Deep brain stimulation of symptom-specific networks in Parkinson's disease.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
31 May 2024
31 May 2024
Historique:
received:
14
03
2023
accepted:
13
05
2024
medline:
1
6
2024
pubmed:
1
6
2024
entrez:
31
5
2024
Statut:
epublish
Résumé
Deep Brain Stimulation can improve tremor, bradykinesia, rigidity, and axial symptoms in patients with Parkinson's disease. Potentially, improving each symptom may require stimulation of different white matter tracts. Here, we study a large cohort of patients (N = 237 from five centers) to identify tracts associated with improvements in each of the four symptom domains. Tremor improvements were associated with stimulation of tracts connected to primary motor cortex and cerebellum. In contrast, axial symptoms are associated with stimulation of tracts connected to the supplementary motor cortex and brainstem. Bradykinesia and rigidity improvements are associated with the stimulation of tracts connected to the supplementary motor and premotor cortices, respectively. We introduce an algorithm that uses these symptom-response tracts to suggest optimal stimulation parameters for DBS based on individual patient's symptom profiles. Application of the algorithm illustrates that our symptom-tract library may bear potential in personalizing stimulation treatment based on the symptoms that are most burdensome in an individual patient.
Identifiants
pubmed: 38821913
doi: 10.1038/s41467-024-48731-1
pii: 10.1038/s41467-024-48731-1
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
4662Subventions
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : 424778381 - TRR 295
Informations de copyright
© 2024. The Author(s).
Références
Deuschl, G. et al. A randomized trial of deep-brain stimulation for Parkinson’s disease. N. Engl. J. Med. 355, 896–908 (2006).
pubmed: 16943402
doi: 10.1056/NEJMoa060281
Fasano, A., Aquino, C. C., Krauss, J. K., Honey, C. R. & Bloem, B. R. Axial disability and deep brain stimulation in patients with Parkinson disease. Nat. Rev. Neurol. 11, 98–110 (2015).
pubmed: 25582445
doi: 10.1038/nrneurol.2014.252
Schrader, C. et al. GPi-DBS may induce a hypokinetic gait disorder with freezing of gait in patients with dystonia. Neurology 77, 483–488 (2011).
pubmed: 21775741
doi: 10.1212/WNL.0b013e318227b19e
Barbe, M. T. et al. Deep brain stimulation for freezing of gait in Parkinson’s disease with early motor complications. Mov. Disord. 35, 82–90 (2020).
pubmed: 31755599
doi: 10.1002/mds.27892
Yin, Z. et al. Persistent adverse effects following different targets and periods after bilateral deep brain stimulation in patients with Parkinson’s disease. J. Neurol. Sci. 393, 116–127 (2018).
pubmed: 30153572
doi: 10.1016/j.jns.2018.08.016
Aviles-Olmos, I. et al. Long-term outcome of subthalamic nucleus deep brain stimulation for Parkinson’s disease using an MRI-guided and MRI-verified approach. J. Neurol. Neurosurg. Psychiatry 85, 1419–1425 (2014).
pubmed: 24790212
doi: 10.1136/jnnp-2013-306907
Bejjani, B.-P. et al. Bilateral subthalamic stimulation for Parkinson’s disease by using three-dimensional stereotactic magnetic resonance imaging and electrophysiological guidance. J. Neurosurg. 92, 615–625 (2000).
pubmed: 10761650
doi: 10.3171/jns.2000.92.4.0615
Picillo, M., Lozano, A. M., Kou, N., Puppi Munhoz, R. & Fasano, A. Programming deep brain stimulation for Parkinson’s disease: the toronto western hospital algorithms. Brain Stimul. 9, 425–437 (2016).
pubmed: 26968806
doi: 10.1016/j.brs.2016.02.004
Hassler, R., Riechert, T., Mundinger, F., Umbach, W. & Ganglberger, J. A. Physiological observations in stereotaxic operations in extrapyramidal motor disturbances. Brain 83, 337–350 (1960).
