A lattice computing approach for on-line fMRI analysis

作者：Grana Manuel^*; Savio Alexandre M; Garcia Sebastian Maite; Fernandez Elsa

来源：Image and Vision Computing, 2010, 28(7): 1155-1161.

DOI：10.1016/j.imavis.2009.10.004

• fMRI
• Lattice computing
• Lattice Associative Memories
• Linear mixing model

We introduce an approach to fMRI analysis based on the Endmember Induction Heuristic Algorithm (EIHA). This algorithm uses the Lattice Associative Memory (LAM) to detect Lattice Independent vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Induced endmembers are used to compute the activation levels of voxels as result of an unmixing process. The endmembers correspond to diverse activation patterns, one of these activation patterns corresponds to the resting state of the neuronal tissue. The on-line working of the algorithm does not need neither a previous training process nor a priori models of the data. Results on a case study compare with the results given by the state of art SPM software.

• 出版日期2010-7