1 00:00:00,180 --> 00:00:09,330 So what I'll talk to you today about is really the neuroscience that I've been doing over the 20 years with many great collaborators. 2 00:00:09,330 --> 00:00:15,750 And it's really a story that that sort of takes its beginning a long time ago. 3 00:00:15,750 --> 00:00:19,200 But before we get into the sort of the history of it, I thought I'd just share it, 4 00:00:19,200 --> 00:00:30,170 share video with you that sort of encapsulates that, I think what is both pleasurable but also deeply meaningful. 5 00:00:30,170 --> 00:00:35,120 Hi, girls. It's August six, is that right? 6 00:00:35,120 --> 00:01:14,630 Yes, August six, 2012 daddy is going to play a song when he's ready. You girls ready? 7 00:01:14,630 --> 00:01:19,970 So why are they so cute? Why is it that, you know, they're moving in time together? 8 00:01:19,970 --> 00:01:27,160 It sort of points to many of the things that I think we find pleasurable. But it I think foremost also points to the sociality 9 00:01:27,160 --> 00:01:32,360 the sort of the idea that a lot of the things we find pleasurable is really being with other people. 10 00:01:32,360 --> 00:01:36,470 And, of course, is the one thing that pandemic's has more or less deprived us. 11 00:01:36,470 --> 00:01:41,680 But let's just let's just think about what is going on in our brains as we go through this. 12 00:01:41,680 --> 00:01:47,270 So today I'm going to talk first about emotion, because that's really where everything starts, I think. 13 00:01:47,270 --> 00:01:50,960 Talk about how we could reverse engineer the human brain. 14 00:01:50,960 --> 00:02:00,170 Talk about how it is that within those emotions, we have pleasure and pleasure cycles, which are, of course, largely built for survival. 15 00:02:00,170 --> 00:02:04,370 I'll talk about how one can look at that on a computer, 16 00:02:04,370 --> 00:02:08,180 built computational models and actually get a handle on how it is that which 17 00:02:08,180 --> 00:02:12,710 switches through these different parts of the cycle and even brain states. 18 00:02:12,710 --> 00:02:19,580 And think about how that might relate to consciousness. Think about how that, in fact, might relate to human flourishing. 19 00:02:19,580 --> 00:02:27,860 So let's get started with that. So many, many years ago, I came to Oxford to do my doctorate on emotion and I wrote this speeches, 20 00:02:27,860 --> 00:02:31,520 as you can see here, called the functional neuroanatomy of Emotion. 21 00:02:31,520 --> 00:02:37,220 And I think it was because I came to realise that really if one wanted to understand how the human brain worked. 22 00:02:37,220 --> 00:02:41,990 One really has to understand how it is that we solve really difficult questions, 23 00:02:41,990 --> 00:02:47,150 really difficult problems in the world and how we use emotions to guide our behaviour. 24 00:02:47,150 --> 00:02:52,220 I've sort of stayed in that field some years ago. I wrote a textbook on the on the matter. 25 00:02:52,220 --> 00:02:59,750 But one of the things I came to realise is, I assure you and this talk is that emotion is this sort of multifaceted molecule. 26 00:02:59,750 --> 00:03:03,860 And yet it consists of these atoms. And those are really the pleasures and the pains. 27 00:03:03,860 --> 00:03:10,160 And those are the ones that one could try to understand in order to stand what it is that is going on. 28 00:03:10,160 --> 00:03:14,650 When we have emotions and when we act in the world, what our emotions. 29 00:03:14,650 --> 00:03:17,990 Yeah. Just a random selection of different people having emotions. 30 00:03:17,990 --> 00:03:25,280 And you immediately recognise that these are emotions and that you can probably recognise what the emotions are as well. 31 00:03:25,280 --> 00:03:30,260 The one thing, of course, which is important about emotions is that we have them on different timescales. 32 00:03:30,260 --> 00:03:35,990 We have the immediate expressions that I just showed you, which are usually having autonomic changes at the same time. 33 00:03:35,990 --> 00:03:39,260 One gets nervous. One starts to sweat all of those kind of things. 34 00:03:39,260 --> 00:03:47,600 And then we can report on those over minutes and hours, which then turned into moods, perhaps even disorders and the certain personality traits. 35 00:03:47,600 --> 00:03:52,340 So there's a timescale to it that can last essentially a lifetime. 36 00:03:52,340 --> 00:03:54,220 But how does one define them? 37 00:03:54,220 --> 00:04:03,650 As always, one can go back to the Greeks and one can read what Plato wrote, sort of talking in, say, having Socrates say pleasure. 38 00:04:03,650 --> 00:04:10,490 So emotions are the pleasures and the pain and pains of the soul. Aristotle was slightly more sort of elaborate. 39 00:04:10,490 --> 00:04:14,420 He said the emotions are all those feelings that so change men as to affect the 40 00:04:14,420 --> 00:04:22,590 judgements and that are also attended by pain and pleasure pleasures already. Then Head had the idea that pain and pleasure must be important. 41 00:04:22,590 --> 00:04:28,380 Of course, what then happened was that, you know, the centuries passed, the millennials went over us, 42 00:04:28,380 --> 00:04:35,310 and people like Darvin decided that he could perhaps think about how this might work in other animals. 43 00:04:35,310 --> 00:04:39,150 And he said that the emotions are really expressive behaviours that communicate 44 00:04:39,150 --> 00:04:43,620 information from one animal to another about what is likely to happen in emotions, 45 00:04:43,620 --> 00:04:48,120 therefore affect the chances of survival of the individual demonstrating the behaviour. 46 00:04:48,120 --> 00:04:51,060 So there's a key key word here, namely the survival. 47 00:04:51,060 --> 00:04:57,330 And this, of course, is one of the things that emotion and specifically pleasure and pleasure cycles are good for. 48 00:04:57,330 --> 00:05:02,280 But having said that, of course, along came cognitive neuroscience and Lisa Feldman. 49 00:05:02,280 --> 00:05:08,510 Barrett has been a very vocal proponent of trying to really sort of parcel out what is going on. 50 00:05:08,510 --> 00:05:17,670 And she she holds that emotions are constructed, events that arise from this simultaneous combination of three or more basic psychological primitives, 51 00:05:17,670 --> 00:05:21,870 core effort, categorisation and executive attention. 52 00:05:21,870 --> 00:05:29,140 Now, the key thing here is that the core effect is really what I'm talking about when I'm talking about pleasure and pain. 53 00:05:29,140 --> 00:05:38,440 It's also clear, as I already alluded to, that when these systems are not no longer working particularly well, we get the burden of mental disease. 54 00:05:38,440 --> 00:05:41,650 And as you know, this is a quite a large burden, of course, 55 00:05:41,650 --> 00:05:47,350 not getting better by the pandemic where we're not able to actually enjoy the things we normally enjoy. 56 00:05:47,350 --> 00:05:54,330 Now. When people then started thinking about what might be going on in the brain, which of course is what my talk is about today. 57 00:05:54,330 --> 00:05:57,720 They happened upon the structures, which are sort of in the middle of the brain, 58 00:05:57,720 --> 00:06:03,540 on the midline in structures that are really what we call the limbic brain, the limbic. 