Physiologic artifacts in resting state oscillations in young children: methodological considerations for noisy data

详细信息    查看全文

• 作者：Kevin McEvoy ; Kyle Hasenstab ; Damla Senturk ; Andrew Sanders…
• 关键词：Quantitative EEG ; Artifacts ; Resting state ; Children
• 刊名：Brain Imaging and Behavior
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：9
• 期：1
• 页码：104-114
• 全文大小：1,150 KB
• 参考文献：1. Barry, RJ, Clarke, AR, Hajos, M, McCarthy, R, Selikowitz, M, Dupuy, FE (2010) Resting-state EEG gamma activity in children with attention-deficit/hyperactivity disorder. Clinical Neurophysiology 121: pp. 1871-77 CrossRef
  2. Benninger, C, Matthis, P, Scheffner, D (1984) EEG development of healthy boys and girls. Results of a longitudinal study. Electroencephalography and Clinical Neurophysiology 57: pp. 1-12 CrossRef
  3. Bonfiglio, L, Olcese, U, Rossi, B, Frisoli, A, Arrighi, P, Greco, G, Carozzo, S, Andre, P, Bergamasco, M, Carboncini, MC (2013) Cortical source of blink-related delta oscillations and their correlation with levels of consciousness. Human Brain Mapping 34: pp. 2178-89 CrossRef
  4. Bosl, W, Tierney, A, Tager-Flusberg, H, Nelson, C (2011) EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine 9: pp. 18 CrossRef
  5. Cantor, DS, Chabot, R (2009) QEEG studies in the assessment and treatment of childhood disorders. Clinical EEG and Neuroscience 40: pp. 113-21 CrossRef
  6. Clarke, AR, Barry, RJ, Dupuy, FE, Heckel, LD, McCarthy, R, Selikowitz, M, Johnstone, SJ (2011) Behavioural differences between EEG-defined subgroups of children with Attention-Deficit/Hyperactivity Disorder. Clinical Neurophysiology 122: pp. 1333-41 CrossRef
  7. Dawson, G, Jones, EJ, Merkle, K, Venema, K, Lowy, R, Faja, S, Webb, SJ (2012) Early behavioral intervention is associated with normalized brain activity in young children with autism. Journal of the American Academy of Child and Adolescent Psychiatry 51: pp. 1150-59 CrossRef
  8. Deen, B, Pelphrey, K (2012) Perspective: brain scans need a rethink. Nature 491: pp. S20 CrossRef
  9. Drongelen, W (2007) Signal processing for neuroscientists : introduction to the analysis of physiological signals. Amsterdam. Elsevier/Academic Press, Burlington
  10. Elliott, CD (1993) Differential abilities scale (DAS). Child Assessment News 3: pp. 1-10
  11. Ferree, TC, Luu, P, Russell, GS, Tucker, DM (2001) Scalp electrode impedance, infection risk, and EEG data quality. Clinical Neurophysiology 112: pp. 536-44 CrossRef
  12. Fletcher, EM, Kussmaul, CL, Mangun, GR (1996) Estimation of interpolation errors in scalp topographic mapping. Electroencephalography and Clinical Neurophysiology 98: pp. 422-34 CrossRef
  13. Freeman, WJ, Holmes, MD, Burke, BC, Vanhatalo, S (2003) Spatial spectra of scalp EEG and EMG from awake humans. Clinical Neurophysiology 114: pp. 1053-68 CrossRef
  14. Goncharova, II, McFarland, DJ, Vaughan, TM, Wolpaw, JR (2003) EMG contamination of EEG: spectral and topographical characteristics. Clinical Neurophysiology 114: pp. 1580-93 CrossRef
  15. Gou, Z, Choudhury, N, Benasich, AA (2011) Resting frontal gamma power at 16, 24 and 36?months predicts individual differences in language and cognition at 4 and 5?years. Behavioural Brain Research 220: pp. 263-70 CrossRef
  16. Grasser, T, Lothar, S, Joachim, M (1985) The transfer of EOG activity into the EEG for eyes open and closed. Electroencephalography and Clinical Neurophysiology 61: pp. 181-93 CrossRef
  17. Gratton, G, Coles, MG, Donchin, E (1983) A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology 55: pp.
• 刊物主题：Neurosciences; Neuroradiology; Neuropsychology; Psychiatry;
• 出版者：Springer US
• ISSN：1931-7565

文摘

We quantified the potential effects of physiologic artifact on the estimation of EEG band power in a cohort of typically developing children in order to guide artifact rejection methods in quantitative EEG data analysis in developmental populations. High density EEG was recorded for 2?min while children, ages 2-, watched a video of bubbles. Segments of data were categorized as blinks, saccades, EMG or artifact-free, and both absolute and relative power in the theta (4-?Hz), alpha (8-2?Hz), beta (13-0?Hz) and gamma (35-5?Hz) bands were calculated in 9 regions for each category. Using a linear mixed model approach with artifact type, region and their interaction as predictors, we compared mean band power between clean data and each type of artifact. We found significant differences in mean relative and absolute power between artifacts and artifact-free segments in all frequency bands. The magnitude and direction of the differences varied based on power type, region, and frequency band. The most significant differences in mean band power were found in the gamma band for EMG artifact and the theta band for ocular artifacts. Artifact detection strategies need to be sensitive to the oscillations of interest for a given analysis, with the most conservative approach being the removal of all EMG and ocular artifact from EEG data. Quantitative EEG holds considerable promise as a clinical biomarker of both typical and atypical development. However, there needs to be transparency in the choice of power type, regions of interest, and frequency band, as each of these variables are differentially vulnerable to noise, and therefore, their interpretation depends on the methods used to identify and remove artifacts.