fMRI Data Analysis With Nonstationary Noise Models: A Bayesian Approach

Luo, Hui; Puthusserypady, Sadasivan

doi:10.1109/tbme.2007.902591

Cited by 19 publications

(7 citation statements)

References 36 publications

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“…To improve the random permutation test, it is possible to use noise models that are temporally non-stationary (Milosavljevic et al, 1995;Long et al, 2005;Luo and Puthusserypady, 2007). The important aspect is that there now exists an objective way to compare the correctness of different parametric and non-parametric approaches.…”

Section: Future Workmentioning

confidence: 99%

Does parametric fMRI analysis with SPM yield valid results?—An empirical study of 1484 rest datasets

et al. 2012

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The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1% -70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1% -19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

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Section: Future Workmentioning

confidence: 99%

Does parametric fMRI analysis with SPM yield valid results?—An empirical study of 1484 rest datasets

et al. 2012

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“…To infer meaningful neuroscientific patterns within fMRI data, various computational/statistical methods have been proposed, including the widely-used general linear model (GLM) for tfMRI (Friston et al, 1994; Worsley, 1997), independent component analysis (ICA) for rsfMRI (McKeown et al, 1998), as well as many others methods including wavelet algorithms (Bullmore et al, 2003; Shimizu et al, 2004), Markov random field (MRF) models (Descombes et al, 1998), mixture models (Hartvig et al, 2000), autoregressive spatial models (Woolrich et al, 2004), Bayesian approaches (Luo et al, 2007). In these methods, GLM is one of the most widely used methods due to its effectiveness, simplicity, robustness and wide availability (Friston et al, 1994; Worsley et al, 1997; Lv et al, 2014a; Lv et al, 2014b).…”

Section: Introductionmentioning

confidence: 99%

Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations

Zhang

et al. 2015

Brain Imaging and Behavior

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A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.

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“…These ROC curves are also an instrument to contrast analysis techniques (e.g. Lange et al, 1999;Luo and Puthusserypady, 2007;Hansen et al, 2001;Skudlarski et al, 1999). Our concern is that the size of the systematic drop in the ROC curve we saw in this study, can be different for other analysis techniques as was demonstrated in simulation study III.…”

Section: Discussionmentioning

confidence: 77%

“…However, a problem arises when the drop in sensitivity and specificity would not be equal between different analysis methods and so, when comparing these methods, using white noise only would result in wrong conclusions about the validity of the methods. In particular ROC curves are a valuable tool to validate new analysis techniques against more established ones (see for example Luo and Puthusserypady, 2007;Lange et al, 1999;Hansen et al, 2001;Skudlarski et al, 1999). The method that would result in the highest ROC curve is most likely the best method to use.…”

Section: Resultsmentioning

confidence: 99%

How ignoring physiological noise can bias the conclusions from fMRI simulation results

Welvaert

Rosseel

2012

Journal of Neuroscience Methods

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fMRI Data Analysis With Nonstationary Noise Models: A Bayesian Approach

Cited by 19 publications

References 36 publications

Does parametric fMRI analysis with SPM yield valid results?—An empirical study of 1484 rest datasets

Does parametric fMRI analysis with SPM yield valid results?—An empirical study of 1484 rest datasets

Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations

How ignoring physiological noise can bias the conclusions from fMRI simulation results

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