Impact of autocorrelation on functional connectivity

Arbabshirani, Mohammad R.; Damaraju, Eswar; Phlypo, Ronald; Plis, Sergey M.; Allen, Elena A.; Ma, Sai; Mathalon, Daniel H.; Preda, Adrian; Vaidya, Jatin G.; Adalı, Tülay; Calhoun, Vince D.

doi:10.1016/j.neuroimage.2014.07.045

Cited by 93 publications

(96 citation statements)

References 52 publications

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“…Is ψ homogeneous across the brain?). 7) It is well known that serial correlation intrinsically exists in the residuals or unaccounted-for part of the time series model for FMRI data due to physiological (cardiac and respiratory) confounds and thermal fluctuations in the scaner, which may lead to biased ISC estimates when heterogeneity of serial correlation occurs across subjects or brain regions (Arbabshirani et al, 2014). Nevertheless, recent investigation indicates that the impact of biased estimations on statistical inferences is minimal or even negligible (Arbabshirani et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

et al. 2016

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FMRI data acquisition under naturalistic and continuous stimuli (e.g., watching a video or listening to music) has become popular recently due to the fact that it entails less manipulation and more realistic/complex contexts involved in the task, compared to the conventional task-based experimental designs. The synchronization or response similarities among subjects are typically measured through inter-subject correlation (ISC) between any pair of subjects. At the group level, summarizing the collection of ISC values is complicated by their intercorrelations, which necessarily lead to the violation of independence assumed in typical parametric approaches such as Student’s t-test. Nonparametric methods, such as bootstrapping and permutation testing, have previously been adopted for testing purposes by resampling the time series of each subject, but the quantitative validity of these specific approaches in terms of controllability of false positive rate (FPR) has never been explored before. Here we survey the methods of ISC group analysis that have been employed in the literature, and discuss the issues involved in those methods. We then propose less computationally intensive nonparametric methods that can be performed at the group level (for both one- and two-sample analyses), as compared to the popular method of circularly shifting the EPI time series at the individual level. As part of the new approaches, subject-wise (SW) resampling is adopted instead of element-wise (EW) resampling, so that exchangeability and independence assumptions are satisfied, and the patterned correlation structure among the ISC values can be more accurately captured. We examine the FPR controllability and power achievement of all the methods through simulations, as well as their performance when applied to a real experimental dataset. The new methodologies are shown to be both efficient and robust, and they have been implemented into an open source program, 3dNPT, in AFNI (http://afni.nimh.nih.gov).

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Section: Discussionmentioning

confidence: 99%

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

et al. 2016

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“…Correcting for this autocorrelation prior to calculating correlation values has been tried, and although it did not significantly impact network results in healthy participants (Arbabshirani et al, 2014), it may impact quantitative comparison of connectivity metrics between cohorts with different inherent autocorrelation properties.…”

Section: Discussionmentioning

confidence: 99%

Potential pitfalls when denoising resting state fMRI data using nuisance regression

2017

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In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the “cleaned” residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series.

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“…Individual and spatial differences in neurovascular coupling have been described (Schippers et al, 2011), and autocorrelation effects have predominantly been treated as a source of noise in the functional imaging literature. However, a previous study evaluating the relationship between autocorrelation of fMRI time series and traditional functional connectivity found that correcting for autocorrelation had little effect on traditional functional connectivity (Arbabshirani et al, 2014). Our results suggest that at least a component of the autocorrelative structure of BOLD signal may be due to differences in neural activity and could be related to cognition.…”

Section: Discussionmentioning

confidence: 99%

Sustained versus instantaneous connectivity differentiates cognitive functions of processing speed and episodic memory

King

Anderson

2018

Human Brain Mapping

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Synchrony of brain activity over time describes the functional connectivity between brain regions but does not address the temporal component of this relationship. We propose a complementary method of analysis by introducing the width of cross-correlation curves between functional MRI (fMRI) time series as a metric of the relative duration of synchronous activity between brain regions, or "sustained connectivity". Using resting-state fMRI, cognitive, and demographics data from 1,003 subjects included in the Human Connectome Project, we find that sustained connectivity is a reproducible trait in individuals, heritable, more transient in females, and shows changes with age in early adulthood. Sustained connectivity in sensory brain regions is specifically associated with differences in processing speed across subjects, particularly in men. In contrast, traditional functional connectivity was correlated with a measure of episodic memory, but not with processing speed. Individual differences in hemodynamic response function (HRF) are closely approximated by sustained connectivity and width of the HRF is also correlated with processing speed across individuals, suggesting that variability in hemodynamic response may be influenced by transient versus sustained neural activity rather than simply differences in vascularity and signal transduction. Sustained connectivity may provide new opportunities to study brain dynamics in clinical populations.

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Impact of autocorrelation on functional connectivity

Cited by 93 publications

References 52 publications

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level

Potential pitfalls when denoising resting state fMRI data using nuisance regression

Sustained versus instantaneous connectivity differentiates cognitive functions of processing speed and episodic memory

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