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Tractography patterns of subthalamic nucleus deep brain stimulation
Open accessAbstract
Deep brain stimulation therapy is an effective symptomatic treatment for Parkinson's disease, yet the precise mechanisms responsible for its therapeutic effects remain unclear. Although the targets of deep brain stimulation are grey matter structures, axonal modulation is known to play an important role in deep brain stimulation's therapeutic mechanism. Several white matter structures in proximity to the subthalamic nucleus have been implicated in the clinical benefits of deep brain stimulation for Parkinson's disease. We assessed the connectivity patterns that characterize clinically beneficial electrodes in Parkinson's disease patients, after deep brain stimulation of the subthalamic nucleus. We evaluated 22 patients with Parkinson's disease (11 females, age 57 ± 9.1 years, disease duration 13.3 ± 6.3 years) who received bilateral deep brain stimulation of the subthalamic nucleus at the National Institutes of Health. During an initial electrode screening session, one month after deep brain stimulation implantation, the clinical benefits of each contact were determined. The electrode was localized by coregistering preoperative magnetic resonance imaging and postoperative computer tomography images and the volume of tissue activated was estimated from stimulation voltage and impedance. Brain connectivity for the volume of tissue activated of deep brain stimulation contacts was assessed using probabilistic tractography with diffusion-tensor data. Areas most frequently connected to clinically effective contacts included the thalamus, substantia nigra, brainstem and superior frontal gyrus. A series of discriminant analyses demonstrated that the strength of connectivity to the superior frontal gyrus and the thalamus were positively associated with clinical effectiveness. The connectivity patterns observed in our study suggest that the modulation of white matter tracts directed to the superior frontal gyrus and the thalamus is associated with favourable clinical outcomes and may contribute to the therapeutic effects of deep brain stimulation. Our method can be further developed to reliably identify effective deep brain stimulation contacts and aid in the programming process.
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“Preoperative tractography can guide deep brain stimulation (DBS) or MR-guided focused ultrasound (MRgFUS) planning, identify the structural connectivity of the volume of tissue activated, and help avoid pathways associated with motor, cognitive, or mood-related adverse effects (Pujol et al., 2017; Rajamani et al., 2024; Vanegas-Arroyave et al., 2016).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Vanegas-Arroyave et al., 2016
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Cites
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Hughes, A. J., Daniel, S. E., Kilford, L., & Lees, A. J. (1992) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.55.3.181 · Cited by 10,378
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
Systematic review of levodopa dose equivalency reporting in Parkinson's disease
Tomlinson, C. L., Stowe, R. L., Patel, S., Rick, C., Gray, R. G., & Clarke, C. E. (2010) · Movement Disorders · doi:10.1002/mds.23429 · Cited by 4,219
Neurodegenerative Disorders: Parkinson's Disease and Huntington's Disease
Hague, S., Klaffke, S., & Bandmann, O. (2006) · IMPERIAL COLLEGE PRESS eBooks · doi:10.1142/9781860948961_0007 · Cited by 2,767
Toward a quantitative assessment of diffusion anisotropy
Pierpaoli, C. M., & Basser, P. J. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910360612 · Cited by 2,392
Weaver, F. M. (2009) · JAMA · doi:10.1001/jama.2008.929 · Cited by 1,535
Core assessment program for surgical interventional therapies in Parkinson's disease (CAPSIT-PD)
Defer, G., Widner, H., Mari�, R.-M., Rémy, P., & Levivier, M. (1999) · Movement Disorders · doi:10.1002/1531-8257(199907)14:4 · Cited by 863
Kühn, A. A., Kempf, F., Brücke, C., Gaynor, L. D., Martínez‐Torres, I., Pogosyan, A. H., Trottenberg, T., Kupsch, A. R., Schneider, G., Hariz, M. I., et al. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0282-08.2008 · Cited by 860
Cellular Effects of Deep Brain Stimulation: Model-Based Analysis of Activation and Inhibition
