Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis

Objective: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson’s Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibili...

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Autores:
Jaramillo-Jiménez, Alberto
Tovar-Rios, Diego Alejandro
Ospina Galindez, Johann Alexis
Mantilla-Ramos, Yorguin José
Loaiza-López, Daniel
Henao Isaza, Verónica
Zapata Saldarriaga, Luisa María
Valeria Cadavid Castro
Suárez-Revelo, Jazmin Ximena
Bocanegra, Yamilé
Lopera, Francisco
Pineda-Salazar, David Antonio
Tobón Quintero, Carlos Andrés
Ochoa-Gómez, John Fredy
Borda, Miguel Germán
Aarsland, Dag
Laura Bonanni
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/15852
Acceso en línea:
https://hdl.handle.net/10614/15852
https://doi.org/10.1016/j.clinph.2023.03.363
https://red.uao.edu.co/
Palabra clave:
Functional data analysis
Electroencephalography
Parkinson’s disease
Alpha rhythm
Theta rhythm
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openAccess
License
Derechos reservados - Elsevier, 2023
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oai_identifier_str oai:red.uao.edu.co:10614/15852
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network_name_str RED: Repositorio Educativo Digital UAO
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dc.title.eng.fl_str_mv Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
title Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
spellingShingle Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
Functional data analysis
Electroencephalography
Parkinson’s disease
Alpha rhythm
Theta rhythm
title_short Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
title_full Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
title_fullStr Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
title_full_unstemmed Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
title_sort Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis
dc.creator.fl_str_mv Jaramillo-Jiménez, Alberto
Tovar-Rios, Diego Alejandro
Ospina Galindez, Johann Alexis
Mantilla-Ramos, Yorguin José
Loaiza-López, Daniel
Henao Isaza, Verónica
Zapata Saldarriaga, Luisa María
Valeria Cadavid Castro
Suárez-Revelo, Jazmin Ximena
Bocanegra, Yamilé
Lopera, Francisco
Pineda-Salazar, David Antonio
Tobón Quintero, Carlos Andrés
Ochoa-Gómez, John Fredy
Borda, Miguel Germán
Aarsland, Dag
Laura Bonanni
dc.contributor.author.none.fl_str_mv Jaramillo-Jiménez, Alberto
Tovar-Rios, Diego Alejandro
Ospina Galindez, Johann Alexis
Mantilla-Ramos, Yorguin José
Loaiza-López, Daniel
Henao Isaza, Verónica
Zapata Saldarriaga, Luisa María
Valeria Cadavid Castro
Suárez-Revelo, Jazmin Ximena
Bocanegra, Yamilé
Lopera, Francisco
Pineda-Salazar, David Antonio
Tobón Quintero, Carlos Andrés
Ochoa-Gómez, John Fredy
Borda, Miguel Germán
Aarsland, Dag
Laura Bonanni
dc.subject.proposal.eng.fl_str_mv Functional data analysis
Electroencephalography
Parkinson’s disease
Alpha rhythm
Theta rhythm
topic Functional data analysis
Electroencephalography
Parkinson’s disease
Alpha rhythm
Theta rhythm
description Objective: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson’s Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. Methods: We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-toepoch change of each feature. Results: For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar
publishDate 2023
dc.date.issued.none.fl_str_mv 2023
dc.date.accessioned.none.fl_str_mv 2024-10-08T13:16:48Z
dc.date.available.none.fl_str_mv 2024-10-08T13:16:48Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Jaramillo-Jiménez, A., et. al. (2023). Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis. Clinical Neurophysiology. volumen 151. p.p. 28-40. ISSN: 1388-2457. Online ISSN: 1872-8952. https://doi.org/10.1016/j.clinph.2023.03.363
dc.identifier.issn.spa.fl_str_mv 13882457
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/15852
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.clinph.2023.03.363
dc.identifier.eissn.spa.fl_str_mv 18728952
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
identifier_str_mv Jaramillo-Jiménez, A., et. al. (2023). Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis. Clinical Neurophysiology. volumen 151. p.p. 28-40. ISSN: 1388-2457. Online ISSN: 1872-8952. https://doi.org/10.1016/j.clinph.2023.03.363
13882457
18728952
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
url https://hdl.handle.net/10614/15852
https://doi.org/10.1016/j.clinph.2023.03.363
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 40
dc.relation.citationstartpage.spa.fl_str_mv 28
dc.relation.citationvolume.spa.fl_str_mv 151
dc.relation.ispartofjournal.eng.fl_str_mv Clinical Neurophysiology
dc.relation.references.none.fl_str_mv Al-Qazzaz NK, Ali SHBM, Ahmad SA, Chellappan K, Islam MS, Escudero J. Role of EEG as Biomarker in the Early Detection and Classification of Dementia. Sci World J 2014. https://doi.org/10.1155/2014/906038.
