Journal article
Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages
It has been previously observed that spatial independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA).
In each case we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both inter-subject and intra-subject spatial normalization.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 2007 |
| Pages: | 1058-1068 |
| ISSN: | 1558254x and 02780062 |
| Types: | Journal article |
| DOI: | 10.1109/TMI.2007.896928 |
| ORCIDs: | Hansen, Lars Kai |
Algorithms Artifacts Biomedical engineering Biomedical imaging Brain Data analysis Evoked Potentials Functional magnetic-resonance imaging (fMRI) Humans Image Enhancement Image Interpretation, Computer-Assisted Image analysis Image registration Independent component analysis Magnetic Resonance Imaging Magnetic analysis Neuroimaging Positron emission tomography Principal Component Analysis Principal component analysis Reproducibility of Results Sensitivity and Specificity biomedical MRI functional magnetic-resonance imaging functional neuroimages image registration independent component analysis intersubject spatial normalization intrasubject spatial normalization medical image processing neuro imaging neurophysiology principal component analysis spatial alignment operator spatial alignment transformations
