Conference site » Proceedings

Nitime: time-series analysis for neuroimaging data

Ariel Rokem - University of California, Berkeley, Berkeley, CA USA
Michael Trumpis - University of California, Berkeley, Berkeley, CA USA
Fernando Pérez - 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.


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

BibTeX entry

Full text PDF

Copyright The content of the articles of the Proceedings of the Python in Science Conference is copyrighted and owned by their original authors.
Terms of use For republication or other use of the material published, please contact the copyright owners to obtain permission.