title: Summarizing complexity in high dimensions.
abstract: High-dimensional, multispectral data on complex physical systems are increasingly common. As the amount of information in data sets increases, the difficulty of effectively utilizing it also increases. For such data, summary information is required for understanding and modeling the underlying dynamics. It is here proposed to use an extension of computational mechanics [C. R. Shalizi and J. P. Crutchfield, J. Stat. Phys. 104, 817 (2001)] to arbitrary spatiotemporal and spectral dimension, for providing such summary information. An example of the use of these tools to identify state evolution in the brain, an archetypal, complex biophysical system, serves as an illustration.
authors.affiliation: Department of Radiology, University of California-San Francisco, San Francisco, California 94121, USA.
authors.forename: Karl
authors.initials: K
authors.lastname: Young
authors.forename: Yue
authors.initials: Y
authors.lastname: Chen
authors.forename: John
authors.initials: J
authors.lastname: Kornak
authors.forename: Gerald B
authors.initials: GB
authors.lastname: Matson
authors.forename: Norbert
authors.initials: N
authors.lastname: Schuff
mesh_terms: D000544:Alzheimer Disease / Q000473:pathology; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*