Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates.
EEG
Parkinson's disease
functional connectivity
oscillations
Journal
Movement disorders : official journal of the Movement Disorder Society
ISSN: 1531-8257
Titre abrégé: Mov Disord
Pays: United States
ID NLM: 8610688
Informations de publication
Date de publication:
20 Oct 2023
20 Oct 2023
Historique:
revised:
25
08
2023
received:
03
05
2023
accepted:
11
09
2023
medline:
20
10
2023
pubmed:
20
10
2023
entrez:
20
10
2023
Statut:
aheadofprint
Résumé
Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD). In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms. We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach. PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC. Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.
Sections du résumé
BACKGROUND
BACKGROUND
Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD).
OBJECTIVES
OBJECTIVE
In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms.
METHODS
METHODS
We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach.
RESULTS
RESULTS
PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC.
CONCLUSIONS
CONCLUSIONS
Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : BRIC 2019 (INAIL) to AS
Organisme : Fondazione Baroni to NM and AS
Organisme : RF-2018-12365509 to NM and AS
Organisme : NEXTGENERATIONEU (NGEU) by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006)
ID : DN.155311.10.2022
Informations de copyright
© 2023 International Parkinson and Movement Disorder Society.
Références
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(1):121-134. https://doi.org/10.1007/s00441-004-0956-9
Berg D, Borghammer P, Fereshtehnejad SM, et al. Prodromal Parkinson disease subtypes - key to understanding heterogeneity. Nat Rev Neurol 2021;17(6):349-361. https://doi.org/10.1038/s41582-021-00486-9
Madrid J, Benninger DH. Non-invasive brain stimulation for Parkinson's disease: clinical evidence, latest concepts and future goals: a systematic review. J Neurosci Methods 2021;347. https://doi.org/10.1016/j.jneumeth.2020.108957
Mezias C, Rey N, Brundin P, Raj A. Neural connectivity predicts spreading of alpha-synuclein pathology in fibril-injected mouse models: involvement of retrograde and anterograde axonal propagation. Neurobiol Dis 2020;134. https://doi.org/10.1016/j.nbd.2019.104623
Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M. Resting state fMRI in Alzheimer's disease: beyond the default mode network. Neurobiol Aging 2012;33(8):1564-1578. https://doi.org/10.1016/j.neurobiolaging.2011.06.007
Luo Y, Sun T, Ma C, et al. Alterations of brain networks in Alzheimer's disease and mild cognitive impairment: a resting state fMRI study based on a population-specific brain template. Neuroscience 2021;452. https://doi.org/10.1016/j.neuroscience.2020.10.023
Babiloni C, Del Percio C, Pascarelli MT, et al. Abnormalities of functional cortical source connectivity of resting-state electroencephalographic alpha rhythms are similar in patients with mild cognitive impairment due to Alzheimer's and Lewy body diseases. Neurobiol Aging 2019;77:112-127. https://doi.org/10.1016/j.neurobiolaging.2019.01.013
Tessitore A, Cirillo M, De MR. Functional connectivity signatures of Parkinson's disease. 2019;9:637-652. https://doi.org/10.3233/JPD-191592
Gratton C, Koller JM, Shannon W, et al. Emergent functional network effects in Parkinson disease. Cereb Cortex 2019;29(6):2509-2523. https://doi.org/10.1093/cercor/bhy121
De MR, Agosta F, Basaia S, Siciliano M. Functional connectomics and disease progression in drug-Naïve Parkinson's disease patients. 2021;36(7):1-15. https://doi.org/10.1002/mds.28541
Rolinski M, Griffanti L, Szewczyk-Krolikowski K, et al. Aberrant functional connectivity within the basal ganglia of patients with Parkinson's disease. Neuroimage Clin 2015;8:126-132. https://doi.org/10.1016/j.nicl.2015.04.003
