Bayesian technique for investigating linearity in event‐related BOLD fMRI

Kershaw, Jeff; Kashikura, Kenichi; Zhang, Xiaojiang; Abe, Sumiko; Kanno, Iwao

doi:10.1002/mrm.1143

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Some studies have included a post-hoc analysis to account for the expected response transients 13,20,33 , but experiments have not yet been carried out that measure the temporal summation of the fMRI responses while explicitly controlling or compensating for response transients, adaptation and attention. So, the temporal-summation experiments completed so far are inconclusive because Temporal summation holds up well in some experiments, but not in others 13,20,24,[26][27][28][29][30][31][32][33] . The failure can be traced to the fact that very short stimulus presentations evoke disproportionately large fMRI responses, compared with what is expected from the responses to long stimulus presentations.…”

Section: Temporal Summation Of Fmri Responsesmentioning

confidence: 88%

What does fMRI tell us about neuronal activity?

2002

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In recent years, cognitive neuroscientists have taken great advantage of functional magnetic resonance imaging (fMRI) as a non-invasive method of measuring neuronal activity in the human brain. But what exactly does fMRI tell us? We know that its signals arise from changes in local haemodynamics that, in turn, result from alterations in neuronal activity, but exactly how neuronal activity, haemodynamics and fMRI signals are related is unclear. It has been assumed that the fMRI signal is proportional to the local average neuronal activity, but many factors can influence the relationship between the two. A clearer understanding of how neuronal activity influences the fMRI signal is needed if we are correctly to interpret functional imaging data.

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Section: Temporal Summation Of Fmri Responsesmentioning

confidence: 88%

What does fMRI tell us about neuronal activity?

2002

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“…However, the difficulty in determining a mathematical model that accounts for BOLD responses due to brief as well as sustained stimuli, as reported here, is consistent with a previous finding in the visual system that incorporated an exponentially adapting neural activity waveform under LTI (23). Bayesian statistics applied to individual subject BOLD responses in the visual cortex suggest that there may be a crossover from a linear to a nonlinear regime as a function of stimulus duration (35), and others have quantified the effect using non‐physiologically‐relevant mathematical functions (36). Nonlinearity may arise from the vasculature; however, the literature on this subject is conflicting.…”

Section: Discussionmentioning

confidence: 99%

Assessing linear time‐invariance in human primary somatosensory cortex with BOLD fMRI using vibrotactile stimuli

Nangini

MacIntosh

Tam

et al. 2005

Magnetic Resonance in Med

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The assumption of linear time-invariance (LTI) in the human primary somatosensory cortex (SI) is assessed for fMRI signals generated by variable-duration vibrotactile stimuli. Predictions based on time-shifted summation (TSS) of responses to 2 s stimuli overestimate observed BOLD signal amplitudes in response to longer-duration stimuli, in agreement with previous findings in other primary sensory cortices. To interpret these results, we undertook an alternative approach for LTI assessment by characterizing BOLD signals using two biophysical models. The first model assumes that the input stimulus envelope is proportional to neural activity. The second assumes that neural activity exhibits both transient and steady-state components, consistent with extensive electrophysiological data, and fits the experimental data better. Although nonlinearity remains evident for short stimulus durations, the latter model shows that the TSS procedure to assess LTI overestimates the BOLD signal because the temporal characteristics of neural activity have not been considered adequately.

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“…Further investigation is in progress to determine a method for truly characterizing phase synchronization in fMRI time series. This future work is relevant to the issues raised in several studies concerning the linearity of fMRI data [Berns et al, 1999;Kershaw et al, 2001;Vasquez and Noll, 1998]. If we assume that there is an interesting source of nonlinearity present in fMRI data, we are left with the question of how to account for nonlinear interactions between regions of the brain.…”

Section: Discussionmentioning

confidence: 95%

Characterizing instantaneous phase relationships in whole‐brain fMRI activation data

Laird

Rogers

Carew

et al. 2002

Human Brain Mapping

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Typically, fMRI data is processed in the time domain with linear methods such as regression and correlation analysis. We propose that the theory of phase synchronization may be used to more completely understand the dynamics of interacting systems, and can be applied to fMRI data as a novel method of detecting activation. Generalized synchronization is a phenomenon that occurs when there is a nonlinear functional relationship present between two or more coupled, oscillatory systems, whereas phase synchronization is defined as the locking of the phases while the amplitudes may vary. In this study, we developed an application of phase synchronization analysis that is appropriate for fMRI data, in which the phase locking condition is investigated between a voxel time series and the reference function of the task performed. A synchronization index is calculated to quantify the level of phase locking, and a nonparametric permutation test is used to determine the statistical significance of the results. We performed the phase synchronization analysis on the data from five volunteers for an event-related finger-tapping task. Functional maps were created that provide information on the interrelations between the instantaneous phases of the reference function and the voxel time series in a whole-brain fMRI activation data set. We conclude that this method of analysis is useful for revealing additional information on the complex nature of the fMRI time series.

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Bayesian technique for investigating linearity in event‐related BOLD fMRI

Cited by 11 publications

References 24 publications

What does fMRI tell us about neuronal activity?

What does fMRI tell us about neuronal activity?

Assessing linear time‐invariance in human primary somatosensory cortex with BOLD fMRI using vibrotactile stimuli

Characterizing instantaneous phase relationships in whole‐brain fMRI activation data

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