Alan Anticevic, PhD. February 2022

January 18, 2023

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Title: Mapping neuro-behavioral heterogeneity of psychedelic neurobiology in humans

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00:17Welcome, everyone.
00:18It's a pleasure today to
00:21have Alan antiseptic here.
00:23Alan, I think many of you know,
00:24is an associate professor in
00:26the Department of Psychiatry.
00:29As well as many other hats.
00:31And his was a really a pioneer in our
00:34in our community and bringing high
00:37resolution human Connectome Project
00:39style imaging to the community and
00:42then applying innovative computational
00:45strategies to analyzing these data.
00:48And then in more recent years,
00:49it started to do some some really
00:52exciting work in looking at the
00:55brain effects first of ketamine
00:56in his earlier work and then
00:59more recently of psilocybin,
01:00LSD and other psychedelics,
01:02which is the focus today.
01:04So Alan,
01:04thank you so much for being
01:05with us and we look forward
01:07to to learning from you.
01:10Thanks, Chris. Mark Gustavo.
01:13Anytime at all.
01:15It's really with this pandemic,
01:17I feel like I haven't seen you
01:20guys in forever and it's feeling
01:22more disconnected than ever,
01:24but it's it's it's really.
01:26I'm excited to tell you
01:28what we've been up to so.
01:30The the title is so let's see mapping
01:35or behavioral heterogeneity of
01:37psychedelic neurology and humans.
01:38So that's. An overly ambitious,
01:41probably not true title.
01:42So we're not quite there
01:44and this is aspirational.
01:45And So what I'd like to kind of
01:48talk about is as as Chris noted,
01:50what are the techniques and approaches
01:52we're bringing to bear towards
01:53this goal and some of the work
01:55that's been coming out of the lab.
01:57So it will be a hybrid of sort of the, the,
02:00the theme and the ethos of what we're doing,
02:03how some of the efforts in in mapping
02:08variation in clinical populations.
02:11Can be related to neuroimaging
02:14effects of pharmacological compounds
02:16with the focus on psychedelics.
02:18So that's kind of the idea,
02:19right.
02:19And so I I want to just disclose
02:23that I'm a member of the TAB
02:26Technology Advisory Board for Nemora
02:30Therapeutic Therapeutics I consult.
02:32For Gilgamesh and I just Co founded
02:35a biotech with my colleague John
02:38Murray that's called Manifest
02:41Technologies and I I say this
02:43really proudly because without the
02:46support of Yale the spin out what it
02:48would not be possible and so we're
02:50we're really excited about this
02:52work as it has this potential so.
02:54Basically the way I kind of
02:56think about the entire space.
03:01Neuropsychiatric mapping is
03:02really about the challenge and the
03:04opportunity in front of us, right?
03:06So there's two ways to think about it.
03:08And why has this opportunity not
03:11been realized in the field of brain
03:13behavioral health and I'll use this term
03:16brain behavioral health because I I I
03:19actually think it's important that we
03:22destigmatize this terminology, right.
03:25So it's a difficulties in regulating
03:29brain behavioral relationships that that
03:32we're after and then I'll talk about a
03:35framework for this quantitative and and.
03:38The neurobiological framework for mapping
03:40brain behavioral relationships with the
03:42assistance obviously of pharmacological or
03:44imaging as a key tool in order to do this,
03:47right. So what's the challenge?
03:50And so this is one of many challenges,
03:52but in my mind a very important one,
03:54right, which is heterogeneity.
03:56And by heterogeneity,
03:57I mean both within a human being over
04:00time and across people in relation
04:03to brain behavioral variation, right.
04:06And so this is an old problem.
04:09We know this, right?
04:10And so, but there are.
04:12Questions that arise because of this problem
04:16and the opportunity in front of us is if we.
04:19Have a drug like any compound.
04:23And psychedelics are a great example,
04:26right?
04:26How do we select the optimal person
04:29who will benefit from that compound?
04:31We don't have a principled quantitative,
04:33rationally guided framework for this,
04:36for anything in our field.
04:38And then in the future,
04:40right,
04:40if we do have a molecule right
04:42before we do not have a molecule,
04:43if we don't have a compound that
04:45crosses the blood brain barrier
04:47safely or any therapeutic,
04:48how do we develop one with
04:50individual precision as the goal,
04:52not patients versus controls,
04:55but individual people, right.
04:57So that's the opportunity as I see it,
05:00right and so.
05:01It's that how can we target
05:03specific people with?
05:05Quantitative precision.
05:06So this problem I see is mapping one
05:10to many levels of analysis, right?
05:12This is really what stands in front of us,
05:14so I don't have to tell you guys that.
05:17Polygenic disturbances and variants.
05:22Variations with rare mutations, right?
05:27Basically those are the two lowest
05:29level possibilities that our field
05:31is studying and how they can affect.
05:34Molecules, synapses and cells and the
05:36balance between those cells, right?
05:38That's that's at the very
05:40baseline of the problem, right?
05:42In turn,
05:43how do we take that information and
05:45map it onto system level observation?
05:48Some people would say this is an ill
05:49posed problem because it's impossible.
05:51There's just too many mappings.
05:52But you know,
05:53I'll leave that for debate later.
05:55And then finally,
05:56how do we link this to Spectra of
05:59behavioral disturbances, right.
06:00And so I'd like to argue that this
06:02mapping is fundamentally unknown.
06:04We don't know it,
06:05and if somebody argues that we do,
06:06I think that they're flying.
06:09Even in circuits where we understand
06:11our biology very well, like fear,
06:13we still can't treat PTSD, right?
06:15So just this mapping is not accomplished,
06:18right?
06:19And I actually think that system
06:21level observations at the
06:22level of neural systems is where the
06:24right link to behavior should be,
06:26not at the level of a synapse,
06:28because the heterogeneity just explodes,
06:30the combinatorics become impossible to
06:32intractable to quantify or deal with.
