Shift-invariant multilinear decomposition of neuroimaging data.
We present an algorithm for multilinear decomposition that allows for arbitrary shifts along one modality. The method is applied to neural activity arranged in the three modalities space, time, and trial. Thus, the algorithm models neural activity as a linear superposition of components with a fixed time course that may vary across either trials or space in its overall intensity and latency. Its utility is demonstrated on simulated data as well as actual EEG, and fMRI data. We show how shift-invariant multilinear decompositions of multiway data can successfully cope with variable latencies in data derived from neural activity--a problem that has caused degenerate solutions especially in modeling neuroimaging data with instantaneous multilinear decompositions. Our algorithm is available for download at www.erpwavelab.org.
Mørup
Morten
M
Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark.
Hansen
Lars Kai
LK
Arnfred
Sidse Marie
SM
Lim
Lek-Heng
LH
Madsen
Kristoffer Hougaard
KH
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*