Three years have passed since I joined my current research lab, first as an undergraduate research intern, then as a PhD student. Three years have I felt constantly grateful to my supervisor and fellow labmates; three years have I occasionally considered quitting this program and moving elsewhere. As with many other PhD students, I’ve had my ups and downs. Here I would like to write an interim summary for my past three years working as a researcher at the intersection of neuroscience and data science.
My field in a nutshell
When I tell someone that I am a neuroscientist, they’d usually picture me as someone who keeps monkeys in the lab and conduct animal experiments. Well, my research involves neither, since our lab is a pure data lab (means that we do not collect data ourselves, instead we use public data collected by other groups) and studies only human data. The landscape of neuroscience is vast and diverse. So where do I find myself in this diverse field?
Network neuroscience
There is one stream of neuroscience research called the “network neuroscience“, which is roughly the tribe where I belong. Basically, network neuroscientists view the brain as an interconnected network instead of an aggregate of individual parts. Before, people used to believe that brain functions are highly localized (i.e. different parts of the brain are associated with different traits and abilities), which was only partially correct, as scientists later discovered. For instance, the occurrence of certain mental diseases is dependent on the disconnection between multiple parts of the brain (sometimes referred to as a brain circuitry), instead of originating from the damage of a particular brain region.
About a decade ago, my current supervisor published an impactful paper on the discovery of large-scale functional brain network with functional MRI (fMRI) data. He performed a clustering algorithm on the fMRI data and divided the cerebral cortex (a sheet of neural tissue that is critical to your cognition!) into a few networks, for instance, the visual network (where the neurons work hard to make you see well), or the default network (which is only most active when you’re not thinking hard, like when you’re meditating).
My current work is, in principle, an extension of his work, whereby I delineate the cerebral cortex into even smaller functional areas, still with fMRI data. My supervisor’s work is sort of like dividing the entire map of the brain into multiple states (brain networks), while I would be dividing the entire map into smaller cities (brain areas), according to spatial characteristics as well as signal similarity of individual brain units.
Call me a mapmaker for the human cerebral cortex!
Hamming’s question
Sometimes, I’d ask myself Hamming’s famous question for every researcher:
“Are you working on the most important questions in your field? If not, why aren’t you?”
In my opinion, the most important question in neuroscience is the “binding problem” - why does cognition arise from this symphony of electrical and chemical signals across a massive number of neurons? I hope I could work on that question, but as far as I know, any existing answer to this question is pure speculation and completely non-falsifiable (I do have one favourite speculation given by Karl Friston). To understand how the brain works is exponentially harder than trying to understand, for example, how the heart works. The heart has four hollow compartments and pumps blood under electrical pulses - an elegant, simple piece of machinery. The brain? 100 billion neurons constantly talk with one another and produces thoughts, emotions, drives, and so many other functions. It is a complicated machinery that is impossible to fathom. It is so complicated that it becomes so unpredictable, while such unpredictability underpins the beautifully complex humanity and who we are.
To be a good neuroscientist, you have to be comfortable with making speculations rather than constructing solid theories. The brain is not made up of 0s and 1s. It’s made up of neurons which are totally unlike logical gates - individual neurons take various shapes and functions, and they do not often work in the same way.
If this kind of uncertainty does not unsettle but excites you, you may enjoy neuroscience!
Life as a Ph.D.
Disclaimer: the experience of pursuing a Ph.D. could vary dramatically from field to field, from country to country, from group to group, and of course from person to person. What I am about to discuss is a personal narration of my own journey, which is not likely representative of an “averaged Ph.D. journey”. This is not a guide to “whether you should go for a Ph.D.”, either. I’d leave that difficult question to someone else!
Hardships
Doing a Ph.D. is hard. For some, hardship is the endless work hours. For some (like me), hardship is the immense difficulty of the problems at hand. For instance, it took me about two years to crack my very first research project. For most of the days during these two years, I’d wake up with these unsettling voices at the back of my mind: “Is this problem solvable? Am I going down the wrong path? When can this project conclude so that I could move on to the next one?” This immense burden of uncertainty has been both a painful and thrilling experience for me.
It was painful since I could not run away from it; whenever I’m awake, my research question may surface from my mind and start to bug me. It was thrilling since, sometimes even in the shower, I’d think about my research questions, and it was a beautiful experience when the “Eureka” moments kicked in. I’d rush out of the shower and try out my new ideas (which rarely works as well as I imagined, of course).
“No pains, no gains” applies particularly well when you work on hard problems that no one has ever attempted to solve before. To be a good researcher, you have to genuinely love solving hard problems. Research is not a lucrative career, and those who stay most likely truly enjoy solving puzzles.
Independent thinker
The first significant trait of an independent thinker is that he is not satisfied at just doing something well. He is most satisfied when he is able to do something well and differently. He enjoys taking the extra mile to think of new ways of doing things (in a better way), and this pursuit of novelty is independent of external rewards.
The second trait of an independent thinker is the ability to ask good questions. Most people are good problem solvers after some solid training in a specific field, but there is no systematic way of training for one to become good at asking questions.
Being an independent thinker may come naturally to some people. While for most of us, we were instructed to think “within the box” through our conventional education system. Students are optimized for taking standardized exams that are full of close-ended questions with unrealistic constraints (such that there exists only one possible answer). After a decade of training in cracking standardized exams, it usually takes some effort to be able to think independently again.
For myself, pursuing a Ph.D. has made me a more independent thinker. I think this is one of the most commonly shared attributes for people who have earned a Ph.D. degree.
Concluding thoughts
The dad of some friend of mine has advised her that, before pursuing a Ph.D., you should better work in the industry for a few years. If not, you’d probably chose a field without much practical value in the industry, and your research would very likely stay unimpactful for decades.
I think there is some wisdom in the above claim. Most human beings enjoy creating stuff, particularly stuff that is to be used by others. If your research is a few feet above people’s daily lives, you may not gain as much satisfaction as being able to invent something that’s going to be used widely, for example, a new drug that could cure Alzheimer’s disease.
Another thing to consider is that stuff with greater business value (means that many people find your product useful) pays off well. If you have the chance to pursue A that is more lucrative and impactful than B, while A and B are both intellectually challenging, why not go for A?