Wu T, Wang L, Chen Y, Zhao C, Li K, Chan P. Changes of functional connectivity of the motor network in the resting state in Parkinson's disease. Neurosci Lett 2009;460(1):6-10. https://doi.org/10.1016/j.neulet.2009.05.046
Esposito F, Tessitore A, Giordano A, et al. Rhythm-specific modulation of the sensorimotor network in drug-naïve patients with Parkinson's disease by levodopa. Brain 2013;136(3):710-725. https://doi.org/10.1093/brain/awt007
Wu T, Long X, Wang L, et al. Functional connectivity of cortical motor areas in the resting state in Parkinson's disease. Hum Brain Mapp 2011;32(9):1443-1457. https://doi.org/10.1002/hbm.21118
Tessitore A, Esposito F, Vitale C, et al. Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology 2012;79(23):2226-2232. https://doi.org/10.1212/WNL.0b013e31827689d6
Wolters AF, van de Weijer SCF, Leentjens AFG, Duits AA, Jacobs HIL, Kuijf ML. Resting-state fMRI in Parkinson's disease patients with cognitive impairment: a meta-analysis. Parkinsonism Relat Disord 2019;62:16-27. https://doi.org/10.1016/j.parkreldis.2018.12.016
Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 1990;87(24):9868-9872. https://doi.org/10.1073/pnas.87.24.9868
Nentwich M, Ai L, Madsen J, et al. NeuroImage Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI. Neuroimage 2020;218(May):117001. https://doi.org/10.1016/j.neuroimage.2020.117001
Duan F, Huang Z, Sun Z, et al. Topological network analysis of early Alzheimer's disease based on resting-state EEG. IEEE Trans Neural Syst Rehabil Eng 2020;28(10):2164-2172. https://doi.org/10.1109/TNSRE.2020.3014951
Aoki Y, Kazui H, Pascal-Marqui RD, et al. EEG resting-state networks in dementia with Lewy bodies associated with clinical symptoms. Neuropsychobiology 2019;77(4):206-218. https://doi.org/10.1159/000495620
Kühn AA, Williams D, Kupsch A, et al. Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. Brain 2004;127(4):735-746. https://doi.org/10.1093/brain/awh106
Conti M, Bovenzi R, Garasto E, et al. Brain functional connectivity in de novo Parkinson's disease patients based on clinical EEG. Front Neurol 2022;13. https://doi.org/10.3389/fneur.2022.844745
Yassine S, Gschwandtner U, Auffret M, et al. Functional brain dysconnectivity in Parkinson's disease: a 5-year longitudinal study. Mov Disord 2022;37(7):1444-1453. https://doi.org/10.1002/mds.29026
Berg D, Postuma RB, Bloem B, et al. Time to redefine PD? Introductory statement of the MDS task force on the definition of Parkinson's disease. Mov Disord 2014;29(4):454-462. https://doi.org/10.1002/mds.25844
Sciacca G, Mostile G, Disilvestro I, Donzuso G, Nicoletti A, Zappia M. Long-duration response to levodopa, motor learning, and neuroplasticity in early Parkinson's disease. Mov Disord 2023;38(4):626-635. https://doi.org/10.1002/mds.29344
Goetz CG, Poewe W, Rascol O, et al. Movement Disorder Society task force report on the Hoehn and Yahr staging scale: status and recommendations. Mov Disord 2004;19(9):1020-1028. https://doi.org/10.1002/mds.20213
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12(3):189-198. https://doi.org/10.1016/0022-3956(75)90026-6
Goetz CG, Tilley BC, Shaftman SR, 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(15):2129-2170. https://doi.org/10.1002/mds.22340
Chaudhuri KR, Martinez-Martin P, Brown RG, et al. The metric properties of a novel non-motor symptoms scale for Parkinson's disease: results from an international pilot study. Mov Disord. 2007;22(13):1901-1911. https://doi.org/10.1002/mds.21596
Postuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 2015;30(12):1591-1601. https://doi.org/10.1002/mds.26424
Schade S, Mollenhauer B, Trenkwalder C. Levodopa equivalent dose conversion factors: an updated proposal including Opicapone and safinamide. Mov Disord Clin Pract 2020;7(3):343-345. https://doi.org/10.1002/mdc3.12921
Committee C, Langston JW, Widner H, et al. Core assessment program for intracerebral transplantations (CAPIT). Mov Disord 1992; 7(1):2-13.
