Next week's presentation should be of interest to anyone who is
involved in neuroimage analysis. Please note time and location are not
usual for a journal club talk.
We are planning to arrange a TCON number and Webex desktop sharing for
this presentation. I do not know yet if there is a limitation on the
number of connections we will be able to handle. If you are interested
in joining this talk through TCON/Webex, please reply to this email.
Access details will be provided to the interested parties as soon as
they are available.
Speaker: Satrajit Ghosh, PhD, http://www.mit.edu/~satra
Speaker Affiliation: RLE MIT, HST Harvard-MIT
Time: 2:00 PM - 3:00 PM
Location: Boston -- 1249 Boylston St, 2nd floor conference room
Title: Nipype - A Python framework for neuroimaging
Nipype is a project under the umbrella of Nipy, an effort to develop
open-source, community-developed neuroimaging tools in Python. The
goals of Nipype are two-fold: 1) to provide a uniform interface to
existing neuroimaging software packages; and 2) to provide a pipelined
environment for efficient batch-processing that can tie together
different neuroimaging data analysis algorithms.
The interface component of nipype provides access to command-line,
matlab-mediated, and pure-python based algorithms from packages such
as FSL, SPM, AFNI and Freesurfer, along with the growing number of
algorithms being developed in Python. The uniform calling-convention
of the nipype interface across all these packages reduces the learning
curve associated with understanding the algorithms, the API and the
user interface in the separate packages.
The interface component extends easily to a rich pipeline environment,
able to interchange processing steps between different packages and
iterate over a set of parameters, along with providing automated
provenance tracking. The structure of the pipeline allows the user to
easily add data and change parameters, and the pipeline will run only
the steps necessary to update the new data or analysis parameters.
Because it is written in Python, the pipeline can also take advantage
of standard Python packages for future integration with a variety of
database systems for storing processed data and metadata.
By exposing a consistent interface to the external packages,
researchers are able to explore a wide range of imaging algorithms and
configure their own analysis pipeline which best fits their data and
research objectives, and perform their analysis in a highly structured
environment. The nipype framework is accessible to the wide range of
programming expertise often found in neuroimaging, allowing for both
easy-to-use high-level scripting and low-level algorithm development
for unlimited customization. We will explain the software architecture
and challenges in interfacing the external packages, and demonstrate
the flexibility of nipype in performing an analysis.
This work is partially supported by NIH grant R03 EB008673 (NIBIB;
PIs: Ghosh, Whitfield-Gabrieli).
Satrajit Ghosh is a Research Scientist in the Research Laboratory of
Electronics at MIT. Dr. Ghosh received his undergraduate degree in
Computer Science specializing in artificial intelligence and his
graduate degree in Computational and Cognitive Neuroscience,
specializing in functional imaging and computational modeling of
speech motor control. His current research focuses on developing
software related to nipype, relating macro-neuroanatomy and function
and understanding mechanisms of speech production and perception.
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