Cortical network organization reflects clinical response to subthalamic nucleus deep brain stimulation in Parkinson's disease
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
34448523
PubMed Central
PMC8559467
DOI
10.1002/hbm.25642
Knihovny.cz E-zdroje
- Klíčová slova
- deep brain stimulation, high-density EEG, network analysis, subthalamic nucleus,
- MeSH
- elektroencefalografie MeSH
- hluboká mozková stimulace * MeSH
- hodnocení výsledků zdravotní péče MeSH
- lidé středního věku MeSH
- lidé MeSH
- mozková kůra patofyziologie MeSH
- nervová síť patofyziologie MeSH
- nucleus subthalamicus patofyziologie MeSH
- Parkinsonova nemoc patofyziologie terapie MeSH
- psychomotorický výkon fyziologie MeSH
- senioři MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The degree of response to subthalamic nucleus deep brain stimulation (STN-DBS) is individual and hardly predictable. We hypothesized that DBS-related changes in cortical network organization are related to the clinical effect. Network analysis based on graph theory was used to evaluate the high-density electroencephalography (HDEEG) recorded during a visual three-stimuli paradigm in 32 Parkinson's disease (PD) patients treated by STN-DBS in stimulation "off" and "on" states. Preprocessed scalp data were reconstructed into the source space and correlated to the behavioral parameters. In the majority of patients (n = 26), STN-DBS did not lead to changes in global network organization in large-scale brain networks. In a subgroup of suboptimal responders (n = 6), identified according to reaction times (RT) and clinical parameters (lower Unified Parkinson's Disease Rating Scale [UPDRS] score improvement after DBS and worse performance in memory tests), decreased global connectivity in the 1-8 Hz frequency range and regional node strength in frontal areas were detected. The important role of the supplementary motor area for the optimal DBS response was demonstrated by the increased node strength and eigenvector centrality in good responders. This response was missing in the suboptimal responders. Cortical topologic architecture is modified by the response to STN-DBS leading to a dysfunction of the large-scale networks in suboptimal responders.
Faculty of Informatics Masaryk University Brno Czech Republic
Institute of Scientific Instruments of the Czech Academy of Sciences v v i Brno Czech Republic
Zobrazit více v PubMed
Aron, A. R. , Behrens, T. E. , Smith, S. , Frank, M. J. , & Poldrack, R. A. (2007). Triangulating a cognitive control network using diffusion‐weighted magnetic resonance imaging (MRI) and functional MRI. Journal of Neuroscience, 27(14), 3743–3752. 10.1523/JNEUROSCI.0519-07.2007 PubMed DOI PMC
Bassett, D. S. , & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–364. 10.1038/nn.4502 PubMed DOI PMC
Benabid, A. L. , Chabardes, S. , Mitrofanis, J. , & Pollak, P. (2009). Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease. Lancet Neurology, 8(1), 67–81. 10.1016/S1474-4422(08)70291-6 PubMed DOI
Bočková, M. , Chládek, J. , Šímová, L. , Jurák, P. , Halámek, J. , & Rektor, I. (2013). Oscillatory changes in cognitive networks activated during a three‐stimulus visual paradigm: An intracerebral study. Clinical Neurophysiology, 124(2), 283–291. 10.1016/j.clinph.2012.07.009 PubMed DOI
Bočková, M. , Lamoš, M. , Klimeš, P. , Jurák, P. , Halámek, J. , Goldemundová, S. , … Rektor, I. (2020). Suboptimal response to STN‐DBS in Parkinson's disease can be identified via reaction times in a motor cognitive paradigm. Journal of Neural Transmission, 127(12), 1579–1588. 10.1007/s00702-020-02254-3 PubMed DOI
Bostan, A. C. , Dum, R. P. , & Strick, P. L. (2018). Functional anatomy of basal ganglia circuits with the cerebral cortex and the cerebellum. Progress in neurological surgery (Vol. 33, pp. 50–61). Basel, Switzerland: Karger AG. 10.1159/000480748 PubMed DOI
Brittain, J.‐S. , Sharott, A. , & Brown, P. (2014). The highs and lows of beta activity in cortico‐basal ganglia loops. European Journal of Neuroscience, 39(11), 1951–1959. 10.1111/ejn.12574 PubMed DOI PMC
Brown, P. (2003). Oscillatory nature of human basal ganglia activity: Relationship to the pathophysiology of Parkinson's disease. Movement Disorders, 18(4), 357–363. 10.1002/mds.10358 PubMed DOI
Coito, A. , Michel, C. M. , van Mierlo, P. , Vulliemoz, S. , & Plomp, G. (2016). Directed functional brain connectivity based on EEG source imaging: Methodology and application to temporal lobe epilepsy. IEEE Transactions on Biomedical Engineering, 63(12), 2619–2628. 10.1109/TBME.2016.2619665 PubMed DOI
Coito, A. , Michel, C. M. , Vulliemoz, S. , & Plomp, G. (2019). Directed functional connections underlying spontaneous brain activity. Human Brain Mapping, 40(3), 879–888. 10.1002/hbm.24418 PubMed DOI PMC
Cosgrove, J. , Picardi, C. , Smith, S. L. , Lones, M. A. , Jamieson, S. , & Alty, J. E. (2016). Investigating the relationship between reaction time and cognition in Parkinson's disease. Movement Disorders, 31, S458–S458.