Nuwer MR. 10-10 electrode system for EEG recording. Clin Neurophysiol 2018;129(5):1103. https://doi.org/10.1016/j.clinph.2018.01.065
Yassine S, Gschwandtner U, Auffret M, et al. Identification of Parkinson's disease subtypes from resting-state electroencephalography. Mov Disord. 2023;38(8):1451-1460. https://doi.org/10.1002/mds.29451
Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw 2000;13(4-5):411-430. https://doi.org/10.1016/S0893-6080(00)00026-5
Conti M, Stefani A, Bovenzi R, et al. STN-DBS induces acute changes in β-band cortical functional connectivity in patients with Parkinson's disease. Brain Sci 2022;12(12):1606. https://doi.org/10.3390/brainsci12121606
Gaser C, Dahnke R. CAT - A computational anatomy toolbox for the analysis of structural MRI data. GaserHBM2016. [1] C Gaser and R Dahnke, “GaserHBM2016,” vol 32, no 7, p 7743, 2012 2012;32(7):7743.
Jatoi MA, Kamel N, Faye I, Malik AS, Bornot JM, Begum T. BEM based solution of forward problem for brain source estimation. IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings, Kuala Lumpur, Malaysia. IEEE Xplore; 2016:180-185. https://doi.org/10.1109/ICSIPA.2015.7412186.
Grech R, Cassar T, Muscat J, et al. Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil 2008;5:1-33. https://doi.org/10.1186/1743-0003-5-25
Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31(3):968-980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Hardmeier M, Hatz F, Bousleiman H, Schindler C, Stam CJ, Fuhr P. Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS One 2014;9(10):e108648. https://doi.org/10.1371/journal.pone.0108648
McCormick DA, Bal T. Sleep and arousal: thalamocortical mechanisms. Annu Rev Neurosci 1997;20:185-215. https://doi.org/10.1146/annurev.neuro.20.1.185
Foffani G, Alegre M. Brain oscillations and Parkinson disease. Handbook of Clinical Neurology. Vol. 184. Elsevier, U.S; 2022;259-271. https://doi.org/10.1016/B978-0-12-819410-2.00014-X.
Jwo DJ, Chang WY, Wu IH. Windowing techniques, the Welch method for improvement of power Spectrum estimation. ComputMater Continua 2021;67(3):3983-4003. https://doi.org/10.32604/cmc.2021.014752
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage 2010;53(4):1197-1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
Serin E, Zalesky A, Matory A, Walter H, Kruschwitz JD. NBS-predict: a prediction-based extension of the network-based statistic. Neuroimage 2021;244. https://doi.org/10.1016/j.neuroimage.2021.118625
Santini A, Man A, Voidăzan S. Accuracy of diagnostic tests. The. J Crit Care Med 2021;7(3):241-248. https://doi.org/10.2478/jccm-2021-0022
Streiner DL, Norman GR. “Precision” and “accuracy”: two terms that are neither. J Clin Epidemiol 2006;59(4):327-330. https://doi.org/10.1016/j.jclinepi.2005.09.005
McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012;22(3):276-282. https://doi.org/10.11613/bm.2012.031
Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011;2011. https://doi.org/10.1155/2011/879716
Varangis E, Habeck CG, Razlighi QR, Stern Y. The effect of aging on resting state connectivity of predefined networks in the brain. Front Aging Neurosci 2019;11:234. https://doi.org/10.3389/fnagi.2019.00234
Tomasi D, Volkow ND. Laterality patterns of brain functional connectivity: gender effects. Cereb Cortex 2012;22(6):1455-1462. https://doi.org/10.1093/cercor/bhr230
De Micco R, Esposito F, di Nardo F, et al. Sex-related pattern of intrinsic brain connectivity in drug-naïve Parkinson's disease patients. Mov Disord 2019;34(7):997-1005. https://doi.org/10.1002/mds.27725
Ara A, Marco-Pallarés J. Different theta connectivity patterns underlie pleasantness evoked by familiar and unfamiliar music. Sci Rep 2021;11(1):18523. https://doi.org/10.1038/s41598-021-98033-5