59 00:06:03,540 --> 00:06:10,920 Really just being meaning war. And there was a number of theories out there mclaine help that there was something about the primitive brain, 60 00:06:10,920 --> 00:06:18,510 which, of course, doesn't really hold up. But that was his idea. But Page was really the first one in 1949 to highlight this. 61 00:06:18,510 --> 00:06:27,840 And since then, there's been great strides making Jonah Do has made a career out of telling us what the amygdala studying what the immigrant is doing. 62 00:06:27,840 --> 00:06:33,180 And he's done some remarkable studies in rats showing that the amygdala is very important for fear conditioning. 63 00:06:33,180 --> 00:06:38,910 In fact, he was so successful that for years people thought that the chemical really was deficit it. 64 00:06:38,910 --> 00:06:46,060 Now we know that that is not true. And we also know that the amygdala probably has been superseded by the prefrontal cortex. 65 00:06:46,060 --> 00:06:52,860 And in humans, after all, it's been about a hundred million years since we shared a common ancestor with with the rodents. 66 00:06:52,860 --> 00:07:01,410 And in fact, Antonieta Mashal wrote his seminal book called The Cats Era from 1994, where he was claiming, 67 00:07:01,410 --> 00:07:06,210 based amongst other things, and was his patient, but also on this particular patient, we were not his patient. 68 00:07:06,210 --> 00:07:12,780 Both was Phineas Gauge, who was a railway engineer who basically had the job of making controlled explosions. 69 00:07:12,780 --> 00:07:19,890 And one day he forgot to put the sand in and this tampering rod went straight through his prefrontal cortex and amazingly, he survived. 70 00:07:19,890 --> 00:07:25,110 And they found the tampering, what, about hundred and fifty metres from there? And it can now be found in Harbord. 71 00:07:25,110 --> 00:07:30,840 But the key thing is what he changed. NIRS was no longer Phineas Gauge. 72 00:07:30,840 --> 00:07:36,040 We don't know a lot about this, but the measure was right to say that the ventromedial prefrontal cortex, 73 00:07:36,040 --> 00:07:45,330 so the orbital frontal cortex reaches over the eyeballs and the middle part here does seem to play a huge part in having emotions, reasons and so on. 74 00:07:45,330 --> 00:07:49,140 But what he KOMY forgets to say is that this is not a new idea. 75 00:07:49,140 --> 00:07:53,190 James Lang, of course, you point could say that that really is setting the scene. 76 00:07:53,190 --> 00:08:00,990 And one Anata, this amazing neurologist, wrote a fantastic paper from Seventy-one called The Problem The Frontal Lobe and a reinterpretation, 77 00:08:00,990 --> 00:08:06,030 which is really about linking the body with the brain. 78 00:08:06,030 --> 00:08:12,870 And so along came the revolution of neuro imaging, which is, of course, one of the things that I've been part of. 79 00:08:12,870 --> 00:08:17,640 And with that revolution, of course, also came the new phrenology of new imaging. 80 00:08:17,640 --> 00:08:22,740 So here's just a meta analysis from 2002 where they looked at all the studies and they 81 00:08:22,740 --> 00:08:27,360 tried to classify the different different emotions and see where you get blocks. 82 00:08:27,360 --> 00:08:31,530 And as you can see, there are blocks all over the brain. What does that mean? 83 00:08:31,530 --> 00:08:36,300 Does that mean that one probably shouldn't think about localising it? 84 00:08:36,300 --> 00:08:42,720 Is it so that you can't have it one to one mapping where you see Amygdaloids fear orbitofrontal cortex or something else? 85 00:08:42,720 --> 00:08:48,750 Or should we really be starting to think about networks? Should we really be starting to think about how it is that we have these overlapping 86 00:08:48,750 --> 00:08:53,970 networks and really look at the complexity and the dynamics of this over these timescales? 87 00:08:53,970 --> 00:08:58,770 I've been talking about. I certainly think that is the way it should happen. 88 00:08:58,770 --> 00:09:03,820 And so there I was as I started my doctorate back in 98. 89 00:09:03,820 --> 00:09:09,100 Coming from computer science and wanting to try to understand how the human brain worked. 90 00:09:09,100 --> 00:09:14,100 I came across this beautiful data that suggested that really we are prediction machines. 91 00:09:14,100 --> 00:09:19,000 Here's an image of essentially black and white, and most of you won't be able to recognise it. 92 00:09:19,000 --> 00:09:26,620 And yet the moment I've shown you my two daughters playing in the in the sun, you can't help but see this because now you'd have a template. 93 00:09:26,620 --> 00:09:34,150 Now you have something that you can predict from. And this is true in the visual domain, but it's also equally true in the auditory domain. 94 00:09:34,150 --> 00:09:41,410 Here is what happens when you have a child with a cochlear implant, which is basically a way of making that child being able to hear. 95 00:09:41,410 --> 00:09:46,090 And the way that happens is basically by downsampling the signal. 96 00:09:46,090 --> 00:09:55,250 So give me let me give you an example of something that is being said and let's see whether that you can try to understand what is being said. 97 00:09:55,250 --> 00:10:00,740 It's very difficult for most of you, we will sound like complete noise, and yet if I play you the roar signal. 98 00:10:00,740 --> 00:10:05,660 So what we hear. We don't have a cochlear implant. It's not downsampled. We get this. 99 00:10:05,660 --> 00:10:12,350 The wife helped her husband. Now listen to the downsampled version again. 100 00:10:12,350 --> 00:10:15,520 And you can immediately hear exactly what is going on. 101 00:10:15,520 --> 00:10:21,170 So in other words, your brain. Now it has a template is able to predict and to work out what it is. 102 00:10:21,170 --> 00:10:29,790 What is this signalling that noise? And so, in many ways, as a computer scientist, I realised that really evolution has been survival machines. 103 00:10:29,790 --> 00:10:35,570 At the end of the day, what is important is that we survive not just this individual, but also as a species. 104 00:10:35,570 --> 00:10:41,540 And really what happens is that we've built a system that has hierarchical prediction. 105 00:10:41,540 --> 00:10:46,790 Other people have had this idea. Culturist and of course. Keep it. Bownds and Clark is another one. 106 00:10:46,790 --> 00:10:53,510 And really, what this basis. This idea that we have predictions and that we have these emotions that helps 107 00:10:53,510 --> 00:10:58,150 guide those predictions is that we can solve what is known as NPR problems, 108 00:10:58,150 --> 00:11:03,650 nonpoor and human problems, problems that are so hard that most computers can't fix them. 109 00:11:03,650 --> 00:11:10,760 And so it means that in order to really reverse engineer the brain, we need to understand this system and we need to understand it over time. 110 00:11:10,760 --> 00:11:17,060 We need to understand the long lasting kind of cycles that I've talked about, but also the very short ones. 111 00:11:17,060 --> 00:11:21,380 So I didn't have my coffee just before now. And of course, I'm thinking about coffee. 112 00:11:21,380 --> 00:11:23,840 So there's a pleasure cycle. We'll see in a moment. 113 00:11:23,840 --> 00:11:32,030 But also, if we understood how that worked, we also much more likely to be able to understand what happens when there are imbalances, 114 00:11:32,030 --> 00:11:40,100 anhedonia, the lack of pleasure, apathy, the lack of wanting things are fundamentally changed in most new psychiatric disorders. 