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Prediction of Movement Ratings and Deep Brain Stimulation Parameters in Idiopathic Parkinson’s Disease

Published:November 14, 2022DOI:https://doi.org/10.1016/j.neurom.2022.09.010

      Abstract

      Background

      Deep brain stimulation (DBS) parameter fine-tuning after lead implantation is laborious work because of the almost uncountable possible combinations. Patients and practitioners often gain the perception that assistive devices could be beneficial for adjusting settings effectively.

      Objective

      We aimed at a proof-of-principle study to assess the benefits of noninvasive movement recordings as a means to predict best DBS settings.

      Materials and Methods

      For this study, 32 patients with idiopathic Parkinson’s disease, under chronic subthalamic nucleus stimulation with directional leads, were recorded. During monopolar review, each available contact was activated with currents between 0.5 and 5 mA, and diadochokinesia, rigidity, and tapping ability were rated clinically. Moreover, participants’ movements were measured during four simple hand movement tasks while wearing a commercially available armband carrying an inertial measurement unit (IMU). We trained random forest models to learn the relations between clinical ratings, electrode settings, and movement features obtained from the IMU.

      Results

      Firstly, we could show that clinical mobility ratings can be predicted from IMU features with correlations of up to r = 0.68 between true and predicted values. Secondly, these features also enabled a prediction of DBS parameters, which showed correlations of up to approximately r = 0.8 with clinically optimal DBS settings and were associated with congruent volumes of tissue activated.

      Conclusion

      Movement recordings from customer-grade mobile IMU carrying devices are promising candidates, not only for remote symptom assessment but also for closed-loop DBS parameter adjustment, and could thus extend the list of available aids for effective programming beyond imaging techniques.

