基于图形模型的多变量分析（GAMMA）：一个开源的、跨平台的神经影像学数据分析软件包。
Graphical model based multivariate analysis (GAMMA): an open-source, cross-platform neuroimaging data analysis software package.
机构信息
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
出版信息
Neuroinformatics. 2012 Apr;10(2):119-27. doi: 10.1007/s12021-011-9129-7.
Abstract
The GAMMA suite is an open-source, cross-platform data-mining software package designed to analyze neuroimaging data. Analyzing brain image volumes is a very challenging problem, due to undersampling and the potential for multivariate nonlinear interactions among variables. The GAMMA suite provides a set of tools to facilitate the analysis of neuroimaging data.
摘要
GAMMA 套件是一个开源的、跨平台的数据挖掘软件包，旨在分析神经影像学数据。分析脑影像体积是一个非常具有挑战性的问题，因为存在欠采样和变量之间可能存在多元非线性相互作用的问题。GAMMA 套件提供了一组工具，以方便神经影像学数据的分析。
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本文引用的文献
1
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.随机松弛，吉布斯分布，以及贝叶斯图像恢复。
IEEE Trans Pattern Anal Mach Intell. 1984 Jun;6(6):721-41. doi: 10.1109/tpami.1984.4767596.
2
Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation.用于功能性脑图谱的非参数分层贝叶斯模型
Conf Comput Vis Pattern Recognit Workshops. 2010 Jun 13:15-22. doi: 10.1109/CVPRW.2010.5543434.
3
Voxelwise Bayesian lesion-deficit analysis.体素级贝叶斯损伤-缺陷分析
Neuroimage. 2008 May 1;40(4):1633-42. doi: 10.1016/j.neuroimage.2008.01.014. Epub 2008 Jan 26.
4
Clinical diagnosis based on bayesian classification of functional magnetic-resonance data.基于功能磁共振数据贝叶斯分类的临床诊断
Neuroinformatics. 2007 Fall;5(3):178-88. doi: 10.1007/s12021-007-0007-2.
5
Quantitative comparison of functional MRI and direct electrocortical stimulation for functional mapping.功能磁共振成像与直接皮层电刺激用于功能图谱绘制的定量比较。
Int J Med Robot. 2007 Sep;3(3):262-70. doi: 10.1002/rcs.149.
6
Graphical-model-based multivariate analysis of functional magnetic-resonance data.基于图形模型的功能磁共振数据多变量分析
Neuroimage. 2007 Apr 1;35(2):635-47. doi: 10.1016/j.neuroimage.2006.11.040. Epub 2007 Jan 25.
7
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IEEE Trans Med Imaging. 2005 Oct;24(10):1237-48. doi: 10.1109/TMI.2005.854305.
8
Network analysis of mild cognitive impairment.轻度认知障碍的网络分析
Neuroimage. 2006 Feb 15;29(4):1252-9. doi: 10.1016/j.neuroimage.2005.08.020. Epub 2005 Oct 5.
9
Fully automatic segmentation of white matter hyperintensities in MR images of the elderly.老年人磁共振图像中白质高信号的全自动分割
Neuroimage. 2005 Nov 15;28(3):607-17. doi: 10.1016/j.neuroimage.2005.06.061. Epub 2005 Aug 29.
10
Models of brain function in neuroimaging.
Annu Rev Psychol. 2005;56:57-87. doi: 10.1146/annurev.psych.56.091103.070311.
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