Delorme, A. , & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single‐trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. 10.1016/j.jneumeth.2003.10.009 PubMed DOI
Ewert, S. , Plettig, P. , Li, N. , Chakravarty, M. M. , Collins, D. L. , Herrington, T. M. , … Horn, A. (2018). Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity. NeuroImage, 170, 271–282. 10.1016/j.neuroimage.2017.05.015 PubMed DOI
Fornito, A. , Zalesky, A. , & Bullmore, E. T. (2016). Fundamentals of brain network analysis. Cambridge, MA: Academic Press. 10.1016/C2012-0-06036-X DOI
Geraedts, V. J. , Boon, L. I. , Marinus, J. , Gouw, A. A. , Van Hilten, J. J. , Stam, C. J. , … Contarino, M. F. (2018). Clinical correlates of quantitative EEG in Parkinson disease: A systematic review. Neurology, 91(19), 871–883. 10.1212/WNL.0000000000006473 PubMed DOI
Geraedts, V. J. , Marinus, J. , Gouw, A. A. , Mosch, A. , Stam, C. J. , van Hilten, J. J. , … Tannemaat, M. R. (2018). Quantitative EEG reflects non‐dopaminergic disease severity in Parkinson's disease. Clinical Neurophysiology, 129(8), 1748–1755. 10.1016/j.clinph.2018.04.752 PubMed DOI
Hatz, F. , Meyer, A. , Zimmermann, R. , Gschwandtner, U. , & Fuhr, P. (2017). Apathy in patients with Parkinson's disease correlates with alteration of left fronto‐polar electroencephalographic connectivity. Frontiers in Aging Neuroscience, 9, 1–8. 10.3389/fnagi.2017.00262 PubMed DOI PMC
Horn, A. , & Kühn, A. A. (2015). Lead‐DBS: A toolbox for deep brain stimulation electrode localizations and visualizations. NeuroImage, 107, 127–135. 10.1016/j.neuroimage.2014.12.002 PubMed DOI
Jordan, N. , Sagar, H. J. , & Cooper, J. A. (1992). Cognitive components of reaction time in Parkinson's disease. Journal of Neurology, Neurosurgery & Psychiatry, 55(8), 658–664. 10.1136/jnnp.55.8.658 PubMed DOI PMC
Klimes, P. , Jurak, P. , Halamek, J. , Roman, R. , Chladek, J. , & Brazdil, M. (2018). Changes in connectivity and local synchrony after cognitive stimulation—Intracerebral EEG study. Biomedical Signal Processing and Control, 45, 136–143. 10.1016/j.bspc.2018.05.043 DOI
Kojovic, M. , Mir, P. , Trender‐Gerhard, I. , Schneider, S. A. , Pareés, I. , Edwards, M. J. , … Jahanshahi, M. (2014). Motivational modulation of bradykinesia in Parkinson's disease off and on dopaminergic medication. Journal of Neurology, 261(6), 1080–1089. 10.1007/s00415-014-7315-x PubMed DOI PMC
Kumru, H. , Summerfield, C. , Valldeoriola, F. , & Valls‐Solé, J. (2004). Effects of subthalamic nucleus stimulation on characteristics of EMG activity underlying reaction time in Parkinson's disease. Movement Disorders, 19(1), 94–100. 10.1002/mds.10638 PubMed DOI
Litvak, V. , Florin, E. , Tamás, G. , Groppa, S. , & Muthuraman, M. (2021). EEG and MEG primers for tracking DBS network effects. NeuroImage, 224, 117447. 10.1016/j.neuroimage.2020.117447 PubMed DOI
Litvak, V. , Jha, A. , Eusebio, A. , Oostenveld, R. , Foltynie, T. , Limousin, P. , … Brown, P. (2011). Resting oscillatory cortico‐subthalamic connectivity in patients with Parkinson's disease. Brain, 134(2), 359–374. 10.1093/brain/awq332 PubMed DOI
Lowe, M. J. , Mock, B. J. , & Sorenson, J. A. (1998). Functional connectivity in single and multislice echoplanar imaging using resting‐state fluctuations. NeuroImage, 7(2), 119–132. 10.1006/nimg.1997.0315 PubMed DOI
Pfurtscheller, G. (2001). Functional brain imaging based on ERD/ERS. Vision Research, 41(10–11), 1257–1260. 10.1016/S0042-6989(00)00235-2 PubMed DOI
Plesinger, F. , Jurco, J. , Halamek, J. , & Jurak, P. (2016). SignalPlant: An open signal processing software platform. Physiological Measurement, 37(7), N38–N48. 10.1088/0967-3334/37/7/N38 PubMed DOI
Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118, 2128–2148. 10.1016/j.clinph.2007.04.019 PubMed DOI PMC
Ponsen, M. M. , Stam, C. J. , Bosboom, J. L. W. , Berendse, H. W. , & Hillebrand, A. (2013). A three dimensional anatomical view of oscillatory resting‐state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas‐based beamforming. NeuroImage: Clinical, 2, 95–102. 10.1016/j.nicl.2012.11.007 PubMed DOI PMC
Rubinov, M. , & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. 10.1016/j.neuroimage.2009.10.003 PubMed DOI
Schuepbach, W. M. M. , Rau, J. , Knudsen, K. , Volkmann, J. , Krack, P. , Timmermann, L. , … Deuschl, G. (2013). Neurostimulation for Parkinson's disease with early motor complications. New England Journal of Medicine, 368(7), 610–622. 10.1056/NEJMoa1205158 PubMed DOI
Seeber, M. , Cantonas, L.‐M. , Hoevels, M. , Sesia, T. , Visser‐Vandewalle, V. , & Michel, C. M. (2019). Subcortical electrophysiological activity is detectable with high‐density EEG source imaging. Nature Communications, 10(1), 753. 10.1038/s41467-019-08725-w PubMed DOI PMC
Stam, C. J. , Nolte, G. , & Daffertshofer, A. (2007). Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11), 1178–1193. 10.1002/hbm.20346 PubMed DOI PMC
Utianski, R. L. , Caviness, J. N. , van Straaten, E. C. W. , Beach, T. G. , Dugger, B. N. , Shill, H. A. , … Hentz, J. G. (2016). Graph theory network function in Parkinson's disease assessed with electroencephalography. Clinical Neurophysiology, 127(5), 2228–2236. 10.1016/j.clinph.2016.02.017 PubMed DOI
Zavala, B. , Tan, H. , Ashkan, K. , Foltynie, T. , Limousin, P. , Zrinzo, L. , … Brown, P. (2016). Human subthalamic nucleus–medial frontal cortex theta phase coherence is involved in conflict and error related cortical monitoring. NeuroImage, 137, 178–187. 10.1016/j.neuroimage.2016.05.031 PubMed DOI PMC
Zavala, B. A. , Tan, H. , Little, S. , Ashkan, K. , Hariz, M. , Foltynie, T. , … Brown, P. (2014). Midline frontal cortex low‐frequency activity drives subthalamic nucleus oscillations during conflict. Journal of Neuroscience, 34(21), 7322–7333. 10.1523/JNEUROSCI.1169-14.2014 PubMed DOI PMC