pubmed: 13852002
doi: 10.1093/brain/83.2.337
McGregor, M. M. & Nelson, A. B. Circuit mechanisms of Parkinson’s disease. Neuron 101, 1042–1056 (2019).
pubmed: 30897356
doi: 10.1016/j.neuron.2019.03.004
Strotzer, Q. D. et al. Deep brain stimulation: Connectivity profile for bradykinesia alleviation. Ann. Neurol. 85, 852–864 (2019).
pubmed: 30937956
doi: 10.1002/ana.25475
Akram, H. et al. Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson’s disease. NeuroImage 158, 332–345 (2017).
pubmed: 28711737
doi: 10.1016/j.neuroimage.2017.07.012
Ni, Z., Pinto, A. D., Lang, A. E. & Chen, R. Involvement of the cerebellothalamocortical pathway in Parkinson disease. Ann. Neurol. 68, 816–824 (2010).
pubmed: 21194152
doi: 10.1002/ana.22221
Helmich, R. C., Toni, I., Deuschl, G. & Bloem, B. R. The Pathophysiology of Essential Tremor and Parkinson’s Tremor. Curr. Neurol. Neurosci. Rep. 13, 378 (2013).
pubmed: 23893097
doi: 10.1007/s11910-013-0378-8
Sturman, M. M., Vaillancourt, D. E., Metman, L. V., Bakay, R. A. E. & Corcos, D. M. Effects of subthalamic nucleus stimulation and medication on resting and postural tremor in Parkinson’s disease. Brain 127, 2131–2143 (2004).
pubmed: 15240437
doi: 10.1093/brain/awh237
Li, N. et al. A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder. Nat. Commun. 11, 3364 (2020).
pubmed: 32620886
pmcid: 7335093
doi: 10.1038/s41467-020-16734-3
Grill, W. M., Snyder, A. N. & Miocinovic, S. Deep brain stimulation creates an informational lesion of the stimulated nucleus. NeuroReport 15, 1137–1140 (2004).
pubmed: 15129161
doi: 10.1097/00001756-200405190-00011
Hollunder, B. & Horn, A. Mapping the dysfunctome provides an avenue for targeted brain circuit therapy. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01572-z (2024).
doi: 10.1038/s41593-024-01572-z
pubmed: 38388734
pmcid: 10917675
Horn, A. et al. Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease. Ann. Neurol. 82, 67–78 (2017).
pubmed: 28586141
pmcid: 5880678
doi: 10.1002/ana.24974
Treu, S. et al. Deep brain stimulation: Imaging on a group level. NeuroImage 219, 117018 (2020).
pubmed: 32505698
doi: 10.1016/j.neuroimage.2020.117018
Horn, A. et al. Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. NeuroImage 184, 293–316 (2019).
pubmed: 30179717
doi: 10.1016/j.neuroimage.2018.08.068
Neudorfer, C. et al. Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks. NeuroImage 268, 119862 (2023).
pubmed: 36610682
doi: 10.1016/j.neuroimage.2023.119862
Irmen, F. et al. Left prefrontal impact links subthalamic stimulation with depressive symptoms. Ann. Neurol. https://doi.org/10.1002/ana.25734 (2020).
doi: 10.1002/ana.25734
pubmed: 32239535
Al-Fatly, B. et al. Connectivity profile of thalamic deep brain stimulation to effectively treat essential tremor. Brain J. Neurol. 18, 130 (2019).
