1 00:00:00,600 --> 00:00:04,050 So the next speaker is Clouse, he's actually my boss and I have a microphone, 2 00:00:04,050 --> 00:00:10,800 has it feels exactly oddly powerful position to be in, but I promise not to abuse it and change originally in Bonn. 3 00:00:10,800 --> 00:00:16,020 And then his Takakura training was in Aberdeen and his first academic post was in Edinburgh. 4 00:00:16,020 --> 00:00:23,830 But he's been with us in Oxford for about nine years now. And I think he's going to talk about the research that we're involved in the Y2K. 5 00:00:23,830 --> 00:00:28,410 Thank you. OK, now for something completely different. 6 00:00:28,410 --> 00:00:35,250 Well, not completely different because it's about people getting older. 7 00:00:35,250 --> 00:00:40,560 One of the things I started when I arrived here was linking up with Whitehall two study. 8 00:00:40,560 --> 00:00:54,670 And this is not really a study of civil servants as such, but see them as a group of people initially about, oh, there's the food bank is missing. 9 00:00:54,670 --> 00:01:08,070 And yes, I used to get so initially they were 100 people who worked in any of the offices in London associated with government. 10 00:01:08,070 --> 00:01:17,130 And the reason for this study was that people wanted to find out what the risk factors for heart attacks were. 11 00:01:17,130 --> 00:01:25,170 This was an 85 or so. And, you know, both of you with a medical background, you'd be familiar with the Framingham study, you know, 12 00:01:25,170 --> 00:01:29,850 where our whole community was followed up and people looked at various potential risk factors to 13 00:01:29,850 --> 00:01:37,470 see what predicts mortality and particular heart attacks and similar cardiovascular incidents. 14 00:01:37,470 --> 00:01:44,070 So in a way, this was trying to replicate this. And that always been this assumption that if you were in middle management, 15 00:01:44,070 --> 00:01:48,810 that's the worst place you can be because you're kicked from above, but also from below. 16 00:01:48,810 --> 00:01:55,680 So work stress is at its absolute maximum and Whitehall did really for the first time. 17 00:01:55,680 --> 00:02:01,530 And that's why it's so important to make sure that the lower down the pecking order you are, 18 00:02:01,530 --> 00:02:08,130 the higher is the the various risk factors and the higher the likelihood that you are going to 19 00:02:08,130 --> 00:02:14,300 suffer from cardiovascular disease and risk factors to identify where those we all know about them. 20 00:02:14,300 --> 00:02:22,800 Now, you hear about them in the news all the time. But this was identified in Whitehall that was continued into Whitehall, too. 21 00:02:22,800 --> 00:02:30,750 And the study was extended not only, you know, beyond heart disease to cancers, chronic lung disease, 22 00:02:30,750 --> 00:02:38,880 other types of diseases, but also depression, suicide, sickness, absence, back pain and general feelings of ill health. 23 00:02:38,880 --> 00:02:49,770 And to the general purpose of the study is to take a very detailed inventory of people's habits about the general state of health, 24 00:02:49,770 --> 00:02:55,350 about their work situations. You know, are there where are there in the hierarchy? 25 00:02:55,350 --> 00:03:04,500 What are their their social networks? What quality does their workplace have in terms of potential stress causation? 26 00:03:04,500 --> 00:03:07,500 In fact, initially it was called the Stress and Health Study. 27 00:03:07,500 --> 00:03:16,050 So it's all about identifying what's generally described as stress and the effect on on ill health. 28 00:03:16,050 --> 00:03:22,260 And from an epidemiologist epidemiologists point of view, really, the idea was to to build models, 29 00:03:22,260 --> 00:03:31,800 mathematical models predicting health outcome by all sorts of observations that are taken throughout the lifetime. 30 00:03:31,800 --> 00:03:45,930 Now, the way the study was organised was that it started in 85 with 10000 odd participants, and they were then followed up every few years. 31 00:03:45,930 --> 00:03:52,380 We're now at phase 12. But, you know, this is just the first 11 phase of it. 32 00:03:52,380 --> 00:04:01,530 And you can see that the average age obviously, you know, goes up and in between each of the large inventories, 33 00:04:01,530 --> 00:04:06,990 as it were, where the person they examined are about five years. 34 00:04:06,990 --> 00:04:17,550 And in order to to cover all important factors, they would, you know, collect the standard descriptive values, then general habits, 35 00:04:17,550 --> 00:04:30,180 you know, like alcohol, sleep, diet, smoking, et cetera, some health markers, anything from you know, from from body size, weight, 36 00:04:30,180 --> 00:04:40,800 et cetera, to some biochemical markers, inflammatory markers, blood pressure, cetera, then the social environment, 37 00:04:40,800 --> 00:04:47,490 the amount of support at work environment, um, 38 00:04:47,490 --> 00:04:53,970 details about employment in particular when they retire and what the activity level is after retirement. 39 00:04:53,970 --> 00:05:00,060 Lots of health outcomes, which partially would have been linked to other databases, like, for instance, if someone. 40 00:05:00,060 --> 00:05:13,890 By way, trying to track down the death certificate and find the cause of death and then a detailed physical workup with a number of examinations, 41 00:05:13,890 --> 00:05:23,400 cognitive tests, memory tests, etc., physical function tests that people have to walk, have to breathe and to diagnostic apparatus. 42 00:05:23,400 --> 00:05:35,610 And so a more detailed psychiatric assessment if they are entered into the oxygen component of the study. 