Creating the most complex map in Human History 🧠

Images of functional connectivity between regions of interest in the brain based on the dataset I downloaded and processed

Intro to Connectomics

Imagine you are driving someplace new. A place you have not been in a while. Most people, including myself, would use Google Maps, or WaZe, to find their way to their destination. Consider, what would you do if you did not have a digital map available? You might ask someone else. Well… slight problem with that. What if no one else whom you had asked had ever been where you wanted to go? How would you find your way to your destination then? Probably with A LOT of frustration, trial and error, and rigorous testing. These three key elements are intrinsic to the study of the brain. An organ so complex and so mysterious, that even with all the technological advancements in human history; fire, cars, computers, self-driving cars, and space exploration being just a few, we still haven’t been able to map the brain out. However, 86 billion neurons and trillions of synapses to map is quite a feat. The field of science dedicated to completing the task of mapping the brain is called connectomics.

Image of a 500-year old map that helped guide Chris Columbus

“Sounds Unnecessary”.

That’s what I said when I first started hearing about connectomics. Why invest all this manpower, money, time, and energy into mapping something which we already understand well enough to cut into? After all, a surgeon can navigate the brain well enough to clip an aneurysm (dilated blood vessel at risk of hemorrhaging) or dissect a tumor from a patients brain. We can even sever the connection between the two hemispheres (a corpus callosotomy).

Connectomics is about creating a detailed mapping system like Google Maps. Right now we have a ancient understanding. Imagine trying to find your way from the Empire State Building, NY to the Hollywood Hills, LA with a map that was used by Christopher Columbus. This is what we’re doing right now. We’re trying to create a new map; one which is more efficient, effective, realistic, and representative of the thousands of illnesses which affect the brain.

This new map of the human brain has not yet been created, and no one knows what it’ll look like when we get there. It will take time, effort, and determination from teams across the world to accomplish this, but in doing so it will allow us to understand the brain — one of the most fundamental things in our universe — much better. The difference in our ability to understand mental illnesses and treat patients will be night and day. Just as the difference between our travel today and that of ancient European explorers.

MRI and fMRI — Functional Connectivity

Image of an MRI machine from Radiology Affiliates Imaging
Diagram from ResearchGate illustrates the relationship between neural activity and BOLD signals

Functional Connectivity

Connectomics is defined by slightly different individual neuroscientists and physicians, but they overlap to come to a definition similar to the following:

Image of different nodes identified during default preprocessing with CONN toolbox
An example of a connectome ring. Different colors represent the strength of connections: blue represents the lack of functional connections and red represent strong functional connections

Making a Movie — Structural Connectivity

Mapping the structural connections is different than mapping the functional ones. Imagine identifying the tastes of a food vs the ingredients that make up that food (if there were 86 billion different ingredients). This task requires numerous steps that all need precision and time. The video below gives a 2 minute summary of how images of different brain slices are taken:

Image from ResearchGate shows the process of analyzing brain slices
Slide from Sebastian Seung: I am my connectome
Slide from Sebastian Seung: I am my connectome

Deep Learning

Deep Learning is a part of machine learning, which is a part of artificial intelligence. It focuses on using the human brain as inspiration to develop various models called “Neural Networks”. There are a variety of types of neural networks; some are used for image classification by Tesla, while others can be used to diagnose patients.

This part of the code I used to create a Multi-Layer Perceptron (a type of feed-forward neural network) that is used for image classification. It predicts the image passed in is the number “5” (trained on numbers 0–9) with ~98% accuracy.

Wrapping it Up 🔑

Connectomics is revolutionizing the way that we approach biology, and it will forever change the way we treat and diagnose patients.

  1. Connectomics can be used to treat various diseases including schizophrenia, dementia, bipolar, and many others. By mapping the individual connections in the brain we can better understand these illnesses and personalize treatment to the patients that suffer from them.
  2. Connectomics can potentially also be used to “download” or store memories. This is because it is believed our memories are stored in our synapses and the mapping of someone’s synapses would allow their memories to be recognized and thus, downloaded.
  3. Connectomics can also forever change artificial intelligence. Neuroscience and machine learning give back to each other and as our understanding of the brain develops so will the development of machine learning.

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Adam Gulamhusein

Adam Gulamhusein

TEDx Speaker | HYRS Alum (Neurosurgical RA) | TKS Student | SHAD Alum | 2021 Calgary Brain Bee Winner