115 00:11:40,100 --> 00:11:47,480 And really a bit of understanding might be able to give us a better understanding of how we could intervene. 116 00:11:47,480 --> 00:11:53,600 And so I think that's all well and good, but of course, the key issue, how is the brain organised to start with? 117 00:11:53,600 --> 00:12:01,220 And again, people have been thinking about this for a long time. One of my favourite examples is the Massive Metronomes, his 1998 paper in Brain, 118 00:12:01,220 --> 00:12:09,170 where he basically shows that there's a hierarchy of things that the primary visual cortex V1 sends 119 00:12:09,170 --> 00:12:15,050 signal on to higher and higher levels and they're up and downstream connexions between these things. 120 00:12:15,050 --> 00:12:21,860 And the key thing is that they're organised so that as you get higher and higher up, you get more and more integrative. 121 00:12:21,860 --> 00:12:27,710 And here he's just showing what happens. The same for the auditory system and ensuring showing what happens when you then integrate that. 122 00:12:27,710 --> 00:12:33,080 When you integrate that model. And in fact, Bernard Barres had a wonderful idea that he said, really, 123 00:12:33,080 --> 00:12:36,560 what is going on here is that you have all of this information coming into a global 124 00:12:36,560 --> 00:12:42,290 workspace and basically then that information is broadcast to the rest of the world. 125 00:12:42,290 --> 00:12:46,550 Now we'll get back to the global workspace, because I think that's a key idea here. 126 00:12:46,550 --> 00:12:50,390 Dominant shellers, of course, immediately said, you know, in their theories, 127 00:12:50,390 --> 00:12:55,850 that is really about how it is that we bring all of these things together in a in a 128 00:12:55,850 --> 00:13:01,820 in a in a system that is able to actually do these things with scheduling and so on. 129 00:13:01,820 --> 00:13:09,110 So just as an aside, because this is also, I think, what is important when one does this kind of work, 130 00:13:09,110 --> 00:13:16,580 when we did the first experiments of just looking at what happens when you just get the different kind of five sensors in, do you bring the notes? 131 00:13:16,580 --> 00:13:24,440 Of course, there are more than five sensors, but just the classical ones, the auditory, the visual, this multisensory, the smell and the taste. 132 00:13:24,440 --> 00:13:30,650 What happens then is we were able to make sculptures of those. So just basically the low was part of the hierarchy. 133 00:13:30,650 --> 00:13:34,970 And here's just a close up, one of those with my good friend, Andy Catrell. 134 00:13:34,970 --> 00:13:39,770 So in other words, we basically were able to show what are the parts that are involved in this? 135 00:13:39,770 --> 00:13:43,910 The outer ring. But the key thing, of course, if one wants to understand the system in reverse, 136 00:13:43,910 --> 00:13:49,110 engineers understand what happens in the middle in this global workspace and shampooer. 137 00:13:49,110 --> 00:13:54,740 As you understand, the hand came up with an influential implementation of Barzeh ideas, 138 00:13:54,740 --> 00:13:59,840 namely to say that really when we doing things, we have things coming in perceptual systems. 139 00:13:59,840 --> 00:14:05,580 We have the past. We have the value system, which is the emotional system, and then we have an attentional system. 140 00:14:05,580 --> 00:14:10,100 And that basically predicates and allows us to basically look at the future. 141 00:14:10,100 --> 00:14:15,620 And they also did a number of beautiful experiments that allowed us them to say that really one 142 00:14:15,620 --> 00:14:20,930 shows things that subliminal strength is not able to get in and therefore is not conscious of it. 143 00:14:20,930 --> 00:14:26,900 And you can even see things that you're not conscious of, like if there was a gorilla walking through the room and I'm focussed on talking to you. 144 00:14:26,900 --> 00:14:31,700 I may not be able to see it. I could have attentional blindness to that. 145 00:14:31,700 --> 00:14:38,510 So that's kind of interesting. But in order to get to there, we need to first think about the reward system and the story of pleasure cycles. 146 00:14:38,510 --> 00:14:42,590 I think it's an exciting one. As I said, you know, right now I'm on wanting coffee. 147 00:14:42,590 --> 00:14:48,710 I'm thinking about coughing. I'm thinking about how I could potentially run out and come back and then finally have a bit of pleasure food. 148 00:14:48,710 --> 00:14:53,450 Of course, it happens in the beginning for the pleasures of a bit later. And then there will be a phase transition. 149 00:14:53,450 --> 00:14:56,930 Once I've had that on me to join then and I'm able to do something else. 150 00:14:56,930 --> 00:15:05,030 Now, if I was addicted to coffee, which of course I'm not, I would spend all my time here and probably wouldn't spend very much time here or here. 151 00:15:05,030 --> 00:15:12,140 So in other words, you could see how it is if you could identify what is one of the circuits that are doing this that could basically 152 00:15:12,140 --> 00:15:19,370 see whether one could find out what is going on and how potentially to help you not get stuck in those attractors. 153 00:15:19,370 --> 00:15:19,970 So in other words, 154 00:15:19,970 --> 00:15:28,850 you can have prediction and meta stable status cycles and networks you need is in order to have the resource allocation for survival. 155 00:15:28,850 --> 00:15:33,770 But we also limited we have a limited bandwidth in the inner circle, as it were. 156 00:15:33,770 --> 00:15:39,920 But key to this is that these rewards are motivational magnets, just like those two babies that you saw in the beginning. 157 00:15:39,920 --> 00:15:44,960 You can't help but actually look at them even if you haven't had children yet. 158 00:15:44,960 --> 00:15:49,460 And really, the pleasure psychosis is essential for the necessary decision making. 159 00:15:49,460 --> 00:15:52,010 And so to think about that together with Christine, 160 00:15:52,010 --> 00:15:58,070 aren't we sort of started thinking about how it is that you have in this state where I'm sitting here thinking about the coffee, 161 00:15:58,070 --> 00:16:02,240 you have all these experience, all these things coming in, perhaps even the smell of coffee, 162 00:16:02,240 --> 00:16:06,620 and you have to make predictions about what you need to do in order to get to that goal. 163 00:16:06,620 --> 00:16:10,550 And so in a very kind of a very kind of simple way, 164 00:16:10,550 --> 00:16:15,680 one could start to think about how it is that you have this search tree of different possibilities 165 00:16:15,680 --> 00:16:21,800 and really the reward values and your past experience allows you to discard suboptimal options. 166 00:16:21,800 --> 00:16:25,910 And this just doesn't happen now. It happens throughout the day. 167 00:16:25,910 --> 00:16:31,340 Only three coffees. But, you know, it could be many more. And, of course, there could be many different kind of cycles. 168 00:16:31,340 --> 00:16:35,720 There could be things that I'm doing as well. And as long as I'm engaged in that cycle. 169 00:16:35,720 --> 00:16:42,350 That, of course, could and will very highly influence the well-being that I have over the lifespan. 