43 00:05:35,610 --> 00:05:46,980 So what we did was we got people's agreement to be contacted and said the way we did this was just select them randomly from this group. 44 00:05:46,980 --> 00:05:55,740 Obviously, if you pick people who volunteer, you get a certain type of people, maybe they're likely to be better educated, healthier, et cetera. 45 00:05:55,740 --> 00:06:03,870 So we selected them randomly and then they were contacted at phase 11 and asked, would you be prepared to go to Oxford and take part in this? 46 00:06:03,870 --> 00:06:09,970 So we have a list to work for. We for them up. We screen them. If people have metal bits that conferences have an MRI scanner. 47 00:06:09,970 --> 00:06:16,350 So we have to exclude a certain number of people. And then we arrange a visit to Oxford. 48 00:06:16,350 --> 00:06:21,720 They come, you know, having filled in detailed questionnaires and we have administrative stuff to do, 49 00:06:21,720 --> 00:06:27,960 like consent to investigations and so on, that have a detailed psychological test, 50 00:06:27,960 --> 00:06:35,010 actually looking at the various components of mental functioning memory, concentration, executive function, 51 00:06:35,010 --> 00:06:41,980 et cetera, that have a systematic psychiatric interview at that age to on average, about 70. 52 00:06:41,980 --> 00:06:46,650 Now we're particularly interested in cognitive and dementia, but also in depression, 53 00:06:46,650 --> 00:06:51,390 because that's even more common than the cognitive impairment in that age group. 54 00:06:51,390 --> 00:06:59,250 And then to have a detailed MRI scan, which may give you details of what we actually looking for in a minute, 55 00:06:59,250 --> 00:07:07,830 plus various tests to have another walking test. We test we take blood and saliva for people who agree to this test, all sorts of other things, 56 00:07:07,830 --> 00:07:17,440 including, you know, the bacterial makeup of the jested trap, et cetera. 57 00:07:17,440 --> 00:07:26,740 And then when they finish, they get a little stressful to a lot of experience, which hopefully wasn't too unpleasant. 58 00:07:26,740 --> 00:07:35,890 Um, right. So what we looked at, for example, with the MRI is we looked at the structure of the brain. 59 00:07:35,890 --> 00:07:41,170 That's a typical MRI scan, looking from the front, from the side and from the top. 60 00:07:41,170 --> 00:07:50,440 And so because we are going to process about 800 of these imaging modality, you can't do these things by hand. 61 00:07:50,440 --> 00:07:58,960 So it's done automatically. In Oxford, we have Premarin, which is the centre for ephemerally, 62 00:07:58,960 --> 00:08:04,630 which is mainly used by experimental psychology and psychology to some extent of neurology. 63 00:08:04,630 --> 00:08:08,830 And they have expertise in analysis software that deals with this. 64 00:08:08,830 --> 00:08:17,950 So what we can do, for example, is we can extract the brain, discard all the other tissues like bone and skin and so on and so forth. 65 00:08:17,950 --> 00:08:24,880 And then within the brain, we can break it up into grey matter and white matter and cerebrospinal fluid, 66 00:08:24,880 --> 00:08:28,420 and then we can go further down and identify certain structures, 67 00:08:28,420 --> 00:08:36,670 for example, basal ganglia and all that can be done more or less automatic, systematically, quickly and in an objective fashion. 68 00:08:36,670 --> 00:08:41,380 And then we find one important structure in the brain is the hippocampus, 69 00:08:41,380 --> 00:08:48,220 which is the structure which is at the inner side of the temporal lobes, and that's mainly associated with memory. 70 00:08:48,220 --> 00:08:53,590 And that's, for example, the structure that this tends to shrink in Alzheimer's disease. 71 00:08:53,590 --> 00:09:02,980 And if you look at the first world country, that is probably 500 or so and you can see that there are normally distributed. 72 00:09:02,980 --> 00:09:08,860 So there's a range within people and then at the left and towards smaller statistical competence, a little bit of a tail. 73 00:09:08,860 --> 00:09:13,180 So as a subgroup of people who already have shrinkage in the hippocampus, 74 00:09:13,180 --> 00:09:22,460 they are selected in the sense that they manage to get to Oxford, managed to find a within the realm of that geriatric hospital. 75 00:09:22,460 --> 00:09:29,600 So most of the people who arrive with us are not demented, but many of them already have memory impairment, 76 00:09:29,600 --> 00:09:36,700 even the ones who've driven to Oxford and quite a few of them have a reduced size of hippocampus, 77 00:09:36,700 --> 00:09:47,200 which, you know, we obviously would be interested in and try to relate to their positive performance as the same on the other side. 78 00:09:47,200 --> 00:09:55,450 Then we do some diagnostic scans. Typically, as you get older, you develop sort of changes in the structure of white matter. 79 00:09:55,450 --> 00:10:03,100 You can see it here. There's quite a bit sometimes called white matter, hyper intensities. 80 00:10:03,100 --> 00:10:10,210 And so they are supposedly related to vascular changes, small vessels getting blocked, 81 00:10:10,210 --> 00:10:14,080 you know, micro strokes if you want to, so you don't have to have a stroke. 82 00:10:14,080 --> 00:10:18,190 But most people who are in this age group already have these changes. 83 00:10:18,190 --> 00:10:25,480 And the degree to which they have these changes may be related to cognitive function, but they're also linked to, for instance, to depression. 84 00:10:25,480 --> 00:10:34,210 And more commonly, people become depressed. So we do a number of scans like that because the difference is that people start scanning. 