Uusberg A, Thiruchselvam R, Gross JJ. Using distraction to regulate emotion: insights from EEG theta dynamics. Int J Psychophysiol 2014;91(3):254-260. https://doi.org/10.1016/j.ijpsycho.2014.01.006
Karakaş S. A review of theta oscillation and its functional correlates. Int J Psychophysiol 2020;157:82-99. https://doi.org/10.1016/j.ijpsycho.2020.04.008
Mysin I, Shubina L. From mechanisms to functions: the role of theta and gamma coherence in the intrahippocampal circuits. Hippocampus 2022;32(5):342-358. https://doi.org/10.1002/hipo.23410
Huang MH, Fan SY, Lin IM. EEG coherences of the fronto-limbic circuit between patients with major depressive disorder and healthy controls. J Affect Disord 2023;331:112-120. https://doi.org/10.1016/J.JAD.2023.03.055
Wen X, Wu X, Liu J, Li K, Yao L. Abnormal baseline brain activity in non-depressed Parkinson's disease and depressed Parkinson's disease: a resting-state functional magnetic resonance imaging study. PloS One 2013;8(5):e63691. https://doi.org/10.1371/journal.pone.0063691
Leocani L, Comi G. EEG coherence in pathological conditions. J Clin Neurophysiol 1999;16(6):548-555. https://doi.org/10.1097/00004691-199911000-00006
Canuet L, Tellado I, Couceiro V, et al. Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study. PloS One 2012;7(9):e46289. https://doi.org/10.1371/journal.pone.0046289
Rocca MA, Valsasina P, Absinta M, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 2010;74(16):1252-1259. https://doi.org/10.1212/WNL.0b013e3181d9ed91
Babiloni C, Lizio R, Marzano N, et al. Brain neural synchronization and functional coupling in Alzheimer's disease as revealed by resting state EEG rhythms. Int J Psychophysiol 2016;103:88-102. https://doi.org/10.1016/j.ijpsycho.2015.02.008
Gurja JP, Muthukrishnan SP, Tripathi M, Sharma R. Reduced resting-state cortical alpha connectivity reflects distinct functional brain dysconnectivity in Alzheimer's disease and mild cognitive impairment. Brain. Connect 2022;12(2):134-145. https://doi.org/10.1089/brain.2020.0926
Babiloni C, Frisoni GB, Vecchio F, et al. Global functional coupling of resting EEG rhythms is related to white-matter lesions along the cholinergic tracts in subjects with amnesic mild cognitive impairment. J Alzheimers Dis 2010;19(3):859-871. https://doi.org/10.3233/JAD-2010-1290
van der Zee S, Kanel P, Gerritsen MJJ, et al. Altered cholinergic innervation in De novo Parkinson's disease with and without cognitive impairment. Mov Disord 2022;37(4):713-723. https://doi.org/10.1002/mds.28913
Canu E, Agosta F, Sarasso E, et al. Brain structural and functional connectivity in Parkinson's disease with freezing of gait. Hum Brain Mapp 2015;36(12):5064-5078. https://doi.org/10.1002/hbm.22994
Tessitore A, Amboni M, Esposito F, et al. Resting-state brain connectivity in patients with Parkinson's disease and freezing of gait. Parkinsonism Relat Disord 2012;18(6):781-787. https://doi.org/10.1016/j.parkreldis.2012.03.018
Fling BW, Cohen RG, Mancini M, et al. Functional reorganization of the locomotor network in parkinson patients with freezing of gait. PloS One 2014;9(6):e100291. https://doi.org/10.1371/journal.pone.0100291
He S, Deli A, Fischer P, et al. Gait-phase modulates alpha and beta oscillations in the pedunculopontine nucleus. J Neurosci 2021;41(41):8390-8402. https://doi.org/10.1523/JNEUROSCI.0770-21.2021
Stefani A, Lozano AM, Peppe A, et al. Bilateral deep brain stimulation of the pedunculopontine and subthalamic nuclei in severe Parkinson's disease. Brain 2007;130(6):1596-1607. https://doi.org/10.1093/brain/awl346
Dalrymple WA, Huss DS, Blair J, et al. Cholinergic nucleus 4 atrophy and gait impairment in Parkinson's disease. J Neurol 2021;268(1):95-101. https://doi.org/10.1007/s00415-020-10111-2