170 00:16:42,350 --> 00:16:47,230 So in my work, I've looked at probably all of the pleasures that are here. 171 00:16:47,230 --> 00:16:53,630 I'm just showing you for food from the top. And what you and social on the right drugs, 172 00:16:53,630 --> 00:17:02,460 methamphetamine giving to dropping is oxygen undergraduates and gambling again, gambling that if octomom graduates. 173 00:17:02,460 --> 00:17:10,540 And when we use these scanners, what we found is that the orbital frontal cortex, the part that you saw was the MASHU, but that I am also. 174 00:17:10,540 --> 00:17:18,460 That clearly is coming up here. The parts of the brain that adjust over the orbits, the eyeballs is really the engaged version. 175 00:17:18,460 --> 00:17:25,150 And, of course, with subcortical regions, as you can see here, when it comes to the drugs, but also put the other one of those not showing. 176 00:17:25,150 --> 00:17:28,150 But I think key here is that this one system for all of these things, 177 00:17:28,150 --> 00:17:35,830 it seems to focus on one system and that one system includes but is not limited to the orbital frontal cortex. 178 00:17:35,830 --> 00:17:41,710 And much of my career has really been spent trying to try to find out what the orbital frontal cortex, 179 00:17:41,710 --> 00:17:47,170 where things are when things happen and what is going on. 180 00:17:47,170 --> 00:17:51,610 And one of the few pleasures that I couldn't do myself, I had to bring in that doctor, 181 00:17:51,610 --> 00:17:56,500 collaborator Janika Grigoriadis, who sort of Masters and Johnson's rolled into one. 182 00:17:56,500 --> 00:18:03,430 And he did this beautiful study where he looked at what happens when when women fake orgasms and when they have orgasms. 183 00:18:03,430 --> 00:18:06,970 And what he found was that when they have real orgasm, as you can see, 184 00:18:06,970 --> 00:18:13,330 you get a very large engagement of the orbiter for the cortex compared to when you are just imitating. 185 00:18:13,330 --> 00:18:20,740 Which is not surprising, given that the pleasure circuit, of course, has to engage the very same one that you used for so many, I think. 186 00:18:20,740 --> 00:18:25,480 Of course, it's a bit frivolous to talk about orgasms here on a on a Thursday afternoon. 187 00:18:25,480 --> 00:18:31,090 But really, I think what is exciting here is really the kind of orchestration of of of activity, 188 00:18:31,090 --> 00:18:34,450 how it is that one goes from desire and how different regions are active. 189 00:18:34,450 --> 00:18:39,370 When you aroused, when you get to the plateau phase and even when you get to the orgasm phase. 190 00:18:39,370 --> 00:18:40,780 So this kind of orchestration, 191 00:18:40,780 --> 00:18:49,660 this kind of choreography of different brain regions changing is really what we are trying to understand when we understanding pleasure, 192 00:18:49,660 --> 00:18:56,530 not just on a single level, but on a whole brain level. And of course, is very similar if one was to look at music. 193 00:18:56,530 --> 00:19:02,050 So in beautiful work by and Lord and Robert Santora from McGill, 194 00:19:02,050 --> 00:19:10,180 they were able to show that when you have the kind of music that where your hair stands on end, you get a very similar kind of network engaged. 195 00:19:10,180 --> 00:19:14,680 Now, they weren't able to say anything about the win because this was a pit study, 196 00:19:14,680 --> 00:19:20,770 but they were very much able to show that it's the same network that is engaged when it comes to these kind of pleasures. 197 00:19:20,770 --> 00:19:24,360 So that's kind of exciting is one of the things that we do in the centre in all 198 00:19:24,360 --> 00:19:28,630 who's at the music in the brain centre where we have all kinds of fancy machines, 199 00:19:28,630 --> 00:19:36,940 but also extraordinarily gifted musicians, including our director Peter Russo, who's here playing the bass. 200 00:19:36,940 --> 00:19:42,100 And as you can see with Graham, and we will continue to grow because we've been extended for another five years. 201 00:19:42,100 --> 00:19:47,950 So, again, if music is your pleasure, you should definitely talk to me afterwards. 202 00:19:47,950 --> 00:19:50,950 But before we sort of look at the commonality of things, 203 00:19:50,950 --> 00:19:57,050 let's maybe just focus on the social pleasures and let me focus on perhaps what is the most simple. 204 00:19:57,050 --> 00:20:02,720 Even those social pleasures are never simple, but the most simple of all of the pleasures, namely that like. 205 00:20:02,720 --> 00:20:18,490 I mean, look at these babies recession. It just goes on and on. 206 00:20:18,490 --> 00:20:22,660 So why are they so cute? Why do they look so cute and why is it that we hear them this cute? 207 00:20:22,660 --> 00:20:26,320 Well, it's very clear that we must be motivated to do something about them because, 208 00:20:26,320 --> 00:20:30,670 of course, the species survival and the survival of the species depends on it. 209 00:20:30,670 --> 00:20:34,420 They're vulnerable. They come out too early. So we need to take care of them. 210 00:20:34,420 --> 00:20:40,550 And if there's something wrong with the parent, if postnatal depression, that causes a problem, that potentially could need some help. 211 00:20:40,550 --> 00:20:48,160 But also, if there's something wrong, the baby like a cleft lips or the template doesn't fit the kind of expectations we have of what babies are. 212 00:20:48,160 --> 00:20:52,750 So I got very excited by this back in the early 2000s and we started using this kind of scanner, 213 00:20:52,750 --> 00:20:56,780 which is an image scanner, what some people call an advanced hairdryer. 214 00:20:56,780 --> 00:21:00,580 But what is cool about it is that it tells you something about the when it tells 215 00:21:00,580 --> 00:21:04,900 you something about how things happen over milliseconds because unlike bold. 216 00:21:04,900 --> 00:21:06,340 So that is if I am right. 217 00:21:06,340 --> 00:21:14,140 It tells you basically how these different things are moving about on a millisecond to whether thousands of a second around the brain. 218 00:21:14,140 --> 00:21:19,900 And I was interested to look at the current Rentz idea of the of the Kenshin schema. 219 00:21:19,900 --> 00:21:25,390 The idea that there's something special about babies. And so we took happy babies. 220 00:21:25,390 --> 00:21:29,530 No neutral babies and sad babies and matched them to some adults. 221 00:21:29,530 --> 00:21:31,630 And then we started looking at what happened in the brain. 222 00:21:31,630 --> 00:21:35,920 And at that time, everybody thought that it would just get activity in the fusiform face area, 223 00:21:35,920 --> 00:21:41,320 which is the part of the brain that Oliver Sacks described when he talked about the man who mistook his wife for a hat. 224 00:21:41,320 --> 00:21:46,270 Because if that part of the brain is no longer functioning, you're unable to recognise faces. 225 00:21:46,270 --> 00:21:52,480 Now, you get that, as you can see here, after about one hundred and thirty milliseconds, both for the adults and for the babies, 226 00:21:52,480 --> 00:21:58,570 but only for the babies, do you get this activity in the emotional part of the brain very quickly. 227 00:21:58,570 --> 00:22:02,390 And then he sort of dies out. So in other words, it looks as if. 228 00:22:02,390 --> 00:22:06,890 What is very special about babies is that they at the same time as you recognise them as being babies, 229 00:22:06,890 --> 00:22:12,320 you get the signal that says I must be taking care of that baby. 230 00:22:12,320 --> 00:22:21,050 And we were able in later work again with Christine Parsons and Katie Young and with the restart to show and with Ellen Stein, of course, 231 00:22:21,050 --> 00:22:28,460 to show that there is something very special about if there's something wrong with the template, if there's just a tiny change in the mouth. 