Horn, A. et al. Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia. Proc. Natl Acad. Sci. 119, e2114985119 (2022).
pubmed: 35357970
pmcid: 9168456
doi: 10.1073/pnas.2114985119
Hollunder, B. et al. Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation. Nat. Neurosci. 27, 573–586 (2024).
pubmed: 38388734
pmcid: 10917675
doi: 10.1038/s41593-024-01570-1
Hollunder, B. et al. Toward personalized medicine in connectomic deep brain stimulation. Prog. Neurobiol. 210, 102211 (2022).
pubmed: 34958874
doi: 10.1016/j.pneurobio.2021.102211
Middlebrooks, E. H. et al. Neuroimaging advances in deep brain stimulation: review of indications, anatomy, and brain connectomics. Am. J. Neuroradiol. 41, 1558–1568 (2020).
pubmed: 32816768
pmcid: 7583111
doi: 10.3174/ajnr.A6693
Baldermann, J. C. et al. Connectivity profile predictive of effective deep brain stimulation in obsessive-compulsive disorder. Biol. Psychiatry 85, 735–743 (2019).
pubmed: 30777287
doi: 10.1016/j.biopsych.2018.12.019
Coenen, V. A. et al. The dentato-rubro-thalamic tract as the potential common deep brain stimulation target for tremor of various origin: an observational case series. Acta Neurochir. (Wien.) 18, 130–14 (2020).
Helmich, R. C., Janssen, M. J. R., Oyen, W. J. G., Bloem, B. R. & Toni, I. Pallidal dysfunction drives a cerebellothalamic circuit into Parkinson tremor. Ann. Neurol. 69, 269–281 (2011).
pubmed: 21387372
doi: 10.1002/ana.22361
Helmich, R. C., Hallett, M., Deuschl, G., Toni, I. & Bloem, B. R. Cerebral causes and consequences of parkinsonian resting tremor: a tale of two circuits?. Brain J. Neurol. 135, 3206–3226 (2012).
doi: 10.1093/brain/aws023
Kühn, A. A. et al. Pathological synchronisation in the subthalamic nucleus of patients with Parkinson’s disease relates to both bradykinesia and rigidity. Exp. Neurol. 215, 380–387 (2009).
pubmed: 19070616
doi: 10.1016/j.expneurol.2008.11.008
Mazzone, P., Sposato, S., Insola, A. & Scarnati, E. The clinical effects of deep brain stimulation of the pedunculopontine tegmental nucleus in movement disorders may not be related to the anatomical target, leads location, and setup of electrical stimulation. Neurosurgery 73, 894 (2013).
pubmed: 23867299
doi: 10.1227/NEU.0000000000000108
Zrinzo, L. et al. Stereotactic localization of the human pedunculopontine nucleus: atlas-based coordinates and validation of a magnetic resonance imaging protocol for direct localization. Brain 131, 1588–1598 (2008).
pubmed: 18467343
doi: 10.1093/brain/awn075
Golestanirad, L., Elahi, B., Graham, S. J., Das, S. & Wald, L. L. Efficacy and Safety of Pedunculopontine Nuclei (PPN) Deep Brain Stimulation in the Treatment of Gait Disorders: A Meta-Analysis of Clinical Studies. Can. J. Neurol. Sci. J. Can. Sci. Neurol. 43, 120–126 (2016).
doi: 10.1017/cjn.2015.318
Vorwerk, J., Oostenveld, R., Piastra, M. C., Magyari, L. & Wolters, C. H. The FieldTrip-SimBio pipeline for EEG forward solutions. Biomed. Eng. OnLine 17, 37 (2018).
pubmed: 29580236
pmcid: 5870695
doi: 10.1186/s12938-018-0463-y
Butenko, K., Bahls, C., Schröder, M., Köhling, R. & van Rienen, U. OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling. PLoS Comput. Biol. 16, e1008023 (2020).
pubmed: 32628719
pmcid: 7384674
doi: 10.1371/journal.pcbi.1008023
Dembek, T. A. et al. Directional DBS increases side‐effect thresholds—A prospective, double‐blind trial. Mov. Disord. 32, 1380–1388 (2017).
pubmed: 28843009
doi: 10.1002/mds.27093
Timmermann, 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. 14, 693–701 (2015).
