Connectome Harmonics

During 2021 I worked with researchers at the Johns Hopkins CPCR and UNC to develop a new tool for connectome-specific harmonic waves, a technique first introduced by Selen Atasoy at Oxford for measuring whole brain network states in a fairly simple decomposition to around 100 structural/functional 'modes'. We wanted to make the technique usable by a larger number of scientists in the field by open-sourcing the code and analysis pipelines, and making use of more modern techniques from the software development community like containerization. It was fascinating, although not that surprising, to see how much cloud computing, open source neuro data, and improved code made collaboration on the project smoother and the reach broader. The long-term impact, I hope, is that more technical talent in other fields start to see neuro data as a resource for building magical applications in addition to understanding what the brain is doing - the best example I've seen of this recently is "Mind's Eye". 

So-called 'connectome harmonics', introduced in 2016 in this publication of Atasoy's in Nature, are a beautiful way of describing oscillations of activity throughout the brain and have proven useful for characterizing states of neural activity. There are plenty of descriptions of the math underneath connectome harmonics, which borrow from graph theory and show up in other unexpected places, but I often feel the significance of the approach can be lost between visualizations and jargon-filled language of the formal process. In my mind CSHW and related techniques provide a way to simplify the representation of the brain from the activity of 100 billion neurons (plus other emergent phenomena like local field potentials) to 100 dimensions that still give a rich understanding of the brain's state at a given moment in time. By describing such a state as "just" characterized by these 100 or so dimensions, we can further start to think about distance functions between these points (comparing one brainstate to another mathematically), trajectories through this high dimensional space (comparing brainstates over time), and critically, benchmarking such states across different interventions (when I do X to the brain, what trajectory does it follow?). 

I spend much of my time now working on new technologies that will modulate brain activity towards some desired end - be that something relatively mundane like sleep or focus, or more exotic like 'advanced meditation'.  A conventional "functional localization" view of neuroscience might tell us that we should target changing activity in the hypothalamus for modulating sleep or locus coeruleus for modulating arousal but I imagine there are far better metrics for both the endpoint (what is "sleep" or "good sleep", actually?) and more refined strategies for where to target that will fall out of a better understanding of these endpoints. Ultimately, I hope a union of mental models from science, engineering and math all contribute to directly improving people's state of mind.