232 00:22:28,460 --> 00:22:35,690 We notice immediately. And interestingly, when you look at the brains of, again, non parents having a look at this, 233 00:22:35,690 --> 00:22:42,360 what you find is that there's a much diminished response in the orbital frontal cortex and also, by the way, in the fusiform face area. 234 00:22:42,360 --> 00:22:47,190 So in other words, this prediction goes awry. And this, of course, could be a problem. 235 00:22:47,190 --> 00:22:54,050 And unless one does something about it, one could then later have problem with the bonding process. 236 00:22:54,050 --> 00:23:00,800 But, of course, now that we know, we can start to think about how we could intervene in that, in effect, in a set of studies that we are conducting, 237 00:23:00,800 --> 00:23:07,130 we have shown that at least the way that nurses and surgeons report on these babies is that they say, 238 00:23:07,130 --> 00:23:15,140 can you see how cute their baby is because they've changed their template? And how do we change the template so that we can engage in a natural way? 239 00:23:15,140 --> 00:23:16,130 So I think that's exciting. 240 00:23:16,130 --> 00:23:23,000 But it's also exciting to see that, again, if we look at the auditory domain here, just looking at comparing infant and adult crying, 241 00:23:23,000 --> 00:23:27,770 only the infant crying gets you activity in the orbital frontal cortex again, 242 00:23:27,770 --> 00:23:32,330 telling us that this early, early response that is guiding our things is really found here. 243 00:23:32,330 --> 00:23:35,240 And it's found in people who are not parents. 244 00:23:35,240 --> 00:23:42,470 And so, in fact, for the last five years in another project I've been investigating, funded by the U.S., see what happens as we become parents. 245 00:23:42,470 --> 00:23:52,700 What happens if we scan people before we want to have children, after we have the child and then a year later and both the mother and the father? 246 00:23:52,700 --> 00:23:59,240 So, again, we haven't published on this yet, but it does look as if there are very significant changes in the brain when you become a parent. 247 00:23:59,240 --> 00:24:03,800 And all of us who are parents will know that this is true for many different ways. 248 00:24:03,800 --> 00:24:08,900 But there are clearly changes in your brain. Clearly changes in your hedonic system. 249 00:24:08,900 --> 00:24:17,270 So together with Roger Chris, I we sort of investigated all of this and we basically wrote this paper where we state 250 00:24:17,270 --> 00:24:24,220 that by revisiting this conundrum in philosophy about high and low pressure pleasures. 251 00:24:24,220 --> 00:24:28,730 And we concluded based on this kind of research and the research of all the people in the field, 252 00:24:28,730 --> 00:24:33,500 that really it seems to be like in Lord of the Rings is one to the mall. 253 00:24:33,500 --> 00:24:39,560 There is a network that is engaged that is similar no matter what kind of pleasure it is. 254 00:24:39,560 --> 00:24:45,410 There is, of course, an interesting question, and that is why does it feel different? 255 00:24:45,410 --> 00:24:50,330 But it's the same system and that system here we show in the rat because one can study pleasure in the Raptors. 256 00:24:50,330 --> 00:24:55,280 Can Berridge from University of Michigan has shown beautifully by giving rats a little 257 00:24:55,280 --> 00:24:59,030 bit of sugar water and basically measuring how many times they lift their lips. 258 00:24:59,030 --> 00:25:05,690 Which, of course, you can also do babies who can report on it. But when you do that, you find the circuitry and you see the orbitofrontal cortex. 259 00:25:05,690 --> 00:25:12,590 But you also see other regions like the nucleus accumbens and ventral part of them that are part of this network. 260 00:25:12,590 --> 00:25:17,000 This kind of workspace that basically allows us to feel pleasure. 261 00:25:17,000 --> 00:25:19,730 And if something happens to them and of course, in rats, they've tried that. 262 00:25:19,730 --> 00:25:25,130 If you take out the ventral palette, them the rat is never able to show that pleasure reaction again. 263 00:25:25,130 --> 00:25:27,740 And here we've tried to extrapolate it in the human brain. 264 00:25:27,740 --> 00:25:32,960 And again, what you can see is that a lot of these regions are very deep in the brain and fairly well protected, 265 00:25:32,960 --> 00:25:37,790 which, of course, is well, because otherwise it'd be very difficult to survive. 266 00:25:37,790 --> 00:25:43,160 So in other words, what we're really talking about here is going back to Aristotle's radical idea that there's 267 00:25:43,160 --> 00:25:49,100 a difference between who donia this idea of heater's sweet taste of honey as pleasure, 268 00:25:49,100 --> 00:25:54,950 which is essential for food, for survival, with a beautiful food, for the individual being six, 269 00:25:54,950 --> 00:25:59,000 or actually being able to propagate the species and of course, the social aspects. 270 00:25:59,000 --> 00:26:06,650 And then you turn Monia, namely, the life will lift embedded in meaningful values together with a sense and engagement. 271 00:26:06,650 --> 00:26:13,940 It's subtle and complex. And what we can say, though, is that the lack of of well-being and mental health can be devastating. 272 00:26:13,940 --> 00:26:18,620 But what we also know is that it's very difficult to measure. It's very difficult to find somebody. 273 00:26:18,620 --> 00:26:24,290 And like a rare butterfly. How do you find somebody who is actually flourishing in a brain scanner? 274 00:26:24,290 --> 00:26:25,760 I think it is possible to do that. 275 00:26:25,760 --> 00:26:35,350 And we'll talk a little bit about how that might be possible through music, through social, through psychedelics and through meditation. 276 00:26:35,350 --> 00:26:38,390 But in order to be able to do that, one needs one more ingredients. 277 00:26:38,390 --> 00:26:44,920 And over the last, again, almost 20 years, I worked with this wonderful man, Gustavo Decco, 278 00:26:44,920 --> 00:26:51,050 from Barcelona to try to see whether we can actually simulate the human brain on a computer. 279 00:26:51,050 --> 00:26:54,940 And as you can see from this neurone paper, we have great expectations that by doing so, 280 00:26:54,940 --> 00:27:01,960 we are able to take the data off normal and off people with disease and try to see whether we can characterise 281 00:27:01,960 --> 00:27:07,450 that on a computer model and then treat that computer model just like we would treat an experimental animal. 282 00:27:07,450 --> 00:27:09,160 But unlike with an experimental animal, 283 00:27:09,160 --> 00:27:19,660 we can basically we can sobek this model to extensive research over a month and even years to find out how did we rebalance that. 284 00:27:19,660 --> 00:27:26,230 In order to do that, though? I have to bore you with some really exciting science, which is what is called complex networks dynamics. 285 00:27:26,230 --> 00:27:33,850 And at the top, you see a sort of a video of a larval superfish and you see from time to time that you get these kind of bursts of activity. 