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Titolo:
Temporal autocorrelation in univariate linear modeling of FMRI data
Autore:
Woolrich, MW; Ripley, BD; Brady, M; Smith, SM;
Indirizzi:
Univ Oxford, Oxford Ctr Funct Magnet Resonance Imagingn Brain, Oxford OX3 9DU, England Univ Oxford Oxford England OX3 9DU agingn Brain, Oxford OX3 9DU, England Univ Oxford, Dept Engn Sci, Oxford OX3 9DU, England Univ Oxford Oxford England OX3 9DU ept Engn Sci, Oxford OX3 9DU, England Univ Oxford, Dept Stat, Oxford OX3 9DU, England Univ Oxford Oxford England OX3 9DU d, Dept Stat, Oxford OX3 9DU, England
Titolo Testata:
NEUROIMAGE
fascicolo: 6, volume: 14, anno: 2001,
pagine: 1370 - 1386
SICI:
1053-8119(200112)14:6<1370:TAIULM>2.0.ZU;2-C
Fonte:
ISI
Lingua:
ENG
Soggetto:
EVENT-RELATED FMRI; TIME-SERIES ANALYSIS; FUNCTIONAL MRI; EMPIRICAL ANALYSES; NULL-HYPOTHESIS; STATISTICS; NOISE; RATES;
Keywords:
FMRI analysis; GLM; temporal filtering; temporal autocorrelation; spatial filtering; single-event; autoregressive model; spectral density estimation; multitapering;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
24
Recensione:
Indirizzi per estratti:
Indirizzo: Woolrich, MW Univ Oxford, Oxford Ctr Funct Magnet Resonance Imagingn Brain, Oxford OX3 9DU, England Univ Oxford Oxford England OX3 9DU Oxford OX3 9DU, England
Citazione:
M.W. Woolrich et al., "Temporal autocorrelation in univariate linear modeling of FMRI data", NEUROIMAGE, 14(6), 2001, pp. 1370-1386

Abstract

In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or "prewhitening," gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currentlynot available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, butonly within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 x 10(-5). (C) 2001 Academic Press.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 02/04/20 alle ore 21:21:05