p = 0.011) patients (Figure 1B). Similarly, the fraction of power in the theta band was significantly lower in DYT‐SGCE (4.42%) compared to the iDYT (7.91%, p = 0.002) and DYT‐THAP1 (7.00%, p = 0.019) (Figure 1B,C). The fraction of gamma power for the DYT‐VPS16 group (37.04%) was significantly higher than the remaining groups, apart from DYT‐SGCE (Figure 1B).
We investigated whether the observed electrophysiological differences were linked to motor symptom severity rather than genetic etiology. This was accomplished by calculating Spearman's correlation between the median values of electrophysiological features for patients and their corresponding baseline BFMDRS scores, as well as the percentage change observed in these scores between the preoperative and postoperative evaluations (Figure 1D). We observed a moderate effect size (0.4–0.6) in the relationship between certain clinical and spectral feature pairs, but none reached statistical significance (p > 0.05, Spearman's correlation with FDR correction) (Figure 1D). Although no significant correlation was found, the power spectral characteristics of the GPi in iDYT cases appeared to be more associated with disease severity compared to genetic dystonia syndromes (Figure S4).
Additionally, we sought to elucidate the spatial characteristics of GPi electrophysiology along MER trajectories by measuring the linear relationship between the normalized depths (GPi entry = 0, GPi exit = 1) and spectral features. The beta/theta and beta/alpha ratios (r s = 0.30, p = 0.049) demonstrated significant linear characteristics between the borders of the GPi in DYT‐VPS16 patients (Figure 1E). Finally, no evidence was found to support the role of genetic etiology in spatially modulating the electrophysiological properties of MER recordings within the GPi (LMM, Wald test with Holm–Bonferroni correction, p > 0.05) (Table S5).
4. Discussion
Here, we presented the potential of dystonia genetic etiology to influence the low‐frequency components of GPi electrophysiology. Reductions in theta and alpha band activities were observed in DYT‐SGCE compared to DYT‐THAP1 and iDYT patients. These differences cannot be attributed to the severity of dystonia, as no significant correlation was found between these components and the severity of motor symptoms. We further demonstrated that the genetic etiology of dystonia is irrelevant to MER‐based lead localization in GPi‐DBS surgeries, as the spatial characteristics of spectral features remain consistent when genetic factors are treated as random effects in LMM.
Weill and colleagues proposed that genetic heterogeneity in PD is not associated with robust electrophysiological differences in STN [15]. In our case, we found significant differences between the low‐frequency activity of DYT‐SGCE from DYT‐THAP1 and iDYT in the GPi. It has been proposed that distinct oscillatory circuit disruptions may underlie dystonia and parkinsonism, even in the same anatomical structure [9]. In this context, the effects of genetic etiology on different movement disorders and brain regions can exhibit differences. Our results align with this study, as we did not observe any impact of genetic etiology on the spatial characteristics of GPi electrophysiology.
We previously analyzed the pallidal single‐unit activity in a larger genetic dystonia cohort [17], including the patients analyzed here. We observed convergence among dystonia genes toward either strong bursts or tonic behavior, with SGCE and THAP1 exhibiting opposite behaviors. This is consistent with the differing low‐frequency activity of the GPi associated with these two genes at the population level in the present work [17].
It should be noted that our study has several limitations. The limited sample size may impact the findings, though the high number of single MER epochs could mitigate this to some extent. The limited number of trajectories per patient prevented a consistent evaluation of potential spatial confounding factors along the anteroposterior and mediolateral directions. Lastly, electrophysiological activity recorded from the GPi using microelectrodes reflects considerably smaller neuronal populations compared to LFPs recorded with DBS macroelectrodes. Therefore, the potential effects of genetic etiology should also be investigated in LFP recordings.
In conclusion, we suggest the genetic etiology may potentially impact low‐frequency activity within the GPi, especially in patients with DYT‐SGCE, where these bands seem to be underrepresented. An adaptive DBS paradigm for dystonia based on the spectral fluctuations of recorded activity should then take into account potential differences between genetic dystonia profiles. However, further studies with larger sample sizes and a broader range of dystonia genes are needed to evaluate the clinical relevance of our hypothesis.
