Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI

Wang, Sijia; Peterson, Daniel J.; Gatenby, J. Christopher; Li, Wenbin; Grabowski, Thomas J.; Madhyastha, Tara M.

doi:10.3389/fninf.2017.00017

Cited by 177 publications

(145 citation statements)

References 26 publications

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“…Functional data was slice time corrected using AFNI's 3dTshift and motion corrected using FSL's mcflirt [42]. Fieldmap-less distortion correction was performed by co-registering the functional image to the samesubject T1w image with intensity inverted [43] constrained with an average fieldmap template [44], implemented with antsRegistration.…”

Section: Msc Functional Preprocessingmentioning

confidence: 99%

High-amplitude co-fluctuations in cortical activity drive functional connectivity

Esfahlani

Faskowitz

et al. 2019

Preprint

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Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here, we decompose resting-state functional connectivity using a "temporal unwrapping" procedure to assess the contributions of moment-tomoment activity co-fluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of co-fluctuations of network organization with fluctuations in the BOLD time series. We show that, surprisingly, only a small fraction of frames exhibiting the strongest co-fluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network's modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Our approach reveals fine-scale temporal structure of resting-state functional connectivity, and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of new directions for future research.

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Section: Msc Functional Preprocessingmentioning

confidence: 99%

High-amplitude co-fluctuations in cortical activity drive functional connectivity

Esfahlani

Faskowitz

et al. 2019

Preprint

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“…The diffusion images were then brain‐masked (BET, Brain Extraction Tool, FSL) and the least‐squares fit diffusion tensor was generated with nonlinear optimization to obtain FA values with the Camino software package . EPI geometric distortion correction was performed by nonlinear registration to the approved T1‐weighted image with the Symmetric Normalization (SyN) feature of ANTs (Advanced Normalization Tools) …”

Section: Methodsmentioning

confidence: 99%

“…27 EPI geometric distortion correction was performed by nonlinear registration to the approved T1-weighted image with the Symmetric Normalization (SyN) feature of ANTs (Advanced Normalization Tools). 28,29 Following distortion correction, the T1-weighted image was used to warp the Freesurfer-derived white matter parcellation onto the diffusion image with ANTs. 28 Finally, FA was computed for the a priori regions of interest (ROIs) by averaging the FA of each voxel within the ROI mask (combining the Freesurfer labels contained in Table 1 generated the ROI masks).…”

Section: Image Processingmentioning

confidence: 99%

Brief Computer‐Based Information Processing Measures are Linked to White Matter Integrity in Pediatric‐Onset Multiple Sclerosis

Bartlett

Shaw

Schwarz

et al. 2018

Journal of Neuroimaging

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BACKGROUND AND PURPOSE Pediatric‐onset multiple sclerosis (POMS) is a demyelinating disorder with unique clinical challenges. A brief computer‐administered cognitive screening battery measuring processing speed (Cogstate) and the Brief International Cognitive Assessment in MS (BICAMS) detect cognitive impairment in POMS. The neuroanatomic correlates of these deficits are incompletely understood. The purpose of this study is to define the neuroanatomic underpinnings of deficits identified with cognitive screening batteries in POMS. METHODS Participants with POMS and age‐matched healthy controls (HCs) were screened with Cogstate and BICAMS. Diffusion tensor imaging assessed region‐wise and tractography‐based fractional anisotropy (FA). RESULTS The POMS (n = 15) and HC (n = 21) groups were matched on age (mean ages 17.9 ± 3.2 vs. 17.8 ± 3.3 years, respectively) and on an estimate of general intellectual functioning. The Cogstate composite revealed significant slowing in POMS relative to HCs (P = .004), but the BICAMS composite did not significantly distinguish the groups (P = .10). The Cogstate composite showed moderate‐to‐strong correlations with regional FA (r = –.67 to –.82) and significantly associated with uncinate fasciculus FA following multiple comparisons correction (P = .002) in POMS. However, the BICAMS composite measure showed only weak‐to‐moderate correlations with FA in POMS (r = –.19 to –.57), with none surviving multiple comparisons correction. CONCLUSIONS Computer‐administered measures of cognitive processing are particularly sensitive in POMS and are closely linked to white matter FA.

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“…Functional data were slice-time corrected using 3dTshift from AFNI v16.2.07 (70) and realigned to a mean reference image using mcflirt (71). 'Fieldmap-less' distortion correction was performed by co-registering the functional image to the intensity-inverted T1w image (72,73) constrained with an EPI distortion atlas presented in Treiber et al (74) and implemented with antsRegistration (ANTs). This was followed by co-registration to the corresponding T1w using boundary-based registration (75) with nine degrees of freedom (bbregister within FreeSurfer v6.0.1).…”

Section: Section I: Visualizing Voxel Time Seriesmentioning

confidence: 99%

Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem

Aquino

Fulcher

Parkes

et al. 2019

Preprint

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One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER. resting-state | fMRI | global signal regression | denoising | GSR | head motion | artifact | DiCER

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Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI

Cited by 177 publications

References 26 publications

High-amplitude co-fluctuations in cortical activity drive functional connectivity

High-amplitude co-fluctuations in cortical activity drive functional connectivity

Brief Computer‐Based Information Processing Measures are Linked to White Matter Integrity in Pediatric‐Onset Multiple Sclerosis

Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem

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