286 00:27:33,850 --> 00:27:37,780 It means that the brain is critical, as has been shown. 287 00:27:37,780 --> 00:27:46,450 And it means that reading, in order to understand that we need some of the tools that have been been basically been built in physics and mathematics. 288 00:27:46,450 --> 00:27:54,960 And one of the first tools is, B, to take Thomas Aquinas equip, sort of talking in Aristotelian manners. 289 00:27:54,960 --> 00:27:59,040 He said quite quit receiver to a modem recipient is receive better. 290 00:27:59,040 --> 00:28:01,600 The content is shaped by the container. 291 00:28:01,600 --> 00:28:08,920 So what we do is we take the wiring of the brain, the truck, top graphy, as is known, that we can pull out of the living brain. 292 00:28:08,920 --> 00:28:13,150 We pass on the brain to make the problem a little bit less. 293 00:28:13,150 --> 00:28:18,910 Rather than doing an order billions of neurones, we're doing it on large brain areas that seems to be doing the same thing. 294 00:28:18,910 --> 00:28:22,660 And then we try to build a model that can fit the data that we get, 295 00:28:22,660 --> 00:28:28,270 either from it from Orion or from G, from any thing really as long as its whole brain. 296 00:28:28,270 --> 00:28:34,600 Because, again, it's important that we're not just modelling a small part of the brain, but that we are modelling the whole dynamics of this. 297 00:28:34,600 --> 00:28:40,300 And just to give you a flavour and it can only be a flavour. But of course, for those who are interested, we should talk much more about this. 298 00:28:40,300 --> 00:28:45,430 But really, we need a term in our models that describes the intrinsic dynamics of the market. 299 00:28:45,430 --> 00:28:51,070 We need something that describes the KOPLIK. And as you can see here, the yellow is not directly connected to the green. 300 00:28:51,070 --> 00:28:58,290 And so really needs to go through the red. And of course, that can happen once you have a dynamic model. 301 00:28:58,290 --> 00:29:00,700 He has some equations that worry too much about them. 302 00:29:00,700 --> 00:29:07,390 But just to show that there are many different ways that one can describe these neural masses and they have various pros and cons, all of them. 303 00:29:07,390 --> 00:29:13,150 But the one that we like most is really this one. Hofbauer Vacation Modern, which is is due at Landauer model, 304 00:29:13,150 --> 00:29:20,820 which is a beautiful way of actually capturing a lot of the information that is being encoded in the human brain. 305 00:29:20,820 --> 00:29:28,320 And in order to do that, we can then perform interpretive studies of the brain, not just as here and beautiful stories of muscle mass in any. 306 00:29:28,320 --> 00:29:32,650 He is trying to show what happens when you give to your miss on the outside or when you're 307 00:29:32,650 --> 00:29:37,750 using deep brain stimulation is not sure you in a moment to stimulate deep in the brain. 308 00:29:37,750 --> 00:29:42,300 By doing this, we can actually find out what are the mechanisms that are causing those changes. 309 00:29:42,300 --> 00:29:46,600 So in other words, we can take the healthy brain. Look at the Healtheon Working Network. 310 00:29:46,600 --> 00:29:52,080 We can take the Zis brain and we can try to stimulate and see whether we can form what we call a 311 00:29:52,080 --> 00:29:58,840 static recovery of turning that disease brain or that different brain state into a healthy one. 312 00:29:58,840 --> 00:30:04,300 So in order to do that, we got quite excited. And this is work with anchors Stefano. 313 00:30:04,300 --> 00:30:10,480 Was a our postdoc. There was a Puchi student with me who was able to show that what happens when you 314 00:30:10,480 --> 00:30:17,320 fall asleep and basically one can train a network to find out what are the stakes. 315 00:30:17,320 --> 00:30:24,780 That happens when you fall asleep. And classically in in in medicine, it's been said that there's a awake. 316 00:30:24,780 --> 00:30:29,170 Then there's in one. Then there's in two. And then they're slowly asleep or deep sleep. 317 00:30:29,170 --> 00:30:34,720 But what you can see that this unguided algorithm was showing us is that there are many more states. 318 00:30:34,720 --> 00:30:39,610 And in fact, Angus was able to show that as you fall deeper and deeper into sleep, 319 00:30:39,610 --> 00:30:45,420 you have a change in each of these represent the brain network and you can see how much time is being spent when you're awake. 320 00:30:45,420 --> 00:30:49,880 So a lot of in state 10, which, of course, is a set of brain regions. 321 00:30:49,880 --> 00:30:56,590 And I'm not showing you here, but that you can find in the paper that basically is the interface transitioning and finally getting to in three. 322 00:30:56,590 --> 00:31:00,280 Now, thinking about this in a more reductive manner, 323 00:31:00,280 --> 00:31:07,630 could one find a way of stimulating the brain to the you can go from deep sleep all the way up here to force this transition that I showed 324 00:31:07,630 --> 00:31:14,570 you earlier by having a computer model of the deep sleep and of the awake and see whether you can force it one way or the other way. 325 00:31:14,570 --> 00:31:22,330 And so we were able to do this recently in a paper that came out in PNAS where we basically were able to model the data and find out 326 00:31:22,330 --> 00:31:29,640 by trial and error an exhaustive search where to stimulate in order to force the model from one way to another is just some data. 327 00:31:29,640 --> 00:31:33,640 There's no you with here's what the data looks like from the empirical data. 328 00:31:33,640 --> 00:31:40,350 This is what the model of. For when you're awake, this is what happens when you are deep sleep and this is what the model looks like. 329 00:31:40,350 --> 00:31:45,360 And this is basically the regions that are involved in it. Now, within stimulated each one of them. 330 00:31:45,360 --> 00:31:51,060 And as you can see, as you stimulate this model, you find that the money comes from sleep to awake. 331 00:31:51,060 --> 00:31:56,880 There are lots of places that you can stimulate in order to wake the brain up by synchronising if you noice it. 332 00:31:56,880 --> 00:32:00,790 However, there is no way that you can do it, on the other hand, is much, much harder, 333 00:32:00,790 --> 00:32:05,310 as you can see on the scale here, too weak to take the awake brain and fall asleep. 334 00:32:05,310 --> 00:32:10,680 And this, of course, fits really nicely with what we feel as well. This is like a toy example. 335 00:32:10,680 --> 00:32:15,930 This is not something that is clinically relevant yet, but it shows in principle that we can do these things. 336 00:32:15,930 --> 00:32:21,150 And I think that's very exciting because it means that we can now take these networks from people with 337 00:32:21,150 --> 00:32:28,550 disease and think about how it is that we could use these models to force them into a normal state. 338 00:32:28,550 --> 00:32:33,120 But rather than doing it just brute force, we could go back to this idea of the hierarchy. 339 00:32:33,120 --> 00:32:38,380 And we worked very, very hard, Gustavo and I, to basically. 340 00:32:38,380 --> 00:32:41,980 Work out how we could find out what the Herrity of the brain is, 341 00:32:41,980 --> 00:32:47,200 and we developed a method called Normalised Ulbrich Transfer Entropy that allows us to take any time series 342 00:32:47,200 --> 00:32:53,320 and basically find out whether it's forcing another timesharing or is being caused by another typesetters. 