Modeling state-related fMRI activity using change-point theory

Lindquist, Martin A.; Waugh, Christian E.; Wager, Tor D.

doi:10.1016/j.neuroimage.2007.01.004

Cited by 99 publications

(120 citation statements)

References 58 publications

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“…(see Chen & Gupta, 2012). Current applications in the field of behavioral sciences include detection of workload changes using heart rate variability (Hoover, Singh, Fishel-Brown, & Muth, 2011), capturing active state transition in fMRI activity (Lindquist, Waugh, & Wager, 2007) and revealing cardio-respiratory changes preceding the occurrence of panic attacks (Rosenfield, Zhou, Wilhelm, Conrad, Roth, & Meuret, 2010). Until recently, research on this topic focused almost exclusively on univariate time series, yielding approaches to detect changes in mean, and, in some cases, variance and/or autocorrelation.…”

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confidence: 99%

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

et al. 2016

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Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

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confidence: 99%

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

et al. 2016

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“…The fMRI data has some fluctuation according to stimuli or disease. Several change point methods have been proposed for fMRI signals (Lindquist et al, 2007(Lindquist et al, , 2014. Were acquired functional magnetic resonance imaging (fMRI) time series data from five healthy volunteers in the resting state to estimate functional connectivity between 90 cortical and subcortical regions.…”

Section: Applicationsmentioning

confidence: 99%

Variance components estimation in the presence of drift

Kim

Ogden

2016

CSAM

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Variance components should be estimated based on mean change when the mean of the observations drift gradually over time. Consistent estimators for the variance components are studied for a particular modeling situation with some underlying functions or drift. We propose a new variance estimator with Fourier estimation of variations. The consistency of the proposed estimator is proved asymptotically. The proposed procedures are studied and compared empirically with the variance estimators removing trends. The result shows that our variance estimator has a smaller mean square error and depends on drift patterns. We estimate and apply the variance to Nile River flow data and resting state fMRI data.

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“…In addition, unpredicted activation may provide novel information about task-unrelated brain activity during resting periods. Therefore, there is a critical need for alternative approaches to fMRI analysis which do not require the onsets of cortical responses to be specified, and which allow the use of more unconstrained experimental paradigms [Faisan et al, 2007;Hutchinson et al, 2009;Lindquist et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

“…Probabilistic frameworks based on Hidden Markov modes [Faisan et al, 2007;Hutchinson et al, 2009] have enabled spatio-temporal mapping of the response but have not been used for single trial analysis. Alternatively, change point theory methods have been proposed to estimate the onsets and durations of activations by modeling the voxel time series as a mixture of two Gaussian distributions (baseline and activation) [Lindquist et al, 2007]. Before fitting the Gaussian mixture model, Lindquist et al [2007] suggested first temporally smoothing the voxel time series with an Exponentially Weighted Moving Average (EWMA) filter and then testing for significant changes in the filtered time series by using a Hotelling T 2 -test comparing to a baseline state.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, change point theory methods have been proposed to estimate the onsets and durations of activations by modeling the voxel time series as a mixture of two Gaussian distributions (baseline and activation) [Lindquist et al, 2007]. Before fitting the Gaussian mixture model, Lindquist et al [2007] suggested first temporally smoothing the voxel time series with an Exponentially Weighted Moving Average (EWMA) filter and then testing for significant changes in the filtered time series by using a Hotelling T 2 -test comparing to a baseline state. However, change point theory methods have only been used to detect activation associated with single-epoch paradigms with prolonged activations.…”

Section: Introductionmentioning

confidence: 99%

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Detection and characterization of single‐trial fMRI bold responses: Paradigm free mapping

Caballero-Gaudes¹,

Petridou²,

Dryden³

et al. 2010

Human Brain Mapping

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This work presents a novel method of mapping the brain's response to single stimuli in space and time without prior knowledge of the paradigm timing: paradigm free mapping (PFM). This method is based on deconvolution of the hemodynamic response from the voxel time series assuming a linear response and using a ridge-regression algorithm. Statistical inference is performed by defining a spatio-temporal t-statistic and by controlling for multiple comparisons using the false discovery rate procedure. The methodology was validated on five subjects who performed self-paced and visually cued finger tapping at 7 Tesla, with moderate (TR = 2 s) and high (TR = 0.4 s) temporal resolution. The results demonstrate that detection of single-trial BOLD events is feasible without a priori information on the stimulus paradigm. The proposed method opens up the possibility of designing temporally unconstrained paradigms to study the cortical response to unpredictable mental events.

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Modeling state-related fMRI activity using change-point theory

Cited by 99 publications

References 58 publications

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Variance components estimation in the presence of drift

Detection and characterization of single‐trial fMRI bold responses: Paradigm free mapping

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