      Keywords

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      References

        • Postuma R.B.
        • Berg D.
        • Stern M.
        • et al.
        MDS clinical diagnostic criteria for Parkinson’s disease.
        Mov Disord. 2015; 30: 1591-1601https://doi.org/10.1002/mds.26424
        • Albin R.L.
        • Young A.B.
        • Penney J.B.
        The functional anatomy of basal ganglia disorders.
        Trends Neurosci. 1989; 12: 366-375https://doi.org/10.1016/0166-2236(89)90074-x
        • Braak H.
        • Ghebremedhin E.
        • Rüb U.
        • Bratzke H.
        • Del Tredici K.
        Stages in the development of Parkinson’s disease-related pathology.
        Cell Tissue Res. 2004; 318: 121-134https://doi.org/10.1007/s00441-004-0956-9
        • Poewe W.
        • Seppi K.
        • Tanner C.M.
        • et al.
        Parkinson disease.
        Nat Rev Dis Primers. 2017; 317013https://doi.org/10.1038/nrdp.2017.13
        • Stocchi F.
        • Jenner P.
        • Obeso J.A.
        When do levodopa motor fluctuations first appear in Parkinson’s disease?.
        Eur Neurol. 2010; 63: 257-266https://doi.org/10.1159/000300647
        • Gray R.
        • Ives N.
        • et al.
        • PD Med Collaborative Group
        Long-term effectiveness of dopamine agonists and monoamine oxidase B inhibitors compared with levodopa as initial treatment for Parkinson’s disease (PD MED): a large, open-label, pragmatic randomised trial.
        Lancet. 2014; 384: 1196-1205https://doi.org/10.1016/S0140-6736(14)60683-8
        • Straka I.
        • Minár M.
        • Škorvánek M.
        • et al.
        Adherence to pharmacotherapy in patients with Parkinson’s disease taking three and more daily doses of medication.
        Front Neurol. 2019; 10: 799https://doi.org/10.3389/fneur.2019.00799
        • Fenoy A.J.
        • Simpson R.K.
        Risks of common complications in deep brain stimulation surgery: management and avoidance.
        J Neurosurg. 2014; 120: 132-139https://doi.org/10.3171/2013.10.JNS131225
        • Vitek J.L.
        • Jain R.
        • Chen L.
        • et al.
        Subthalamic nucleus deep brain stimulation with a multiple independent constant current-controlled device in Parkinson’s disease (INTREPID): a multicentre, double-blind, randomised, sham-controlled study.
        Lancet Neurol. 2020; 19: 491-501https://doi.org/10.1016/S1474-4422(20)30108-3
        • Schuepbach W.M.M.
        • Rau J.
        • Knudsen K.
        • et al.
        Neurostimulation for Parkinson’s disease with early motor complications.
        N Engl J Med. 2013; 368: 610-622https://doi.org/10.1056/NEJMoa1205158
        • Timmermann L.
        • Jain R.
        • Chen L.
        • et al.
        Multiple-source current steering in subthalamic nucleus deep brain stimulation for Parkinson’s disease (the VANTAGE study): a non-randomised, prospective, multicentre, open-label study.
        Lancet Neurol. 2015; 14: 693-701https://doi.org/10.1016/S1474-4422(15)00087-3
        • Pollo C.
        • Kaelin-Lang A.
        • Oertel M.F.
        • et al.
        Directional deep brain stimulation: an intraoperative double-blind pilot study.
        Brain. 2014; 137: 2015-2026https://doi.org/10.1093/brain/awu102
        • Waldthaler J.
        • Bopp M.
        • Kühn N.
        • et al.
        Imaging-based programming of subthalamic nucleus deep brain stimulation in Parkinson’s disease.
        Brain Stimul. 2021; 14: 1109-1117https://doi.org/10.1016/j.brs.2021.07.064
        • Gilron R.
        • Little S.
        • Perrone R.
        • et al.
        Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease.
        Nat Biotechnol. 2021; 39: 1078-1085https://doi.org/10.1038/s41587-021-00897-5
        • Little S.
        • Pogosyan A.
        • Neal S.
        • et al.
        Adaptive deep brain stimulation in advanced Parkinson disease.
        Ann Neurol. 2013; 74: 449-457https://doi.org/10.1002/ana.23951
        • Herz D.M.
        • Little S.
        • Pedrosa D.J.
        • et al.
        Mechanisms underlying decision-making as revealed by deep-brain stimulation in patients with Parkinson’s disease.
        Curr Biol. 2018; 28: 1169-1178.e6https://doi.org/10.1016/j.cub.2018.02.057
        • Cagnan H.
        • Pedrosa D.
        • Little S.
        • et al.
        Stimulating at the right time: phase-specific deep brain stimulation.
        Brain. 2017; 140: 132-145https://doi.org/10.1093/brain/aww286
        • Kleinholdermann U.
        • Melsbach J.
        • Pedrosa D.J.
        Remote assessment of idiopathic Parkinson’s disease : developments in diagnostics, monitoring and treatment. Article in German.
        Nervenarzt. 2019; 90: 1232-1238https://doi.org/10.1007/s00115-019-00818-7
        • Hossein Tabatabaei S.A.
        • Pedrosa D.
        • Eggers C.
        • et al.
        Machine learning techniques for Parkinson’s disease detection using wearables during a timed-up-and-go-test.
        Curr Dir Biomed Eng. 2020; 6: 376-379https://doi.org/10.1515/cdbme-2020-3097
        • Kleinholdermann U.
        • Wullstein M.
        • Pedrosa D.
        Prediction of motor Unified Parkinson’s Disease Rating Scale scores in patients with Parkinson’s disease using surface electromyography.
        Clin Neurophysiol. 2021; 132: 1708-1713https://doi.org/10.1016/j.clinph.2021.01.031
        • Ancona S.
        • Faraci F.D.
        • Khatab E.
        • et al.
        Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature.
        J Neurol. 2022; 269: 100-110https://doi.org/10.1007/s00415-020-10350-3
        • Sica M.
        • Tedesco S.
        • Crowe C.
        • et al.
        Continuous home monitoring of Parkinson’s disease using inertial sensors: a systematic review.
        PLoS One. 2021; 16e0246528https://doi.org/10.1371/journal.pone.0246528
        • Tomlinson C.L.
        • Stowe R.
        • Patel S.
        • Rick C.
        • Gray R.
        • Clarke C.E.
        Systematic review of levodopa dose equivalency reporting in Parkinson’s disease.
        Mov Disord. 2010; 25: 2649-2653https://doi.org/10.1002/mds.23429
        • Goetz C.G.
        • Tilley B.C.
        • Shaftman S.R.
        • et al.
        Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.
        Mov Disord. 2008; 23: 2129-2170https://doi.org/10.1002/mds.22340
        • R Core Team
        R: A Language and Environment for Statistical Computing.
