Improved autoregressive model for correction of noise serial correlation in fast fMRI

Luo, Qing; Misaki, Masaya; Mulyana, Ben; Wong, Chung-Ki; Bodurka, Jerzy

doi:10.1002/mrm.28203

Cited by 10 publications

(17 citation statements)

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“…Our investigation on AR effects ( Appendix C ) confirmed a previous study ( Olszowy et al, 2019 ) and indicated that the AR structure in the residuals varies substantially across regions, tasks and subjects. Therefore, we recommend that, to obtain reasonably accurate standard errors for effect estimates, a GLS model with the temporal structure in the residuals be accounted for with preferably AR(2) or ARMA(1, 1) for a TR around 2 s. With shorter TRs, a higher-order AR structure would be likely needed ( Luo et al, 2020 ; Olszowy et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

et al. 2021

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In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling , all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category (“face”), and may inflate the statistical evidence when the trial sample size is not large enough. Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling . To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision. Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists.

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Section: Discussionmentioning

confidence: 99%

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

et al. 2021

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“…Our investigation on AR effects (Appendix B) confirmed a previous study (Olszowy et al, 2019) and indicated that the AR structure in the residuals varies substantially across regions, tasks and subjects. Therefore, we recommend that, to obtain reasonably accurate standard errors for effect estimates, a GLS model with the temporal structure in the residuals be accounted for with preferably AR(2) or ARMA(1, 1) for a TR around 2 s. With shorter TRs, a higher-order AR structure would be likely needed (Olszowy et al, 2019;Luo et al, 2020).…”

Section: Additional Trial-level Modeling Issuesmentioning

confidence: 99%

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

Chen

Padmala

Chen

et al. 2020

Preprint

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In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling, all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category ("face"). Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling. To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision.Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists. and so on. The reliability of a task-based experiment hinges on having a reasonably large number of repetitions associated with a condition. Such repetitions are usually termed "trials", and each trial is considered to be an instantiation of an idealized condition. For example, in an emotion study with three conditions (positive, neutral and negative), the investigator may show 20 different human faces of each emotional valence to the subject in the scanner. From the statistical perspective, the number of trials serves as the sample size for each condition and, per the law of large numbers in probability theory, the average effect estimate for a specific conditio...

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“…Local prewhitening can lead to noisier estimates of the prewhitening parameters, though smoothing can help combat this. While previous work based on volumetric fMRI found smoothing to be detrimental because of mixing signals across tissue classes (Luo et al, 2020), our use of surface-based smoothing largely avoids this limitation. Using cortical surface fMRI data also has the advantage of reduced dimensionality and the option to further reduce dimensionality through resampling without significant loss of spatial resolution.…”

Section: Variable Autocorrelation Across the Cortex Results In Spatia...mentioning

confidence: 97%

Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation

et al. 2023

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IntroductionAnalysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform “prewhitening” to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates.MethodsIn this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI.ResultsWe find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives.ConclusionOur analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.

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Improved autoregressive model for correction of noise serial correlation in fast fMRI

Cited by 10 publications

References 50 publications

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

To pool or not to pool: Can we ignore cross-trial variability in FMRI?

Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation

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