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Published in Andrei I. Holodny, Functional Neuroimaging, 2019
During a typical five- to six-minute paradigm, thousands of raw images are thus obtained. A voxel-by-voxel analysis of the signal intensity changes that occur over time (i.e., fMRI time series) must be conducted to obtain fMRI activation maps. These maps are actually statistical probability maps that are obtained through extensive statistical analysis and then overlaid on 3D structural brain image sets. There are numerous approaches to fMRI data analysis, and, unfortunately, there has been little progress made to date with respect to national standardization of fMRI data processing approaches. In general, t-test, crosscorrelation, general linear model (GLM) and independent component analysis methods have been used. The GLM is the most commonly used approach worldwide at this time, and the most popular software package utilizing this approach has been statistical parametric mapping (SPM) (SPM—SPM99 through SPM5), which is a software package developed by the Wellcome Department of Cognitive Neurology in London (3). The GLM assumes that the experimental data are composed of a linear combination of different model factors, along with uncorrelated noise (4). However, many other commercial and institutional internally developed statistical software packages are available. An alternative approach to statistical analysis is the independent components analysis (ICA), which is a data-driven analysis method that identifies spatially stationary sets of voxels whose activity varies together over time and is maximally distinguishable from that of other sets (4).
Organic Chemicals
Published in William J. Rea, Kalpana D. Patel, Reversibility of Chronic Disease and Hypersensitivity, Volume 4, 2017
William J. Rea, Kalpana D. Patel
The statistical parametric mapping (SPM) group maps show areas of activation consistent with other fMRI studies using working memory paradigms in normal controls. Significant activated clusters were detected in the anterior cingulated cortex (ACC), DLPFC, parietal cortex (PC), and insular cortices (ICs). Due to the imbalance in race (no African-Americans in the control group), the between group comparisons were performed on Caucasians only. After correcting for VIQ and lead exposure, SPM maps showed that relative to controls, solvent-exposed subjects had reduced activation in areas in the ACC and bilateral DLPFC. Accounting for task performance differences, the ANCOVA showed that the solvent-exposed subjects had significantly reduced activation in the left DLPFC and increased activation in the left parietal regions compared with controls. Among solvent-exposed subjects, lifetime solvent exposure was significantly and negatively correlated with activation detected in the ACC, DLPFC, and PC after control for confounders Z. Percent activation values that were extracted from the ROIs also showed significant correlations with lifetime solvent exposure.
Neurobiology of Dyspnea: An Overview
Published in Donald A. Mahler, Denis E. O’Donnell, Dyspnea, 2014
Karleyton C. Evans, Robert B. Banzett
There are now several validated statistical software packages that are specifically designed to preprocess and analyze neuroimaging data that include Statistical Parametric Mapping (http://www.fil.ion.ucl.ac.uk/spm), Functional MRI of the Brain Software Library (http://fsl.fmrib.ox.ac.uk), and Analysis of Functional Neuroimages (http://afni.nimh.nih.gov). Additional information regarding image preprocessing and analyses is now available in several texts; for example, the comprehensive text by Friston et al.24
Striatal functional connectivity in chronic ketamine users: a pilot study
Published in The American Journal of Drug and Alcohol Abuse, 2020
Chia-Chun Hung, Sheng Zhang, Chun-Ming Chen, Jeng-Ren Duann, Ching-Po Lin, Tony Szu-Hsien Lee, Chiang-Shan R. Li
Brain imaging data were preprocessed using Statistical Parametric Mapping (SPM 12, Wellcome Department of Imaging Neuroscience, University College London, U.K.). We followed standard procedures in image preprocessing, as in recent work (56–60). Images of each individual subject were first realigned (motion corrected) and corrected for slice timing. A mean functional image volume was constructed for each subject per run from the realigned image volumes. These mean images were co-registered with the high-resolution structural image and then segmented for normalization with affine registration followed by nonlinear transformation (61,62). The normalization parameters determined for the structure volume were then applied to the corresponding functional image volumes for each subject. Finally, the images were smoothed with a Gaussian kernel of 8 mm at Full Width at Half Maximum.
Interchangeability of position tracking technologies; can we merge the data?
Published in Science and Medicine in Football, 2020
Matt Taberner, Jason O’Keefe, David Flower, Jack Phillips, Graeme Close, Daniel Dylan Cohen, Chris Richter, Christopher Carling
To examine the interchangeability between positional tracking variables derived from the GPS-1, GPS-2 and TRACAB, a Bland–Altman plot and regression analysis were used. The resulting correlation coefficient (Pearson) was used to examine shared variation (r2< .3 small, .3 < r2< .5 moderate and r2 > .5 large), while the standard error estimate (SEE) as well as the confidence interval (95 and 99%) of the square root of the error from the regression equation was used to assess confidence in the observed values. To evaluate the existence of proportional bias, the percentage difference between the devices was regressed to their average (Bland and Altman 1999). In addition to the test of relationship, a two-tailed paired-sample t-test was used to examine differences between devices. Data were analysed using statistical parametric mapping (spm0d version 0.4), and an alpha level of = 0.05 was utilised. Data analysis was performed in MATLAB (The MathWorks, Massachusetts, USA).
Intragastric quinine administration decreases hedonic eating in healthy women through peptide-mediated gut-brain signaling mechanisms
Published in Nutritional Neuroscience, 2019
Julie Iven, Jessica R. Biesiekierski, Dongxing Zhao, Eveline Deloose, Owen G. O’Daly, Inge Depoortere, Jan Tack, Lukas Van Oudenhove
Data were analyzed using Statistical Parametric Mapping (SPM12, Wellcome Trust Centre for Neuroimaging, UCL, London, UK) implemented in MATLAB R2014b (The MathWorks Inc., Natick, MA, USA). All individual images were preprocessed using the standard procedures implemented in SPM12, including spatial realignment as correction for small movements during the scan and co-registration of each functional image to the structural image of each subject and segmentation of the structural image. The structural image was used for each participant as reference for the spatial normalization to the EPI template image supplied with SPM12, based on information obtained during the segmentation step. Spatial smoothing using an 8×8×8 mm3 Gaussian smoothing kernel was applied to the normalized images to improve the signal-to-noise ratio.