        R Foundation for Statistical Computing, 2018
        • Du Y.-C.
        • Lin C.-H.
        • Shyu L.-Y.
        • Chen T.
        Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis.
        Expert Syst Appl. 2010; 37: 4283-4291https://doi.org/10.1016/j.eswa.2009.11.072
        • Horn A.
        • Kühn A.A.
        Lead-DBS: a toolbox for deep brain stimulation electrode localizations and visualizations.
        NeuroImage. 2015; 107: 127-135https://doi.org/10.1016/j.neuroimage.2014.12.002
        • Horn A.
        • Reich M.
        • Vorwerk J.
        • et al.
        Connectivity Predicts deep brain stimulation outcome in Parkinson disease.
        Ann Neurol. 2017; 82: 67-78https://doi.org/10.1002/ana.24974
        • Vescio B.
        • Quattrone A.
        • Nisticò R.
        • Crasà M.
        • Quattrone A.
        Wearable devices for assessment of tremor.
        Front Neurol. 2021; 12680011https://doi.org/10.3389/fneur.2021.680011
        • Salarian A.
        • Russmann H.
        • Wider C.
        • Burkhard P.R.
        • Vingerhoets F.J.G.
        • Aminian K.
        Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system.
        IEEE Trans Biomed Eng. 2007; 54: 313-322https://doi.org/10.1109/TBME.2006.886670
        • Kassavetis P.
        • Saifee T.A.
        • Roussos G.
        • et al.
        Developing a tool for remote digital assessment of Parkinson’s disease.
        Mov Disord Clin Pract. 2016; 3: 59-64https://doi.org/10.1002/mdc3.12239
        • Samà A.
        • Pérez-López C.
        • Rodríguez-Martín D.
        • et al.
        Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor.
        Comput Biol Med. 2017; 84: 114-123https://doi.org/10.1016/j.compbiomed.2017.03.020
        • Abou L.
        • Peters J.
        • Wong E.
        • et al.
        Gait and balance assessments using smartphone applications in Parkinson’s disease: a systematic review.
        J Med Syst. 2021; 45: 87https://doi.org/10.1007/s10916-021-01760-5
        • Tsanas A.
        • Little M.A.
        • McSharry P.E.
        • Ramig L.O.
        Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests.
        IEEE Trans Biomed Eng. 2010; 57: 884-893https://doi.org/10.1109/TBME.2009.2036000
        • Beck C.A.
        • Beran D.B.
        • Biglan K.M.
        • et al.
        National randomized controlled trial of virtual house calls for Parkinson disease.
        Neurology. 2017; 89: 1152-1161https://doi.org/10.1212/WNL.0000000000004357
        • Tarolli C.G.
        • Andrzejewski K.
        • Zimmerman G.A.
        • et al.
        Feasibility, reliability, and value of remote video-based trial visits in Parkinson’s disease.
        J Parkinsons Dis. 2020; 10: 1779-1786https://doi.org/10.3233/JPD-202163
        • Poonja S.
        • Miyasaki J.
        • Fu X.
        • et al.
        The trajectory of motor deterioration to death in Parkinson’s disease.
        Front Neurol. 2021; 12670567https://doi.org/10.3389/fneur.2021.670567
        • Wijers A.
        • Hochstenbach L.
        • Tissingh G.
        Telemonitoring via questionnaires reduces outpatient healthcare consumption in Parkinson’s disease.
        Mov Disord Clin Pract. 2021; 8: 1075-1082https://doi.org/10.1002/mdc3.13280
        • Lange F.
        • Steigerwald F.
        • Malzacher T.
        • et al.
        Reduced programming time and strong symptom control even in chronic course through imaging-based DBS programming.
        Front Neurol. 2021; 12785529https://doi.org/10.3389/fneur.2021.785529
        • Dayal V.
        • Limousin P.
        • Foltynie T.
        Subthalamic nucleus deep brain stimulation in Parkinson’s disease: the effect of varying stimulation parameters.
        J Parkinsons Dis. 2017; 7: 235-245https://doi.org/10.3233/JPD-171077
        • Su D.
        • Chen H.
        • Hu W.
        • et al.
        Frequency-dependent effects of subthalamic deep brain stimulation on motor symptoms in Parkinson’s disease: a meta-analysis of controlled trials.
        Sci Rep. 2018; 814456https://doi.org/10.1038/s41598-018-32161-3
        • Steigerwald F.
        • Timmermann L.
        • Kühn A.
        • et al.
        Pulse duration settings in subthalamic stimulation for Parkinson’s disease.
        Mov Disord. 2018; 33: 165-169https://doi.org/10.1002/mds.27238
        • Meidahl A.C.
        • Tinkhauser G.
        • Herz D.M.
        • Cagnan H.
        • Debarros J.
        • Brown P.
        Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy.
        Mov Disord. 2017; 32: 810-819https://doi.org/10.1002/mds.27022
        • Telkes I.
        • Sabourin S.
        • Durphy J.
        • et al.
        Functional use of directional local field potentials in the subthalamic nucleus deep brain stimulation.
        Front Hum Neurosci. 2020; 14: 145https://doi.org/10.3389/fnhum.2020.00145
        • Tinkhauser G.
        • Pogosyan A.
        • Debove I.
        • et al.
        Directional local field potentials: a tool to optimize deep brain stimulation.
        Mov Disord. 2018; 33: 159-164https://doi.org/10.1002/mds.27215
        • Little S.
        • Beudel M.
        • Zrinzo L.
        • et al.
        Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease.
        J Neurol Neurosurg Psychiatry. 2016; 87: 717-721https://doi.org/10.1136/jnnp-2015-310972
        • Swann N.C.
        • de Hemptinne C.
        • Thompson M.C.
        • et al.
        Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing.
        J Neural Eng. 2018; 15046006https://doi.org/10.1088/1741-2552/aabc9b
        • Stanslaski S.
        • Herron J.
        • Chouinard T.
        • et al.
        A chronically implantable neural coprocessor for investigating the treatment of neurological disorders.
        IEEE Trans Biomed Circuits Syst. 2018; 12: 1230-1245https://doi.org/10.1109/TBCAS.2018.2880148
        • Roediger J.
        • Dembek T.A.
        • Wenzel G.
        • Butenko K.
        • Kühn A.A.
        • Horn A.
        StimFit—a data-driven algorithm for automated deep brain stimulation programming.
        Mov Disord. 2022; 37: 574-584https://doi.org/10.1002/mds.28878
        • Schlachetzki J.C.M.
        • Barth J.
        • Marxreiter F.
        • et al.
        Wearable sensors objectively measure gait parameters in Parkinson’s disease.
        PLoS One. 2017; 12e0183989https://doi.org/10.1371/journal.pone.0183989

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