pubmed: 26027940
doi: 10.1016/S1474-4422(15)00087-3
Roediger, J. et al. Automated deep brain stimulation programming based on electrode location: a randomised, crossover trial using a data-driven algorithm. Lancet Digit. Health 5, e59–e70 (2023).
pubmed: 36528541
doi: 10.1016/S2589-7500(22)00214-X
Roediger, J. et al. StimFit—a data-driven algorithm for automated deep brain stimulation programming. Mov. Disord. 37, 574–584 (2022).
pubmed: 34837245
doi: 10.1002/mds.28878
Makris, N. et al. Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive disorder (OCD). Brain Imaging Behav. 10, 1054–1067 (2016).
pubmed: 26518214
pmcid: 4851930
doi: 10.1007/s11682-015-9462-9
Lujan, J. L. et al. Tractography-Activation Models Applied to Subcallosal Cingulate Deep Brain Stimulation. Brain Stimul. 6, 737–739 (2013).
pubmed: 23602025
pmcid: 3772993
doi: 10.1016/j.brs.2013.03.008
Coenen, V. A. et al. Machine learning—aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET. Acta Neurochir. (Wien.) 161, 1559–1569 (2019).
pubmed: 31144167
doi: 10.1007/s00701-019-03947-9
Hollunder, B., Ganos, C. & Horn, A. Deep Brain Stimulation: From Sweet Spots to Sweet Networks? Biol. Psychiatry Cogn. Neurosci. Neuroimaging 6, 939–941 (2021).
pubmed: 34625219
Merk, T. et al. Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Exp. Neurol. 351, 113993 (2022).
pubmed: 35104499
pmcid: 10521329
doi: 10.1016/j.expneurol.2022.113993
Horn, A. et al. Teaching NeuroImages: In vivo visualization of Edinger comb and Wilson pencils. Neurology 92, e1663–e1664 (2019).
pubmed: 30936236
pmcid: 6448452
doi: 10.1212/WNL.0000000000007252
Alho, E. J. L. et al. The Ansa Subthalamica: A Neglected Fiber Tract. Mov. Disord. 35, 75–80 (2020).
pubmed: 31758733
doi: 10.1002/mds.27901
Petersen, M. V. et al. Holographic Reconstruction of Axonal Pathways in the Human Brain. Neuron 104, 1056–1064.e3 (2019).
pubmed: 31708306
pmcid: 6948195
doi: 10.1016/j.neuron.2019.09.030
Horn, A. et al. Deep brain stimulation induced normalization of the human functional connectome in Parkinson’s disease. Brain J. Neurol. 18, 130–15 (2019).
Wang, Q. et al. Normative vs. patient-specific brain connectivity in deep brain stimulation. NeuroImage 224, 117307 (2020).
pubmed: 32861787
doi: 10.1016/j.neuroimage.2020.117307
Horn, A. & Fox, M. D. Opportunities of Connectomic Neuromodulation. NeuroImage 117180 (2020) https://doi.org/10.1016/j.neuroimage.2020.117180 .
Ewert, S. et al. Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei. NeuroImage 184, 586–598 (2019).
pubmed: 30267856
doi: 10.1016/j.neuroimage.2018.09.061
Oxenford, S. et al. WarpDrive: Improving spatial normalization using manual refinements. Med. Image Anal. 91, 103041 (2024).
pubmed: 38007978
doi: 10.1016/j.media.2023.103041
Schönecker, T., Kupsch, A., Kühn, A. A., Schneider, G.-H. & Hoffmann, K.-T. Automated optimization of subcortical cerebral MR imaging−atlas coregistration for improved postoperative electrode localization in deep brain stimulation. Am. J. Neuroradiol. 30, 1914–1921 (2009).
pubmed: 19713324
pmcid: 7051288
doi: 10.3174/ajnr.A1741
Husch, A., V. Petersen, M., Gemmar, P., Goncalves, J. & Hertel, F. PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation. NeuroImage Clin. 17, 80–89 (2018).