Author Contributions
Ahmet Kaymak: conceptualization, methodology, software, data curation, investigation, validation, formal analysis, visualization, project administration, writing – original draft, writing – review and editing. Luigi M. Romito: conceptualization, methodology, data curation, validation, supervision, project administration, resources, writing – original draft, writing – review and editing. Fabiana Colucci: data curation, investigation, writing – review and editing. Nico Golfrè Andreasi: data curation, writing – review and editing. Roberta Telese: data curation. Sara Rinaldo: data curation. Vincenzo Levi: data curation, writing – review and editing. Giovanna Zorzi: data curation. Zvi Israel: supervision. David Arkadir: supervision, writing – review and editing. Hagai Bergman: supervision. Miryam Carecchio: supervision. Holger Prokisch: supervision. Michael Zech: supervision, writing – review and editing. Barbara Garavaglia: data curation, investigation, validation. Alberto Mazzoni: conceptualization, methodology, validation, supervision, funding acquisition, project administration, writing – original draft, writing – review and editing. Roberto Eleopra: supervision.
Ethics Statement
The study was approved by the Ethical Committee of Fondazione IRCCS Istituto Neurologico Carlo Besta.
Consent
Informed consent was obtained from patients and their legal representatives.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1.
Acknowledgments
The authors express their gratitude to Francesco Carella, who performed most of the intraoperative microrecordings. Open access publishing facilitated by Scuola Superiore Sant'Anna, as part of the Wiley ‐ CRUI‐CARE agreement.
Funding: This research is supported by #Nextgenerationeu (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project THE (IECS00000017)—Tuscany Health Ecosystem (DN. 1553 11.10.2022). A.K. was supported by the Italian Ministry of Research, under the complementary actions to the NRRP “Fit4MedRob—Fit for Medical Robotics” Grant (# PNC0000007). L.R. received research support from the Italian Ministry of Health (IMH) grant GR‐2009‐1594645 and from the European Academy of Neurology—EAN (formerly European Federation of Neurological Societies) Fellowship Award to young Neurologists 2007—EFNS Fellowship 2007. B.G. and G.Z. received support and grants from The Italian Ministry of Health (IMH) and the Pierfranco e Luisa Mariani Foundation. M.Z. receives research support from the German Research Foundation (DFG 458949627; ZE 1213/2–1) and acknowledges grant support from the European Joint Programme on Rare Diseases (EJP RD Joint Transnational Call 2022), the German Federal Ministry of Education and Research (BMBF, Bonn, Germany), the PreDYT project (PREdictive biomarkers in DYsTonia, 01GM2302), the Federal Ministry of Education and Research (BMBF) and the Free State of Bavaria under the Excellence Strategy of the Federal Government and the Länder, as well as the Technical University of Munich Institute for Advanced Study. D.A. is supported by a research grant from the Dystonia Medical Research Foundation (DMRF).
Ahmet Kaymak and Luigi M. Romito contributed equally to this work.
The last two authors share senior authorship.
Contributor Information
Luigi M. Romito, Email: luigi.romito@istituto-besta.it.
Alberto Mazzoni, Email: alberto.mazzoni@santannapisa.it.
Data Availability Statement
The processed spectral datasets (both spectral features and normalized PSD) from MER data supporting the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.14800798 in a tabular and fully anonymized format.
The complete code for data processing, descriptive analyses, and plotting is available online on the GitHub repository: https://github.com/ahmetofficial/Genetic‐DYT‐GPi‐Spectral‐Activity/.