Amalric M, Pattij T, Sotiropoulos I, Silva JM, Sousa N, Ztaou S, et al. Where Dopaminergic and Cholinergic Systems Interact: A Gateway for Tuning Neurodegenerative Disorders. Front Behav Neurosci 2021;15:147. https://doi.org/10.3389/FNBEH.2021.661973/BIBTEX.
Anjum MF, Dasgupta S, Mudumbai R, Singh A, Cavanagh JF, Narayanan NS. Linear predictive coding distinguishes spectral EEG features of Parkinson’s disease. Parkinsonism Relat Disord 2020;79:79–85. https://doi.org/10.1016/J. PARKRELDIS.2020.08.001.
Appelhoff S, Hurst AJ, Lawrence A, Li A, Mantilla Ramos YJ, O’Reilly C, et al. PyPREP: A Python implementation of the preprocessing pipeline (PREP) for EEG data. 2022. https://doi.org/10.5281/ZENODO.6363576.
Arnaldi D, De Carli F, Famà F, Brugnolo A, Girtler N, Picco A, et al. Prediction of cognitive worsening in de novo Parkinson’s disease: Clinical use of biomarkers. Mov Disord 2017;32:1738–47. https://doi.org/10.1002/mds.27190.
Babiloni C, Barry RJ, Bas ar E, Blinowska KJ, Cichocki A, Drinkenburg WHIM, et al. International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies. Clin Neurophysiol 2020:285–307. https://doi.org/10.1016/j.clinph.2019.06.234.
Babiloni C, Blinowska K, Bonanni L, Cichocki A, De Haan W, Del Percio C, et al. What electrophysiology tells us about Alzheimer’s disease: a window into the synchronisation and connectivity of brain neurons. Neurobiol Aging 2020b;85:58–73. https://doi.org/10.1016/j.neurobiolaging.2019.09.008.
Barzegaran E, Vildavski VY, Knyazeva MG. Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior. Sci Reports 2017;2017(71):7. https://doi.org/10.1038/s41598-017- 08421-z.
Beer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, et al. Longitudinal ComBat: A method for harmonising longitudinal multi-scanner imaging data. Neuroimage 2020;220. https://doi.org/10.1016/J. NEUROIMAGE.2020.117129 117129.
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Bigdely-Shamlo N, Touryan J, Ojeda A, Kothe C, Mullen T, Robbins K. Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies. Neuroimage 2020;207. https://doi.org/10.1016/j.neuroimage.2019.116361 116361.
Bonanni L, Franciotti R, Nobili F, Kramberger MG, Taylor J-P, Garcia-Ptacek S, et al. EEG Markers of Dementia with Lewy Bodies: A Multicenter Cohort Study. J Alzheimer’s Dis 2016;54:1649–57. https://doi.org/10.3233/JAD-160435.
Bonanni L, Perfetti B, Bifolchetti S, Taylor J-P, Franciotti R, Parnetti L, et al. Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies. Neurobiol Aging 2015;36:434. https://doi.org/10.1016/J.NEUROBIOLAGING.2014.07.009.
Bonanni L, Thomas A, Tiraboschi P, Perfetti B, Varanese S, Onofrj M. EEG comparisons in early Alzheimer’s disease, dementia with Lewy bodies and Parkinson’s disease with dementia patients with a 2-year follow-up. Brain 2008;131:690–705. https://doi.org/10.1093/brain/awm322.
Carmona Arroyave JA, Tobón Quintero CA, Suárez Revelo JJ, Ochoa Gómez JF, García YB, Gómez LM, et al. Resting functional connectivity and mild cognitive impairment in Parkinson’s disease. An electroencephalogram study. Future Neurol 2019;14:FNL18. https://doi.org/10.2217/fnl-2018-0048.
Carrarini C, Calisi D, De Rosa MA, Di Iorio A, D’Ardes D, Pellegrino R, et al. QEEG abnormalities in cognitively unimpaired patients with delirium. J Neurol Neurosurg Psychiatry 2023:94. https://doi.org/10.1136/JNNP-2022-330010.
Castellanos NP, Makarov VA. Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods 2006;158:300–12. https://doi.org/10.1016/j.jneumeth.2006.05.033.
Coffey N, Hinde J. Analysing time-course microarray data using functional data analysis - A review. Stat Appl Genet Mol Biol 2011:10. https://doi.org/10.2202/ 1544-6115.1671/MACHINEREADABLECITATION/RIS.
Donoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, et al. Parameterising neural power spectra into periodic and aperiodic components. Nat Neurosci 2020 2312 2020;23:1655–65. https://doi.org/10.1038/s41593-020-00744-x.
Eubank RL. Nonparametric Regression and Spline Smoothing. Second Edition. Taylor & Francis; 1999. 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;12:189–98. https://doi.org/10.1016/0022-3956(75)90026-6.
Franciotti R, Pilotto A, Moretti DV, Falasca NW, Arnaldi D, Taylor J-P, et al. Anterior EEG slowing in dementia with Lewy bodies: a multicenter European cohort study. Neurobiol Aging 2020;93:55–60. https://doi.org/10.1016/j. neurobiolaging.2020.04.023.
Garcés P, Vicente R, Wibral M, Pineda-Pardo J ángel, López ME, Aurtenetxe S, et al. Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment. Front Aging Neurosci 2013;5:100. https://doi.org/10.3389/ FNAGI.2013.00100/BIBTEX.
George JS, Strunk J, Mak-Mccully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in Parkinson’s disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage (Amst) 2013;3:261. https://doi.org/10.1016/J.NICL.2013.07.013.
Gibb WRG, Lees AJ. The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease. J Neurol Neurosurg Psychiatry 1988;51:745–52. https://doi.org/10.1136/JNNP.51.6.745.
Goetz CC. The Unified Parkinson’s Disease Rating Scale (UPDRS): Status and recommendations. Mov Disord 2003;18:738–50. https://doi.org/10.1002/ mds.10473.
Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. MEG and EEG data analysis with MNE-Python. Front Neurosci 2013:267. https://doi. org/10.3389/FNINS.2013.00267/BIBTEX.
Jackson N, Cole SR, Voytek B, Swann NC. Characteristics of Waveform Shape in Parkinson’s Disease Detected with Scalp Electroencephalography. ENeuro 2019:6. https://doi.org/10.1523/ENEURO.0151-19.2019.
Jaramillo-Jimenez A, Suarez-Revelo JX, Ochoa-Gomez JF, Carmona Arroyave JA, Bocanegra Y, Lopera F, et al. Resting-state EEG alpha/theta ratio related to neuropsychological test performance in Parkinson’s Disease. Clin Neurophysiol 2021;132:756–64. https://doi.org/10.1016/j.clinph.2021.01.001.
Jennings JL, Peraza LR, Baker M, Alter K, Taylor J-P, Bauer R. Investigating the power of eyes open resting state EEG for assisting in dementia diagnosis. Alzheimer’s Res Ther 2022;2022(141):14. https://doi.org/10.1186/S13195-022-01046-Z.
Kehagia AA, Barker RA, Robbins TW. Cognitive impairment in Parkinson’s disease: The dual syndrome hypothesis. Neurodegener Dis 2012;11:79–92. https://doi. org/10.1159/000341998.
Kimchi EY, Neelagiri A, Whitt W, Sagi AR, Ryan SL, Gadbois G, et al. Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes. Neurology 2019;93:e1260.
Law ZK, Todd C, Mehraram R, Schumacher J, Baker MR, LeBeau FEN, et al. The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies—A Systematic Review. Diagnostics 2020;10:616. https://doi.org/10.3390/DIAGNOSTICS10090616.
Li M, Wang Y, Lopez-Naranjo C, Hu S, Reyes RCG, Paz-Linares D, et al. Harmonized- Multinational qEEG norms (HarMNqEEG). Neuroimage 2022;256. https://doi. org/10.1016/J.NEUROIMAGE.2022.119190 119190.
Litvan I, Goldman JG, Tröster AI, Schmand BA, Weintraub D, Petersen RC, et al. Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Mov Disord 2012;27:349–56. https://doi.org/10.1002/mds.24893.
Llinás RR, Ribary U, Jeanmonod D, Kronberg E, Mitra PP. Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterised by magnetoencephalography. Proc Natl Acad Sci U S A 1999;96:15222–7. https:// doi.org/10.1073/pnas.96.26.15222.
Lopes da Silva F. Neural mechanisms underlying brain waves: from neural membranes to networks. Electroencephalogr Clin Neurophysiol 1991;79:81–93. https://doi.org/10.1016/0013-4694(91)90044-5.
Mari-Acevedo J, Yelvington K, Tatum WO. Normal EEG variants. Handb Clin Neurol 2019;160:143–60. https://doi.org/10.1016/B978-0-444-64032-1.00009-6.
