Detecting spatiotemporal nonlinear dynamics in resting state of human brain based on fMRI datasets
ABSTRACT In this work, a nonlinear dynamics method, coupled map lattices, was applied to functional magnetic resonance imaging (fMRI) datasets to examine the spatiotemporal properties of resting state blood oxygen level-dependent (BOLD) fluctuations. Spatiotemporal Lyapunov Exponent (SPLE) was calculated to study the deterministic nonlinearity in resting state human brain of nine subjects based on fMRI datasets. The results show that there is nonlinearity and determinism in resting state human brain. Furthermore, the results demonstrate that there is a spatiotemporal chaos phenomenon in resting state brain, and suggest that fluctuations of fMRI data in resting state brain cannot be fully attributed to nuclear magnetic resonance noise. At the same time, the spatiotemporal chaos phenomenon suggests that the correlation between voxels varies with time and there is a dynamic functional connection or network in resting state human brain.
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ABSTRACT: Fractional Gaussian noise (fGn) provides a parsimonious model for stationary increments of a self-similar process parameterised by the Hurst exponent, H, and variance, sigma2. Fractional Gaussian noise with H < 0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with H > 0.5 demonstrates 1/f, long memory or persistent behaviour; and the special case of fGn with H = 0.5 corresponds to classical Gaussian white noise. We comparatively evaluate four possible estimators of fGn parameters, one method implemented in the time domain and three in the wavelet domain. We show that a wavelet-based maximum likelihood (ML) estimator yields the most efficient estimates of H and sigma2 in simulated fGn with 0 < H < 1. Applying this estimator to fMRI data acquired in the "resting" state from healthy young and older volunteers, we show empirically that fGn provides an accommodating model for diverse species of fMRI noise, assuming adequate preprocessing to correct effects of head movement, and that voxels with H > 0.5 tend to be concentrated in cortex whereas voxels with H < 0.5 are more frequently located in ventricles and sulcal CSF. The wavelet-ML estimator can be generalised to estimate the parameter vector beta for general linear modelling (GLM) of a physiological response to experimental stimulation and we demonstrate nominal type I error control in multiple testing of beta, divided by its standard error, in simulated and biological data under the null hypothesis beta = 0. We illustrate these methods principally by showing that there are significant differences between patients with early Alzheimer's disease (AD) and age-matched comparison subjects in the persistence of fGn in the medial and lateral temporal lobes, insula, dorsal cingulate/medial premotor cortex, and left pre- and postcentral gyrus: patients with AD had greater persistence of resting fMRI noise (larger H) in these regions. Comparable abnormalities in the AD patients were also identified by a permutation test of local differences in the first-order autoregression AR(1) coefficient, which was significantly more positive in patients. However, we found that the Hurst exponent provided a more sensitive metric than the AR(1) coefficient to detect these differences, perhaps because neurophysiological changes in early AD are naturally better described in terms of abnormal salience of long memory dynamics than a change in the strength of association between immediately consecutive time points. We conclude that parsimonious mapping of fMRI noise properties in terms of fGn parameters efficiently estimated in the wavelet domain is feasible and can enhance insight into the pathophysiology of Alzheimer's disease.NeuroImage 04/2005; 25(1):141-58. · 6.13 Impact Factor
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ABSTRACT: Nonlinear effects in fMRI BOLD data may substantially influence estimates of task-related activations, particularly in rapid event-related designs. If the BOLD response to each stimulus is assumed to be independent of the stimulation history, nonlinear interactions create a prediction error that may reduce sensitivity. When stimulus density differs among conditions, nonlinear effects can cause artifactual differences in activation. This situation can occur in rapid event-related designs or when comparing blocks of unequal lengths. We present data showing substantial nonlinear history effects for stimuli 1 s apart and use estimates of nonlinearities in response magnitude, onset time, and time to peak to form a low-dimensional parameterization of these nonlinear effects. Our estimates of nonlinearity appear relatively consistent throughout the brain, and these estimates can be used to form adjusted linear predictors for future rapid event-related fMRI studies. Adjusting the linear model for these known nonlinear effects results in a substantially better model fit. The biggest advantages to using predictors adjusted for known nonlinear effects are (1) higher sensitivity at the individual subject level of analysis, (2) better control of confounds related to nonlinear effects, and (3) more accurate estimates of design efficiency in experimental fMRI design.NeuroImage 04/2005; 25(1):206-18. · 6.13 Impact Factor
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ABSTRACT: In this paper, we propose a generic framework for the analysis of steady-state fMRI datasets, applied here to resting-state datasets. Our approach avoids the introduction of user-defined seed regions for the study of spontaneous activity. Unlike existing techniques, it yields a sparse representation of resting-state activity networks which can be characterized and investigated fairly easily in a semi-interactive fashion. We proceed in several steps, based on the idea that spectral coherence of the fMRI time courses in the low frequency band carries the information of interest. In particular, we address the question of building adapted representations of the data from the spectral coherence matrix. We analyze nine datasets taken from three subjects and show resting-state networks validated by EEG-fMRI simultaneous acquisition literature, with low intra-subject variability; we also discuss the merits of different (rapid/slow) fMRI acquisition schemes.NeuroImage 02/2006; 29(1):321-7. · 6.13 Impact Factor