Welcome to NeurAL’s documentation!

This library is designed to provide easy shortcut methods for processing neuroimaging data with Python. The functions are simply meant as wrappers for different command line programs and simple functions to replace common scripts.

For a more comprehensive analysis library, see the NIPy project: http://nipy.org

This library is written to try to be generic, wrapping multiple neuroimaging packages, although (because of author bias) it’s currently dominated by AFNI functions.

Usage Example:

Just a quick example of how you might use it:

import neural as nl

with nl.run_in('data_dir/subject1'):
        nl.calc(['dataset.nii.gz','mask.nii.gz'],'a*step(b)',prefix='dataset_masked.nii.gz')
        nl.thresh('dataset_masked.nii.gz',p=0.005,prefix='dataset_masked_p0.005.nii.gz')

        nl.affine_align('anatomy.nii.gz','TT_N27.nii.gz',skull_strip='anatomy.nii.gz')
        nl.affine_apply('dataset_masked_p0.005.nii.gz','anatomy_aff.1D')

General Structure

The library contains several groups of functions, organized into several modules. When using the functions, you can pretty much ignore the module hierchy (just call neural.func() rather than neural.module.func(). The modules are primarily there for conceptual organization and keeping the documentation simple. All of the following modules are imported into the main level, and don’t need to be referred to in code:

neural.wrappers, neural.utils, neural.dsets, neural.decon, neural.alignment, neural.dicom, neural.preprocess, neural.stats

For example, to call the method neural.wrappers.calc(), you just need to call neural.calc()

Modules:

Wrapper Functions

Wrappers for simple generic functions can be found in the module neural.wrappers. Calling these functions will try to find an analysis package to implement the function (based on the preferences you set).

Useful Utilities

Generic, non-imaging specific methods for useful functions are located in neural.utils. Simple dataset identification and manipulation methods can be found in neural.dsets.

Simple Analysis

Methods to implement simple analyses are organized by topic. Linear and non-linear alignment methods can be found in neural.alignment. DICOM image manipulation and dataset creation methods are in neural.dicom. Simple preprocessing and dataset statistic methods are in neural.preprocess and neural.stats. Functional connectivity analyses can be found in neural.connectivity.

Indices and tables