06:35So why have we not solved this problem right?
06:39Why is this opportunity not been realized?
06:41And so I think that.
06:44I might argue my many reasons,
06:46but there's some legacy barriers,
06:48right, that we're still
06:49trying to overcome as a field.
06:50And I and the legacy approach are called
06:54legacy because it's historically important
06:57to acknowledge that this this is.
07:00The framework that we have been
07:02operating under is has tremendous
07:03utility for what it was designed
07:05for and by that I mean DSM, right?
07:07It does what it was built for to do,
07:11which is it reliably gets me and
07:14everybody else to agree that some person
07:18has X out of P symptoms over T time.
07:22That it does quantitatively accomplishes
07:24that, so we can reliably agree.
07:27To categorize a human being as
07:29you showed X symptoms out of,
07:32you know, some rubric overtime.
07:35And then we give you a label.
07:36I'd like to argue that's ill
07:38fitting for what we need,
07:39actually not wrong for what it was
07:42built to do, just not what we need.
07:45And then further,
07:46we lack data and methods to
07:47quantitatively molecular benchmark
07:49brain behavior relationships, right?
07:51So the legacy approach doesn't have it.
07:52And then we don't have technological
07:54solutions to actually scale this with
07:56the kinds of observations that are so
07:58important in the area of psychedelic
08:00medicine for precision therapeutics,
08:02right.
08:02So this is a dystopian vision of the future,
08:07right,
08:07that I'd like to show sometimes like
08:10basically where we use computer assisted
08:12decision making or machine learning informed.
08:15Decisions with multimodal data where
08:16the brain is in the middle, right?
08:18I'm not taking the person out of this.
08:20I'm not, you know,
08:21reductionist to that point,
08:23but the organ has to be in
08:25the middle in my mind.
08:27And then we're hopefully optimizing these
08:29two decisions through an iterative cycle,
08:32right?
08:32But we're not there and and so,
08:34So what do we do about that?
08:36So my group here and our other collaborators
08:40here at Yale are really approaching this in,
08:43in the following way.
08:45So let me walk you through the framework.
08:48So we have to 1st agree that the
08:52neurobehavioral mapping problem is
08:54quantitatively A must, we have to do that.
08:57Correctly.
08:57And I'll explain what I mean by that, right?
08:59Because if if we are using an
09:02ill fitting framework to map
09:04onto neurobehavioral variation,
09:05then just won't work.
09:07We won't even translate what we want.
09:10Gene expression alterations right
09:12and disturbances in the way
09:14that the circuits are formed are
09:17it's useful information.
09:19We harness that from say the island
09:22human brain Atlas to inform our our
09:24our models that then can simulate
09:27pharmacological fMRI effects which in
09:29turn can be fixed individual people.
09:31So this is just one way
09:33that we're approaching this
09:35problem in the area of just mental
09:37health and and psychedelic pharmacology.
09:39Specifically, but there's there's many
09:41other tools that we're leaving on the table,
09:44but this is what I'm going to talk about,
09:46right. So again I'd like to argue
09:47that you know Chris asked me to talk
09:50about neuroimaging today specifically
09:51and to really have that focus.
09:53So I'd like to argue that new imaging
09:56is not an option anymore in our work.
09:59And by that I mean normalizing broadly,
10:01right? But a necessity, because here,
10:04here's a choice, right?
10:05This is literally what we had on the table,
10:08right? But we need this.
10:12And so I'd like to argue that if if you
10:15present the problem this way, right,
10:16like how can we not leverage brain data
10:19in the service of decision making as a must,
10:22specifically for things as complicated
10:24as psychedelic neurobiology at
10:26the individual level, right.
10:28So.
10:28So, OK, so how can we exploit imaging
10:31in the service of this goal?
10:33So that's the rest of the talk.
10:35So for those of you who haven't
10:38really thought about.
10:40Structural functional
10:41multimodal imaging recently,
10:43this is a very useful reminder of the
10:45resolution of what imaging straddles,
10:47right.
10:47So on the Y axis is the size of
10:50the observation and time is on the
10:54the X axis and and you'll notice
10:56that human imaging across modality
10:58straddles a good bit of the space.
11:00We're not helpless right.
11:01Like we can actually measure signals
11:04in various ways in relation to various
11:06phenomena, but we still are, you know,
11:08out of reach of certain levels of analysis.
11:10Like with human imaging with symptoms
11:12improving but still not quite there.
11:14And we also don't have just one
11:16way to measure this,
11:17right.
11:17We have multiple modalities that now can
11:20combine that give you different slices
11:23of the signal that this incredibly
11:26complex piece of tissue produces,
11:28right.
11:29And so,
11:29so I'm going to talk about specifically
11:32throughout the rest of talk about bold F MRI,
11:34right, which is a measure that is is.
11:39One of the expertise areas in my group,
11:41right so.
11:44Specifically,
11:44the phenomena that for the rest
11:46of the the talk will focus on is
11:49this idea of resting state, right?
11:51And so this is now a household name.
11:53I, you know,
11:54I don't have to go over this
11:55in a lot of detail,
11:56but I'd like to kind of just
11:58review the history of it.
11:59I always like to do this because I
12:01like to remind people that in 1995,
12:03right, broad Biswal.
12:05Actually observe this simply
12:08by taking signal.
12:10Out of 1 hemisphere of the motor
12:13cortex and asking where in the
12:15brain is there time dependent
12:17covariation of signal within a person
12:20and then average across people.
12:21And he saw this map right which is
12:24bilateral motor cortex comes out.
12:26And people thought this was junk.
12:28This was a compound, right?
12:30And they ignored it.
12:31In fact, he was attacked for it quite a lot,
12:33right? And then only a
12:36decade later with some.
