Healthy aging changes in conventional frequency bands of neuroelectric brain activity reconstructed from resting-state MEG

Scally B, Burke MR, Bunce D, Delvenne JF. Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging. Neurobiol Aging. 2018;71:149–55. https://doi.org/10.1016/J.NEUROBIOLAGING.2018.07.004.

Article  PubMed  Google Scholar 

Perinelli A, Assecondi S, Tagliabue CF, Mazza V. Power shift and connectivity changes in healthy aging during resting-state EEG. NeuroImage. 2022;256:119247. https://doi.org/10.1016/j.neuroimage.2022.119247.

Article  PubMed  Google Scholar 

Hinault T, Baillet S, Courtney SM. Age-related changes of deep-brain neurophysiological activity. Cereb Cortex. 2023;33(7):3960–8. https://doi.org/10.1093/CERCOR/BHAC319.

Article  CAS  PubMed  Google Scholar 

Shafto MA, Tyler LK, Dixon M, Taylor JR, Rowe JB, Cusack R, Calder AJ, Marslen-Wilson WD, Duncan J, Dalgleish T, Henson RN, Brayne C, Matthews FE. Cam-CAN: the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology. 2014;14(1):1–25. https://doi.org/10.1186/S12883-014-0204-1.

Article  Google Scholar 

Green E, Shafto MA, Matthews FE, Cam-CAN, White SR. Adult lifespan cognitive variability in the cross-sectional cam-can cohort. Int J Environ Res Public Health. 2015;12(12):15516. https://doi.org/10.3390/IJERPH121215003.

Gómez C, Pérez-Macías JM, Poza J, Fernández A, Hornero R. Spectral changes in spontaneous MEG activity across the lifespan. J Neural Eng. 2013;10(6). https://doi.org/10.1088/1741-2560/10/6/066006.

Stier C, Braun C, Focke NK. Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness. NeuroImage. 2023;278. https://doi.org/10.1016/J.NEUROIMAGE.2023.120275.

Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys. 1993;65(2):413. https://doi.org/10.1103/RevModPhys.65.413.

Article  Google Scholar 

Llinás RR, Ribary U, Jeanmonod D, Kronberg E, Mitra PP. Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc Natl Acad Sci U S A. 1999;96(26):15222–7. https://doi.org/10.1073/PNAS.96.26.15222.

Article  PubMed  PubMed Central  Google Scholar 

Baillet S. Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci. 2017;20(3):327–39. https://doi.org/10.1038/nn.4504.

Article  CAS  PubMed  Google Scholar 

Little S, Bonaiuto J, Meyer SS, Lopez J, Bestmann S, Barnes G. Quantifying the performance of MEG source reconstruction using resting state data. NeuroImage. 2018;181:453–60. https://doi.org/10.1016/j.neuroimage.2018.07.030.

Article  PubMed  Google Scholar 

Hämäläinen MS, Ilmoniemi RJ. Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput. 1994;32(1):35–42. https://doi.org/10.1007/BF02512476.

Article  PubMed  Google Scholar 

Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24(Suppl D):5–12.

PubMed  Google Scholar 

Belardinelli P, Ortiz E, Barnes G, Noppeney U, Preissl H. Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches. PLoS ONE. 2012;7(12). https://doi.org/10.1371/journal.pone.0051985.

Friston K, Harrison L, Daunizeau J, Kiebel S, Phillips C, Trujillo-Barreto N, Henson R, Flandin G, Mattout J. Multiple sparse priors for the M/EEG inverse problem. NeuroImage. 2008;39(3):1104–20. https://doi.org/10.1016/j.neuroimage.2007.09.048.

Article  PubMed  Google Scholar 

Llinás RR, Ustinin MN. Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization. Front Neural Circuits. 2014;8:75300. https://doi.org/10.3389/FNCIR.2014.00043.

