Reaching into an Abyss – Challenges in Computational Neuroscience and Graduate School

group shot of students at SAMSI

Students attending the Undergraduate Workshop at SAMSI.

The following was written by Praveen Suthaharan, an undergraduate student from North Carolina State University who recently attended the SAMSI Undergraduate Workshop on Computational Neuroscience.

Continually baffling researchers across the globe, the 3 pounds of matter that sits in our skull holds many mysteries that have yet to be discovered. Brain research, or Neuroscience, is on the verge of revolutionizing our world. In the past few years, by taking advantage of the advancements made in the computing world, several neuroscientists have delved into the brain trying to unfold many of its hidden intricacies. I, too, aspire to be part of this rising era of computational neuroscience research.

I’m an undergrad, majoring in Statistics and Neurobiology, at North Carolina State University. I plan to pursue a PhD in Computational Neuroscience. My exposure to the coursework in Statistics and Neurobiology has made me curious about the areas of study that lie at the intersection of the two fields. This curiosity has led me to steadfastly chase the inevitable question of, what IS computational neuroscience? This year’s SAMSI undergraduate workshop has served as a portal for me to explore this question that stemmed from my curiosity.

It was a Saturday morning and I could see new prospects for my future as I stepped into SAMSI and grabbed my official name tag. My pulse rate started beating fast, with a sense of excitement, as I walked into the conference room to a group of other dedicated and driven prospective scientists. The series of presentations started with a high note as Dr. Ciprian Crainiceanu began his talk with a tutorial on clinical brain imaging. Given the time, he provided a fast-paced, yet comprehensive lecture on ‘neurohacking’ and on the process of how brain images are coded into computable values for the purpose of monitoring/detecting changes in the brain. His presentation set the tone for our next presenter, Dr. Ana-Maria Staicu, who provided deep insight on the applications of an interesting image processing technique (anisotropic diffusion) on a well-known neurological disorder known as Multiple Sclerosis. At this very moment, as the momentum of wanting to think began to fade, I got distracted.

As the aroma of freshly baked bread hit my olfactory senses with a blast of pleasant sensation, I glanced at the time knowing it was lunch time. Immediately as we vacated the conference room, an announcement about taking a group photo was broadcasted to the students. We all congregated outside of SAMSI like any group of young, excited individuals – confused, yet composed.

people on the shuttle going to Duke University

Riding the bus to Duke.

With a blink of an eye, we were all set to board the shuttle to the Center of Neuroimaging at Duke. Here, we visited Dzirasa’s lab. We were all given an overview of the research lab and a tour of the facility. This visit has strengthened my interest in computational neuroscience research, and will be looking forward to applying to Duke for grad school.

Person talking at the Duke Lab

Stephen Mague talks to the students about Dzirasa’s Lab at Duke.

On our way back to SAMSI, the desire to acquire more knowledge grew inside of me as I was eager to learn about the applications of Fourier Transform (FT) within neuroscience, to interactively work with brain data using various programming languages, and to attend the graduate school panel discussion. Benjamin Risk, a postdoc who works at SAMSI, engaged us with a tutorial on image reconstruction using Discrete Fourier Transformation (DFT). The ability to manipulate images through mathematical approaches was mind-blowing, especially knowing that these approaches have been invaluable to neuroscience research. Following Benjamin’s talk, Sarah Vallélian introduced her presentation with a tutorial on Computed Tomography (CT). She discussed about several useful signal processing techniques, including back-projection, filtered back-projection, and Hilbert Transform, and gave us the opportunity to work with CT data using some of these techniques. As much as the other students enjoyed these presentations, I believe these interactive activities (i.e., using R, Matlab, and python) served as the best part of this workshop, allowing us to fiddle with the data and providing us with the initial steps to computational neuroscience research.

As the panel discussion about graduate studies commenced, my ears were engaged in the conversation as I was absorbing various useful information coming from insightful graduate students. I have come to realize that research mirrors an abyss – it’s a never ending path of glory. This appreciation of mine for research has now become my driving force to pursue graduate school. With that, the first day came to a close with an enticing dinner. The food formed this perfect taste combination that left my mouth revitalized and extremely satisfied. SAMSI definitely knows how to treat prospective scientists!

Ezra Miller, Duke, giving a lecture at the workshop.

Ezra Miller, Duke, giving a lecture at the workshop.