343 00:32:53,320 --> 00:32:58,630 And if you do that, you can basically establish from this matrix of all these different brain regions, 344 00:32:58,630 --> 00:33:04,180 you can establish the hierarchy and you can establish the regions in the brain that get a lot of information 345 00:33:04,180 --> 00:33:09,670 in and basically keep that information to themselves until they broadcast it to the rest of the brain. 346 00:33:09,670 --> 00:33:13,130 And you can do that not just when you're resting, but when you're doing many different tasks. 347 00:33:13,130 --> 00:33:18,730 And in this case, in a thousand people, we had seven tasks. So we look at what is common to all of them. 348 00:33:18,730 --> 00:33:21,940 And then although I will show you here, you can be did in the paper, 349 00:33:21,940 --> 00:33:27,880 we create a model where like a model, hi, Hydra, Multiheaded, Hydra, we cut out these regions. 350 00:33:27,880 --> 00:33:33,490 And when we do that, we are able to show that if we cut out the ones that we've identified that are now in the global workspace, 351 00:33:33,490 --> 00:33:40,960 the whole system breaks down. If we only take out a few ones of them, the system is still able to recover, which is good. 352 00:33:40,960 --> 00:33:47,380 But of course, it also means we can then think about how can we then make this system rebalanced? 353 00:33:47,380 --> 00:33:53,860 So when you do that, you find that again, these are the regions that are in the visual cortex, so much entry cortex. 354 00:33:53,860 --> 00:33:57,940 Those are the ones that just put a lot of information forward. 355 00:33:57,940 --> 00:34:01,390 So they're the lowest part of the hierarchy by the integrated part. 356 00:34:01,390 --> 00:34:03,490 And this is the brain sort of seen from above. 357 00:34:03,490 --> 00:34:11,830 Just like one would see a map are basically the regions that are on the midline, the ones that are to do with emotions and with attention and so on. 358 00:34:11,830 --> 00:34:17,920 And I'm just showing you this other slide just to see that if one changes the population, you get very similar results. 359 00:34:17,920 --> 00:34:27,010 But the money shot, if you like, is this what you find when you look at all these seven task and the resting is that you get this network of regions, 360 00:34:27,010 --> 00:34:31,990 these regions that are really to do with perceptual systems. They're really to do with long term memories. 361 00:34:31,990 --> 00:34:35,170 Paya's like the hippocampus. They're to do with evaluative. 362 00:34:35,170 --> 00:34:42,880 The amygdala is in there, the the the nucleus accumbens, the right eagerness is singular and so on. 363 00:34:42,880 --> 00:34:47,920 So they are regions here that really in many ways substantiate the idea. 364 00:34:47,920 --> 00:34:54,990 And the theory that that Shannon Dramane shampoo your shoe and stand Hinden and Bernard past came up with. 365 00:34:54,990 --> 00:34:57,820 So for the first time ever, I think we've identified what are the regions. 366 00:34:57,820 --> 00:35:05,290 And of course, if one then wants to change the state from one state to another rather than trying to stimulate everywhere, 367 00:35:05,290 --> 00:35:11,650 like in the visual cortex, when we know that the visual cortex is not really something that necessarily changes anything. 368 00:35:11,650 --> 00:35:15,680 We could then start with the top of the hierarchy, the parts of the brain, 369 00:35:15,680 --> 00:35:22,600 the the hierarchy of elders that basically are making the decisions and making the transitions. 370 00:35:22,600 --> 00:35:28,000 So I think that's potentially very interesting. Of course, it fits very nicely with the kind of work that I've done with sequences where we've 371 00:35:28,000 --> 00:35:32,440 tried to work out what happens when you are giving people deep brain stimulation. 372 00:35:32,440 --> 00:35:35,470 We do this on a routine basis based on people's great discovery. 373 00:35:35,470 --> 00:35:40,750 They knew that if you stimulate a place like this ophthalmic nucleus in people with Parkinson's, 374 00:35:40,750 --> 00:35:47,230 you can basically Cuil the tremor or as TIPO has always shown and here you see him with 375 00:35:47,230 --> 00:35:54,530 somebody you might know is that if you stimulate forth somewhere where you have chronic. 376 00:35:54,530 --> 00:35:59,300 Debilitating pain, namely in a phantom limb. So in a hand that is no longer there. 377 00:35:59,300 --> 00:36:05,840 If one stimulates the pirika Dr. Grey at 20 hertz, you can make that pain go away completely. 378 00:36:05,840 --> 00:36:09,080 Now, interestingly, and we do this on table in these patients, 379 00:36:09,080 --> 00:36:17,230 when you do this and you then stimulate them and you listen to what they tell you, if you stimulate a knighthood in exactly the same place, 380 00:36:17,230 --> 00:36:25,760 the pain, the pain becomes even worse than before, suggesting, as we've already guessed, that pain and pleasure really two sides of the same coin. 381 00:36:25,760 --> 00:36:29,000 And if you then put them in a scanner and you turn the stimulator on and off, 382 00:36:29,000 --> 00:36:33,320 what you find is that the orbital frontal cortex seems to be very much active. 383 00:36:33,320 --> 00:36:38,330 When you get the pain relief or the pleasure of moving from one state to another. 384 00:36:38,330 --> 00:36:44,870 And so, again, with anyone I talk to about this, she couldn't help herself or say we got a built, we got to build some sculptures of theirs. 385 00:36:44,870 --> 00:36:51,350 And we made this wonderful sculpture where we were able to basically track where this point we're stimulating, 386 00:36:51,350 --> 00:36:56,990 where this bifurcating point that either give you pain pleasure, where that projects in the rest of the brain. 387 00:36:56,990 --> 00:37:03,530 And it's a I think, a beautiful kind of thing, a sort of a pain and pleasure made flesh. 388 00:37:03,530 --> 00:37:13,250 But of course, the brain is a large place. And I just wanted to give you a sense of what can be accomplished if one is actually stimulating. 389 00:37:13,250 --> 00:37:20,850 And this is for for basically to try to to do simulant stimulation. 390 00:37:20,850 --> 00:37:24,820 And this is a beautiful study where they stimulate in different parts of the single. 391 00:37:24,820 --> 00:37:30,980 And remember, the cingulate is just part over your of the sort of where you can see here in the midbrain if you stop your 392 00:37:30,980 --> 00:37:36,740 fingers or the way sort of four or five centimetres in and you can see that there is a certain place in our. 393 00:37:36,740 --> 00:37:45,760 Now just play your video where they get some very interesting results. It's like you do it. 394 00:37:45,760 --> 00:37:54,090 It's. Yes. 395 00:37:54,090 --> 00:38:03,820 I. Think you can do that. I think they're like I crazy. 396 00:38:03,820 --> 00:38:17,170 He plays into it every single time you see Michael. 397 00:38:17,170 --> 00:38:31,230 So they should be. This is awesome. 398 00:38:31,230 --> 00:38:38,380 To me, that is a silly assignment. 399 00:38:38,380 --> 00:38:46,660 I get against them. Yes. 400 00:38:46,660 --> 00:39:01,820 I think it's an evil. And so I encourage you to go and actually have a look at this, 401 00:39:01,820 --> 00:39:08,600 so it appears that they've found a place in the brain where in voluntarily the patient can't help but feel good. 402 00:39:08,600 --> 00:39:11,690 So I think one of the things we we've taken discussion is just think about whether 403 00:39:11,690 --> 00:39:17,420 this really are such pleasure electrodes where you are perturbing the system. 