pubmed: 29062684
doi: 10.1016/j.nicl.2017.10.004
Lofredi, R. et al. Interrater reliability of deep brain stimulation electrode localizations. NeuroImage 262, 119552 (2022).
pubmed: 35981644
doi: 10.1016/j.neuroimage.2022.119552
Gunalan, K. et al. Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example. PLOS ONE 12, e0176132 (2017).
pubmed: 28441410
pmcid: 5404874
doi: 10.1371/journal.pone.0176132
Howell, B. & McIntyre, C. C. Analyzing the tradeoff between electrical complexity and accuracy in patient-specific computational models of deep brain stimulation. J. Neural Eng. 13, 036023 (2016).
pubmed: 27172137
pmcid: 5259803
doi: 10.1088/1741-2560/13/3/036023
Åström, M. et al. Method for patient-specific finite element modeling and simulation of deep brain stimulation. Med. Biol. Eng. Comput. 47, 21–28 (2009).
Horn, A. & Kühn, A. A. Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations. NeuroImage 107, 127–135 (2015).
pubmed: 25498389
doi: 10.1016/j.neuroimage.2014.12.002
Avants, B. B., Epstein, C. L., Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008).
pubmed: 17659998
doi: 10.1016/j.media.2007.06.004
Ewert, S. et al. Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity. NeuroImage 170, 271–282 (2018).
pubmed: 28536045
doi: 10.1016/j.neuroimage.2017.05.015
Poldrack, R. A., Huckins, G. & Varoquaux, G. Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry 77, 534–540 (2020).
Butenko, K. et al. Linking profiles of pathway activation with clinical motor improvements – A retrospective computational study. NeuroImage Clin. 36, 103185 (2022).
pubmed: 36099807
pmcid: 9474565
doi: 10.1016/j.nicl.2022.103185
Zhang, S. & Arfanakis, K. Evaluation of standardized and study-specific diffusion tensor imaging templates of the adult human brain: Template characteristics, spatial normalization accuracy, and detection of small inter-group FA differences. NeuroImage 172, 40–50 (2018).
pubmed: 29414497
doi: 10.1016/j.neuroimage.2018.01.046
Butson, C. R. & McIntyre, C. C. Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation. Clin. Neurophysiol. 116, 2490–2500 (2005).
pubmed: 16125463
doi: 10.1016/j.clinph.2005.06.023
McIntyre, C. C., Richardson, A. G. & Grill, W. M. Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle. J. Neurophysiol. 87, 995–1006 (2002).
pubmed: 11826063
doi: 10.1152/jn.00353.2001
Fox, C. A., Rafols, J. A. & Cowan, W. M. Computer measurements of axis cylinder diameters of radial fibers and “comb” bundle fibers. J. Comp. Neurol. 159, 201–223 (1975).
pubmed: 803515
doi: 10.1002/cne.901590204
Mathai, A., Wichmann, T. & Smith, Y. More than meets the Eye—Myelinated axons crowd the subthalamic nucleus. Mov. Disord. 28, 1811–1815 (2013).
pubmed: 23852565
doi: 10.1002/mds.25603
Verhaart, W. J. C. Fiber analysis of the basal ganglia. J. Comp. Neurol. 93, 425–440 (1950).
pubmed: 14803571
doi: 10.1002/cne.900930307
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).
pubmed: 17695343
doi: 10.3758/BF03193146
Schmitz-Hübsch, T. The caudal zona incerta does not prove suitable as a target for deep brain stimulation in Parkinson’s disease. J. Neurol. 267, 591–606 (2014).
Coulombe, V. et al. A Topographic Atlas of the Human Brainstem in the Ponto-Mesencephalic Junction Plane. Front. Neuroanat. 15, 627656 (2021).
pubmed: 34483849
pmcid: 8414831
doi: 10.3389/fnana.2021.627656