Article  Google Scholar 

Llinás RR, Ustinin MN, Rykunov SD, Boyko AI, Sychev VV, Walton KD, Rabello GM, Garcia J. Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data. Front Neurosci. 2015;9(OCT):155316. https://doi.org/10.3389/FNINS.2015.00373.

Article  Google Scholar 

Ustinin MN, Boyko AI, Rykunov SD. Functional tomography of complex systems using spectral analysis of multichannel measurement data. Pattern Recognit Image Anal. 2023;33(4):1344–74. https://doi.org/10.1134/S1054661823040491.

Article  Google Scholar 

Llinás RR, Rykunov S, Walton KD, Boyko A, Ustinin M. Splitting of the magnetic encephalogram into «brain» and «non-brain» physiological signals based on the joint analysis of frequency-pattern functional tomograms and magnetic resonance images. Front Neural Circuits. 2022;16:834434. https://doi.org/10.3389/FNCIR.2022.834434.

Article  PubMed  PubMed Central  Google Scholar 

Taulu S, Kajola M. Presentation of electromagnetic multichannel data: the signal space separation method. J Appl Phys. 2005;97(12):1. https://doi.org/10.1063/1.1935742/893620.

Article  Google Scholar 

Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys Med Biol. 2006;51(7):1759. https://doi.org/10.1088/0031-9155/51/7/008.

Article  CAS  PubMed  Google Scholar 

Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, Hämäläinen MS. MEG and EEG data analysis with MNE-Python. Front Neurosci. 2013;7(267):1–13. https://doi.org/10.3389/fnins.2013.00267.

Article  Google Scholar 

Frigo M, Johnson SG. The design and implementation of fftw3. Proc IEEE. 2005;93(2):216–31. https://doi.org/10.1109/JPROC.2004.840301.

Article  Google Scholar 

Oostenveld R, Fries P, Maris E, Schoffelen J-M. Fieldtrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011(1):156869. https://doi.org/10.1155/2011/156869.

Article  PubMed  Google Scholar 

Huo Y, Xu Z, Xiong Y, Aboud K, Parvathaneni P, Bao S, Bermudez C, Resnick SM, Cutting LE, Landman BA. 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage. 2019;194:105–19. https://doi.org/10.1016/J.NEUROIMAGE.2019.03.041.

Article  PubMed  Google Scholar 

Zhang Z, Zhang H, Zhao L, Chen T, Arık S, Pfister T. Nested hierarchical transformer: towards accurate, data-efficient and interpretable visual understanding. Proc AAAI Conf Artif Intell. 2022;36(3):3417–25. arXiv:2105.12723. https://doi.org/10.1609/AAAI.V36I3.20252.

Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, Murrey B, Myronenko A, Zhao C, Yang D, Nath V, He Y, Xu Z, Hatamizadeh A, Myronenko A, Zhu W, Liu Y, Zheng M, Tang Y, Yang I, Zephyr M, Hashemian B, Alle S, Darestani MZ, Budd C, Modat M, Vercauteren T, Wang G, Li Y, Hu Y, Fu Y, Gorman B, Johnson H, Genereaux B, Erdal BS, Gupta V, Diaz-Pinto A, Dourson A, Maier-Hein L, Jaeger PF, Baumgartner M, Kalpathy-Cramer J, Flores M, Kirby J, Cooper LAD, Roth HR, Xu D, Bericat D, Floca R, Zhou SK, Shuaib H, Farahani K, Maier-Hein KH, Aylward S, Dogra P, Ourselin S, Feng A. MONAI: an open-source framework for deep learning in healthcare. 2022. https://doi.org/10.48550/arXiv.2211.02701. https://arxiv.org/abs/2211.02701v1.

Sarvas J. Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys Med Biol. 1987;32(1):11. https://doi.org/10.1088/0031-9155/32/1/004.

Pearson RK. Outliers in process modeling and identification. IEEE Trans Control Syst Technol. 2002;10(1):55–63. https://doi.org/10.1109/87.974338.

留言 (0)

沒有登入
gif