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Titolo:
Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains
Autore:
Bullmore, E; Long, C; Suckling, J; Fadili, J; Calvert, G; Zelaya, F; Carpenter, TA; Brammer, M;
Indirizzi:
Univ Cambridge, Dept Psychiat, Cambridge, England Univ Cambridge Cambridge England dge, Dept Psychiat, Cambridge, England Univ Cambridge, Wolfson Brain Imaging Ctr, Cambridge, England Univ Cambridge Cambridge England Brain Imaging Ctr, Cambridge, England Univ Cambridge, Dept Expt Psychol, Cambridge, England Univ Cambridge Cambridge England Dept Expt Psychol, Cambridge, England Univ London Kings Coll, Inst Psychiat, London WC2R 2LS, England Univ London Kings Coll London England WC2R 2LS London WC2R 2LS, England Guys Kings & St Thomass Med Sch, London, England Guys Kings & St Thomass Med Sch London England Med Sch, London, England Univ Oxford, FMRIB Ctr, Oxford, England Univ Oxford Oxford EnglandUniv Oxford, FMRIB Ctr, Oxford, England
Titolo Testata:
HUMAN BRAIN MAPPING
fascicolo: 2, volume: 12, anno: 2001,
pagine: 61 - 78
SICI:
1065-9471(200102)12:2<61:CNACII>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Soggetto:
FRACTIONAL BROWNIAN-MOTION; MAGNETIC-RESONANCE IMAGES; EMPIRICAL ANALYSES; FRACTAL ANALYSIS; NULL-HYPOTHESIS; COEFFICIENTS; STATISTICS; SIGNAL;
Keywords:
fractional noise; neuroimaging; brain mapping; nonparametric; high field MRI;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
59
Recensione:
Indirizzi per estratti:
Indirizzo: Bullmore, E Addenbrookes Hosp, Dept Psychiat, Brain Mapping Unit, Box 255,Cambridge CB2 2QQ, England Addenbrookes Hosp Box 255 Cambridge England CB2 2QQ , England
Citazione:
E. Bullmore et al., "Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains", HUM BRAIN M, 12(2), 2001, pp. 61-78

Abstract

Even in the absence of an experimental effect, functional magnetic resonance imaging (fMRT) time series generally demonstrate serial dependence. Thiscolored noise or endogenous autocorrelation typically has disproportionatespectral power at low frequencies, i.e., its spectrum is 1/f-like. Variouspre-whitening and pre-coloring strategies have been proposed to make validinference on standardised test statistics estimated by time series regression in this context of residually autocorrelated errors. Here we introduce a new method based on random permutation after orthogonal transformation ofthe observed time series to the wavelet domain. This scheme exploits the general whitening or decorrelating property of the discrete wavelet transform and is implemented using a Daubechies wavelet with four vanishing momentsto ensure exchangeability of wavelet coefficients within each scale of decomposition. For 1/f-like or fractal noises, e.g., realisations of fractional Brownian motion (fBm) parameterised by Hurst exponent 0 < H < 1, this resampling algorithm exactly preserves wavelet-based estimates of the second order stochastic properties of the (possibly nonstationary) time series. Performance of the method is assessed empirically using 1/f-like noise simulated by multiple physical relaxation processes, and experimental fMRI data. Nominal type 1 error control in brain activation mapping is demonstrated by analysis of 13 images acquired under null or resting conditions. Compared to autoregressive pre-whitening methods for computational inference, a key advantage of wavelet resampling seems to be its robustness in activation mapping of experimental fMRI data acquired at 3 Tesla field strength. We conclude that wavelet resampling may be a generally useful method for inference on naturally complex time series. (C) 2001 Wiley-Liss, Inc.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 23/01/20 alle ore 06:25:01