The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition

Van der Worp HB, Kraaier V, Wieneke GH, Van Huffelen AC. Quantitative EEG during progressive hypocarbia and hypoxia. Hyperventilation-induced EEG changes reconsidered. Electroencephalogr Clin Neurophysiol. 1991;79(5):335–41. https://doi.org/10.1016/0013-4694(91)90197-c.

Article  Google Scholar 

Mazzucchi E, et al. Hyperventilation in patients with focal epilepsy: electromagnetic tomography, functional connectivity and graph theory - a possible tool in epilepsy diagnosis? J Clin Neurophysiol. 2017;34(1):92–9. https://doi.org/10.1097/WNP.0000000000000329.

Article  Google Scholar 

Mäkiranta MJ, et al. BOLD-contrast functional MRI signal changes related to intermittent rhythmic delta activity in EEG during voluntary hyperventilation-simultaneous EEG and fMRI study. Neuroimage. 2004;22(1):222–31. https://doi.org/10.1016/j.neuroimage.2004.01.004.

Article  Google Scholar 

Khachidze I, Gugushvili M, Advadze M. EEG characteristics to hyperventilation by age and sex in patients with various neurological disorders. Front Neurol. 2021;12:727297. https://doi.org/10.3389/fneur.2021.727297.

Article  Google Scholar 

Plouin P, Kaminska A, Moutard ML, Soufflet C. Developmental aspects of normal EEG. Handb Clin Neurol. 2013;111:79–85. https://doi.org/10.1016/B978-0-444-52891-9.00007-5.

Article  Google Scholar 

Kennealy JA, Penovich PE, Moore-Nease SE. EEG and spectral analysis in acute hyperventilation. Electroencephalogr Clin Neurophysiol. 1986;63(2):98–106. https://doi.org/10.1016/0013-4694(86)90002-7.

Article  CAS  Google Scholar 

Brian JE. Carbon dioxide and the cerebral circulation. Anesthesiology. 1998;88(5):1365–86. https://doi.org/10.1097/00000542-199805000-00029.

Article  Google Scholar 

Petersen RC, et al. Apolipoprotein E status as a predictor of the development of Alzheimer's disease in memory-impaired individuals. JAMA. 1995;273(16):1274–8.

Article  CAS  Google Scholar 

Petersen RC, et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–92. https://doi.org/10.1001/archneur.58.12.1985.

Article  CAS  Google Scholar 

Scheltens P, Fox N, Barkhof F, De Carli C. Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. Lancet Neurol. 2002;1(1):13–21. https://doi.org/10.1016/s1474-4422(02)00002-9.

Article  Google Scholar 

Ponomareva NV, Korovaitseva GI, Rogaev EI. EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiol Aging. 2008;29(6):819–27. https://doi.org/10.1016/j.neurobiolaging.2006.12.019.

Article  CAS  Google Scholar 

Vecchio F, Miraglia F, Bramanti P, Rossini PM. Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data. J Alzheimers Dis. 2014;41(4):1239–49. https://doi.org/10.3233/JAD-140090.

Article  Google Scholar 

Rossini PM, Di Iorio R, Granata G, Miraglia F, Vecchio F. From mild cognitive impairment to Alzheimer's disease: a new perspective in the "Land" of human brain reactivity and connectivity. J Alzheimers Dis. 2016;53(4):1389–93. https://doi.org/10.3233/jad-160482.

Article  Google Scholar 

Miraglia F, et al. Brain connectivity and graph theory analysis in Alzheimer's and Parkinson's disease: the contribution of electrophysiological techniques. Brain Sci. 2022;12(3):402. https://doi.org/10.3390/brainsci12030402.

Article  CAS  Google Scholar 

Başar E, Schürmann M. Toward new theories of brain function and brain dynamics. Int J Psychophysiol. 2001;39(2-3):87–9. https://doi.org/10.1016/s0167-8760(00)00134-3.

Article  Google Scholar 

Miller EK, Wilson MA. All my circuits: using multiple electrodes to understand functioning neural networks. Neuron. 2008;60(3):483–8. https://doi.org/10.1016/j.neuron.2008.10.033.

Article  CAS  Google Scholar 

Friston KJ, Büchel C. CHAPTER 37 - Functional connectivity: eigenimages and multivariate analyses. In: Friston K, Ashburner J, Kiebel S, Nichols T, Penny W, editors. Statistical parametric mapping: Academic Press; 2007. p. 492–507. https://doi.org/10.1016/B978-012372560-8/50037-1.

Chapter  Google Scholar 

Vecchio F, Miraglia F, Maria RP. Connectome: graph theory application in functional brain network architecture. Clin Neurophysiol Pract. 2017;2:206–13. https://doi.org/10.1016/j.cnp.2017.09.003.

Article  Google Scholar 

Vecchio F, et al. Graph theory on brain cortical sources in Parkinson's disease: the analysis of 'Small World' organization from EEG. Sensors (Basel). 2021;21(21):31. https://doi.org/10.3390/s21217266.

Article  Google Scholar 

Winblad B, et al. Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med. 2004;256(3):240–6. https://doi.org/10.1111/j.1365-2796.2004.01380.x.

Article  CAS  Google Scholar 

Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med. 2011;364(23):2227–34. https://doi.org/10.1056/NEJMcp0910237.

Article  CAS  Google Scholar 

McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34(7):939–44. https://doi.org/10.1212/wnl.34.7.939.

Article  CAS  Google Scholar 

Miraglia F, et al. Assessing the dependence of the number of EEG channels in the brain networks' modulations. Brain Res Bull. 2021;167:33–6. https://doi.org/10.1016/j.brainresbull.2020.11.014.