McIntyre, C. C., Grill, W. M., Sherman, D. L., & Thakor, N. V. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00989.2003 · Cited by 817
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Sammartino, F., Krishna, V., & Rezai, A. R. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805353-9.00011-5 · Cited by 5
Tractography patterns of pedunculopontine nucleus deep brain stimulation
Raghu, A. L. B., Parker, T., Zand, A. P. D., Payne, S. J., Andersson, J., Stein, J. F., Aziz, T. Z., & Green, A. L. (2021) · Journal of Neural Transmission · doi:10.1007/s00702-021-02327-x · Cited by 4
DBS in the restoration of motor functional recovery following spinal cord injury
Li, W., Qu, W., Li, Y., Wang, S., Liu, D., Deng, L., & Wang, Y. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1442281 · Cited by 5
Shehab, S. A. S., D’Souza, C. M., Ljubisavljević, M., & Redgrave, P. (2018) · Brain Research · doi:10.1016/j.brainres.2018.01.034 · Cited by 5
High impedance analysis in recordings of deep brain stimulation surgery
Ali, H. A. M., Abdullah, S. S., & Faraj, M. K. (2021) · Interdisciplinary Neurosurgery · doi:10.1016/j.inat.2021.101115 · Cited by 5
White Matter Connectivity between Structures of the Basal Ganglia using 3T and 7T
Shim, J., & Baek, H. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.12.034 · Cited by 4
Schumacher, F. K., Schumacher, L. V., Amtage, F., Horn, A., Egger, K. E., Piroth, T., Weiller, C., Schelter, B. O., Coenen, V. A., & Kaller, C. P. (2021) · Scientific Reports · doi:10.1038/s41598-021-81535-7 · Cited by 4
Liu, J., Chen, S., Chen, J., Wang, B., Zhang, Q., Xiao, L., Zhang, D., & Cai, X. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.05.150 · Cited by 4
Generation of Mouse Basal Ganglia Diffusion Tractography Using 9.4T MRI
Shim, J., Im, S.-J., Kim, A.-Y., Kim, Y., Kim, E. B., & Baek, H. (2019) · Experimental Neurobiology · doi:10.5607/en.2019.28.2.300 · Cited by 4
Pearce, P., Bulluss, K. J., Xu, S. S., Kim, B., Milicevic, M., Perera, T., & Thevathasan, W. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254504 · Cited by 3
McKavanagh, A., Ridzuan-Allen, A., Kreilkamp, B. A. K., Chen, Y., Manjón, J. V., Pierrick Coupé, A., Bracewell, R. M., Das, K., Taylor, P. N., Marson, A. G., et al. (2023) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2023.109084 · Cited by 3
Optimal Stimulation Sites and Connectomes for GPi and STN ‐ DBS in Cervical Dystonia
Xue, T., Qiu, Y., Tian, W., Xie, H., Fan, S., Fan, H., Xie, M., Ye, M., Wang, Z., Ning, T., et al. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70561 · Cited by 3
Xu, J., Liu, B., Shang, G., Liu, S., Feng, Z., Zhang, Y., Yang, H., Liu, D., Chang, Q., Yuhan, C., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-086098 · Cited by 2
Emerging Techniques for the Personalization of Deep Brain Stimulation Programming
Santyr, B. G., Boutet, A., Ajala, A., Germann, J., Qiu, J., Fasano, A. A., Lozano, A. M., & Kucharczyk, W. (2025) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1017/cjn.2025.29 · Cited by 3
Predicting treatment response based on DBS connectivity
Horn, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821861-7.00015-4 · Cited by 2
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Výtvarová, E., Lamoš, M., Hlinka, J., Goldemundová, S., Rektor, I., & Bočková, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-80630-9 · Cited by 2
Loução, R., Köcher, M., Brandt, G. A., Petry‐Schmelzer, J. N., Barbe, M. T., Dafsari, H. S., Mana, J., Jech, R., Wirths, J., Visser-Vandewalle, V. E. R. M., et al. (2025) · Stereotactic and Functional Neurosurgery · doi:10.1159/000546716 · Cited by 2
Imaging: Patient Selection, Targeting, and Outcome Biomarkers
Krishna, V., Young, N. A., & Sammartino, F. (2020) · Springer eBooks · doi:10.1007/978-3-030-34906-6_36 · Cited by 1
Arévalo-Sáenz, A., Manzanares, L. L., García, M. N., Pastor, J., Vega-Zelaya, L., & Torres, C. V. (2022) · Revista de Neurología · doi:10.33588/rn.7404.2021196 · Cited by 1
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“…(DBS) or MR-guided focused ultrasound (MRgFUS) planning, identify the structural connectivity of the volume of tissue activated, and help avoid pathways associated with motor, cognitive, or mood-related adverse effects ( Pujol et al., 2017; Rajamani et al., 2024; Vanegas-Arroyave et al., 2016).”
