Learning about Clinical Brain Imaging using R at SAMSI’s Computational Neuroscience Workshop

The following was written by Katy Wang, attendee from the University of California, Riverside, of the Undergraduate Workshop focusing on Computational Neuroscience.

Dr. Ciprian Crainceanu

Dr. Ciprian Crainiceanu.

Of the seven presentations at SAMSI’s Computational Neuroscience Workshop from October 19-20th, the one that was most memorable to me was given by Dr. Ciprian Crainiceanu, a professor in the Department of Biostatistics at Johns Hopkins University. Dr. Crainiceanu’s presentation on neurohacking in R really stood out to me because I learned how to preprocess images, read, write, plot, and manipulate neuroimaging data in R.

You may be wondering “what exactly is neurohacking and how is the application of statistics used in clinical brain imaging?” As defined by Dr. Crainiceanu, “neurohacking is the continuous process of using, improving, and designing the simplest open source scripted software that depends on the minimum number of software platforms and is dedicated to improving the correctness, reproducibility, and speed of neuroimage data analysis.” The goal of neurohacking is the “democratization of neuroimaging data analysis,” in other words, to make neuroimaging data analysis possible for all people to understand. Throughout the presentation, we were shown an image of an axial slice of the T1-w image of the brain that contained a multiple sclerosis lesion surrounded by a hyper intense ring, which indicated blood with a higher concentration of gadolinium chelate. After taking the region of interest, a matrix is used with numbers corresponding to that particular section of the brain in order to understand what the dynamics of blood flow are into the lesion. To see if anything has changed (e.g, Did the brain tumor get bigger? Was the cancer eliminated by surgery?), the follow-up T1-w is subtracted from a baseline T1-w volume. A template-based analysis is also used in which an MNI T1 template is used to see which parts of the brain it maps to. The results are later quantified and mapped to neuroimage.

Students interacting with the lecturer

Students interacting with the lecturer.

Although there was not enough time to actually work with the data during the presentation, Dr. Crainiceanu offered a clear explanation on neuroimaging, an impressive tutorial with Powerpoint slides on how to set up the data, information on data structure and operations (working with various file types, visualization and data manipulation) preprocessing (inhomogeneity correction, intensity normalization, tools in R), registration, segmentation, dynamic visualization in R, and many resources in order to work with and become more familiar with working with the data. Furthermore, we were given suggested prerequisites and coursework, such as (1) Linux/Unix; (2) a basic knowledge of programming; (3) a basic knowledge of array data structures (e.g. 2d and 3d arrays), and most importantly (4) an interest in “hacking” with neuroimaging data! You may also find these Coursera Data Science Specialization courses offered on behalf of Johns Hopkins University as a helpful resource.

All in all, SAMSI’s undergraduate workshop was truly a great learning experience! I went into the workshop with very limited knowledge in computational neuroscience but came out of the workshop with several Word documents of notes, many data files/tutorials, and resources to enhance my knowledge of mathematical and statistical methods in neuroscience.

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