Three-dimensional MR-encephalography: Fast volumetric brain imaging using rosette trajectories

Zahneisen, Benjamin; Grotz, Thimo; Lee, Kuan J.; Ohlendorf, Sabine; Reisert, Marco; Zaitsev, Maxim; Hennig, Jürgen

doi:10.1002/mrm.22711

Cited by 64 publications

(77 citation statements)

References 28 publications

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“…An aim of ongoing efforts is to achieve an acquisition speed for volume data previously only possible for single slice data. Approaches investigated currently are compressed sensing [23], parallel imaging [24] and EEG-like MR imaging [25].…”

Section: Towards Higher Temporal and Spatial Resolutionmentioning

confidence: 99%

fMRI analysis on the GPU—Possibilities and challenges

Eklund

Andersson

Knutsson

2012

Computer Methods and Programs in Biomedicine

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Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on the GPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s if three GPUs are used, compared to 0.5 -2.5 h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.

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Section: Towards Higher Temporal and Spatial Resolutionmentioning

confidence: 99%

fMRI analysis on the GPU—Possibilities and challenges

Eklund

Andersson

Knutsson

2012

Computer Methods and Programs in Biomedicine

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“…. , 6, and the width of the time windows (e.g. 11 network observations) that are centered on these points.…”

Section: Synthetic Time-varying Networkmentioning

confidence: 99%

“…Such data may be obtained, for example, from electroencephalography (EEG), magnetoencephalography (MEG), magnetic resonance encephalography recordings, 6 and also, with limitations, from resting state functional magnetic resonance imaging (fMRI) scans. 7 The results of the time-variant connectivity analysis can be represented e.g.…”

mentioning

confidence: 99%

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

Schmidt

Piper

Pester

et al. 2018

Int. J. Neur. Syst.

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Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

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“…In fact, previous works have indicated the influence of the number of gradient directions on data acquisitions (Landman et al 2007;Tijssen et al 2009). Recently, several promising imaging techniques have been proposed, including MRencephalography (Zahneisen et al 2011) and multiband echo planar imaging (Moeller et al 2010). Using the multiband scanning protocol, additional gradient directions can be acquired in the same scan duration without a significant loss in spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Association between In-scanner Head Motion with Cerebral White Matter Microstructure: A Multiband Diffusion-weighted MRI study

Kong

2014

Preprint

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Diffusion-weighted MRI (DW-MRI) has emerged as a promising neuroimaging technique used to depict the biological microstructural properties of the human brain white matter.However, like any other MRI technique, DW-MRI remains subject to head motion during scanning. The association between motion and diffusion metrics is rarely understood.Previous studies have indicated that there are some regions showing significant relationship with diffusion metrics from traditional DW-MRI data with relative few gradient directions (e.g., 30 directions). As imaging techniques improves, additional gradient directions can be acquired in the same scan duration without a significant loss in spatial resolution. The current study examined the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS), with a multiband diffusion data (i.e., 137 directions). The diffusion metrics used in this study not only the included the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also a newly proposed inter-voxel metric, local diffusion homogeneity (LDH). The positive association was observed with MD, while the negative association with LDH. No significant association between motion and FA was observed. These results indicate that there is a similar link between motion and diffusion metrics in the multiband diffusion data. Finally, the motion-diffusion association is discussed. directions). As imaging techniques improves, additional gradient directions can be acquired in the same scan duration without a significant loss in spatial resolution. The current study examined the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS), with a multiband diffusion data (i.e., 137 directions). The diffusion metrics used in this study not only the included the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also a newly proposed inter-voxel metric, local diffusion homogeneity (LDH). The positive association was observed with MD, while the negative association with LDH. No significant association between motion and FA was observed. These results indicate that there is a similar link between motion and diffusion metrics in the multiband diffusion data. Finally, the motion-diffusion association is discussed. PeerJ PrePrints

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Three-dimensional MR-encephalography: Fast volumetric brain imaging using rosette trajectories

Cited by 64 publications

References 28 publications

fMRI analysis on the GPU—Possibilities and challenges

fMRI analysis on the GPU—Possibilities and challenges

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

Association between In-scanner Head Motion with Cerebral White Matter Microstructure: A Multiband Diffusion-weighted MRI study

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