Massa F, Meli R, Grazzini M, Famà F, De Carli F, Filippi L, et al. Utility of quantitative EEG in early Lewy body disease. Park Relat Disord 2020;75:70–5. https://doi. org/10.1016/j.parkreldis.2020.05.007.
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695–9. https://doi.org/ 10.1111/j.1532-5415.2005.53221.x.
Ou Z, Pan J, Tang S, Duan D, Yu D, Nong H, et al. Global Trends in the Incidence, Prevalence, and Years Lived With Disability of Parkinson’s Disease in 204 Countries/Territories From 1990 to 2019. Front Public Heal 2021;9. https://doi. org/10.3389/FPUBH.2021.776847/FULL 776847.
dc.rights.spa.fl_str_mv Derechos reservados - Elsevier, 2023
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spelling Jaramillo-Jiménez, AlbertoTovar-Rios, Diego AlejandroOspina Galindez, Johann Alexisvirtual::5714-1Mantilla-Ramos, Yorguin JoséLoaiza-López, DanielHenao Isaza, VerónicaZapata Saldarriaga, Luisa MaríaValeria Cadavid CastroSuárez-Revelo, Jazmin XimenaBocanegra, YamiléLopera, FranciscoPineda-Salazar, David AntonioTobón Quintero, Carlos AndrésOchoa-Gómez, John FredyBorda, Miguel GermánAarsland, DagLaura Bonanni2024-10-08T13:16:48Z2024-10-08T13:16:48Z2023Jaramillo-Jiménez, A., et. al. (2023). Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysis. Clinical Neurophysiology. volumen 151. p.p. 28-40. ISSN: 1388-2457. Online ISSN: 1872-8952. https://doi.org/10.1016/j.clinph.2023.03.36313882457https://hdl.handle.net/10614/15852https://doi.org/10.1016/j.clinph.2023.03.36318728952Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Objective: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson’s Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. Methods: We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-toepoch change of each feature. Results: For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar13 páginasapplication/pdfengElsevierDerechos reservados - Elsevier, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Spectral features of resting-state EEG in Parkinson’s Disease: A multicenter study using functional data analysisArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a854028151Clinical NeurophysiologyAl-Qazzaz NK, Ali SHBM, Ahmad SA, Chellappan K, Islam MS, Escudero J. Role of EEG as Biomarker in the Early Detection and Classification of Dementia. Sci World J 2014. https://doi.org/10.1155/2014/906038.Amalric M, Pattij T, Sotiropoulos I, Silva JM, Sousa N, Ztaou S, et al. Where Dopaminergic and Cholinergic Systems Interact: A Gateway for Tuning Neurodegenerative Disorders. Front Behav Neurosci 2021;15:147. https://doi.org/10.3389/FNBEH.2021.661973/BIBTEX.Anjum MF, Dasgupta S, Mudumbai R, Singh A, Cavanagh JF, Narayanan NS. Linear predictive coding distinguishes spectral EEG features of Parkinson’s disease. Parkinsonism Relat Disord 2020;79:79–85. https://doi.org/10.1016/J. PARKRELDIS.2020.08.001.Appelhoff S, Hurst AJ, Lawrence A, Li A, Mantilla Ramos YJ, O’Reilly C, et al. PyPREP: A Python implementation of the preprocessing pipeline (PREP) for EEG data. 2022. https://doi.org/10.5281/ZENODO.6363576.Arnaldi D, De Carli F, Famà F, Brugnolo A, Girtler N, Picco A, et al. Prediction of cognitive worsening in de novo Parkinson’s disease: Clinical use of biomarkers. Mov Disord 2017;32:1738–47. https://doi.org/10.1002/mds.27190.Babiloni C, Barry RJ, Bas ar E, Blinowska KJ, Cichocki A, Drinkenburg WHIM, et al. International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies. Clin Neurophysiol 2020:285–307. https://doi.org/10.1016/j.clinph.2019.06.234.Babiloni C, Blinowska K, Bonanni L, Cichocki A, De Haan W, Del Percio C, et al. What electrophysiology tells us about Alzheimer’s disease: a window into the synchronisation and connectivity of brain neurons. Neurobiol Aging 2020b;85:58–73. https://doi.org/10.1016/j.neurobiolaging.2019.09.008.Barzegaran E, Vildavski VY, Knyazeva MG. Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior. Sci Reports 2017;2017(71):7. https://doi.org/10.1038/s41598-017- 08421-z.Beer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, et al. Longitudinal ComBat: A method for harmonising longitudinal multi-scanner imaging data. 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