12:40Advances from Michael Greicius
12:41and then Marcus Raichle.
12:43Did this phenomena really
12:45become a mainstream?
12:46And so now if you Fast forward
12:48what is now a decade ago,
12:49which is hard to believe right,
12:50that Thomas Yao and Randy Buckner
12:54actually mapped comprehensively right.
12:56Large scale networks
12:58across individuals right.
12:59And this is this is now a joke
13:03like we we know we can do this now
13:04and every single person right.
13:05But it was controversial
13:07then and so then 2016 onward.
13:09Human connectome produced sparse
13:11relation which gives you an index of
13:14the boundaries between the areas that
13:16is comprehensive but not definitive.
13:18This will probably improve it or actively,
13:20but I'd like to tell you like we've
13:22made some serious progress, right,
13:23with human or imaging and we can exploit it,
13:26right?
13:27And furthermore,
13:28this is something I will repeat
13:31in every talk I give.
13:34So the reason why the Human
13:36Connectome project pipelines and the
13:38entire effort towards so impactful,
13:40which is why I obviously drink the
13:42kool-aid because I trained there, but.
13:46The human brain and the cortical
13:49mantle is A2 dimensional surface
13:51wrapped around white matter that is
13:54about 4 millimeters thick and it's the
13:57size of a pizza and that geometry matters.
13:59It matters fundamentally when you're
14:01going to do precision medicine
14:03analytics with single subject
14:05human cortical surfaces, right?
14:07In fact,
14:07just before this talk I got off
14:09the call with one of my students,
14:10Amber Howell,
14:11deeply debating the importance of
14:13importance of social curvature
14:14and depth on a single subject
14:16level in relation to.
14:17Diffusivity of white matter tracts,
14:19and turns out it matters.
14:20It matters a lot anyway.
14:23Analyzing your data on the surface,
14:25I think is is. I must like you.
14:29You are blind without that to
14:31individual variation, right?
14:33So.
14:34When you do this right,
14:36then you can produce metrics in the
14:39geometry of the cortical surface
14:41that can quantify some signal in
14:44every cortical parcel or area.
14:46I'll use the term parcel because
14:48that's the formal definition.
14:49And then you can operate with those
14:51metrics to analyze it within a
14:53subject across people in various ways,
14:55right.
14:55So furthermore,
14:56what we've done in my group out of necessity,
14:59right,
15:00we started to use the cortical parcellation
15:02from the Human Connectome Project,
15:04which is shown here.
15:06These little borders,
15:07but then we use the network partitions from
15:09from other groups and they were great,
15:12they they worked really well.
15:13But then we realized subcortical
15:15coverage is not there.
15:17Like thalamus wasn't covered appropriately,
15:19brainstem wasn't covered.
15:20And you guys know that psychiatric
15:23medication works on subcortical
15:25circuits and precision of isolating
15:28specific voxels in relation to
15:30networks and parcels is really important.
15:32So what I'm showing you here is
15:34work produced by my student Lisa.
15:36Who's extended the cortical partition
15:39into networks Cortically first,
15:41and then studied how subcortical
15:44voxels covary with those networks,
15:47assigning every single voxel in
15:49subcortical brain matter to a
15:52network until it reached split half?
15:55Stability across the entire HCP sample.
15:58And so the reason why we did
15:59this out of necessity is we need
16:01this for clinical application.
16:02We actually needed a whole brain partition
16:04to covers every piece of Gray matter.
16:06We can't leave things on the table.
16:08So now this is published and this is what
16:10we're going to be using for us to talk.
16:11Now just to convince you that
16:13this actually is better, right.
16:15So you can take a dense signal, right?
16:18In other words, at the level of vertex
16:19on a cortical surface, you can parse,
16:22relate it prior to computing some
16:23metric or you can parse relate it.
16:25Post right?
16:26So if the person relation is.
16:29Valid and consistent across subjects,
16:32then this should be better than that.
16:34And that's in fact true, right?
16:35And so this is not news,
16:37not Glasser has shown this
16:38in his work and so on,
16:39but it's just to convince you guys that
16:42these parcellation are actually very,
16:44very useful, not just quantitatively,
16:46but as a feature space reduction,
16:49because now you are no longer
16:50working with 95,000 voxels,
16:52you're working with 700 areas
16:53and now you can do some clever
16:55feature engineering on top of that.
16:57So that matters, right?
16:59So how do we now link this to
17:02molecular mechanism, right,
17:04very broadly speaking and so.
17:07So, so,
17:07so the way we do this and the
17:09way we've approached this across
17:11all compounds ketamine,
17:13single sideband as well as application
17:17to clinical questions is what
17:21are the principal organizational.
17:26Features, if you will, of the human
17:28brain or the mammalian brain in general,
17:30and I'd like to argue one that we know about,
17:33is functional specialization
17:34across the cortical axis.
17:37So we know that lower order and higher
17:39order areas have very distinct patterns of
17:41feed forward and back connections, right?
17:44So that's an organizational principle.
17:45This is a classic picture from the
17:47Fellman and Vanessa and publication,
17:49which also highlights that there is a
17:52hierarchy and information processing from.
17:55Answer to association agents, right?
17:57We also know from work such as this
18:01paper from John Murray while he was
18:04starting here at Yale that there
18:06is a difference in the spontaneous
18:09autocorrelated activity across areas
18:11from non human primate data and this
18:15intrinsic activity scales suggesting
18:17a hierarchy of functional hierarchy.
18:19Furthermore,
18:20we know that during cognitive operations
18:23such as working memory primary.
18:25Visual areas such as Mt do not sustain
18:29signal during the mnemonic phase,
18:32whereas areas such as LP FC sustain
18:35a recurrent reverberatory activity,
18:38right again highlighting distinct
18:40functional specialization in this
18:42very coarse way.
