A Spatio-temporal Regression Model for the Analysis of Functional MRI Data

Katanoda, Kota; Matsuda, Yasumasa; Sugishita, Morihiro

doi:10.1006/nimg.2002.1209

Cited by 70 publications

(47 citation statements)

References 22 publications

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“…Some investigators attempt to capture correlations between the measured brain activity in a given voxel with the activity in neighboring voxels. For example, Katanoda et al (2002) address spatial correlations by incorporating the time series from neighboring (physically contiguous) voxels. Similarly, Gössl et al (2001) and Woolrich et al (2004a) consider correlations between neighboring voxels using a conditional autoregressive (CAR) model in a Bayesian framework, and specifically Woolrich et al (2004a) model correlations as a function of the geometric mean of the number of neighboring voxels.…”

Section: Introductionmentioning

confidence: 99%

“…The spatial modeling approaches of Katanoda et al (2002), Gössl et al (2001), andWoolrich et al (2004a) provide local smoothing, but limiting spatial associations to a very restricted area (e.g. consisting of contiguous voxels) departs from our current knowledge regarding neurophysiology including functional networks that are spatially disperse (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian hierarchical framework for spatial modeling of fMRI data

Bowman

Caffo

Bassett³

et al. 2008

NeuroImage

110

131

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Functional neuroimaging techniques enable investigations into the neural basis of human cognition, emotions, and behaviors. In practice, applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric, neurological, and substance abuse disorders, as well as into the neural responses to their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. One may also extend voxel-level analyses by simultaneously considering the ensemble of voxels constituting an anatomically defined region of interest (ROI) or by considering means or quantiles of the ROI. In this work we present a Bayesian extension of voxel-level analyses that offers several notable benefits. First, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance for regional mean parameters allows for the study of inter-regional functional connectivity, provided enough subjects are available to allow for accurate estimation. Finally, an exchangeable correlation structure within regions allows for the consideration of intra-regional functional connectivity. We perform estimation for our model using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling which, despite the high throughput nature of the data, can be executed quickly (less than 30 minutes). We apply Hosted by The Berkeley Electronic Press in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer's disease. The unifying hierarchical model presented in this manuscript is shown to enhance the interpretation content of these data sets.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian hierarchical framework for spatial modeling of fMRI data

Bowman

Caffo

Bassett³

et al. 2008

NeuroImage

110

131

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“…ReHo measures the regional homogeneity, which reflects local synchrony by calculating similarity of dynamic fluctuations of voxels within a given cluster. This method is based on observations that meaningful fMRI activity is more likely to occur in clusters of several spatially contiguous voxels than in a single voxel 29,30) . It assumes that within a functional cluster, the hemodynamic characteristics of every voxel would be similar or synchronous with that of its neighbors; and such similarity could be changed or modulated by different conditions.…”

Section: Acupuncturementioning

confidence: 99%

Changes of Regional Homogeneity and Amplitude of Low Frequency Fluctuation on Resting-State Induced by Acupuncture

Yeo

2013

Korean Journal of Acupuncture

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Objectives : Our study aimed to investigate the sustained effects of sham (SHAM) and verum acupuncture (ACUP) into the post-stimulus resting state. Methods : In contrast to previous studies, in order to define the changes in resting state induced by acupuncture, changes were evaluated with a multi-method approach by using regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF). Twelve healthy participants received SHAM and ACUP stimulation right GB34 (Yanglingquan) and the neural changes between post-and pre-stimulation were detected. Results : The following results were found; in both ReHo and ALFF, the significant foci of; left and right middle frontal gyrus, left medial frontal gyrus, left superior frontal gyrus, and right posterior cingulate cortex, areas that are known as a default mode network, showed increased connectivity. In addition, in ReHo, but not in ALFF, brain activation changes in the insula, anterior cingulate cortex, and the thalamus, which are associated with acupuncture pain modulation, were found. Conclusions : In this study, results obtained by using ReHo and ALFF, showed that acupuncture can modulate the post-stimulus resting state and that ReHo, but not ALFF, can also detect the neural changes that were induced by the acupuncture stimulations. Although more future studies with ReHo and ALFF will be needed before any firm conclusions can be drawn, our study shows that particularly ReHo could be an interesting method for future clinical neuroimaging studies on acupuncture.

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“…to set up a spatiotemporal formulation. Some of the spatiotemporal models proposed in the literature are Bowman (2007), Penny et al (2005), Katanoda et al (2002) and Woolrich et al (2004a). Although all these models are based on convolution, the four of them present a di↵erent modelisation of the spatiotemporal correlation structure between voxels.…”

Section: Introductionmentioning

confidence: 99%

“…Penny et al (2005) propose a fully Bayesian model with spatial priors defined over regression coe cients of a GLM, using Gaussian Markov random fields (GMRF), and the errors are modelled as an autoregressive process. Katanoda et al (2002) propose a spatiotemporal regression model for each voxel that involves the time series of the neighbouring voxels together with its own. Woolrich et al (2004a) present a fully Bayesian approach, incorporating spatiotemporal noise modelling.…”

Section: Introductionmentioning

confidence: 99%