The Use of New Surgical Technologies for Deep Brain Stimulation
Krishna, V., Sammartino, F., & Rezai, A. R. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805353-9.00034-6
The advances of Deep Brain Stimulation (DBS) for treatment of Parkinson s disease (PD)
Franzoi, A. E. D. A., Bonchoski, A. B., & Koerbel, A. (2018) · Clinical & Biomedical Research · doi:10.4322/2357-9730.86267
Imaging, Technology, and Parkinson’s Disease
Vanegas, N. (2019) · touchREVIEWS in Neurology · doi:10.17925/usn.2019.15.1.18
DBS imaging methods IV: Group analyses
Treu, S., & Horn, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821861-7.00003-8
Masoumi, S., & Côté, D. C. (2021) · Photonics North (PN) · doi:10.1109/pn52152.2021.9695541
Rikitake, M., Hata, J., Iida, M., Seki, F., Ito, R., Komaki, Y., Yamada, C., Yoshimaru, D., Okano, H. J., & Shirakawa, T. (2022) · The Open Neuroimaging Journal · doi:10.2174/18744400-v15-e2202040
Mattis, P. J., Fridman, C. B., & Meltzer, E. P. (2019) · Clinical handbooks in neuropsychology · doi:10.1007/978-3-319-93497-6_35
Neuroanatomical variation in Parkinson’s disease motor subtypes
Boonstra, J. T. (2023) · Research Publications (Maastricht University) · doi:10.26481/dis.20230626jb
Wang, L., Zhao, Y., Ding, Q., Huang, P., Wang, T., Qiu, X., Sun, B., Pan, Y., Li, D., & Voon, V. (2025) · Brain stimulation · doi:10.1016/j.brs.2024.12.551
Wang, X., Zhou, C., Xie, F., Wang, X., Chen, X., Zhang, J., Wang, H., Li, R., He, X., Yang, Z., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-15326-9 · Cited by 1
Palmese, C. A., Wyman‐Chick, K. A., & York, M. K. (2025) · Oxford University Press eBooks · doi:10.1093/9780197689264.003.0015
Schilling, L. M., Ciesla, A.-L., Kube, J. M. V., Dechent, P., Riedel, C. H., & Neef, N. E. (2025) · Clinical Neuroradiology · doi:10.1007/s00062-025-01601-9
Boonstra, J. T., Michielse, S., Temel, Y., Jahanshahi, A., & Roebroeck, A. F. (2026) · Neurobiology of Disease · doi:10.1016/j.nbd.2026.107266
Sensory Function Improves With Subthalamic Nucleus Stimulation in Parkinson’s: A Network Analysis
Permezel, F. E., Alty, J., Harding, I. H., Ding, C., Klassen, B. T., & Thyagarajan, D. (2026) · Neuromodulation Technology at the Neural Interface · doi:10.1016/j.neurom.2026.03.011
Zang, D., Hasuike, K., Qi, Z., Yu, Y. B., & Gui, P. (2026) · Neuroscience Bulletin · doi:10.1007/s12264-026-01677-4
Yuan, Y., Liu, P., Yin, C., Zhu, J., Xu, J., Xiong, H., & Xu, R. (2026) · MedScience · doi:10.1007/s11684-026-1231-1
Metadata
- Authors
- Vanegas-Arroyave, N., Lauro, P. M., Huang, L., Hallett, M., Horovitz, S. G., Zaghloul, K. A., & Lungu, C.
- Affiliations
- National Institutes of Health (US)
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-02-26
- Volume
- 139
- Issue
- 4
- Pages
- 1200-1210
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC5006230
- OpenAlex
- W2283119078
- Cited by
- 137
- References
- 50
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 10 · 2023: 12 · 2022: 18 · 2021: 26 · 2020: 15 · 2019: 16 · 2018: 13 · 2017: 9 · 2016: 4
- FWCI
- 10.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Columbia Academic Commons (Columbia University)
- Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke
- Topics
- Health Sciences › Medicine › Clinical Neurology › Deep Brain Stimulation and Movement Disorders
- Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- deep brain stimulation, subthalamic nucleus, Parkinson's disease, probabilistic tractography, white matter connectivity, superior frontal gyrus, thalamus, substantia nigra, brainstem, electrode selection, clinical outcome prediction, diffusion tensor imaging
- MeSH terms
- Aged, Electrodes, Implanted, Female, Follow-Up Studies, Humans, Male, Middle Aged, Neural Pathways, Parkinson Disease, Subthalamic Nucleus, Deep Brain Stimulation, Diffusion Tensor Imaging
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