85 00:10:34,210 --> 00:10:38,680 You can see something called the microbeads micro stroke. 86 00:10:38,680 --> 00:10:49,540 If you want to move a blood collection which can be picked up from this, then we are looking at so-called diffusion tensor imaging. 87 00:10:49,540 --> 00:10:52,160 That's really quite clever application. 88 00:10:52,160 --> 00:11:01,390 That's clever or glamorise clever in the way that it's clever because it gives a lot of information and you know that process again, 89 00:11:01,390 --> 00:11:09,250 to get into normal space. And then we're able to extract a number of bits of information with top row here. 90 00:11:09,250 --> 00:11:13,990 What you can see the fibres that connect different parts of the brain with each other. 91 00:11:13,990 --> 00:11:19,690 So these are actually the weight of the fibres and the stronger the signal. 92 00:11:19,690 --> 00:11:23,930 This is the teacher that is called fractional isotropic. 93 00:11:23,930 --> 00:11:29,540 So that is the measure that tells you to what extent water diffusion in the brain. 94 00:11:29,540 --> 00:11:39,250 This is confined to one direction. If you think about about a white matter that consists of the cabling of axons if you want to, 95 00:11:39,250 --> 00:11:43,000 and they're well insulated from each other like any electrical cabling. 96 00:11:43,000 --> 00:11:46,990 And if you if insulation breaks down, it doesn't function properly. 97 00:11:46,990 --> 00:11:55,750 But that also means that the water, if it moves in the system, moves along the direction of the axons, not across it. 98 00:11:55,750 --> 00:12:01,840 And the water's constricted to this one direction, the better the quality of the insulation if you want to. 99 00:12:01,840 --> 00:12:10,600 So the FAA gives you a measure of how well the white matter is organised structurally to perform its function. 100 00:12:10,600 --> 00:12:16,090 And then you can look, this is another example of a modality. This just gives you the total amount of. 101 00:12:16,090 --> 00:12:27,130 If you're 17, you know, it's great in the ventricles, so the water can move in all sorts of directions and less so in areas where you have repetition. 102 00:12:27,130 --> 00:12:33,280 And so similarly to what they become politicised. I mentioned that the changes are quite common. 103 00:12:33,280 --> 00:12:42,960 If you look at this measure of white man's integrity to go want to you can see it's, again, the moment to curve and there is a tail on the left side. 104 00:12:42,960 --> 00:12:51,370 There's always a subgroup of people who already have abnormalities in this particular measurement. 105 00:12:51,370 --> 00:12:58,570 And last, at least 12 people in this country for ten minutes not doing anything in particular to keep their eyes open, 106 00:12:58,570 --> 00:13:02,500 but they do what you do if you're lying somewhere, not having anything particular to do. 107 00:13:02,500 --> 00:13:05,860 So they think about things that remember things. 108 00:13:05,860 --> 00:13:14,530 And intriguingly, what you find is that if you compare the correlation of different bits of the brain, 109 00:13:14,530 --> 00:13:18,430 what we actually measure is blood flow to be more accurate, 110 00:13:18,430 --> 00:13:23,980 the amount of oxygenation of blood that gives you an idea of which bits of the brain are active. 111 00:13:23,980 --> 00:13:30,280 And if you compare different different parts of the brain in terms of how they fluctuate over those 10 minutes, 112 00:13:30,280 --> 00:13:33,340 some of them will be correlated and others won't. 113 00:13:33,340 --> 00:13:42,100 If they're correlated, you can assume that there's someone communicating with each other, OK, if they're not correlated, they don't. 114 00:13:42,100 --> 00:13:48,130 And if you look at the whole dataset, you know, the whole brain box, money box, live picture unit, that picture unit, 115 00:13:48,130 --> 00:13:53,410 and you follow that up over ten minutes and the scans are quiet all the time, 116 00:13:53,410 --> 00:13:58,480 you can then identify the networks that are connected with each other and those that are separate. 117 00:13:58,480 --> 00:14:11,520 So, for example, here. You have a network that mainly contains this reading, the central bit of the occipital cortex. 118 00:14:11,520 --> 00:14:24,350 Can you read the writing up here? If you can't read it, what do you think it tells? 119 00:14:24,350 --> 00:14:32,380 Much, yeah, right, right out the back, that's where your primary visual cortex sits, 120 00:14:32,380 --> 00:14:38,250 and although they don't watch any film or do anything more active, that's part of the brain already. 121 00:14:38,250 --> 00:14:44,410 It changes in parallel. And that may be I mean, they usually just look at someone at the scene or at the surface. 122 00:14:44,410 --> 00:14:50,710 That doesn't change. But clearly in the imagination what's happened in the same area. 123 00:14:50,710 --> 00:14:56,650 And for some reason, these bits of the brain already are connected because there's nothing special to look at. 124 00:14:56,650 --> 00:15:05,320 That would be the actual visual cortex that is there to process visual information and to be able to look at directions of movements, 125 00:15:05,320 --> 00:15:17,660 look at shapes, etc. and colours. Um, this would be the system associated with hearing, but with movement. 126 00:15:17,660 --> 00:15:24,830 When you see pictures of this this homunculus, you know, this projection of the human body upside down, 127 00:15:24,830 --> 00:15:32,200 on top, at the bottom, in the middle of the brain, that's reflected in those two areas here. 128 00:15:32,200 --> 00:15:38,650 So this is the sensory motor cortex, which already although the lying there still with nothing is touch. 