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Systems Neuroscience Approaches to Measure Brain Mechanisms Underlying Resilience—Towards Optimizing Performance
Published in Steven Kornguth, Rebecca Steinberg, Michael D. Matthews, Neurocognitive and Physiological Factors During High-Tempo Operations, 2018
Martin P. Paulus, Alan N. Simmons, Eric G. Potterat, Karl F. Van Orden, Judith L. Swain
The basic structural and functional image processing were conducted with the Analysis of Functional Neuroimages (AFNI) software package (Cox 1996). A multivariate regressor approach detailed below was used to relate changes in EPI intensity to differences in task characteristics (Haxby, Hoffman, and Gobbini 2000). EPI images were co-registered using a 3D-coregistration algorithm (Eddy, Fitzgerald, and Noll 1996) that has been developed to minimize the amount of image translation and rotation relative to all other images. Six motion parameters were obtained across the time series for each subject. Three of these motion parameters were used as regressors to adjust EPI intensity changes due to motion artifacts. All slices of the EPI scans were temporally aligned following registration to ensure different relationships with the regressors are not due to the acquisition of different slices at different times during the repetition interval.
An Overview of fMRI
Published in Kaushik Majumdar, A Brief Survey of Quantitative EEG, 2017
UNIX-based open-source software Analysis of Functional NeuroImages (AFNI) too has a very powerful and flexible visualization abilities, including the ability to integrate visualization of volumes and cortical surfaces using the SUMA toolbox. AFNI’s statistical modeling and inference tools have historically been less sophisticated than those available in SPM and FSL. However, recent work has integrated AFNI with the R statistical package, which allows the use of more sophisticated modeling techniques available within R (Poldrack et al., 2011).
Brain-computer interfaces for stroke rehabilitation: summary of the 2016 BCI Meeting in Asilomar
Published in Brain-Computer Interfaces, 2018
Christoph Guger, José del R. Millán, Donatella Mattia, Junichi Ushiba, Surjo R. Soekadar, Vivek Prabhakaran, Natalie Mrachacz-Kersting, Kyousuke Kamada, Brendan Z. Allison
All pre- and post-processing of MRI data was performed using the AFNI software package. The first four volumes of each functional sequence were discarded to allow for signal stabilization. EPI data sets were motion-corrected and then spatially smoothed at 6 mm with a full width at half maximum Gaussian kernel. Each voxel time-series was scaled to a mean of 100, and AFNI’s 3dDeconvolve was used to perform a voxel-wise regression analysis with six motion parameters regressed out.