404 00:39:17,420 --> 00:39:18,020 In this case, 405 00:39:18,020 --> 00:39:25,700 it causes an end to your Lytic patient and you're forcing that patient to basically go to a place just like with the with the with the hand, 406 00:39:25,700 --> 00:39:29,120 with the chronic pain. You're going into a good place, as it were. 407 00:39:29,120 --> 00:39:37,310 But again, let's talk about what that actually might mean, both in terms of the neuroscience, but also very much in terms of the of the ethics. 408 00:39:37,310 --> 00:39:42,020 So is it true then that this target state that we going for? 409 00:39:42,020 --> 00:39:46,610 And remember, there was a target statement. I showed you how you live from deep sleep to awake. 410 00:39:46,610 --> 00:39:50,990 Could there be a target state where you read one really is you demonic? 411 00:39:50,990 --> 00:39:58,380 Everything is. It's good. So one could study that with many different ways, one could study with meditation. 412 00:39:58,380 --> 00:40:04,840 And can we sort of got some beautiful meditation data and we could listen to more looking at Janna's. 413 00:40:04,840 --> 00:40:09,910 This is work with Jamila and Daria and also the psychedelics in work with Robin. 414 00:40:09,910 --> 00:40:13,810 Can't have Herries and various other people. Miss Johnson and music, of course. 415 00:40:13,810 --> 00:40:23,650 These are states where reliably we can't seem to induce something that people describe, at least afterwards, as a very meaningful state. 416 00:40:23,650 --> 00:40:29,010 And so let me just give you the example of psychedelics is the work of Sulin SSR. 417 00:40:29,010 --> 00:40:33,730 Who is this wonderful researcher that I work with many years, who's come up with an alphabet, 418 00:40:33,730 --> 00:40:39,130 if you like, of the sort of the harmonics, the harmonic functions of the brain? 419 00:40:39,130 --> 00:40:43,450 This is a beautiful paper from 2016 that basically gives you an alphabet of which you can 420 00:40:43,450 --> 00:40:48,790 then spill all the different states that one hand we call these functional harmonics. 421 00:40:48,790 --> 00:40:53,860 And basically, it's a framework that allows us to take these basic come on X and then find out how 422 00:40:53,860 --> 00:40:59,380 it is that in different states they spell out different kinds of frequency spectrums. 423 00:40:59,380 --> 00:41:08,920 And if we then take robins in this data with music, without music, we can see that there is a change in the energy spectrum. 424 00:41:08,920 --> 00:41:14,290 When we look at enlistee compared to placebo and you can see there's a very significant difference. 425 00:41:14,290 --> 00:41:18,310 There's also a difference in the criticality in the power law exponents. 426 00:41:18,310 --> 00:41:23,200 But there's also a difference in the way that the repertoire is being expressed. 427 00:41:23,200 --> 00:41:27,940 It looks like that enlistee basically increases the repertoire. 428 00:41:27,940 --> 00:41:35,340 It looks like it's in the brain suddenly is able to explore much more of that landscape that it will go through normally. 429 00:41:35,340 --> 00:41:40,120 And it's true not just for enlistee, but also for psychedelics like psilocybin. 430 00:41:40,120 --> 00:41:45,760 Very similar results, as you can see, for psychedelics or psilocybin, rather. 431 00:41:45,760 --> 00:41:51,340 So that's kind of exciting. But of course, what is really exciting and this is work by one of my student, Chappell Vasek, 432 00:41:51,340 --> 00:41:56,980 begin working on Robyn Stacey, where Robyn gave psychedelics to depressed patients. 433 00:41:56,980 --> 00:42:01,720 And he was able to show that in some of these responders, they got much better. 434 00:42:01,720 --> 00:42:06,220 And it is very, very early on. And then after a while, it faded. 435 00:42:06,220 --> 00:42:11,500 But if you read The Lancet psychiatry, the effects are very strong. Now, he also had new imaging of it. 436 00:42:11,500 --> 00:42:18,730 And what we are doing here is we are trying to see what happens when you have and you use these computational models that we have. 437 00:42:18,730 --> 00:42:25,180 And you can start to think about how it is that we could look at the nonresponders and find out what it is about the brain response that 438 00:42:25,180 --> 00:42:32,650 is different from the responders and see whether we could force at least the computer model to actually give us that kind of response. 439 00:42:32,650 --> 00:42:40,360 So this is an ongoing and really exciting area of research. And I think it has huge potential for actually being able to really help with things like 440 00:42:40,360 --> 00:42:45,970 depression and really understand the mechanisms by which the serotonergic drugs are working. 441 00:42:45,970 --> 00:42:50,680 So I think this is potentially exciting and I think something we can talk about as well. 442 00:42:50,680 --> 00:42:58,210 And I think really that's about as much as I wanted to say. I wanted to say also that it's not just about the brain. 443 00:42:58,210 --> 00:43:03,470 It's about empathy is about other people. For those of you who don't know about it, you should go to my friend Roman Kazunori, 444 00:43:03,470 --> 00:43:10,600 who is standing here with a pair of shoes and try to see how one can, in fact, doesn't really need just to have electrodes put in your brain. 445 00:43:10,600 --> 00:43:15,580 But even listening to a story and walking a mile in somebody's shoes can really change you. 446 00:43:15,580 --> 00:43:17,560 I think that's potentially something else. 447 00:43:17,560 --> 00:43:25,480 And I think it also points to something that I'm very keen on, namely to say that this is really a multidisciplinary endeavour. 448 00:43:25,480 --> 00:43:32,290 What our show is today is just some of the neuroscience, some of the selected bunch of neuroscience that I've done over the last 20 years. 449 00:43:32,290 --> 00:43:40,480 But it's by no means the full story. Really, what is going on here is that one needs to look at this from many different angles. 450 00:43:40,480 --> 00:43:41,500 And with this in mind, 451 00:43:41,500 --> 00:43:51,160 one of the things that will be happening over the next year or so is that we are setting up a new centre centre for eudaimonia and human flourishing, 452 00:43:51,160 --> 00:43:54,400 which we'll be working closely with you here, Hero Centre, 453 00:43:54,400 --> 00:44:02,830 to try to see whether we can't bring these multitudes of perspectives together and try to see whether we can finally catch that butterfly, 454 00:44:02,830 --> 00:44:08,320 or at least part of that beautiful, rare butterfly, which is well-being or flourishing. 455 00:44:08,320 --> 00:44:16,720 And see whether we can't characterise it both in terms of what happens in the brain, how it feels and how we might be able to rebalance it. 456 00:44:16,720 --> 00:44:23,440 So thank you so much. I'll leave you with this image of my my wonderful lab and some of my collaborators. 457 00:44:23,440 --> 00:44:29,680 I should point out Peter Boost and Einstein, Kent Berridge and Peter Whybrow at the top, 458 00:44:29,680 --> 00:44:33,610 but of course, also Gustavo Teiko, who's standing in there in the middle. 459 00:44:33,610 --> 00:44:36,788 Thank you so much.