129 00:15:38,650 --> 00:15:45,460 And then, you know, in spite of this absence of activation, these bits of the brain already correlate activity. 130 00:15:45,460 --> 00:15:53,650 And similarly, you have areas which are of particular interest to us, which are which tend to be activated when nothing else is happening. 131 00:15:53,650 --> 00:16:00,250 And they tend to move to the so-called default mode networks that will act as if you're depressed. 132 00:16:00,250 --> 00:16:06,940 So this is rumination. This may have something to do with activating those, but they are the areas that are more active. 133 00:16:06,940 --> 00:16:16,030 If something is not engaged in the task as opposed to doing a particular task and focussing on doing that task. 134 00:16:16,030 --> 00:16:20,740 And then there are others which are more frontally which have something to do with executive function and planning, 135 00:16:20,740 --> 00:16:29,200 etc. So you can break down the various networks and the brain can see which networks are more active and less active, 136 00:16:29,200 --> 00:16:35,800 depending on, you know, risk factors or mental performance. 137 00:16:35,800 --> 00:16:40,420 OK, so let me just focus on a few questions that one could ask. 138 00:16:40,420 --> 00:16:50,720 Looking at this data set which are relevant. The first three, we have the information which goes back to 95, 139 00:16:50,720 --> 00:16:58,720 which had the risk factors predict brain changes, you know, up to 30 years after the start of the study. 140 00:16:58,720 --> 00:17:02,470 How far back can we go to predict changes? 141 00:17:02,470 --> 00:17:11,560 Does it matter if, for instance, you know, we have a certain type of lifestyle or certain risk factors when we were in our 40s? 142 00:17:11,560 --> 00:17:19,780 That's a matter of what these risk factors were in relation to how our brain looks when we had women 70 years old. 143 00:17:19,780 --> 00:17:25,020 And how quickly do these changes manifest? 144 00:17:25,020 --> 00:17:33,720 Now, this shows you the kind of data we're looking at, so this site and imaging data, which is done in Oxford at the end of the study, 145 00:17:33,720 --> 00:17:43,080 if you want to, and then we've got data, which I showed you before, five years, 10 years, 15 years, 20 years back from the scan. 146 00:17:43,080 --> 00:17:49,710 So essentially, we can look at those data and see whether we can make sense in terms of, 147 00:17:49,710 --> 00:17:56,490 you know, which ones are those important in how the brain looks when people are in the 70s. 148 00:17:56,490 --> 00:18:03,460 And I want to focus on one particular type of risk. It's called the Framingham Stroke Index. 149 00:18:03,460 --> 00:18:12,840 Um, you may have heard of Framingham. This is the the town I think it's in, uh, Massachusetts. 150 00:18:12,840 --> 00:18:14,970 It's in New England anyway. 151 00:18:14,970 --> 00:18:22,620 And there's the whole whole town took part in the study and based on follow up and who developed heart attacks and strokes, 152 00:18:22,620 --> 00:18:28,450 they managed to put together as risk factors and predict whether you're going to develop a stroke. 153 00:18:28,450 --> 00:18:32,280 Now, this is something your GP has in front of him. 154 00:18:32,280 --> 00:18:37,800 You know, when they tried to calculate your risk and see whether you should be on a statin, for example. 155 00:18:37,800 --> 00:18:40,200 And that takes into account all sorts of factors. 156 00:18:40,200 --> 00:18:48,990 Age, sex, men are at greater risk because we would expect compared to premenopausal women and then other things like blood pressure, 157 00:18:48,990 --> 00:18:57,150 like being overweight, like blood lipids, you know, all those risk factors that we know about. 158 00:18:57,150 --> 00:18:58,830 So I thought, 159 00:18:58,830 --> 00:19:12,360 but let's look at this this risk index and see how long the effect lasts and how important it is for what the brain looks like at the moment. 160 00:19:12,360 --> 00:19:20,610 Now, if you think about in theory, what should happen is if you look at the risk that is closest to the scan, 161 00:19:20,610 --> 00:19:28,530 if there's a correlation, that's likely to be the biggest. But then if you go back in time five, 10, 15, 20 years, of course, 162 00:19:28,530 --> 00:19:35,010 things will have happened between between those measurements if you measure the risk at phase three. 163 00:19:35,010 --> 00:19:38,760 Twenty years ago, lots of events happened in person. 164 00:19:38,760 --> 00:19:44,440 I must have put on some weight or increased blood pressure or, you know, the risk profile would have changed. 165 00:19:44,440 --> 00:19:51,750 So if you look at the correlation, it should be the highest closest to the scanner and that should probably go down in a cold, dark time. 166 00:19:51,750 --> 00:20:02,680 Does that make sense? And then if you want to, this, of course, could mean that changes happen in all sorts of people and, you know, 167 00:20:02,680 --> 00:20:11,030 that correlation may be as high and lower than that because a subset of people have changed their body. 168 00:20:11,030 --> 00:20:14,350 But that reduction could be due to other people changing. 169 00:20:14,350 --> 00:20:20,230 So you're not quite sure whether this is actually a change within people or just between different subgroups. 170 00:20:20,230 --> 00:20:21,910 So what you can do is you can look, 171 00:20:21,910 --> 00:20:30,470 this is the uncorrectable correlation at Face 11 and you can check of how much predictive value remains once you correct, 172 00:20:30,470 --> 00:20:40,090 for example, for the first before you do a partial correlation or regression because face the risk of the first nine and 11 are very similar. 