References
- 1. Albanese A., Bhatia K., Bressman S. B., et al., “Phenomenology and Classification of Dystonia: A Consensus Update,” Movement Disorders 28, no. 7 (2013): 863–873, 10.1002/mds.25475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Lange L. M., Gonzalez‐Latapi P., Rajalingam R., et al., “Nomenclature of Genetic Movement Disorders: Recommendations of the International Parkinson and Movement Disorder Society Task Force—An Update,” Movement Disorders 37, no. 5 (2022): 905–935, 10.1002/mds.28982. [DOI] [PubMed] [Google Scholar]
- 3. Tisch S. and Kumar K. R., “Pallidal Deep Brain Stimulation for Monogenic Dystonia: The Effect of Gene on Outcome,” Frontiers in Neurology 11 (2021): 630391, 10.3389/fneur.2020.630391. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Mosher C. P., Mamelak A. N., Malekmohammadi M., Pouratian N., and Rutishauser U., “Distinct Roles of Dorsal and Ventral Subthalamic Neurons in Action Selection and Cancellation,” Neuron 109, no. 5 (2021): 869–881, 10.1016/j.neuron.2020.12.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Kaymak A., Vissani M., Lenge M., et al., “Patterns of Neural Activity and Clinical Outcomes in a Juvenile Huntington's Disease Patient Undergoing Deep Brain Stimulation of the Subthalamic Nucleus,” Deep Brain Stimulation 1 (2023): 15–19, 10.1016/j.jdbs.2023.03.001. [DOI] [Google Scholar]
- 6. Neumann W., Horn A., Ewert S., et al., “A Localized Pallidal Physiomarker in Cervical Dystonia,” Annals of Neurology 82, no. 6 (2017): 912–924, 10.1002/ana.25095. [DOI] [PubMed] [Google Scholar]
- 7. Zhu G., Geng X., Tan Z., et al., “Characteristics of Globus Pallidus Internus Local Field Potentials in Hyperkinetic Disease,” Frontiers in Neurology 9 (2018): 934, 10.3389/fneur.2018.00934. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Geng X., Zhang J., Jiang Y., et al., “Comparison of Oscillatory Activity in Subthalamic Nucleus in Parkinson's Disease and Dystonia,” Neurobiology of Disease 98 (2017): 100–107, 10.1016/j.nbd.2016.12.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Neumann W., Huebl J., Brücke C., et al., “Enhanced Low‐Frequency Oscillatory Activity of the Subthalamic Nucleus in a Patient With Dystonia,” Movement Disorders 27, no. 8 (2012): 1063–1066, 10.1002/mds.25078. [DOI] [PubMed] [Google Scholar]
- 10. Tisch S. and Limousin P., “Neurophysiological Insights in Dystonia and Its Response to Deep Brain Stimulation Treatment,” Experimental Brain Research 238, no. 7–8 (2020): 1645–1657, 10.1007/s00221-020-05833-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Reale C., Panteghini C., Carecchio M., and Garavaglia B., “The Relevance of Gene Panels in Movement Disorders Diagnosis: A Lab Perspective,” European Journal of Paediatric Neurology 22, no. 2 (2018): 285–291, 10.1016/j.ejpn.2018.01.013. [DOI] [PubMed] [Google Scholar]
- 12. Levi V., Zorzi G., Messina G., et al., “Deep Brain Stimulation Versus Pallidotomy for Status Dystonicus: A Single‐Center Case Series,” Journal of Neurosurgery 134, no. 1 (2021): 197–207, 10.3171/2019.10.JNS191691. [DOI] [PubMed] [Google Scholar]
- 13. 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 170 (2018): 271–282, 10.1016/j.neuroimage.2017.05.015. [DOI] [PubMed] [Google Scholar]
- 14. Horn A., Li N., Dembek T. A., et al., “Lead‐DBS v2: Towards a Comprehensive Pipeline for Deep Brain Stimulation Imaging,” NeuroImage 184 (2019): 293–316, 10.1016/j.neuroimage.2018.08.068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Weill C., Gallant A., Baker Erdman H., et al., “The Genetic Etiology of Parkinson's Disease Does Not Robustly Affect Subthalamic Physiology,” Movement Disorders 38, no. 3 (2023): 484–489, 10.1002/mds.29310. [DOI] [PubMed] [Google Scholar]
- 16. Zaidel A., Spivak A., Shpigelman L., Bergman H., and Israel Z., “Delimiting Subterritories of the Human Subthalamic Nucleus by Means of Microelectrode Recordings and a Hidden Markov Model,” Movement Disorders 24, no. 12 (2009): 1785–1793, 10.1002/mds.22674. [DOI] [PubMed] [Google Scholar]
- 17. Kaymak A., Colucci F., Ahmadipour M., et al., “Spiking Patterns in the Globus Pallidus Highlight Convergent Neural Dynamics Across Diverse Genetic Dystonia Syndromes,” Annals of Neurology 30 (2025): 27185, 10.1002/ana.27185. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1.
Data Availability Statement
The processed spectral datasets (both spectral features and normalized PSD) from MER data supporting the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.14800798 in a tabular and fully anonymized format.
The complete code for data processing, descriptive analyses, and plotting is available online on the GitHub repository: https://github.com/ahmetofficial/Genetic‐DYT‐GPi‐Spectral‐Activity/.