Abstract
In the neuroscience research field, specific for medical imaging analysis, how to mining more latent medical information from big medical data is significant for us to find the solution of diseases. In this review, we focus on neuroimaging data that is functional Magnetic Resonance (fMRI) which non-invasive techniques, it already becomes popular tools in the clinical neuroscience and functional cognitive science research, after we get f(MRI)data, we actually have various software and computer programming that including open source and commercial, it's very hard to choose the best software to analyze data, what's worse, it would cause final result imbalance and unstable when we combine more than software together, so that's why we want to make a pipeline to analyze data. On the other hand, with the growing of machine learning, Python has already become one of very hot and popular computer programing, in addition, it is an open source and dynamic computer programming, the communities, libraries and contributors fastly increase in the recent year. Through this review, we hope that can make neuroimaging data analysis more easy, stable and unite base the one platform system.
Keywords: neuroscience; big data; functional Magnetic Resonance (fMRI); pipeline; one platform system
Introduction
When I start to walk into the neuroscience and neuroimaging field, I was deeply attracted by High-Field structural and functional MRI (Magnetic Resonance Imaging). This technique can non-invasively detect brain signal and has substantially high spatial resolution compared with EEG(Electroencephalography, MEG(magnetoencephalogram), etc. However, neuroimaging is a complex field that explores inter-disciplinary studies including physics, engineering, biological science, clinical medicine, physiology, statistics, and much more. The pure (f)MRI data only provide limited information and feedback about the brain, so data mining is a necessary and significant step for us to get more quantitative and broad functional information. When I started to analysis my first batch of (f)MRI data, there existed a lot of software and scripts on the Internet. Unfortunately, First, the source code was extremely messed. Second, the script was written by a variety of programming language, and it hardly connects all code together. Third, according to prior research, if you set up different parameters in the same software, it would affect the final results and make them unstable. Even worse, it's hard for us to combine all kind of software or scripts together, then batch processing (f)MRI data
\cite{Eklund_2015,Pauli_2016,Della_Maggiore_2002}. Fourth, the main computer programming is Matlab for the (f)MRI data analysis, but with the boost of machine learning and deep learning, Python is gradually beyond Matlab. In addition, Python is a totally open-source computer programming language, so compared to Matlab, it can accommodate the huge community contributes to the
Python. What's more, Python has already formed a complete software and powerful community in the (f)MRI data analysis. Finally, compared to other computer programming languages, Python script is an interpretable and
dynamic programming language, the source code is more simple and understandable (Fig. 1).