173 00:20:40,090 --> 00:20:49,160 Once you've accounted for everything that's predictive at phase nine, there's very little left about the intervening time can add to the risk. 174 00:20:49,160 --> 00:20:57,290 So that's correct. The correlation is quite low. And then the further back in time you get, the more the similar the correlations become. 175 00:20:57,290 --> 00:21:00,610 And then you know about phase three, 176 00:21:00,610 --> 00:21:08,320 you would have had the contribution of quite a substantial contribution that is made between phase three and phase 11. 177 00:21:08,320 --> 00:21:12,110 And that will be phase one percent. Make sense of it. 178 00:21:12,110 --> 00:21:23,140 But if you think about those correlations being similar to each other, if you do a partial correlation, you remove whatever variability is there. 179 00:21:23,140 --> 00:21:29,260 You basically are left with very little and then it goes the opposite way the further back you go. 180 00:21:29,260 --> 00:21:33,460 And then if you look at the correlation over time, sometimes you may find something like this. 181 00:21:33,460 --> 00:21:39,910 So the further back you go, the less it's, you know, the risk is correlated with how the brain looks. 182 00:21:39,910 --> 00:21:46,420 But then there's a sort of falls off when it goes down a little bit more closely to the to the actual scan. 183 00:21:46,420 --> 00:21:52,950 Any idea what that. 184 00:21:52,950 --> 00:22:03,210 Exactly, yeah, so these are risk factors and the risk number is essentially the percentage risk of having a stroke in the next 10 years, 185 00:22:03,210 --> 00:22:08,070 you can actually, you know, calculated down to that to that specific meaning. 186 00:22:08,070 --> 00:22:13,850 And of course, they don't they don't have to have a stroke. They have other changes in the brain which may be more sensitive, 187 00:22:13,850 --> 00:22:19,590 be picked up by the scan, but it takes a while for the risk actually to manifest itself. 188 00:22:19,590 --> 00:22:29,430 So, you know, you you would expect that the closer you get there may have been you know, the risk is not yet represented in the pattern you perceive. 189 00:22:29,430 --> 00:22:36,900 OK, that's the theory. That's the practise. So if you look at those those pictures up here, 190 00:22:36,900 --> 00:22:48,210 they're essentially show your areas in the brain where the grey matter is correlated with the risk at phase three five seven nine 11. 191 00:22:48,210 --> 00:22:51,900 I'm just looking at them in particular in the small form, look very similar. 192 00:22:51,900 --> 00:22:59,520 But they are in fact, if you take the average correlation across the whole body and it's sort of more or less at the same level, 193 00:22:59,520 --> 00:23:05,670 if anything, those two are a little bit lower than those two sets of sort of trend for it to get higher. 194 00:23:05,670 --> 00:23:11,580 But by and large, the correlation stays within the same range, a significant correlation. 195 00:23:11,580 --> 00:23:22,440 So the higher the risk of developing the stroke of any of those stages, the greater the atrophy and brain matter at age seven, 196 00:23:22,440 --> 00:23:28,800 been at the time of the Oxford scan, or if you put this in more drastic terms, 197 00:23:28,800 --> 00:23:38,010 the risk you have for the Framingham risk you have when you're 50 years old already 198 00:23:38,010 --> 00:23:43,440 significantly predicts the degree of brain atrophy you have when you're 70, 199 00:23:43,440 --> 00:23:48,300 OK, and that stays the same. But of course, this is across the whole cohort. 200 00:23:48,300 --> 00:23:53,910 You don't know who's contributing to this. So we do this exercise with partial correlation anyway. 201 00:23:53,910 --> 00:24:02,280 And we actually find that if you correlate if you correct the phase eleven risk again for phase nine, it goes down. 202 00:24:02,280 --> 00:24:06,000 And if you correct the full phase seven, it's higher again. 203 00:24:06,000 --> 00:24:13,440 So you get this this negative slope, I suggest it will be part of the time dilution effect. 204 00:24:13,440 --> 00:24:25,920 And there are a number of interesting things to note. One is that the risk at Phase 11 and phase nine are sufficiently similar. 205 00:24:25,920 --> 00:24:31,600 Once you correct the Phase 11 correlation for the first nine, you've got nothing left. 206 00:24:31,600 --> 00:24:45,570 OK, and then on the other hand, if you then go back already from phase seven, along with those other green areas from phase seven onwards, 207 00:24:45,570 --> 00:24:53,610 the the risk that accumulates from then onwards would be reflected in atrophy of those bits of the brain. 208 00:24:53,610 --> 00:25:01,660 OK, and the same is true for the earlier ones. So let's look at that in more detail. 209 00:25:01,660 --> 00:25:03,660 These are the same two lives of the top. 210 00:25:03,660 --> 00:25:18,090 One is the the correlation of the risk 20 years ago with the brain scan now and 15 and five years ago and immediately beforehand. 211 00:25:18,090 --> 00:25:24,060 And this is the risk from the most recent phase. 212 00:25:24,060 --> 00:25:32,760 Correct. So there's nothing there. But then 10 years ago, between 10 years and the presence of the additional risk, 213 00:25:32,760 --> 00:25:37,620 the risk that accumulates this particular found in these areas now these areas here, 214 00:25:37,620 --> 00:25:44,130 any suggestion that it's those two areas of the inside into the temporal lobes, 215 00:25:44,130 --> 00:25:51,390 if a complex find, so that changes the more recent changes occurring in the campus. 216 00:25:51,390 --> 00:26:02,970 But then if you look at the top part of the brain in the frontal cortex, you can see that there are correlations all throughout the places. 