Nitime: time-series analysis for neuroimaging data
Ariel Rokem
arokem@berkeley.edu -
University of California, Berkeley, Berkeley, CA USA
Michael Trumpis
mtrumpis@berkeley.edu -
University of California, Berkeley, Berkeley, CA USA
Fernando Pérez
Fernando.Perez@berkeley.edu -
University of California, Berkeley, Berkeley, CA USA
Nitime is a library for the analysis of time-series developed as part of the
Nipy project, an effort to build open-source libraries for neuroimaging
research. While nitime is developed primarily with neuroimaging data in mind
(espespecially functional Magnetic Resonance Imaging data), its design is
generic enough that it should be useful to other fields with experimental
time-series. The package starts from a purely functional set of algorithms for
time-series analysis, including spectral transforms, event-related analysis and
coherency. An object-oriented layer is separated into lightweight data
container objects for the representation of time-series data and high-level
analyzer objects that couple data storage and algorithms. Each analyzer is
designed to deal with a particular family of analysis methods and exposes a
high-level object oriented interface to the underlying numerical algorithms.
We briefly describe functional neuroimaging and some of the unique
considerations applicable to time-series analysis of data acquired using these
techniques, and provide examples of using nitime to analyze both synthetic data
and real-world neuroimaging time-series.
Citation
A Rokem, M Trumpis, F Pérez, Nitime: time-series analysis for neuroimaging data in Proceedings of the 8th Python in Science conference (SciPy 2009), G Varoquaux, S van der Walt, J Millman (Eds.), pp. 68-75
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