18:43Furthermore, we know that we can leverage.
18:47Microstructure values from the T1 and T2
18:51maps to derive a proxy of myelin cortically,
18:56which shows a hierarchy.
18:58It smoothly varies from
19:00association to sensory areas.
19:02And this is also true in the macaque,
19:05right?
19:05So now we have some clues that the
19:07brain varies hierarchically and this
19:09shouldn't be controversial, right?
19:11Can we leverage this information to
19:15understand how the effects of pharmacology?
19:18Which we'll get to and so.
19:20The the motivation for this work was
19:23driven by a grad student in John's lab,
19:26Josh Burt, who did some really
19:28elegant gene expression mapping.
19:30And I'll walk you through why this matters,
19:31right.
19:31So we have this human myelin map, right?
19:34We've established that it has a
19:36hierarchical organization, right.
19:37And what he's just shown that when
19:39you use Lisas network partition,
19:41but in fact there is a difference
19:43across networks,
19:44right,
19:44that the association networks have less
19:47myelin than sensory somatomotor networks.
19:49OK, just the validity check.
19:51Furthermore,
19:51he's taken mcac myelin values,
19:54and he's taken tracer data,
19:56defining the hierarchy as feed
19:58forward and feedback connections in
20:00collaboration with David Vanessa's group.
20:02And he's correlated.
20:03This again establishing that
20:05there is hierarchy right.
20:07So far,
20:07so good.
20:08Then he went into the alien human
20:11Brain Atlas gene expression database.
20:14And he's taken from every cortical
20:18microarray a probe across something
20:20like 20,000 different genes.
20:23And because the amount of human
20:25brain Atlas has serendipitously,
20:27thank goodness,
20:28scanned every single person of
20:30the six people that they studied.
20:33Postmortem.
20:33We could then reconstruct the cortical
20:37surface right anatomy postmortem and
20:39map it onto the human Connectome Atlas.
20:43Particulate it right using the
20:45parcellation that I just introduced.
20:47Do this for every person from the
20:50island human brain Atlas and then
20:52average it to get the aggregate
20:54group map across every gene
20:57for every cortical parcel.
20:58Now you can imagine why this is powerful.
21:00Now we have an actual gene expression
21:02topography for every gene across
21:04all the areas that we have our
21:05new imaging maps for, right?
21:07So what can we do with this?
21:09So the first question is,
21:10what is the principal gradient,
21:12the first principal component of
21:15gene expression topography, right.
21:16And this is the picture.
21:18This is how it varies, right?
21:19And it varies this way,
21:22so much so that it
21:23did you say that this this expression
21:26is from 6 brains, six people. OK.
21:29So I'm wondering how that limits the power
21:31of this kind of correlation analysis.
21:33If you only have you have a huge number
21:35of genes but you only have 6 replicates
21:38at each for each gene in each parcel,
21:41that's a great question.
21:41So let me go back.
21:43So, So what Chris is really
21:45highlighting is something that is.
21:48Rental, which is you when you're running any
21:52analysis on the matrix of genes by parcels,
21:55notice that their group averaged, right?
21:58So what we do is we first ask, what is the?
22:02Coverage of that microarray probe
22:04in that cortical location, right.
22:07Is there a good signal?
22:08And then we evaluate the differential
22:10stability across individuals, right.
22:12So we want to be confident that it's
22:14consistent across these six people,
22:15right, and that there's good coverage.
22:17Then we produce a single value single number,
22:20which is the average right
22:21across these six people.
22:23All the analysis are done on the data
22:25object that's you're seeing here,
22:27which is at the group parcel level.
22:29In other words,
22:30the principal component does not consider.
22:32Variation across people,
22:34it considers variation across areas,
22:36right,
22:37with the assumption obviously that
22:39people are consistently expressing
22:40these genes in these areas and
22:42that's an empirical question,
22:43right?
22:44One that we keep talking to NIH
22:45and the on human brain that was
22:47folks that they need more brains.
22:49They need to produce this kind of
22:52mapping a sample of sufficient size
22:54that we can interrogate whether
22:56there is true human variability.
22:59And furthermore,
23:00you could imagine this matters for
23:02psychedelic cardiology which is if your 5 HD.
23:04Receptors,
23:04which I'll talk about in a second,
23:06very differentiated across people.
23:07One could argue that some of the
23:09conclusions that will draw are incorrect,
23:11but I think you're,
23:12you're you're as always always go
23:15to the key intuition right away,
23:17which is that these maps are
23:19average across people and therefore
23:22limits generalizability across
23:23the the entire population, right.
23:26We don't know
23:27and also implies that your principal
23:29components are dependent on multiple genes.
23:31They're looking for patterns
23:32of multiple genes across areas
23:33they're not going to give. Exactly.
23:35Whereas if you had a much larger data set,
23:37you could look for components that
23:39are for grades within single genes,
23:41but you can't do that in this data set, so.
23:44Exactly, exactly.
23:45So you could ask the question of is
23:48there a gradient of a single gene across
23:51people in an area or across areas, right.
23:54That's another level of variance
23:55that is left on the table.
23:57But the first question,
23:59the only question really that we could ask
24:01was what's the spatial gradient, right?
24:03What is the spatial topography of the way
24:06these genes vary across cortical areas,
24:08right. And this is how they vary.
24:11And so it turns out that that explains
24:14almost 30% of all the variants, right?
24:16Not, not everything, but a lot, right?
24:18And so now you could say,
24:20oh, what does this look like?
24:21What kind of looks like Milan, right?
24:23So you could quantify that, right?
24:24And it turns out it looks a lot like my own.
24:27In other words,
24:29it varies along a hierarchy, right?
24:31Almost 1/3 of all the variants in
24:34human gene expression in the adult
24:36brains with this these six people
24:39varies along the principal axis,
24:41where it's high in sensory
24:44somatomotor or low.
24:46And low or high in association cortices,
24:50right?
24:51This is this gradient can go both ways,
24:53right?
24:54And this is cool,
24:55because now you can imagine this is
24:57the key way that the brain breaks,
24:59which is across this cortical hierarchy
25:00and its pattern of gene expression.
25:04Can you then look at what genes
25:07are contributing substantially to
25:09that first principal component?
25:11Because you would predict that,
25:13for example, oligodendrocyte genes
25:16might contribute significantly.
25:17So if oligodendrocyte genes covary
25:19with the myelin density map, that's.
25:21A different way of measuring
25:23exactly the same thing.
25:25It's not telling you something new as
25:26opposed to if there are other neural
25:28genes that might be less intuitive
25:29that they should vary and that
25:30might be telling you something new.
25:31So can you dig into the contributors
25:33to this component and start to
25:35make that kind of inference.
25:36Yeah of course you can check what's
25:38the loading of each gene onto this
25:40component and so on so definitely so.
25:42So I don't know if Josh has that
25:44in one of the tables are not
25:45in the paper but we can check.
25:47It's a really good question right and and
25:49allows for another access to exploration
25:51but the point that you're already.
25:53You know, your your questions and and
25:55suggestions are already highlighting
25:57this which is the cortical gene
25:59expression variation is dominated,
26:00right, by a single principle axis
26:02which is highly correlated with these
26:05expressions of hierarchy and that's great.
26:07OK, that's an observation, right.
26:09And these gradients of micro scale
26:11properties then can contribute perhaps
26:13to sensory association specialization,
26:15but furthermore may contribute
26:17to the effects of pharmacology
26:18across these cortical areas, right.
26:21And so this is this is work.
26:23That we've published on and actually
26:25I'm I'm also really proud of this
26:27John and I Co wrote a patient with
26:29our colleague Bill Martin who is
26:30now head of Global Neuro at J&amp;J
26:33and this was just recently awarded.
26:35So now we have a patent on this,
26:36not with that useful for anything
26:38but was it was something that we
26:41wanted to develop and really kind
26:43of drive forward anyway so.
26:46Sharp left turn.
26:47How does this relate to pharmacological
26:50effects of any kind and psychedelic
26:52psychedelics in particular?
26:54You guys are probably thinking when is he
26:55going to get to anything psychedelic related?
26:57Like why am I listening to this?
26:58So, so we're we're getting there, so.
27:03OK, this map and and I had some
27:07set up slides, but,
27:08but I actually want to go a little faster,
27:09right.
27:10So this is a paper published by
27:12Katrine in elife a couple years ago,
27:15right.
27:15And So what she's done is
27:17basically giving people a.
27:22A pill of LSD which targets the
27:25serotonin receptor versus placebo.
27:28And what we've done is we've computed
27:30a map of unresting state of the effect
27:33of LSD on every single person and this
27:36is the average across all the people,
27:39right?
27:41In the effects of LSD on a metric that
27:43we call global brain connectivity.
27:46So let me unpack this a little bit
27:48so you so you develop an intuition.
27:50Every warm area that you see here
27:52is an area that has an elevation
27:56in its brain wide covariation.
27:58When people are given LSD relative
28:00to cell cycle, so in other words,
28:03you could you could say it's
28:04a hyperconnectivity, right?
28:06But I don't like to use that term
28:08before impacting it first, right?
28:10So for instance,
28:11the visual cortex here would.
28:14This the interpretation is that
28:17LSD elevates the connectivity with
28:19the rest of the brain for visual
28:22cortex bilaterally and for sensory
28:25somatomotor cortex and MTV.
28:28But it reduces connectivity in
28:31these association areas, right?
28:34So this is important, right?
28:35In other words,
28:36there is a bidirectional effect
28:38of LSD versus placebo on sensory
28:41versus association regions, right?
28:43At least it appears that way,
28:45right? And so we can now isolate the Type
28:491 error protected effect right by doing
28:52TFCC protection at the whole brain level.
28:55And now you can see this values,
28:57this is what's pulling out the values
28:59out of this area now what Katrina has
29:02also done which is. Pretty clever.
29:05She's given people ketanserin prior to the
29:08administration of LSD right and contains.
29:11Azrin is thought to be a very selective
29:14antagonist of the five HT 2A receptor.
29:17And you'll notice that when people
29:19are pretreated with ketanserin,
29:20they look just like.
29:23Placebo right.
29:24In other words,
29:25there is no effect of LSD in those areas.
29:27And this is true for the areas that
29:29show a reduction in connectivity
29:31by LSD and the regions that show
29:33an increase in connectivity by LSD.
29:36So there is a full blockade of the effect
29:38of LSD by Captain Strand on average, right?
29:41So that's pretty cool, right.
29:43And and I remember showing this to to John
29:47Crystal years ago when we first saw this
29:50effect and saying like look the two maps,
29:53the LSD versus placebo and the
29:55LSD plus captain string versus
29:57LSD are super correlated.
29:58Look, they're almost the same.
30:00And and I'll show you what they
30:02look like side by side, right.
30:04This is same people, two different days.
30:07One day they're given LSD
30:08alone versus placebo,
30:10the other day they're given
30:11contains trend prior to.
30:12Was the and then contrasted to placebo.
30:16And I'm like, this is incredible.
30:17They look the same.
30:18And he just laughed at me.
30:19He was like, ohh,
30:20of course it's pharmacology.
30:21It has to be.
30:22Was like,
30:22well,
30:23I'm so glad it's so obvious to you
30:24that that you're going to get such a
30:27correspondence in the within subject
30:28effect of pharmacological effects on
30:30brain imaging at the surface level.