217 00:26:02,970 --> 00:26:09,450 And in fact, even if you correct for the phase three risk, you remove the face of evidence. 218 00:26:09,450 --> 00:26:20,550 In other words, the risk of having atrophy in those parts of the brain is already mainly determined 20 years before the scan. 219 00:26:20,550 --> 00:26:30,780 So you can start teasing apart the bits of the brain that are responding to the risk and what the dynamic is in terms of time. 220 00:26:30,780 --> 00:26:42,450 This is a similar plot looking at bitmap so you can essentially reduce the white matter to a so-called white matter skeletons. 221 00:26:42,450 --> 00:26:50,240 The skeletons essentially are just that, a representation of the main tracts and. 222 00:26:50,240 --> 00:27:00,780 Roll again, you have the risk of face three five seven nine 11 correlated with fractionalised sexual predators, the quality of life, not a chance. 223 00:27:00,780 --> 00:27:04,540 In other words, the greater the risk, the poorer the quality of things. 224 00:27:04,540 --> 00:27:13,530 Women, I'm the tracks in, you know, from those faces. And interestingly, the risk at phase three doesn't seem to have any effect at all. 225 00:27:13,530 --> 00:27:21,540 Things have happened in between. So the actual risk putting of phase is not correlated with the brain. 226 00:27:21,540 --> 00:27:27,060 20 years afterwards, about 15 years, it starts having an impact and so on. 227 00:27:27,060 --> 00:27:33,540 And if you go the other way around, if you go back from phase 11 and correct the phase nine, 228 00:27:33,540 --> 00:27:41,160 rather than like in the previous case where you have nothing left actually between phase nine and 11 in those five years, 229 00:27:41,160 --> 00:27:47,820 they are reacting to changes already contributes to what marked a deterioration in those areas here. 230 00:27:47,820 --> 00:27:59,460 And then if you go back, it becomes more and more. So the short summary of that is we looked at three different types of tissue, if you want to. 231 00:27:59,460 --> 00:28:03,480 There was white matter and there were two areas of grey matter, grey matter. 232 00:28:03,480 --> 00:28:08,070 This would have been the upper part of the brain. This had been the campus. 233 00:28:08,070 --> 00:28:16,980 And it seems that the campus, the important areas are the most recent ones from phase nine onwards, for the grey matter. 234 00:28:16,980 --> 00:28:20,910 You know, in other parts, if you want to add neocortical areas, 235 00:28:20,910 --> 00:28:32,190 the important phases from the beginning to phase nine and not so much dead and white matter the importance effect start, 236 00:28:32,190 --> 00:28:35,730 but phase five and then go through if you want to. 237 00:28:35,730 --> 00:28:41,340 This reflects the plasticity of these various tissues we know about. 238 00:28:41,340 --> 00:28:44,010 White matter is pretty malleable. 239 00:28:44,010 --> 00:28:52,830 You know, the Black Panthers and dependent on so-called glial cells, which are not those cells that can grow and regrow, 240 00:28:52,830 --> 00:28:58,440 that can form scars if you want to, but they can also adjust the wiring of the brain. 241 00:28:58,440 --> 00:29:00,750 That's part of the plasticity that happens in the brain. 242 00:29:00,750 --> 00:29:10,170 You know, bits of the brain still functioning, but the connexion becomes switch over and kind of go to a different area and the bank are compensating. 243 00:29:10,170 --> 00:29:15,060 So the white matter is pretty variable and plastic. 244 00:29:15,060 --> 00:29:22,620 So the you know, it starts what happened 20 years ago is not recognising, again, 245 00:29:22,620 --> 00:29:27,660 that the other extreme is grey matter, where the cells seem to be fairly static. 246 00:29:27,660 --> 00:29:36,630 So the risk here is recognisable as the term is determining the pattern of brain matter. 247 00:29:36,630 --> 00:29:43,890 And follow up an extreme example for this most recent study, which came from the millennium cohort, 248 00:29:43,890 --> 00:29:52,950 where people were able to follow up a group of mouse 70 year olds who have the intelligence test when they were 11, 249 00:29:52,950 --> 00:30:01,650 OK, and they found that the IQ distribution at age 11 was cold at a certain amount of structures at age 17. 250 00:30:01,650 --> 00:30:10,260 So that's the extreme of how, um, how stable dramatic patterns can be. 251 00:30:10,260 --> 00:30:18,300 So it allows us to identify areas of different risk and put the difference between hippocampus and neocortex. 252 00:30:18,300 --> 00:30:30,830 Any suggestion of why that should be the case in New Haven should be much more plastic than. 253 00:30:30,830 --> 00:30:34,190 This is one part of the paper we know about continuously, 254 00:30:34,190 --> 00:30:41,030 new brain cells that generated generate the plasticity becomes actually one of the highest in the brain. 255 00:30:41,030 --> 00:30:45,710 If you feel like you should enter a London taxi driver, of course, 256 00:30:45,710 --> 00:30:51,500 and have an MRI at the beginning of the end, at the end, it becomes a much bigger event. 257 00:30:51,500 --> 00:31:01,440 That's over a few months. So this is this in a way reflects the fact that it encompasses one bit of the brain that's most plastic. 258 00:31:01,440 --> 00:31:06,660 OK, go to. It's good. 259 00:31:06,660 --> 00:31:16,500 OK. And then you can go one step further and you can look at how the performance in various tasks is related to brain structures. 260 00:31:16,500 --> 00:31:20,010 This is how it looks before it's all tidied up. 261 00:31:20,010 --> 00:31:26,640 These are all the various risks and phase three, five, seven nine, and they all correlate with each other. 