AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach–Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.

References in zbMATH (referenced in 21 articles )

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  1. Adrian, Daniel W.; Maitra, Ranjan; Rowe, Daniel B.: Complex-valued time series modeling for improved activation detection in fMRI studies (2018)
  2. Andrew Beers; James Brown; Ken Chang; Katharina Hoebel; Elizabeth Gerstner; Bruce Rosen; Jayashree Kalpathy-Cramer: DeepNeuro: an open-source deep learning toolbox for neuroimaging (2018) arXiv
  3. Choi, Ji Yeh; Hwang, Heungsun; Timmerman, Marieke E.: Functional parallel factor analysis for functions of one- and two-dimensional arguments (2018)
  4. Frank, Lawrence R.; Galinsky, Vitaly L.: Dynamic multiscale modes of resting state brain activity detected by entropy field decomposition (2016)
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  6. Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying: Spectral properties of the temporal evolution of brain network structure (2015)
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  8. Ruiz Vargas, E.; Mitchell, D. G. V.; Greening, S. G.; Wahl, L. M.: Topology of whole-brain functional MRI networks: improving the truncated scale-free model (2014)
  9. Wang, Xiao-Feng; Li, Yingxing: Bayesian inferences for beta semiparametric-mixed models to analyze longitudinal neuroimaging data (2014)
  10. Rasmussen, Peter M.; Hansen, Lars K.; Madsen, Kristoffer H.; Churchill, Nathan W.; Strother, Stephen C.: Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012) ioport
  11. Karsten Tabelow; Brandon Whitcher: Special Volume on Magnetic Resonance Imaging in R (2011) not zbMATH
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  15. Preusse, Franziska; van der Meer, Elke; Ullwer, Dorothea; Brucks, Martin; Krueger, Frank; Wartenburger, IsabellAl.: Long-term characteristics of analogical processing in high-school students with high fluid intelligence: an fMRI study (2010) MathEduc
  16. Vidal, Camille; Jedynak, Bruno: Learning to match: deriving optimal template-matching algorithms from probabilistic image models (2010) ioport
  17. Yan, Xiaodan: Dissociated emergent-response system and fine-processing system in human neural network and a heuristic neural architecture for autonomous humanoid robots (2010) ioport
  18. Zhang, Chunming: Statistical inference of minimum BD estimators and classifiers for varying-dimensional models (2010)
  19. Bhaumik, Dulal K.; Roy, Anindya; Lazar, Nicole A.; Kapur, Kush; Aryal, Subhash; Sweeney, John A.; Patterson, Dave; Gibbons, Robert D.: Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments (2009)
  20. Zhang, Chunming; Lu, Yuefeng; Johnstone, Tom; Oakes, Terry; Davidson, Richard J.: Efficient modeling and inference for event-related fMRI data (2008)

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