The Human Connectome and ANNs
By: Danilo Pena
Tags: Machine Learning, Artificial Intelligence, Learning, Neuroscience, Healthcare

Photo by Donald Giannatti on Unsplash

Lately, I have been interested in understanding more imaging modalities because I may enter this research area soon. There have been many advances in the medical imaging community — with collaborations in Kaggle being made and NIH releasing images to the public as some examples.

Soon enough, we will see more and more influence of the machine learning community in the everyday healthcare setting (though, of course, with its own set of challenges).

In light of these advances, I would like to highlight a subset of the healthcare imaging field you may find interesting, and potentially how the broader machine learning community can benefit from this area.

The Connectome

If you haven’t seen an example of this, feast your eyes on the really cool connectome.

Fig. 1: From this paper

This imaging technique allows us to understand the structure of the brain.

You can kind of see the structure of the brain when you look at the whole thing. These different fibrous looking lines are edges between the main nodes that are the large white circles.

The nodes are regions of interest or common landmarks within the brain. The lines between them connect the nodes together. The colors represent different degrees of connectedness.

The way the connectome is constructed is actually through looking at diffusion rates of water molecules within the brain. Typically, this type of measurement takes a box of size X within the brain, and then we take a look at how the molecules are diffusing within that box over a set amount of time.

This snapshot will then be summarized by an ellipsoid that carries the average diffusion direction of that box. In the image below, you will find different shapes that can define the average, if you will, diffusion rate of a small area of the brain.

Fig. 2: From this paper

When these are aggregated throughout the entire brain, the output looks like this:

Fig. 3: From this paper

Cool, right?

The different colors can represent connections between certain parts of the brain — or nodes. The different orientations of the ellipsoids allow us to visually understand how diffusion is occuring throughout the brain.

You can trace cruves that span from the left side of the image, to the middle, to the right side of the image. You can trade small curves that appear in the bottom left. Or even stationary connections in the middle with the large blobs.

These are then further coaslesed together to give us highways or common road that diffusion takes throughout the brain. These are indicative of the overall structure of the brain.

Fig. 4: From this paper

These can be further analyzed to give us some scientific insight in how the brain may change over time or finding out which connections are important for specific neuro-related diseases such as Parkinson’s.

I’ll dive deeper into that in another post, but let’s take it to machine learning for now.

Relationship to ANNs

Now that you have a general idea of what the connectome looks like, we can dig into one of the types of analyses I’ve seen repeated in several papers.

As stated earlier, the main nodes within the brain are typically referred to as region of interest (ROIs), and these are parts of the brain where there are a lot of connections and may serve as a common hub of information that is being passed. These nodes are demonstrated as circles in the figure below.

Between the main nodes are edges, or the lines in the figure below. These are the connections between the nodes where information ride on.

As you can see, there are ways to characterize the network of nodes and edges. The paper uses some unique terms like modularity and clustering coefficient among others. However, let’s not worry about that.

Fig. 5: From this paper

What I wanted to show was that there are interesting characteristics that define the snapshot image of the brain for these DTIs. For example, let’s take the purple node and describe it.

From the paper: Betweenness centrality (BC) represents the importance of a particular node for network communication and is conceptualized by the number of shortest paths between any two nodes in the network that has to pass through that particular node. The purple node in the network has a high betweenness centrality because many shortest paths have to flow through it.

This concept is very easy to grasp, and could potentially lend a lot of predictive power when determining whether a particular brain is degenerative. This is one of many network characteristics that can define its structure and integrity.

However, if we look to the broader context of neural networks, maybe we can draw some analogies.

What I mean by this is — what if there were different topologies of the neural network that we used to predict the likelihood of cancer in a patient or the likelihood that a grocery store will achieve its sales goals for the next month.

These topologies are made up of artifical neurons that can have strong and weak connections with one another. Could we come up with methods to better describe them besides weights that minimize the loss function we chose?

Would these descriptions of the network give us more insight as to how the features in our network were correlated? How the network changed over time with more or less training data? Or maybe even allow us to visualize the connections in 3D space to make sense of these “black boxes”.

The list can go on and on.