262 00:31:26,640 --> 00:31:33,480 This is the hippocampus because I'm interested in the campus because that's changed in dementia to some extent in depression. 263 00:31:33,480 --> 00:31:37,800 And this is one of the tests that is thought to depend on income for function. 264 00:31:37,800 --> 00:31:42,250 This is the this is a verbal learning test. 265 00:31:42,250 --> 00:31:53,430 And essentially it consists of 12 words which are sort of fairly random, but not connected in any sort of logical sense. 266 00:31:53,430 --> 00:31:58,440 So the idea, at least theoretically, theoretically, is that you have to learn those 12 words. 267 00:31:58,440 --> 00:32:04,500 So they read to you. You have to repeat them. They read to you again, you have to repeat them again and for a third time. 268 00:32:04,500 --> 00:32:08,730 So in a way, it's a very simple learning test about just giving this word was to learn. 269 00:32:08,730 --> 00:32:12,210 And generally people get better as as they go along. 270 00:32:12,210 --> 00:32:21,390 So three times 12, the maximum score would be thirty six. If you score lower than 18, you're likely to have a problem with your short term. 271 00:32:21,390 --> 00:32:25,710 So that's the test and that tends to be related to the campus. 272 00:32:25,710 --> 00:32:36,390 And if you tie this up a little bit, we find that hippocampal size is related to the risk at phase three. 273 00:32:36,390 --> 00:32:41,250 But then, as you saw, there's most more recent contribution as well. 274 00:32:41,250 --> 00:32:50,370 And the first time it becomes significant because then phase 11. So there's an initial relation to the risk and then the later one. 275 00:32:50,370 --> 00:32:57,270 And then there's also a direct effect of risk on performance of the learning task. 276 00:32:57,270 --> 00:33:06,540 So you can actually model the causal relationship between how in terms of how the vascular risk that people have is 277 00:33:06,540 --> 00:33:15,150 reflected in brain structures and to what extent the change in brain structures affects the performance of particular tests. 278 00:33:15,150 --> 00:33:24,360 And this are presumably has something to do with how this risks this risk affects other bits of the brain, but also for the test. 279 00:33:24,360 --> 00:33:29,400 So just one example, example of how you can tease this further apart. 280 00:33:29,400 --> 00:33:33,150 Right. The next important question is how? 281 00:33:33,150 --> 00:33:43,810 But are there any characteristics that predict good mental performance and do these protect from the effects of brain changes? 282 00:33:43,810 --> 00:33:49,260 If you think of what happens during the lifetime, the risk factors on the minus side. 283 00:33:49,260 --> 00:34:00,120 If you want to add that protective factors, for example, higher education or IQ, certain drugs, particular diets, 284 00:34:00,120 --> 00:34:08,400 supposedly moderate alcohol consumption, but then also mental and physical activities, which social networks, the business, as it were. 285 00:34:08,400 --> 00:34:16,170 So that about factors and the pluses and the negative side. So let's focus on education. 286 00:34:16,170 --> 00:34:22,950 This is education in terms of highest and qualifications, all levels of A-levels and so on. 287 00:34:22,950 --> 00:34:29,220 And this is the year of full time education, as you would expect, the higher achievement amongst we've gone to school, 288 00:34:29,220 --> 00:34:39,510 but also it's also correlated with the actual IQ of this case, a particular measure of IQ that's unlikely to change with age. 289 00:34:39,510 --> 00:34:49,800 And then you go back to our learning test, you perform that task work, learning tasks, the better the higher your initial IQ is. 290 00:34:49,800 --> 00:35:00,240 But also there's this correlation with hippocampus. So the bigger the volume of the hippocampus the brain, the better you are performing these tasks. 291 00:35:00,240 --> 00:35:09,150 And if you look at the correlation between IQ and the the campus and the learning task, you can see there have some correlations. 292 00:35:09,150 --> 00:35:17,670 And when you remove the other relatively other factor, it actually increases from zero to the partial correlation. 293 00:35:17,670 --> 00:35:32,400 Or in other words, if you just look at the contribution of those two predictors, hippocampal size and IQ, if you just add them up, you get 10 percent. 294 00:35:32,400 --> 00:35:37,650 If you actually look how they interact with each other, it's almost 13 percent. 295 00:35:37,650 --> 00:35:48,330 In other words, the best thing is to be interaction between those two factors in the sense that if you have a small IQ, 296 00:35:48,330 --> 00:35:55,260 you sort of you have a small hippocampus, a greater IQ is going to make a relatively bigger contribution to your performance. 297 00:35:55,260 --> 00:36:04,950 So IQ can actually be shown in medical terms to be a factor that protects you from poor performance. 298 00:36:04,950 --> 00:36:12,480 And then finally, that action in particular changes in characteristics in the brain that pertain to IQ. 299 00:36:12,480 --> 00:36:28,420 Of course, it's very abstract. We don't know what it means. And one you recognise and one approach which I have thought about, 300 00:36:28,420 --> 00:36:37,660 is that you and what you have to do is you have to look at people with a small hippocampus and then you compare the two groups, 301 00:36:37,660 --> 00:36:46,150 the one group that performs very well in this case on the verbal memory task, and the other is the group who don't perform well. 302 00:36:46,150 --> 00:36:50,500 So they both have the same degree of hippocampal atrophy. 