Wilson J, Yarnall AJ, Craig CE, et al. Cholinergic basal forebrain volumes predict gait decline in Parkinson's disease. Mov Disord 2021;36(3):611-621. https://doi.org/10.1002/mds.28453
Morris R, Martini DN, Madhyastha T, et al. Overview of the cholinergic contribution to gait, balance and falls in Parkinson's disease. Parkinsonism Relat Disord 2019;63:20-30. https://doi.org/10.1016/j.parkreldis.2019.02.017
Little S, Brown P. The functional role of beta oscillations in Parkinson's disease. Parkinsonism Relat Disord 2014;20(SUPPL.1) S44-S48. https://doi.org/10.1016/S1353-8020(13)70013-0
Oswal A, Brown P, Litvak V. Synchronized neural oscillations and the pathophysiology of Parkinson's disease. Curr Opin Neurol 2013;26(6):662-670. https://doi.org/10.1097/WCO.0000000000000034
Brown P. Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of parkinson's disease. Mov Disord 2003;18(4):357-363. https://doi.org/10.1002/mds.10358
Brown P, Oliviero A, Mazzone P, Insola A, Tonali P, Di Lazzaro V. Dopamine dependency of oscillations between subthalamic nucleus and pallidum in Parkinson's disease. J Neurosci 2001;21(3):1033-1038. https://doi.org/10.1523/jneurosci.21-03-01033.2001
Fogelson N, Williams D, Tijssen M, Van Bruggen G, Speelman H, Brown P. Different functional loops between cerebral cortex and the subthalmic area in parkinson's disease. Cereb Cortex 2006;16(1):64-75. https://doi.org/10.1093/cercor/bhi084
Litvak V, Jha A, Eusebio A, et al. Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson's disease. Brain 2011;134(2):359-374. https://doi.org/10.1093/brain/awq332
Oswal A, Beudel M, Zrinzo L, et al. Deep brain stimulation modulates synchrony within spatially and spectrally distinct resting state networks in Parkinson's disease. Brain 2016;139(5):1482-1496. https://doi.org/10.1093/brain/aww048
Oswal A, Gratwicke J, Akram H, et al. Cortical connectivity of the nucleus basalis of Meynert in Parkinson's disease and Lewy body dementias. Brain 2021;144(3):781-788. https://doi.org/10.1093/brain/awaa411
Bologna M, Paparella G, Fasano A, Hallett M, Berardelli A. Evolving concepts on bradykinesia. Brain 2020;143(3):727-750. https://doi.org/10.1093/brain/awz344
Guerra A, Asci F, D'Onofrio V, et al. Enhancing gamma oscillations restores primary motor cortex plasticity in Parkinson's disease. J Neurosci 2020;40(24):4788-4796. https://doi.org/10.1523/JNEUROSCI.0357-20.2020
Costumero V, Bueichekú E, Adrián-Ventura J, Ávila C. Opening or closing eyes at rest modulates the functional connectivity of V1 with default and salience networks. Sci Rep 2020;10(1):9137. https://doi.org/10.1038/s41598-020-66100-y
Samogin J, Marino M, Porcaro C, et al. Frequency-dependent functional connectivity in resting state networks. Hum Brain Mapp 2020;41(18):5187-5198. https://doi.org/10.1002/hbm.25184
Lau TM, Gwin JT, McDowell KG, Ferris DP. Weighted phase lag index stability as an artifact resistant measure to detect cognitive EEG activity during locomotion. J Neuroeng Rehabil 2012;9:47. https://doi.org/10.1186/1743-0003-9-47
Neumann WJ, Degen K, Schneider GH, et al. Subthalamic synchronized oscillatory activity correlates with motor impairment in patients with Parkinson's disease. Mov Disord 2016;31(11):1748-1751. https://doi.org/10.1002/mds.26759
Espinoza AI, May P, Anjum MF, et al. A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease. Clin Parkinsonism Related Disord 2022;7:100166. https://doi.org/10.1016/j.prdoa.2022.100166
Han CX, Wang J, Yi GS, Che YQ. Investigation of EEG abnormalities in the early stage of Parkinson's disease. Cogn Neurodyn 2013;7(4):351-359. https://doi.org/10.1007/s11571-013-9247-z
Jackson N, Cole SR, Voytek B, Swann NC. Characteristics of waveform shape in Parkinson's disease detected with scalp electroencephalography. eNeuro 2019;6(3):ENEURO.0151. https://doi.org/10.1523/ENEURO.0151-19.2019