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      Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study

      research-article
      PeerJ
      PeerJ Inc.
      White matter, Head motion, Microstructure, Diffusion MRI

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          Abstract

          Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI techniques, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.

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          Most cited references23

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          Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

          Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
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            Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI.

            Parallel imaging in the form of multiband radiofrequency excitation, together with reduced k-space coverage in the phase-encode direction, was applied to human gradient echo functional MRI at 7 T for increased volumetric coverage and concurrent high spatial and temporal resolution. Echo planar imaging with simultaneous acquisition of four coronal slices separated by 44mm and simultaneous 4-fold phase-encoding undersampling, resulting in 16-fold acceleration and up to 16-fold maximal aliasing, was investigated. Task/stimulus-induced signal changes and temporal signal behavior under basal conditions were comparable for multiband and standard single-band excitation and longer pulse repetition times. Robust, whole-brain functional mapping at 7 T, with 2 x 2 x 2mm(3) (pulse repetition time 1.25 sec) and 1 x 1 x 2mm(3) (pulse repetition time 1.5 sec) resolutions, covering fields of view of 256 x 256 x 176 mm(3) and 192 x 172 x 176 mm(3), respectively, was demonstrated with current gradient performance. (c) 2010 Wiley-Liss, Inc.
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              Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy.

              Functional homotopy, the high degree of synchrony in spontaneous activity between geometrically corresponding interhemispheric (i.e., homotopic) regions, is a fundamental characteristic of the intrinsic functional architecture of the brain. However, despite its prominence, the lifespan development of the homotopic resting-state functional connectivity (RSFC) of the human brain is rarely directly examined in functional magnetic resonance imaging studies. Here, we systematically investigated age-related changes in homotopic RSFC in 214 healthy individuals ranging in age from 7 to 85 years. We observed marked age-related changes in homotopic RSFC with regionally specific developmental trajectories of varying levels of complexity. Sensorimotor regions tended to show increasing homotopic RSFC, whereas higher-order processing regions showed decreasing connectivity (i.e., increasing segregation) with age. More complex maturational curves were also detected, with regions such as the insula and lingual gyrus exhibiting quadratic trajectories and the superior frontal gyrus and putamen exhibiting cubic trajectories. Sex-related differences in the developmental trajectory of functional homotopy were detected within dorsolateral prefrontal cortex (Brodmann areas 9 and 46) and amygdala. Evidence of robust developmental effects in homotopic RSFC across the lifespan should serve to motivate studies of the physiological mechanisms underlying functional homotopy in neurodegenerative and psychiatric disorders.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                24 April 2014
                2014
                : 2
                : e366
                Affiliations
                [-1]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University , Beijing, China
                [-2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University , Beijing, China
                Article
                366
                10.7717/peerj.366
                4006224
                24795856
                2377017c-248d-4991-98a2-2069e0c0d428
                © 2014 Kong

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 24 January 2014
                : 9 April 2014
                Funding
                There was no funding source for this study.
                Categories
                Neurology
                Radiology and Medical Imaging

                white matter,head motion,microstructure,diffusion mri
                white matter, head motion, microstructure, diffusion mri

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