30:32Like I wouldn't have guessed that,
30:34right, but this is important,
30:36right,
30:36because it highlights that we can
30:39leverage surface based topography
30:40as an index of the effect of
30:43pharmacology on the human brain, right.
30:45And so now, now we get this map,
30:48this delta map, delta GBC,
30:50right and now we ask.
30:52OK, what about gene expression patterns,
30:54right?
30:54If this is truly related to the
30:56serotonin 5H2 receptor which we
30:58believe contains from this blocking?
31:00Then presumably the 5H2 receptor map
31:03ought to look like this map, right?
31:05So that's what we tried.
31:07We took the gene expression map,
31:08right,
31:09and we computed the correlation
31:11right across these two, right.
31:13And we also took some other target genes,
31:16right,
31:16that are thought in the literature
31:19to be targeted by LSD.
31:22And then we computed a similarity of
31:24correlation between this and this is
31:26serotonin is right up here, right.
31:28So it's the of these, it's the most.
31:31Positively correlated.
31:32And then we repeated this for the
31:34entire distribution of all genes
31:36from the alien human brain Atlas.
31:38And again,
31:39serotonin comes out here in the
31:4195th at almost 96 percentile, right?
31:44So there are some things that
31:45by chance could
31:45be higher, but this is pretty.
31:47Encouraging as a initial proof of
31:49principle that we can actually map
31:51human gene expression in relation
31:52to the pharmacological effects on
31:54the human brain in vivo of a given
31:57pharmacological agent that was blocked by
32:00the hypothesized receptor antagonist, right?
32:03That's in my mind pretty cool.
32:06And so, so you also see the opposite,
32:09which is HR7, which in my mind has
32:11some very interesting pharmacology,
32:13but we won't get into that today, right?
32:15It's just you also see the opposite
32:17effects in this, this kind of analysis.
32:19OK. So. How do we take
32:23this quick question?
32:24If you go back, I'm thinking about
32:27the 1A receptor which is usually
32:29pre synaptic on Axon terminals,
32:31which means that the receptor is
32:34not in the same place as the M RNA.
32:37That's what you're doing here.
32:39When you're when you're looking at the
32:41distribution of gene expression across
32:43the brain, you're looking at M RNA,
32:45which is basically where the cell bodies.
32:49Right and. That should be pretty good
32:52because it's amended and Reddick.
32:53But for the 1A receptor there,
32:55there's going to be a substantial
32:57dissociation between where the M
32:58RNA is and where the receptor is,
33:00which I don't think you can get
33:01it at this technique.
33:02So just for some receptors that may be
33:04applicant, you're absolutely right.
33:06So you're absolutely right.
33:08So what you're highlighting is the nuance,
33:11the Super important nuance between
33:13the ligand and the receptor, right.
33:15And basically the fact that the M RNA
33:16may be coding for the ligand or the
33:18receptor and in the cases where it's
33:20coding for the receptor, this analysis.
33:22Approach may be very informative,
33:24but in the cases where it's
33:25coding for the ligand,
33:27it may or may not be informative.
33:28It's not about whether it's the ligand,
33:29it's where the receptor is on the neuron.
33:33So for pre, for pre synaptic receptors,
33:36for receptors that are
33:37targeted to Axon terminals,
33:39we see this in the animal literature
33:41all the time where the receptor
33:42in the M RNA are in different
33:43places because the receptor is
33:44way out on the Axon terminals,
33:46which is in a different part of
33:48the brain from the cell body.
33:49It's not about the ligand,
33:50it's about where the receptor
33:52is localized in the neuron.
33:53So you're highlighting something
33:55even different, right, which is which
33:56is now beginning to appreciate it.
33:58So you're saying that the postsynaptic
34:01receptor expression of the five HD.
34:03Way. Is potentially captured very well
34:07by the M RNA and the the the probes
34:12whereas the presynaptic 1A may may be a
34:15very different phenomenon because it's
34:17on the presynaptic terminals right.
34:19And therefore you're not binding to it.
34:21And then furthermore furthermore it's
34:23this is where my thinking was going
34:26which is the ligand versus the receptor,
34:29right because now you have that third
34:31axis of variation and so and and to your
34:33point this gets complicated because.
34:35When you give a substance like a psychedelic,
34:38you have post,
34:39you have polysynaptic distal effects, right?
34:43Which is it's going to travel right through
34:46the Axon and potentially shuttle onto the.
34:50Receptors and activate those terminals
34:52on distal neurons that are not in the
34:55local tissue bed of the high expression,
34:57which is why and this is actually
34:59gets really nuanced why looking at
35:01the dense GBC at the voxel level and
35:04partially the GBC doesn't necessarily
35:06fully map onto one another because that
35:09level of granularity begins to matter,
35:11right.
35:11And you can now appreciate right
35:13that if you are averaging signal
35:15within an area or if you're looking
35:17at boxing level pharmacology,
35:18right and furthermore if you can.
35:20Some even deeper into
35:22columnar level pharmacology,
35:23but they're an important ones here, right?
35:25But at the very coarse level,
35:28you can at least begin to
35:30identify these first principles,
35:31which is pharmacological
35:33neuroimaging topographies with GBC,
35:35which is this random freaking measure, right?
35:37That that seemingly has these
35:39properties that we really like maps
35:42onto gene expression gradients, right?
35:44Like who would have thunk it?
35:46And it's not.
35:46And and the important thing about this
35:48is that if you use some graph, theoretical.
35:51Distraction.
35:51Without a surface map,
35:53there's no freaking way you can
35:55get this right.
35:56And that's actually kind of the take
35:58away that I was trying to get at right
36:00is that that this is obscured if you
36:02do not have a surface map, right?
36:04You just can never see it, Umm,
36:07because that's what drives the
36:10correspondence, right, and the location.
36:12So anyway, so, so,
36:13so now I I want to be sensitive to time,
36:16so I may have to kind of speed
36:17up to some of this stuff.
