Tools for Neuroimaging Data Processing
In previous post, we discussed that data from neuroimaging experiments are arrays and how scientific computing with arrays and data visualization were not specific to neuroimaging. Here, we will discuss data science tools tailored to neuroimaging data. First, we will present a survey of neuroimaging-specific software implemented in Python. Next, we will discuss some of the applications of these data science tools to fundamental problems in neuroimaging data analysis.
Neuroimaging in Python
Within the broader ecosystem of Python tools for data science, there is a family of tools specifically focused on neuroimaging (we will refer to them collectively as “NiPy”, which stands for “Neuroimaging in Python”). These software tools, developed by and for neuroimaging researchers, cover a wide range of data analysis tasks on a variety of different kinds of experimental data. According to their Github page the aim of NiPy is to produce a platform-independent Python environment for the analysis of functional brain imaging data using an open development model.
- Provide an open source, mixed language scientific programming environment suitable for rapid development.
- Create software components in this environment to make it easy to develop tools for MRI, EEG, PET and other modalities.
- Create and maintain a wide base of developers to contribute to this platform.
- To maintain and develop this framework as a single, easily installable bundle.
Here, we’ll give a thorough overview of the many tools that are available right now. This ecosystem is dynamically changing and that some of these technologies may eventually change or even vanish. Inevitably, new tools will also be developed. To help you keep an eye on these trends as they develop in the future, we’ll try to give you a sense of how an ecosystem like this one forms and changes.
Software Packages
There are different software packages to preprocess and analyze neuroimaging data. Most of them are open source and free to use. The most popular ones like SPM, FSL, & AFNI are well established and entail many new methods are developed and distributed. The implementation of the best statistical techniques, user friendliness, and computing effectiveness have been the main objectives of these programmes. There are also numerous other speciality packages that concentrate on particular preprocessing steps and analysis such as spatial normalization with ANTs, and connectivity analyses with the conn-toolbox. Also, there has been a surge in the number of new packages for statistics, visualisation, machine learning, and web development due to a growing trend in the data science and scientific computing communities to use the open source Python framework. There are many great tools that are being actively developed such as nilearn, brainiak, neurosynth, nipype, fmriprep, and many more. The neuroimaging community now has much easier access to new sorts of analysis thanks to the very tight integration with numerous cutting edge advances in neighbouring communities like machine learning using scikit-learn, tensorflow, and pytorch.
Loading Data with Nibabel
Reading neuroimaging data from files is usually the first step in practically any analysis of neuroimaging data. The capacity to read neuroimaging data from widely used file formats into the computer’s memory is a prerequisite for all NiPy programmes to some extent. The tool of choice for doing that is called Nibabel. The name hints at the plethora of different file formats that exist to represent and store neuroimaging data. Nibabel, which has been in continuous development since 2007, provides a relatively straightforward common route to read many of these formats. Neuroimaging data is stored in many different file formats, but the NIfTI format—the name stands for Neuroimaging Informatics Technology Initiative—has been increasingly popular in recent years. This is because it was first developed by a team at the US National Institutes of Health. Scans are often stored in the format of NIfTI files .nii which can also be compressed using gzip .nii.gz. These files have structured metadata in the image header and store both 3D and 4D data. For storing raw MRI data, it is the format specified by the BIDS standard, for instance. Nibabel objects can be initialized by simply pointing to a nifti file even if it is compressed through gzip. Nibabel operations allows us to work through some issues that arise when analyzing data from neuroimaging experiments. NiBabel supports an ever growing collection of neuroimaging file formats. To make the most of a file format, it is important to be aware of all of its unique characteristics. In order to achieve this, NiBabel provides high-level, format-independent access to neuroimages as well as an API with varying levels of format-specific access to all information that is accessible in a given file format. Here you can find Nibabel code examples.
Nipype, the Python tool to unify them all
One of the main challenges for anyone doing neuroimaging data analysis is that different algorithms are implemented in different software libraries. Even if you decide that you would like to use NiPy tools for most of your data analysis, you might still find yourself needing to use non-Python tools for some steps in your analysis. For example, to use novel algorithms that are implemented in the popular Software packages such as FSL or Analysis of Functional NeuroImages (AFNI) frameworks for fMRI, or in FreeSurfer for analysis of structural MRI. Creating reproducible data analysis programs that put together such pipelines becomes cumbersome and can be very difficult. Theoretically, you would need to become familiar with the various ways that these various frameworks produce their output and expect their input data to emerge if you were to build an analysis pipeline that combines operations from many frameworks. The difficulty can easily become overwhelming. Fortunately, these challenges have been addressed through the Nipype Python software library. Python interfaces for different neuroimaging software programmes are implemented by Nipype (including those we mentioned above).
The Python project Nipype, an open-source, community-developed effort under the NiPy umbrella, offers a consistent interface to current neuroimaging tools and makes it easier for different packages to interact with one another inside a single workflow. Nipype offers a setting that promotes interactive exploration of algorithms from various packages, such as ANTS, SPM, FSL, FreeSurfer, Camino, MRtrix, MNE, AFNI, and Slicer. It also makes it easier to design workflows both within and between packages and lessens the learning curve associated with using various packages. In order to address the shortcomings of current pipeline systems, Nipype is developing a collaborative platform for neuroimaging software development in a high-level language. The fact that Nipype offers a programming interface that is fairly comparable across all of these different tools is one of its enticing qualities. Moreover, it has the capability of displaying pipelines of operations, where the results of one processing step are used as the inputs for subsequent processing processes. Using such a pipeline as a single programme is crucial for reproducibility as well as ease. Nipype enables users to report precisely what they did with their data to reach a certain result by tracking and recording the set of operations that a dataset goes through during analysis as well as the parameters that are used in each step. Moreover, Nipype enables the usage of a processing pipeline with different datasets. Here you can find Nipype code examples.