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Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp.

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TLDR
This artifact was described in cohorts of children, adolescents, and adults, and its severity (magnitude) was related to the prevalence of motion within a cohort, which is a substantial confound in the examination of single rs-fcMRI datasets and in comparisons of multiple rs-FCMRI datasets.
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This article is published in NeuroImage.The article was published on 2013-08-01 and is currently open access. It has received 308 citations till now. The article focuses on the topics: Artifact (error).

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The WU-Minn Human Connectome Project: An Overview

TL;DR: Progress made during the first half of the Human Connectome Project project in refining the methods for data acquisition and analysis provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
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The minimal preprocessing pipelines for the Human Connectome Project.

TL;DR: The minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space are described.
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Methods to detect, characterize, and remove motion artifact in resting state fMRI

TL;DR: It is found that motion-induced signal changes are often complex and variable waveforms, often shared across nearly all brain voxels, and often persist more than 10s after motion ceases, which increase observed RSFC correlations in a distance-dependent manner.
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DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

TL;DR: The newly developed toolbox, DPABI, which was evolved from REST and DPARSF is introduced, designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.
References
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Journal ArticleDOI

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

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.
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Movement-related effects in fMRI time-series

TL;DR: The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.
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The influence of head motion on intrinsic functional connectivity MRI.

TL;DR: Head motion was associated with decreased functional coupling in the default and frontoparietal control networks--two networks characterized by coupling among distributed regions of association cortex and other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions.
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Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth.

TL;DR: The results demonstrate the pervasive influence of motion on multiple types functional connectivity analysis, and underline the importance of accounting for motion in studies of neurodevelopment.
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Optimizing the order of operations for movement scrubbing: Comment on Power et al.

Joshua Carp
- 01 Aug 2013 - 
TL;DR: Using simulated data, it is shown that deleting and replacing contaminated volumes before temporal filtering removes a greater proportion of artifactual signal while retaining a greaterportion of the original data.
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