scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Integrated strategy for improving functional connectivity mapping using multiecho fMRI

TL;DR: The proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.
Abstract: Functional connectivity analysis of resting state blood oxygen level–dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals—a characteristic property of BOLD signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.
Citations
More filters
Journal ArticleDOI
TL;DR: The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI, and to highlight gaps in current knowledge and avenues for future research.

871 citations


Cites background or methods from "Integrated strategy for improving f..."

  • ...In another study (Kundu et al., 2013), data were acquired at 4 TEs (12, 28, 44, and 60 ms) in order to partition the data into ICA components whose signal reflects artifactual S0 changes (such as those caused by motion) versus relaxation rate changes (such as those caused by neural activity or changes in pCO2), building on the MEICA methods outlined in Kundu et al....

    [...]

  • ...…development of denoising techniques) are themulti-echo approaches,which improve identification of non-BOLD signals (e.g. (Bright and Murphy, 2013; Kundu et al., 2013)), and task fMRI, where an evoked signal of interest should be better isolated by better denoising (e.g., (Christodoulou et al.,…...

    [...]

  • ...…is not yet clear, since methods to counteract motion artifact are still being developed and validated (Bright and Murphy, 2013; Jo et al., 2013; Kundu et al., 2013; Muschelli et al., 2014; Patel et al., 2014; Power et al., 2014; Satterthwaite et al., 2013a; Scheinost et al., 2014; Yan et al.,…...

    [...]

  • ...The full impact of motion artifact on functional connectivity studies of development, aging, and disease is not yet clear, since methods to counteract motion artifact are still being developed and validated (Bright and Murphy, 2013; Jo et al., 2013; Kundu et al., 2013; Muschelli et al., 2014; Patel et al., 2014; Power et al., 2014; Satterthwaite et al., 2013a; Scheinost et al., 2014; Yan et al., 2013a,b; Zeng et al., 2014)....

    [...]

  • ...(Bright and Murphy, 2013; Kundu et al., 2013)), and task fMRI, where an evoked signal of interest should be better isolated by better denoising (e....

    [...]

Journal ArticleDOI
TL;DR: An integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP), and should accelerate progress in understanding the brain in health and disease.
Abstract: Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

793 citations

Journal ArticleDOI
TL;DR: A systematic evaluation of 14 participant‐level confound regression methods for functional connectivity highlights the heterogeneous efficacy of existing methods, and suggests that different confounding regression strategies may be appropriate in the context of specific scientific goals.

790 citations

Journal ArticleDOI
TL;DR: These results indicate that simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts, and group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy.

564 citations

Journal ArticleDOI
TL;DR: The importance of signal denoising as an essential step in the analysis pipeline of task‐based and resting state fMRI studies is summarized and practical recommendations regarding the optimization of the preprocessing pipeline are indicated.

449 citations


Cites methods from "Integrated strategy for improving f..."

  • ...Similar to other ICA-based methods, the final step of ME-ICA is to regress out from the data those ICs that are classified as noise (Kundu et al., 2012; 2013; Olafsson et al., 2015)....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described and techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described.

10,002 citations

Journal ArticleDOI
TL;DR: It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
Abstract: An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

8,766 citations


"Integrated strategy for improving f..." refers background in this paper

  • ...Functional connectivity between brain regions is then typically estimated by the correlation between time series (1)....

    [...]

  • ...become distorted after global signal regression Brain Connectivity 2(1):25–32....

    [...]

Journal ArticleDOI
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.

8,231 citations


"Integrated strategy for improving f..." refers background in this paper

  • ...Because these components are, by construction, independent, this means that the BOLD degrees of freedom for inference are known and can be used to appropriately normalize correlation values (12, 13)....

    [...]

Journal ArticleDOI
TL;DR: The results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

6,411 citations


"Integrated strategy for improving f..." refers background or methods in this paper

  • ...Peltier SJ, Noll DC (2002) T(2)(*) dependence of low frequency functional connectivity....

    [...]

  • ...Finally, it has been suggested that in extreme cases, time points that are severely contaminated by otherwise intractable movement effects may be simply deleted or “scrubbed” from the time series (2)....

    [...]

  • ...Delta variation signal (DVARS), computed as the root mean square (RMS) average of the first derivatives of all fMRI signals, identified time points with rapidly changing fMRI signal (2)....

    [...]

Journal ArticleDOI
TL;DR: The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing, and uses the effective degrees of freedom associated with the error term.

2,647 citations


"Integrated strategy for improving f..." refers background in this paper

  • ...This normalization also controls for variability in BOLD degrees of freedom due to varying BOLD sensitivity with subject motion (14)....

    [...]