36:18So, so basically the point of this is
36:20how do we map this onto neurobehavioral,
36:23geometric models in in clinical
36:26population level analysis,
36:27right.
36:28And so this cartoon is just
36:29highlighting that there's some
36:31brain to behavioral relationship and
36:32that it's probably or some oblique,
36:34maybe even not linear.
36:35And we don't know what it is.
36:37And so this is work really purely done
36:41by my former student now research
36:43scientist here in our department,
36:45Lisa and and she's published
36:48this in life earlier last year.
36:51After a whole saga.
36:53So,
36:54so I I I want to this gets a little
36:56technical, so I'll try to kind of keep
36:58it clear and then get to the key points.
37:01So if we're going to leverage
37:03pharmacological new imaging that's
37:05even benchmark with gene expression or
37:07what have you right to achieve brain
37:10behavioral models that can actually be
37:12deployed for any therapeutic purpose.
37:14There are some criteria that I'd like to
37:16argue we need to really hold in mind, right.
37:19And these criteria are are not exhaustive and
37:21things that I've learned the hard way that,
37:24you know, if you don't do this,
37:26things are just brittle.
37:27So the first one is more
37:29kind of for the whole field,
37:30which is that anything that we
37:32produce as a field has to scale and
37:35interoperate in an informatics way.
37:38So for instance,
37:39what Pronet is doing and what Professor
37:41Woods is doing in our department with
37:43this massive worldwide consortium.
37:45Right.
37:45So, so the days of me working on my PC
37:49and producing imaging are gone, right?
37:51Like it's no longer that.
37:52So then the measure selection,
37:55and I mean the behavioral measure selection
37:58matters here and it matters a lot.
37:59And I'll show you why. Then.
38:02How do we cross validate those
38:04behavioral models,
38:05right.
38:06And this is stuff that has to do with
38:08trustworthiness of the reproducibility
38:10of those models at the behavioral level.
38:14No imaging yet.
38:15This is this is something that that
38:17we also found out is very important.
38:20Then Criterion 4 is how do we then
38:22produce a robust and interpretable
38:25neuroimaging maps that are linkable
38:28to that behavioral variation?
38:29And then finally,
38:30how do we cross validate that
38:32brain behavioral?
38:33Right.
38:33So this is a lot of stuff to cover
38:35in like 15 minutes.
38:36So some of the hit on some of the highlights
38:39and then we'll pause your questions.
38:41So this is.
38:42You know,
38:43a trivial point, right,
38:44it's just hard to do,
38:46which is we need informatics
38:47solutions that scale.
38:48And Yale is at the forefront of this.
38:50I think that what we're doing
38:52in our department,
38:53I'm tremendously proud of and I
38:54think some of the work of faculty
38:56on this call and others like we're
38:58really pushing the boundary of this.
39:00This is the point is really things
39:01have to scale and drop rate if
39:03we're going to develop precision
39:05medicine solutions, right.
39:06So I'll just forward and just
39:08say a key thing inside this
39:10architecture is analytic discovering.
39:12Years.
39:13And so this is the workflow from
39:15Lisa's paper,
39:15just as a shameless plug.
39:17But the point is that analytics have to be
39:20organically built into this for the Discovery
39:22science engine to work where it cannot be.
39:25Data collection devoid of analytics.
39:27It's it's it's all,
39:29it's all combined, right?
39:30So talk through how we do this with
39:32a particular analytic framework using
39:34a data set that's called Beast Snip.
39:37Our colleague,
39:38Godfrey Pearlson was one of the
39:41principal investigators on the original.
39:43Snip that made it into the public domain,
39:44and now they're on to be snipped too.
39:46I don't even know three,
39:47but this is a public domain
39:49datasets that made it into NH.
39:51We downloaded it out of the National
39:53Debt archive and processed it using the
39:56human connectome processing pipelines.
39:58Like that's as much as I'll
40:00say to speed ahead, OK?
40:01So I've shown this several times.
40:04Maybe some of you have seen these data,
40:06but now it's published and I can kind
40:07of show you the full gamut of this.
40:09So the first question that we asked
40:13ourselves was what is the covariance
40:16structure across people in the symptom
40:20geometry of the psychosis spectrum
40:22population of these 436 people?
40:25And you're looking at pans,
40:27these are pans items, right?
40:28The backs is on top backs,
40:31which is the brief.
40:32Assessment of cognition and
40:34then pans items here.
40:35So this is the covariance matrix
40:38and there's some correlation between
40:40these right across 436 people.
40:42So in other words,
40:43they're structure between these symptoms,
40:45which is expected.
40:46This is not new, this is, this makes sense.
40:49But when you plotted across
40:51the DSM categories, right,
40:52where bipolar is shown in yellow,
40:54schizoaffective in this
40:56kind of orangish color,
40:58dark red is schizophrenia and all pro bands,
41:00all patients are shown in black,
41:02you know, I like to argue that.
41:06There's a lot of variation in each one of
41:09these sub scores on the pans and cognition,
41:12but not really clear,
41:13very clear distinctions between
41:15diagnostic categories, right?
41:16And so this is not news.
41:19Psychosis spectrum disorder is heterogeneous,
41:20exhibits covariation symptoms
41:22across clinical scales. OK, great.
41:24You know, cool story Allen.
41:26So now what?
41:26So the question is what is the
41:28dimensionality of this solution,
41:30right.
41:30Is there a low dimensional solution that
41:32we can reduce the map to the brain right.
41:34Can we do that and simply right.
41:37You could ask is there say principal
41:40component analytic solution that
41:42explains these data or a factor analytic
41:44solution or K means clustering solution,
41:47just something that is looking at
41:49the covariance structure of the
41:51data in a lower dimensional space.
41:53And so it turns out yes.
