Colored noise and computational inference in fMRI time series analysis: resampling methods in time and wavelet domains

Bullmore, Edward T.; Long, Chris; Suckling, John; Fadili, Jalal M.; Calvert, Gemma A.; Zelaya, Fernando; Carpenter, Adrian; Brammer, Mick

doi:10.1016/s1053-8119(01)91429-6

Cited by 13 publications

(16 citation statements)

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“…From (12), it is seen that the wavelet coefficients at level are with the same variance . Hence, the submatrix at level is of identical value at the diagonal and of dimension .…”

Section: B Fractional Noise Modelmentioning

confidence: 96%

“…2) Fractional Noise: To investigate the effect of fractional noise or -like noise, we first synthesize realizations of fBm noise based on the statistical model of fBm using the method introduced in [12]. The total length of the simulated fBm noise is with the decomposition level (since ).…”

Section: A Simulated Data 1) Time-varying Variance Noisementioning

confidence: 99%

“…For example, the abrupt movements of the lower jaw of subjects may affect only a few consecutive fMRI images, causing the variance of these images to be high and violating the stationary assumption of the noise. Moreover, the -like noise or nonstationary fractional noises in the fMRI data are reported and investigated by many researchers recently [12]- [14]. These factors show that stationarity assumption of the noise may not be realistic to cope with the complex fMRI data.…”

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confidence: 99%

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fMRI Data Analysis With Nonstationary Noise Models: A Bayesian Approach

Luo

Puthusserypady

2007

IEEE Trans. Biomed. Eng.

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“…From (12), it is seen that the wavelet coefficients at level are with the same variance . Hence, the submatrix at level is of identical value at the diagonal and of dimension .…”

Section: B Fractional Noise Modelmentioning

confidence: 96%

Section: A Simulated Data 1) Time-varying Variance Noisementioning

confidence: 99%

mentioning

confidence: 99%

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fMRI Data Analysis With Nonstationary Noise Models: A Bayesian Approach

Luo

Puthusserypady

2007

IEEE Trans. Biomed. Eng.

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“…Spatially it is used to obtain a sparse representation of the activation map and then the statistical significance of activation is computed on these wavelet coefficients [VBU06]. Alternatively, along the temporal dimension, wavelets are used for de‐noising and whitening the time‐series data [BLS*01] and activity estimation [Mey03].…”

Section: Related Workmentioning

confidence: 99%

Visual Analysis of Brain Activity from fMRI Data

Janoos

Nouanesengsy

Machiraju

et al. 2009

Computer Graphics Forum

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Classically, analysis of the time-varying data acquired during fMRI experiments is done using static activation maps obtained by testing voxels for the presence of significant activity using statistical methods. The models used in these analysis methods have a number of parameters, which profoundly impact the detection of active brain areas. Also, it is hard to study the temporal dependencies and cascading effects of brain activation from these static maps. In this paper, we propose a methodology to visually analyze the time dimension of brain function with a minimum amount of processing, allowing neurologists to verify the correctness of the analysis results, and develop a better understanding of temporal characteristics of the functional behaviour. The system allows studying time-series data through specific volumes-of-interest in the brain-cortex, the selection of which is guided by a hierarchical clustering algorithm performed in the wavelet domain. We also demonstrate the utility of this tool by presenting results on a real data-set.

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“…However, it has become clear that there is a nonlinear relationship between the variation in the fMRI signal and the stimulus presentation [21]; and the hemodynamic response function (HRF) varies spatially and between subjects [22]. Moreover, the structure of noise in fMRI is not well understood and remains a contentious subject [3]. Thus, the validity of the statistical models depends on the extent to which the data satisfies the underlying assumptions.…”

Section: Introductionmentioning

confidence: 99%

Modeling hemodynamic variability with fuzzy features for detecting brain activation from fMR time-series

Zhou¹,

Rajapakse

2007

Neural Comput & Applic

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We propose to detect brain activation from fMR time-series of a group study by modeling fuzzy features. Five discriminating features are automatically extracted from fMRI data by a sequence of temporal-sliding-windows. A fuzzy model based on these features is first derived by a gradient method on a set of initial training data and then incrementally enhanced. The resulting fuzzy activation maps of all subjects are then combined to provide a measure of strength of activation of each voxel, based on the group of subjects. A two-way thresholding scheme is introduced to determine true activated voxels. The method is tested on both synthetic and real fMRI datasets. The method is less vulnerable to correlated noise and able to capture the key activation from a group of subjects by adapting to hemodynamic variability across subjects.

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Colored noise and computational inference in fMRI time series analysis: resampling methods in time and wavelet domains

Cited by 13 publications

References 0 publications

fMRI Data Analysis With Nonstationary Noise Models: A Bayesian Approach

fMRI Data Analysis With Nonstationary Noise Models: A Bayesian Approach

Visual Analysis of Brain Activity from fMRI Data

Modeling hemodynamic variability with fuzzy features for detecting brain activation from fMR time-series

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