303 00:36:50,500 --> 00:36:54,850 So they both have the same degree of organic impairment, if you want to. 304 00:36:54,850 --> 00:36:58,630 But then there's a well performing group and a poorly performing group. 305 00:36:58,630 --> 00:37:02,260 And if you compare the white man for the cabling on somewhere between two groups, 306 00:37:02,260 --> 00:37:06,600 you find that the Musladin group here has a much higher quality of life. 307 00:37:06,600 --> 00:37:12,580 The Connexions, in other words, a good wife, a good a good connectivity, 308 00:37:12,580 --> 00:37:19,660 and the brain compensates to some extent for it will come to actually be interested in what actually protects you. 309 00:37:19,660 --> 00:37:22,250 And that would be certainly one factor. 310 00:37:22,250 --> 00:37:33,460 And, you know, she thought off this index and that is the bigger the verbal memory score and the smaller the hippocampal volume, 311 00:37:33,460 --> 00:37:44,470 the greater resilience. So given the particular hippocampal volume, the guys with a higher memory score are more resilient and vice versa. 312 00:37:44,470 --> 00:37:50,050 So after this couple of months, if you look at this, this resilience index, 313 00:37:50,050 --> 00:37:56,380 I think what you find is that the areas where people have high points of the green, 314 00:37:56,380 --> 00:38:05,530 it's just this character and those it's just a show where changes can occur and the orange and red are the areas where are significant relationships. 315 00:38:05,530 --> 00:38:15,490 So in other words, where the orange colours are, there's a significant positive relationship with resilience or say, again, 316 00:38:15,490 --> 00:38:26,230 if you have high quality white kinds of connexions in those areas, you're more likely to perform well and given a certain size on campus. 317 00:38:26,230 --> 00:38:32,920 And, you know, we always be worried in case this comes about by confounders socially excluded, 318 00:38:32,920 --> 00:38:43,520 things like total grey matter density, stroke risk, alcohol, and you still have this relationship. 319 00:38:43,520 --> 00:38:51,240 So the areas that have better quality by Connexions and the guy is going to have a little. 320 00:38:51,240 --> 00:39:02,030 So that's nice. And then if you add the last one that we had shown before is related to performance, once you remove the effect, I actually that. 321 00:39:02,030 --> 00:39:06,820 So this is not the final proof, but it looks as if maybe the connectivity, 322 00:39:06,820 --> 00:39:13,970 the connectivity of the brain is partially the reason why people have a higher IQ measure. 323 00:39:13,970 --> 00:39:26,780 And both of them together obviously will increase their chances to not present with memory problems in spite of already some developed brain changes. 324 00:39:26,780 --> 00:39:33,170 And then the very last thing is that as you get older, you're the front of your brain becomes more active. 325 00:39:33,170 --> 00:39:40,550 And that's because you already start getting to work harder to achieve the same thing. 326 00:39:40,550 --> 00:39:48,530 Just put the next two minutes. Do you know the the, um, that there's a game called Timboon Family Game? 327 00:39:48,530 --> 00:39:57,960 It's a bit like Sharath, but it's not that you have to act out and talk and you describe something, but you have to avoid certain words. 328 00:39:57,960 --> 00:40:03,620 Okay, so if you, if you let's say you have to get people to get out of the room, 329 00:40:03,620 --> 00:40:11,160 you must not mention hopping Australia and some other words that would obviously be connected. 330 00:40:11,160 --> 00:40:21,900 And if you play in a family context, any book, anybody or anybody over 30 is going to do much worse than the younger members of the family. 331 00:40:21,900 --> 00:40:29,090 And it's really quite entertaining and it's very gratifying for children. 332 00:40:29,090 --> 00:40:33,590 But that may be one sign that as you get older and your frontal lobes have to work harder. 333 00:40:33,590 --> 00:40:41,640 And that's actually not bad, because if you look at particular tasks, you know, they've got to look somewhat active as you get older. 334 00:40:41,640 --> 00:40:48,350 But if you look at specific diseases like Alzheimer's disease, and then you tend to find people who are equally impaired, 335 00:40:48,350 --> 00:41:00,540 equally scoring in terms of their dementia severity score if you want to, and you can divide them into people who have high levels of education, 336 00:41:00,540 --> 00:41:08,600 the ones with intermediate and the lowest education, you find that in order to present with the same degree of dementia, 337 00:41:08,600 --> 00:41:14,240 you have to have a much poorer brain function in the areas that are normally dementia. 338 00:41:14,240 --> 00:41:20,210 If you already have a high IQ and then as you you know, the sort of low, 339 00:41:20,210 --> 00:41:27,500 low education groups need much less of a brain change that could be changed to have the same degree of dementia. 340 00:41:27,500 --> 00:41:38,210 And what you also see is that particularly in this group, but also in the frontal lobe, seem to be more active in those, you know, after two groups. 341 00:41:38,210 --> 00:41:49,250 So it's almost as if the activity of the frontal lobe can compensate to some degree for the established brain damage that comes about the dimension. 342 00:41:49,250 --> 00:41:56,270 And that's maybe one of the reasons why you should activate your brain if you get your frontal lobes to work to some extent, 343 00:41:56,270 --> 00:42:10,750 up to a certain point, you can compensate. OK, and of course, it's not my my work and many other using other people's efforts. 344 00:42:10,750 --> 00:42:15,422 Mr. Confectioner's.