Article  Google Scholar 

Pappalettera C, Miraglia F, Cotelli M, Rossini PM, Vecchio F. Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy. Geroscience. 2022;44(3):1599–607. https://doi.org/10.1007/s11357-022-00552-0.

Article  Google Scholar 

Vecchio F, Miraglia F, Judica E, Cotelli M, Alù F, Rossini PM. Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects. Geroscience. 2020;42(2):575–84. https://doi.org/10.1007/s11357-020-00176-2.

Article  Google Scholar 

Vecchio F, Miraglia F, Alù F, Menna M, Judica E, Cotelli M, Rossini PM. Classification of Alzheimer's disease with respect to physiological aging with innovative EEG biomarkers in a machine learning implementation. J Alzheimers Dis. 2020;75(4):1253–61. https://doi.org/10.3233/JAD-200171.

Article  Google Scholar 

Pappalettera C, Cacciotti A, Nucci L, Miraglia F, Rossini PM, Vecchio F. Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain. Geroscience. 2022. https://doi.org/10.1007/s11357-022-00710-4.

Hoffmann S, Falkenstein M. The correction of eye blink artefacts in the EEG: a comparison of two prominent methods. PLoS One. 2008;3(8):e3004. https://doi.org/10.1371/journal.pone.0003004.

Article  CAS  Google Scholar 

Iriarte J, et al. Independent component analysis as a tool to eliminate artifacts in EEG: A quantitative study. J Clin Neurophysiol. 2003;20(4):249–57. https://doi.org/10.1097/00004691-200307000-00004.

Article  Google Scholar 

Jung TP, et al. Removing electroencephalographic artifacts by blind source separation. Psychophysiology. 2000;37(2):163–78.

Article  CAS  Google Scholar 

Vecchio F, Nucci L, Pappalettera C, Miraglia F, Iacoviello D, Rossini PM. Time-frequency analysis of brain activity in response to directional and non-directional visual stimuli: an event related spectral perturbations (ERSP) study. J Neural Eng. 2022;19(6). https://doi.org/10.1088/1741-2552/ac9c96.

Mulert C, et al. Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage. 2004;22(1):83–94. https://doi.org/10.1016/j.neuroimage.2003.10.051.

Article  Google Scholar 

Vitacco D, Brandeis D, Pascual-Marqui R, Martin E. Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing. Hum Brain Mapp. 2002;17(1):4–12. https://doi.org/10.1002/hbm.10038.

Article  Google Scholar 

Worrell GA, et al. Localization of the epileptic focus by low-resolution electromagnetic tomography in patients with a lesion demonstrated by MRI. Brain Topogr. 2000;12(4):273–82.

Article  CAS  Google Scholar 

Dierks T, et al. Spatial pattern of cerebral glucose metabolism (PET) correlates with localization of intracerebral EEG-generators in Alzheimer's disease. Clin Neurophysiol. 2000;111:1817–24.

Article  CAS  Google Scholar 

Pizzagalli DA, et al. Functional but not structural subgenual prefrontal cortex abnormalities in melancholia. Mol Psychiatry. 2004;9(4):393–405. https://doi.org/10.1038/sj.mp.4001469.

Article  Google Scholar 

Zumsteg D, Wennberg RA, Treyer V, Buck A, Wieser HG. H2(15) O or 13NH3 PET and electromagnetic tomography (LORETA) during partial status epilepticus. Neurology. 2005;65(10):1657–60. https://doi.org/10.1212/01.wnl.0000184516.32369.1a.

Article  CAS  Google Scholar 

Vecchio F, et al. Human brain networks in physiological and pathological aging: reproducibility of electroencephalogram graph theoretical analysis in cortical connectivity. Brain Connect. 2022;12(1):41–51. https://doi.org/10.1089/brain.2020.0824.

Article  Google Scholar 

Kubicki S, Herrmann WM, Fichte K, Freund G. Reflections on the topics: EEG frequency bands and regulation of vigilance. Pharmakopsychiatr Neuropsychopharmakol. 1979;12(2):237–45. https://doi.org/10.1055/s-0028-1094615.

Article  CAS  Google Scholar 

Pascual-Marqui RD. Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. arXiv:0710.3341 [math-ph]. 2007; http://arxiv.org/pdf/0710.3341.

Pascual-Marqui RD, et al. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans A Math Phys Eng Sci. 1952;2011(369):3768–84. https://doi.org/10.1098/rsta.2011.0081.

Article  Google Scholar 

Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998;393(6684):440–2. https://doi.org/10.1038/30918.

Article  CAS  Google Scholar 

Vecchio F, Pappalettera C, Miraglia F, Deinite G, Manenti R, Judica E, Caliandro P, Rossini PM. Prognostic role of hemispherical functional connectivity in stroke: a study via graph theory versus coherence of electroencephalography rhythms. Stroke. 2022. https://doi.org/10.1161/STROKEAHA.122.040747.

Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69. https://doi.org/10.1016/j.neuroimage.2009.10.003.

Article  Google Scholar 

Vecchio F, et al. Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: electroencephalographic connectivity and graph theory combined with apolipoprotein E. Ann Neurol. 2018;84(2):302–14. https://doi.org/10.1002/ana.25289.

Article  CAS  Google Scholar 

Miraglia F, Vecchio F, Rossini PM. Brain electroencephalographic segregation as a biomarker of learning. Neural Netw. 2018;106:168–74. https://doi.org/10.1016/j.neunet.2018.07.005.

Article  Google Scholar 

Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701. https://doi.org/10.1103/PhysRevLett.87.198701.

Article  CAS  Google Scholar 

Bassett DS, Bullmore E. Sma

留言 (0)

沒有登入
gif