To get around the problem of damaging 10,000 neurons just to connect with 1,000, Biohybrid is experimenting with an approach that makes donor neurons a part of the implant itself - potentially allowing for dramatically better connection scaling.
Oh neat, another brain implant startup. I published in this field. If anyone has questions, I’m happy to answer.
Do you see these eventually evolving into more a practical medical purpose or convenience/commodity purpose or both?
The most practical medical purpose I’ve seen is as a prosthetic implant for people with brain/spinal cord damage. Battelle in Ohio developed a very successful implant and has since received DARPA funding: https://www.battelle.org/insights/newsroom/press-release-details/battelle-led-team-wins-darpa-award-to-develop-injectable-bi-directional-brain-computer-interface. I think that article over-sells the product a little bit.
The biggest obstacle to invasive brain-computer implants like this one is their longevity. Inevitably, any metal electrode implanted in the brain gets rejected by the immune system of the brain. It’s a well-studied process where a glial scar forms, neurons move away from the implant, and the overall signal of the device decreases. We need advances in biocompatibility before this really becomes revolutionary.
ETA: This device avoids putting metal in the brain and instead the device sends axons into the brain. Certainly a novel approach which runs into different issues. The new neurons need to be accepted by the brain, and they need to be kept alive by the device.
If they move the cell bodies into the brain and then had the device house axons and dendrites (neuron input and output), they could maybe let the brain keep the device alive. But that is a much more difficult installation procedure
Fascinating, thank you for answering
Have we figured out yet how to deal with tissue rejection without instituting a drug regimen?
They’re culturing the mouse’s own cells, so no risk of rejection.
Between the various cortical layers and white matter, what part of the brain’s structure do these implants typically target? Do they sit on top of the outermost layer of some specific region of the cortex (effectively creating a new layer), or do they make long-distance connections to other brain structures?
A traditional electrode array needs to be as close to the neurons as possible to collect data. So, straight through the dura and pia mater, into the parenchyma where the cell axons and bodies are hanging out. Usually, they collect local data without getting any long distance information - which is a limiting factor to this technology.
The brain needs widespread areas to work in tandem to get most complicated tasks done. An electrode is great for measuring motor activity because those are pretty localized. But, something like memory and language? Not really possible.
There are electrocorticographic devices (ECoG) that places electrodes over a wide area and can rest on the pia mater, on the surface of the brain. Less invasive, but you still need a craniotomy to place the device. They also have less resolution.
What do you see these types of implants being useful for in the near future?
See Alk’s comment above, I touched on medical applications.
As for commercial uses, I see very few. These devices are so invasive, I doubt they could be approved for commercial use.
I think the future of Brain Computer Interfacing lies in Functional Near Infrared Spectroscopy (FNIRS). Basically, it uses the same infrared technology as a pulse oximeter to measure changes in blood flow in your brain. Since it uses light (instead of electricity or magnetism) to measure the brain, it’s resistant to basically all the noise endemic to EEG and MRI. It’s also 100% portable. But, the spatial resolution is pretty low.
HOWEVER, the signals have such high temporal resolution. With a strong enough machine learning algorithm, I wonder if someone could interpret the signal well enough for commercial applications. I saw this first-hand in my PhD - one of our lab techs wrote an algorithm that could read as little as 500ms of data and reasonably predict whether the participant was reading a grammatically simple or complex sentence.
It didn’t get published, sadly, due to lab politics. And, honestly, I don’t have 100% faith in the code he used. But I can’t help but wonder.
Really cool, thanks for the explanations
Deus Ex, here we come.
Hopefully there’s a JC Denton or Adam Jensen in the future, too.
