Effect of spatial alignment transformations in PCA and ICA of functional neuroimages.
It has been previously observed that 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 intersubject and intrasubject spatial normalization.
Lukic
Ana S
AS
Predictek Inc., Chicago, IL 60616, USA.
Wernick
Miles N
MN
Yang
Yongyi
Y
Hansen
Lars Kai
LK
Arfanakis
Konstantinos
K
Strother
Stephen C
SC
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity