ss_proposal1.lyx

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\begin_layout Title
\noindent
Proposal for INCF Training in neuroinformatics: Software development for
 reproducible neuroimaging research in Python.
 
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Topic
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As the complexity of computational methods used in scientific research increases
, there is also an increasing awareness of the need to develop methods to
 maintain the reproducibility of research results (as expressed, for example,
 in an editorial in 
\emph on
Nature
\emph default
 on February 17th, 2011).
 This concern is particularly pertinent for neuroimaging, a field which
 often requires many steps of computation to be performed on large, complex
 data sets.
 Python is increasingly becoming the lingua franca of reproducible research
 in many fields, including neuroscience.
 This course will teach neuroimaging researchers to use modern open-source
 software development tools, in order to conduct reproducible research.
 The course will teach best practices in software development (“software
 carpentry”, 
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LatexCommand href
target "http://software-carpentry.org"

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), as well as specific solutions to analysis problems in neuroimaging, with
 an emphasis on problem-solving with real data.
 The first day of the course will focus on the basic Python stack of tools
 for scientific programming (version control, ipython, numpy, scipy, matplotlib,
 etc.).
 Days 2-4 will each be divided into three two-hour sessions devoted to general
 topics, such as statistics, machine learning, time series analysis, and
 visualization, as well as presenting several neuroimaging-specific Python
 libraries including nipy, dipy, nibabel, nitime, pymvpa, and nipype (see
 http://nipy.org).
 The last day of the course will have the students work in small groups
 (2-3 people) on selected team projects to analyze real data using the tools
 we've presented.
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The course will fill gaps in the traditional training of practitioners of
 neuroimaging: though some scientists in this field receive formal training
 in programming and fundamentals of computer science, there is very little
 training available in neuroinformatics.
 In order to focus the scope of the course, we have defined the following
 three major goals of learning: 
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\begin_layout Enumerate
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Participants will acquire computing practices to facilitate reproducible
 research.
 This included knowledge in practices of scientific software engineering
 and knowledge of specific tools for version control, data handling, collaborati
on and documentation, which facilitate these practices.
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\noindent
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Participants will acquire knowledge and skills in neuroimaging data analysis.
 In particular, participants will acquire domain-specific knowledge in a
 variety of topics in modern neuorimaging, including diffusion imaging,
 time-series analysis, machine learning, graph-theory, etc.
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\noindent
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Through interaction with instructors and practical exercises, participants
 will gain confidence in their programming skills and will increase their
 capacity to productively perform reproducible computational analysis, to
 answer a variety of scientific questions.
 
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Fulfilling these goals will also provide us with a clear measure of the
 success of the training.
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\noindent
Target audience
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Neuroimaging researchers (mainly graduate students and post-doctoral researchers
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status open

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Target groups 3 and 4 in the summary of the 1st INCF Workshop on Needs for
 Training in Neuroinformatics (2008)
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) interested in learning best practices in the use and development of reproducib
le research tools.
 The target audience are researchers with some programming experience in
 Matlab, Python, or C/C++.
 The course will admit 30 participants, to maintain a good instructor/student
 ratio.
 In particular, we will seek to make the course available to members of
 groups under-represented in neuroinformatics.
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\noindent
Organizers
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Matthew Brett, Jarrod Millman, Fernando Perez, Ariel Rokem.
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Dates
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August 5th - August 10th, 2012
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Location
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University of California, Berkeley (USA)
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Time table
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\begin_layout Plain Layout

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\end_layout

\begin_layout Section
\noindent
Content
\end_layout

\begin_layout Subsection
\noindent
Introduction to neuroinformatics and reproducible research (Jarrod)
\end_layout

\begin_layout Subsection
\noindent
Scientific computing with Python
\end_layout

\begin_layout Standard
We discuss the packages that extend Python for numerical computing, beyond
 what is part of the language.
 We present the use of the IPython environment for an efficient workflow
 (including debugging, profiling and access to help), and explore the use
 of Numpy arrays, their connection to other data structures in the language
 (lists, dicts and sets), their numerical capabilities, slicing and indexing,
 masking and the notion of structured arrays.
 We conclude with a brief overview of SciPy.
\end_layout

\begin_layout Subsection
\noindent
Version control and literate programming (Matthew/Fernando)
\end_layout

\begin_layout Subsection
\noindent
Neuroimaging data formats (Matthew/Michael)
\end_layout

\begin_layout Subsection
\noindent
Image processing 
\end_layout

\begin_layout Standard
An overview of existing image processing packages, such as 
\family typewriter
scikits.image, mahotas
\family default
 and 
\family typewriter
simplecv
\family default
 is given.
 We then focus on the built-in tools in the 
\family typewriter
scipy.ndimage 
\family default
package, which include image filtering, geometrical warping, morphology
 and object labelling.
\end_layout

\begin_layout Subsection
\noindent
Statistics (Jonathan)
\end_layout

\begin_layout Subsection
\noindent
Pipelines and data-bases (Satra/Chris) 
\end_layout

\begin_layout Standard
The first part of the session will present building and executing workflows
 for preprocessing of functional data.
 The second part of the session will present the benefits of storing data
 in neuroimaging databases (e.g.
 XNAT) and will demonstrate retrieving, analyzing and storing results in
 such a database.
 The session will focus on using 
\begin_inset Formula $\mathtt{nipype}$
\end_inset

 and 
\begin_inset Formula $\mathtt{pyxnat}$
\end_inset

 software libraries.
\end_layout

\begin_layout Subsection
\noindent
Machine learning (Gael/Yaroslav/Michael)
\end_layout

\begin_layout Subsection
\noindent
Visualization
\end_layout

\begin_layout Standard
Interactive 3D data visualization and plotting is presented using Mayavi....
\begin_inset Newline newline
\end_inset

In the second part, we demonstrate how to create a simple Graphical User
 Interface for a Python class using Traits and TraitsGUI.
 Such GUIs are helpful for instance in data entry or parameter control.
 The created GUIs can be integrated in larger application architectures.
 We give a brief overview of how this is done in the Connectome Viewer which
 extends Mayavi using the Envisage plugin architecture.
\end_layout

\begin_layout Subsection
\noindent
Time-series analysis
\end_layout

\begin_layout Standard
We will present some basic time-series concepts (the Fourier transform,
 auto-regressive models, etc.) and algorithms (filtering, MAR estimation,
 coherency estimation).
 We will demonstrate the use of these algorithms in univariate and multivariate
 analysis of neuroimaging data.
 Exercises will be based on the 
\begin_inset Formula $\mathtt{nitime}$
\end_inset

 software analysis library.
 
\end_layout

\begin_layout Subsection
\noindent
Graph theory
\end_layout

\begin_layout Standard
We will present some graph theoretical tools for analyzing functional networks
 derived from neuroimaging data.
 We will demonstrate the application of these tools in quantifying the network
 properties of the brain (clustering, path length, small-worldness) as well
 as visualization of this complex network data.
 Exercises will be based upon the 
\begin_inset Formula $\mathtt{networkx}$
\end_inset

 python library.
\end_layout

\begin_layout Subsection
\noindent
Diffusion imaging (Eleftherios)
\end_layout

\begin_layout Section
\noindent
Draft of call
\end_layout

\begin_layout Subsection*
\noindent
INCF Summer Course: Software development for reproducible neuroimaging research
 in Python
\end_layout

\begin_layout Subsection*
\noindent
August 5th - August 10th, 2012
\end_layout

\begin_layout Subsection*
\noindent
University of California, Berkeley
\end_layout

\begin_layout Paragraph
\noindent
Berkeley, CA, USA
\end_layout

\begin_layout Paragraph*
\noindent
We hereby invite applications to participate in the graduate summer school
 on software development for neuroimaging.
 
\end_layout

\begin_layout Paragraph*
\noindent
Rationale and goals: This course will teach neuroimaging researchers to
 use modern open-source software development, in order to create tools for
 reproducible research.
 We will focus on best practices in software development, using the programming
 language Python.
 In addition, we will present specific solutions to analysis problems in
 neuroimaging, with an emphasis on problem-solving with real data.
\end_layout

\begin_layout Paragraph*
\noindent
Target audience: Neuroimaging researchers at the graduate student or post-doc
 level with a strong interest in learning best practices in the use and
 development of reproducible research tools.
 We seek to increase the opportunities given to under-represented groups
 and encourage members of these groups to apply.
\end_layout

\begin_layout Paragraph*
\noindent
Requirements: Programming experience in Matlab, Python, or C/C++ is expected.
 
