Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space
dc.contributor.author | Martín-Buro, María Carmen | |
dc.contributor.author | Garcés, Pilar | |
dc.contributor.author | Maestú Unturbe, Fernando | |
dc.date.accessioned | 2023-06-18T06:56:09Z | |
dc.date.available | 2023-06-18T06:56:09Z | |
dc.date.issued | 2015-10-14 | |
dc.description.abstract | Several studies have reported changes in spontaneous brain rhythms that could be used asclinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), low beta (13–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted inhigh within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials. | |
dc.description.department | Depto. de Psicología Experimental, Procesos Cognitivos y Logopedia | |
dc.description.faculty | Fac. de Psicología | |
dc.description.refereed | TRUE | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/39218 | |
dc.identifier.doi | 10.1002/hbm.23027 | |
dc.identifier.issn | 1065-9471 | |
dc.identifier.officialurl | http://dx.doi.org/10.1002/hbm.23027 | |
dc.identifier.relatedurl | http://onlinelibrary.wiley.com/doi/10.1002/hbm.v37.1/issuetoc | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/24621 | |
dc.issue.number | 1 | |
dc.journal.title | Human brain mapping | |
dc.language.iso | eng | |
dc.page.final | 190 | |
dc.page.initial | 179 | |
dc.publisher | Wiley | |
dc.rights.accessRights | open access | |
dc.subject.cdu | 159.95 | |
dc.subject.cdu | 612.8 | |
dc.subject.keyword | MEG | |
dc.subject.keyword | Reliability | |
dc.subject.keyword | Esting state | |
dc.subject.keyword | Rain rhythms | |
dc.subject.keyword | Test-retest | |
dc.subject.keyword | Spectral power | |
dc.subject.keyword | Signal to noise ratio | |
dc.subject.keyword | Intraclass correlation coefficient | |
dc.subject.keyword | Coeficiente de correlación intraclase | |
dc.subject.keyword | Espectro de potencia | |
dc.subject.keyword | Fiabilidad | |
dc.subject.keyword | Magnetoencefalografía | |
dc.subject.keyword | Resting state | |
dc.subject.keyword | Ritmos cerebrales. | |
dc.subject.ucm | Psicología cognitiva | |
dc.subject.ucm | Neurociencias (Biológicas) | |
dc.subject.unesco | 6104.01 Procesos Cognitivos | |
dc.subject.unesco | 2490 Neurociencias | |
dc.title | Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space | |
dc.type | journal article | |
dc.volume.number | 37 | |
dcterms.references | Acar ZA, Makeig S (2010): Neuroelectromagnetic Forward Head Modeling Toolbox. J Neurosci Methods 190:258–270. Assenza G, Pellegrino G, Tombini M, Di Pino G, Tomasevic L, Tecchio F, Di Lazzaro V (2013): Delta waves increase after cortical plasticity induction during wakefulness. Clin Neurophysiol 124:e71–e72. Atcherson SR, Gould HJ, Pousson Ma, Prout TM (2006): Longterm stability of N1 sources using low-resolution electromagnetic tomography. Brain Topogr 19:11–20. Bas¸ar E, Guntekin B (2012): A short review of alpha activity in € cognitive processes and in cognitive impairment. Int J Psychophysiol 86:25–38. Basar E, Basar-Eroglu C, Karakas¸ S, Schurmann M (2001): Gamma, € alpha, delta, and theta oscillations govern cognitive processes. Int J Psychophysiol 39:241–248. Burgess A, Gruzelier J (1993): Individual reliability of amplitude distribution in topographical mapping of EEG. Electroencephalogr Clin Neurophysiol 86:219–223. Buzsaki G (2002): Theta Oscillations in the Hippocampus Review. Neuron 33:325–340. Buzsaki G (2006): Rhythms of the Brain. New York: Oxford University Press. Buzsaki G (2012): Brain rhythms and neural syntax: Implications for efficient coding of cognitive content. Dialogues Clin Neurosci 14:345–367. Buzsaki G, Wang XJ (2012): Mechanisms of gamma oscillations. Annu Rev Neurosci 35:203–225. Cannon RL, Baldwin DR, Shaw TL, Diloreto DJ, Phillips SM, Scruggs AM, Riehl TC (2012): Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days. Neurosci Lett 518:27–31. Cavanagh JF, Frank MJ (2014): Frontal theta as a mechanism for cognitive control. Trends Cogn Sci 18:414–421. Congedo M, John RE, De Ridder D, Prichep L (2010): Group independent component analysis of resting state EEG in large normative samples. Int J Psychophysiol 78:89–99. Cornew L, Roberts TPL, Edgar JC (2013): Resting-State Oscillatory Activity in Autism Spectrum Disorders. J Autism Dev Disord 42:1884–1894. Deuker L, Bullmore ET, Smith M, Christensen S, Nathan PJ, Rockstroh B, Bassett DS (2009): Reproducibility of graph metrics of human brain functional networks. Neuroimage 47:1460–1468. Fehr T, Kissler J, Moratti S, Wienbruch C, Rockstroh B, Elbert T (2001): Source distribution of neuromagnetic slow waves and MEG-delta activity in schizophrenic patients. Biol Psychiatry 50:108–11. Fernandez A, Hornero R, Mayo A, Poza J, Gil-Gregorio P, Ortiz T (2006): MEG spectral profile in Alzheimer’s disease and mild cognitive impairment. Clin Neurophysiol 117:306–314. Fingelkurts A, Fingelkurts A, Ermolaev V, Kaplan A (2006): Stability, reliability and consistency of the compositions of brain oscillations. Int J Psychophysiol 59:116–126. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, Van Der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002): Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355. Garces P, Vicente R, Wibral M, Pineda-Pardo JA, L opez ME, Aurtenetxe S, Marcos A, de Andres ME, Yus M, Sancho M, Maestu F, Fern andez A (2013): Brain-wide slowing of spontaneousalpha rhythms in mild cognitive impairment. Front Aging Neurosci 5:100. Gasser T, B€acher P, Steinberg H, Bacher P (1985): Test-retest reliability of spectral parameters of the EEG. Electroencephalogr Clin Neurophysiol 60:312–319. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Parkkonen L, H€am€al€ainen MS (2014): MNE software for processing MEG and EEG data. Neuroimage 86:446 460. Gross J, Kujala J, Hamalainen M, Timmermann L, Schnitzler A, Salmelin R (2001): Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proc Natl Acad Sci USA 98:694–699. Gudmundsson S, Runarsson TP, Sigurdsson S, Eiriksdottir G, Johnsen K (2007): Reliability of quantitative EEG features. Clin Neurophysiol 118:2162–2171. Hillebrand A, Singh KD, Holliday IE, Furlong PL, Barnes GR (2005): A new approach to neuroimaging with magnetoencephalography. Hum Brain Mapp 25:199–211. Hughes SW, Crunelli V (2005): Thalamic mechanisms of EEG alpha rhythms and their pathological implications. Neuroscientist 11:357–372. Jensen O, Kaiser J, Lachaux J-P (2007): Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci 30:317–324. Jin S-H, Seol J, Kim JS, Chung CK (2011): How reliable are the functional connectivity networks of MEG in resting states? J Neurophysiol 106:2888–2895. Klimesch W (1999): EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev 29:169–195. Knyazev GG (2012): EEG delta oscillations as a correlate of basic homeostatic and motivational processes. Neurosci Biobehav Rev 36:677–695. Kondacs A, Szabo M (1999): Long-term intra-individual variability of the background EEG in normals. Clin Neurophysiol 110: 1708–1716. Leighton BN, Vinogradov S, Guggisberg AG, Fisher M, Findlay AM, Nagarajan SS (2011): Clinical symptoms and alpha bandr Reliabil resting-state functional connectivity imaging in patients with schizophrenia: Implications for novel approaches to treatment. Biol Psychiatry 70:1134–1142. Luckhoo HT, Brookes MJ, Woolrich MW (2014): Multi session statistics on beamformed MEG data. Neuroimage 95:330–335. McEvoy LK, Smith ME, Gevins A (2000): Test-retest reliability of cognitive EEG. Clin Neurophysiol 111:457–463. McGraw K, Wong S (1996): Forming inferences about some intraclass correlation coefficients. Psychol Methods 1:30 42. Van Der Meer ML, Tewarie P, Schoonheim MM, Douw L, Barkhof F, Polman CH, Stam CJ, Hillebrand a (2013): Cognition in MS correlates with resting-state oscillatory brain activity: An explorative MEG source-space study. Neuroimage Clin 2:727–734. Olde Dubbelink KTE, Stoffers D, Deijen JB, Twisk JWR, Stam CJ, Berendse HW (2013): Cognitive decline in Parkinson’s disease is associated with slowing of resting-state brain activity: A longitudinal study. Neurobiol Aging 34:408–418. Oostenveld R, Fries P, Maris E, Schoffelen JMM (2011): FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011: 156869 Pollock VE, Schneider LS, Lyness SA (1991): Reliability of topographic quantitative EEG amplitude in healthy late middleaged and elderly subjects. Electroencephalogr Clin Neurophysiol 79:20–26. Sauseng P, Klimesch W (2008): What does phase information of oscillatory brain activity tell us about cognitive processes? Neurosci Biobehav Rev 32:1001–1013. Schaefer M, Muhlnickel W, Grüsser SM, Flor H (2002): Repro-ducibility and Stability of Neuroelectric Source Imaging in Primary Somatosensory Cortex. Brain Topography 14:179– 189. Schnitzler A, Gross J (2005): Normal and pathological oscillatorycommunication in the brain. Nat Rev Neurosci 6:285–296. Segonne F, Pacheco J, Fischl B (2007): Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans Med Imaging 26:518–529. Sekihara K, Nagarajan SS (2008): Adaptive Spatial Filters for Electromagnetic Brain Imaging. Berlin, Germany: Springer. Shrout P, Fleiss J (1979): Intraclass correlations: Uses in assessing rater reliability. Psychol Bull. 86:420–428. Snyder AZ, Raichle ME (2012): A brief history of the resting state: The Washington University perspective. Neuroimage 62:902–910. Taulu S, Simola J (2006): Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys Med Biol 51:1759–1768. Telesford QK, Burdette JH, Laurienti PJ (2013): An exploration of graph metric reproducibility in complex brain networks. Front Neurosci 7:67. Vlahou EL, Thurm F, Kolassa I-T, Schlee W (2014): Resting-state slow wave power, healthy aging and cognitive performance. Sci Rep 4:5101. Wang X (2010): Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev 1195–1268. Weir JPJ (2005): Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19:231–240. Wienbruch C, Paul I, Bauer S, Kivelitz H (2005): The influence of methylphenidate on the power spectrum of ADHD children - an MEG study. BMC Psychiatry 5:29. | |
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