The next day ended with some more fascinating mathematical/statistical approaches to neuroscience as Dr. Laura Miller and Dr. Ezra miller took the floor. Particularly, Dr. Ezra Miller’s presentation on Topology for Statistical Analysis of Brain Artery Images provided me with a deeper insight on an interesting mathematical approach towards neuroscience. As a matter of fact, his presentation motivated me to immerse myself in Topology and its various applications to neuroscience.

With the end of my undergrad years, just around the corner, new doors to success have emerged with this amazing workshop. Not only did this workshop provide me with a new perspective on my research interest and grad school, but it has also given me the appreciation and audacity to reach into the abyss, knowing that it will lead me on a never ending path of glory. After all, research, in particular, computational neuroscience research, is an abyss – a bottomless pit filled with incessantly approaching questions that permeate your mind with curiosity of the mysteries of the brain.

SAMSI has organized an incredible workshop that I would not think twice about attending in the future.

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Taking a Different Road – Being a Statistics Major

The following is written by Sarah Lotspeich, University of Florida who attended the SAMSI Undergraduate Workshop focusing on Computational Neuroscience.

I declared my Statistics major in the eleventh grade, approximately halfway through my AP Statistics course. As everyone around me pondered medical school and the many types of engineering, I knew that my choice seemed unconventional. Now three years into my undergraduate degree, I have met only a handful of fellow Statistics majors to date. During the third week of October, however, this changed forever as I attended the SAMSI Undergraduate Workshop.

Duke Chapel

Duke Chapel.

It was a gorgeous fall day (a pleasant surprise for me, as my typical “fall” in Gainesville, Florida includes a few fallen leaves and a high temperature in the 80s) in Research Triangle Park, North Carolina. Budding statistics and mathematics students from across the country gathered to explore computational neuroscience, and to enjoy fantastic food. Always eager for an adventure, I flew in as early as possible the day before the workshop to get maximum exploring time in Durham. Perhaps a bit TOO eager, I walked over eight miles through Downtown Durham and to both edges of Duke University’s gorgeous gothic campus.

Dame's Chicken and Waffles

Excellent chicken and waffles place!

Fret not, however, as I was well fueled by Dame’s Chicken and Waffles and fondue from the Little Dipper. Needless to say the local area surpassed my every expectation and left me excited to wear scarves and learn more about statistics the following day. The mingling began at approximately 7:30am the next morning, as over thirty of my fellow “numbers people” bonded over bagels and oatmeal. I was so excited to hear from people who care as much about significance tests and p-values as I do!

The presentations commenced with an absolute bang as Dr. Ciprian Crainiceanu of Johns Hopkins University immersed us in “Neurohacking”. He outlined the basic principles of converting MRI images from picture to a system of numbers, and by the end of the hour left us with a data set and the necessary code to explore it independently. One of my favorite components of the workshop, actually, was the interactive nature of each presentation with the integration of R or Matlab code.

Guest lecturers introduced many fascinating facets of computational neuroscience, and I especially enjoyed how my knowledge on the subject compounded with each additional lecture. As the workshop progressed I found that I was relating information from one speaker’s presentation back to material I learned even hours previously, and even today I walked away with a nice basis on the topic. It very much feels as if I went from zero to one hundred with this material, and I appreciate the challenges posed to us by the complicated subject matter.

Beyond the presentations, the field trip to the laboratory for psychiatric neuroengineering at Duke University provided a “behind-the-scenes” glimpse at the processes of data collection that create the massive sets we dealt with during lecture. I was also just happy for any excuse to ogle the beautiful campus once more. Each new speaker and opportunity brought about new questions to ask and facts to learn, so I was happy for the constantly changing environment of the workshop from lecture to lecture, or even breaks for the field trip or panel.

students by SAMSI sign

From left to right: Jordan Zeldin, Eion Blanchard, Sarah Lotspeich, Michelle Zamperlini.

The many bus rides provided unexpectedly pleasant opportunities to meet new people, as well, as I was shuffled into new groups with each trip. I thoroughly enjoyed swapping stories about my university – about the weather, everyday dress code, the statistics department – with people from other schools! And I was even lucky enough to give suggestions about things to do and places to eat in Florida, as one of my new friends is planning a trip to the Sunshine State soon. Perhaps the most unexpected bonus to this experience was the people.

This was honestly one of the most incredible groups of students, and upon learning more about each person and their involvement I am absolutely honored to have been selected among them for the 2015 SAMSI Undergraduate Workshop. Though the workshop lasted only two day, the people I met and research I was immersed in will carry through my entire career. I cannot emphasize enough the importance of this experience and how strongly I recommend it.

There is a 100% probability that I would love to return to SAMSI sometime in the future.

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.