\end_layout

\begin_layout Paragraph
\noindent
Structure: The first day of the course will focus on basic tools for scientific
 programming (including version control, ipython, numpy, scipy, matplotlib,
 etc.).
 Days 2-4 will each be divided into three two-hour sessions devoted to general
 topics, such as statistics, machine learning, time series analysis, and
 visualization, as well as presenting several neuroimaging-specific Python
 libraries including nipy, dipy, nibabel, nitime, pymvpa, and nipype (see
 http://nipy.org).
 The last day of the course will have the students work in small groups
 (2-3 people) on selected team projects to analyze real data using the tools
 we've presented.
\end_layout

\begin_layout Paragraph*
\noindent
Confirmed tutors: Fernando Perez, Stefan van der Walt, Jarrod Millman, Matthew
 Brett, Caterina Gratton (UC Berkeley), Ariel Rokem and Jonathan Taylor
 (Stanford), Michael Hanke and Yaroslav Halchenko (Dartmouth), Gael Varoquax
 (INSERM), Alex Gramfort (MGH/Harvard), Eleftherios Garyfallidis (Cambridge),
 Satrajit Ghosh (MIT), Chris Filo Gorgolewski (University of Edinburgh),
 Stephan Gerhard (ETH Zurich).
\end_layout

\begin_layout Paragraph*
\noindent
To apply: Please send an email to: nipycourse@gmail.com .
 Please provide us with a CV, as well as a brief (200 words or less) statement
 of your interest in participation in the course.
 To be considered for a travel grant, please mention this in your application
 and ask one reference to provide a letter of recommendation to be sent
 directly to this email address.
 
\end_layout

\begin_layout Section
\noindent
Dissemination plan
\end_layout

\begin_layout Standard
\noindent
\align block
The course will include a tutorial on literate programming with sphinx (
\begin_inset CommandInset href
LatexCommand href
target "http://sphinx.pocoo.org/"

\end_inset

).
 Thus, all the teaching materials (slides, scripts, problem sets and full
 solutions) and the projects written by the participants, will be compiled
 and disseminated on the INCF website, will be integrated into the documentation
 of the nipy software projects and will become part of the online documentation
 of these software projects, which is freely available.
 In addition, the course sessions will be video-taped and made available
 through the INCF.
 
\end_layout

\begin_layout Section
\noindent
Benefit and relevance to INCF mission
\end_layout

\begin_layout Standard
\noindent
\align block
As mentioned in the INCF mission statement, neuroinformatics poses a specific
 challenge for training, as the variety of knowledge and research cultures
 comprising neuroinformatics is only rarely combined in one single place
 and often falls at the boundaries of traditional academic departments.
 The training we propose will draw on the combined experience and knowledge
 of several different neuroinformatics practitioners and provides one of
 these rare opportunities.
\end_layout

\begin_layout Standard
\noindent
\align block
The list of instructors proposed embodies the spirit of international cooperatio
n, which underlies the mission of the INCF, with instructors from the US,
 from France, from Switzerland and from the UK.
 We intend for the student population to embody this spirit, at least in
 part, by inviting international participants to apply for travel grants,
 which will cover the expenses of 5 such students.
\end_layout

\begin_layout Standard
\noindent
\align block
Among the three components of neuroinformatics (tools, models and data-bases),
 this course is aimed mostly at tool-development and data analysis.
 However, other topics such as data-base and data-sharing will be discussed.
 
\end_layout

\begin_layout Standard
\noindent
\align block
Of the core compoetencies defined in the summary of the 1st INCF Workshop
 on Needs for Training in Neuroinformatics held in Edinburgh in July 23-25,
 2008 (page 6), this course will thoroughly address the following three:
\end_layout

\begin_layout Enumerate
\noindent
\align block
“Knowledge of stochastic modeling, time-series analysis, exploratory data
 analysis and applied statistics”.
 These topics are extremely relevant for neuroimaging and will be covered
 both at the theoretical level, as well as at the practical level.
 
\end_layout

\begin_layout Enumerate
\noindent
\align block
“Ability to programme in high-level languages”.
 Although we expect many of the participants to have at least some knowledge
 of programming, one of the the major goals of the course is to increase
 participants’ confidence in their programming skills to a degree where
 they will be able to publicly share the code they use for their analysis,
 making their analysis reproducible by others.
\end_layout

\begin_layout Enumerate
\noindent
\align block
“Knoweldge of imaging techniques and image analysis”: This topic is also
 central to the course an will be covered extensively, both at the theoretical
 level and at the practical level.
\end_layout

\end_body
\end_document