New mechanism #2 — inhibitory interneuron coupling

52:59architecture predicted that there's increased synaptic coupling between inhibitory interneurons. Meaning those negative feedback neurons that I was telling you about, these are sometimes called fast spiking PV plus neurons in the cerebral cortex, those negative feedback neurons in our cerebral cortex have a lot of coupling in between them. So it's like the negative feedback is coupled to the negative feedback which is coupled to the negative feedback, right? That is what the AI model is predicting. So to test this, you go to transcriptomics. Transcriptomics means I'm going to now read the mRNA that is in my cerebral cortex, okay? And I'm going to see what

53:40genes are being expressed Yes. >> in patients that have vegetative state Yes. >> and patients that don't. >> Yes. >> Okay, healthy versus those in coma. Yes. What they find is there's an upregulation of two genes, the VGF and the SCG2. These are genes that when expressed in these interneurons they drive synaptogenesis, meaning they drive this feedback mechanism, this coupling, yes. >> Yeah, the that Right. >> This is really >> This one's pretty significant. >> Yeah. This one's This one's way more than in both those genes. >> Yeah, yeah, yeah, yeah. >> Right? That >> is again, data that's out there. Right.

54:20Right. Right. >> Right. >> Exactly. And And I think the This is kind of again this in terms of like a frame of reference for I I've seen the conversation around {quote} {unquote} AI be very different within some corners of the science community versus the general public because a lot of researchers view it in this way where it's like, "Okay, this can be a sort of intermediary layer where I can rapidly prototype and generate predictions and rapidly be able to test those test that against real data in a way where I don't have to abuse my postdocs." >> Mhm. Yeah, yeah, yeah. Yeah, just just abuse the undergrads.

55:03Here Here's a thousand images labeled them Right. Right. Right. Right. No, right. >> Sorry, undergrads are always going to That's That's tough. >> Yeah, it's tough It's a tough life. We've all been there. We all want those letters of recommendation. You got You got to work the sweat. >> This is really interesting though because now there's the predictions are also on two very different planes. Um >> Yes, that's that's a very good point. >> I mean? Like in terms of what's Yeah, because when we think about those three those three components of our city, right? The cerebral cortex cerebral cortex is the homes and the businesses that PV plus interneuron thing has to do with that part. >> Right. And then and then the the

55:45the brain imaging part. >> Yes. That one had to do with the thalamus and the relay station. >> Correct. Right, right, right. So all three parts of the meso circuit hypothesis are working together and this thing is giving predictions as you said on all of them. Right. >> Right. >> Right. Uh in in very interesting ways and again this this continues to go back to the fact that we have all these existing tools like transcript uh transcriptomics >> Mhm. uh enables the ability to know what genes are being expressed. >> And one thing I just want to say is like to the team that that made this paper happen, right? I admire the resourcefulness. >> Mhm. Right? Because you it's one thing to have an AI model and make predictions

56:27and it's another thing to think, "Okay, here are the predictions, how can I make the argument that this is real?" Right. Right? How can I check? It's always about checking. >> Yes. And they were so resourceful that they found these kinds of data sets, you know? Maybe they talked I don't I don't know exactly the details. I don't know if these are open or not, but you you go talk to somebody who has that data set. You meet someone at a conference and they're like, "Hey, actually you can look into mine, you know, put me as an author and you know? So like >> Yeah, I know. It's very clever. >> Yeah. I I I think that part is nice. >> The whole from the from the ideation of like let's give it a try to the how are we going to be able to prove to reviewer

57:07two that this is not all nonsense. >> Yeah. Yeah, exactly. I got I got um lunch with Daniel about 3 weeks ago and he was telling me about this paper and he he told me like and you know, the thing predicted this and the first thing I asked well, yeah, but like, you know, how do you >> How do you know? How do you know if the model isn't just bullshitting? And then he told me about these two techniques that I was like, "Okay, that's actually pretty dope." >> Yeah. Yeah, yeah. All right. Well, well, as soon as it's out, we'll cover it. Yeah. You know, um So, yeah, this was this was pretty cool. Okay, the final thing we're going to talk about is a proposed strategy for awakening patients Okay. with coma, right? Cuz at the end of the day, that's what really matters. Clinically, we want to That's how That's how it started, and

57:48that's how we're going to end it. So, the intervention that they looked for and how to test was deep brain stimulation. This is again the idea of you you test um you you put in an electrode deep into the brain, and then you give it electrical activity, and you try to wake up the neurons, okay? Now, the researchers here, they what they did was test a bunch of targets in their biophysical model and say, "What if I provide stimulation here? What if I provide stimulation here? What's the best target in my three-component model, right?" Cuz there's a bunch. There's cortex, there's thalamus, there's subthalamic nuclei, there's the pallidum, and each of those has their own little

58:30sub populations. So, we can get really