41:55Right.
41:55So these are the components that come out,
41:57which brings me to criterion too, right?
41:59Can we select the right measures
42:01to map onto the brain? Right.
42:03And can we obtain an interpretable
42:05solution here?
42:06Right.
42:06So, I mean, I'm not going to walk
42:08through these principal components.
42:09More importantly,
42:10when I want to highlight is what the geometry
42:12looks like just so you can get an intuition.
42:15Every dot is a patient.
42:16They're color-coded as noted here,
42:18right?
42:19These arrows in this space are
42:22vectors of the pans and backs.
42:28Average scores. The green arrow is backs,
42:32which is almost perfectly
42:33collinear with the cognition axis.
42:35That's one of the principal components,
42:37the black dots that you may see
42:40here healthy controls right?
42:41And then the two the the the
42:45arrows that are coming up the
42:48the blue and the purple are the.
42:52Negative and positive symptoms,
42:54respectively, right?
42:55Notice that they project onto
42:58this access under an angle.
43:01It's not. They're not collinear,
43:03and they're obliquely rotated, right?
43:06And then there's a global dysfunction
43:09which is all the patients are not
43:11functioning as well as controls.
43:13So it's this PC three that we're
43:15going to talk about, right.
43:17So this is a static picture of that solution,
43:20right.
43:21And if if you can see after correct
43:24schizoaffective is misspelled,
43:26but if you can see the positive
43:28and the negative vectors,
43:29they form a 45 degree angle onto PC-3.
43:32OK.
43:33So now what is this action you
43:35look like numerically, right?
43:36Zoom in and these are the linear combinations
43:39and this solution right in this sample.
43:42So if I plot the DSM categories,
43:44you'd say there's an effect.
43:47Right. They don't differ,
43:49but that's actually the point.
43:51The point is that when you cut
43:54through this geometry with the.
43:56Data-driven solution.
43:57You ought not to see differences in DSM
43:59categories because they don't seem to
44:02actually follow natural variation, right?
44:03And so how can I convince you of that, right?
44:06So.
44:06So let's take a look at the
44:09configuration of the PC-3 items, right.
44:12So typical person would be somewhat
44:14delusional, conceptual, disorganized.
44:16They are hallucinating,
44:17they have some excitement,
44:19grandiosity, right?
44:20But they're not, you know,
44:22purely collinear with the negative symptoms.
44:24They have something, some they don't.
44:26They're a little bit cognitively impaired,
44:27right?
44:28But again not a clean, you know,
44:31one to one mapping between these these axes,
44:33right, between the the subscores of the pans.
44:36So again, you know,
44:37let's put this to the test
44:38I I'm a competitive person,
44:39I like competition, right.
44:40And and I like to, you know,
44:43create competitions,
44:44incentive questions, right.
44:45So let's see is the SM going to
44:48outperform a data-driven solution, right.
44:50Because we need it to map it
44:52onto pharmacology, right?
44:53Like we need something that is robust, so.
44:58Criterion 3.
44:59Before we even get there right,
45:01we have to check that the
45:03solution of this model is stable.
45:05So this is a summary of the leave
45:07each site out cross validation,
45:10a summary of the predicted
45:11versus observed scores,
45:12a summary of the predicted versus
45:15observed single subject scores
45:16from K fold bootstrapping,
45:18and similarity of the actual loadings
45:20on the PCA for leave site out five
45:23fold bootstrapping and split half.
45:25And hopefully this shows you that
45:27the solution is really stable.
45:28Which means that the basement consortium
45:30did a really good job actually,
45:32right.
45:32They collected and and asset clinically
45:34the data in a very consistent way
45:37and we're able to get a pretty
45:39good stable behavioral model,
45:41right. So the PCA variance is generalizes,
45:45the score is generalized and the
45:46PC weights generalized, right.
45:48Otherwise why are we mapping it onto
45:50the brain if it doesn't, right? Cool.
45:52So now let's actually go even further
45:54from DSM and take pans positive symptoms.
45:57Let's give pans a fair shot because
45:59this is what the industry uses, right?
46:01If you're going to test if
46:03a antipsychotic works,
46:04you're going to use pans positive symptoms.
46:06That's your benchmark.
46:07That's the gold standard for the industry,
46:09right. And and this is the
46:13psychosis configuration PCA effect,
46:15which required only only one level
46:18of supervision, which is to pick PCA.
46:21That's it. We just said.
46:22Let's run a PCA.
46:23So now, which one will give you
46:25a better brain map, right?
46:26That's what we care about.
46:28We care which Brain Mac is better.
46:31And so again,
46:31we're going to use GBC as the
46:33brain measure and we're going to
46:36calculate the variation from each.
46:37Parcel to every other parcel using this
46:40quantitative technique that I explained,
46:41right.
46:42And so the intuition again is that we're
46:44going to get this value for every parcel.
46:47We're going to then correlate
46:49the area level signal with the
46:52symptom for every patient.
46:54And then we're going to get in a
46:56cross subject map that tells us
46:58how people vary across the sample
47:00with respect to their GBC, right.
47:02So it's an individual difference analysis.
47:04And so this is the map you get with pans
47:07with 436 people and this is the map.
47:09You get when you use the PC-3.
47:12Now.
47:12I want to just pause here because
47:15hopefully it's self-evident to
47:17everybody which one is better
47:19and if somebody says a.
47:21I I hope they're joking.
47:24So.
47:24This is not nothing done except simply
47:27taking a data-driven behavioral
47:30analysis of pens and backs,
47:33a data-driven neural measure with
47:35the only piece of supervision being.
47:38Reduction of the FC matrix using GBC.
47:41And then you get this slice through
47:43the geometry that hopefully one
47:45could argue is is better and
47:47quantitatively it is better, right?
47:49You can actually check that statistically
47:51that the variance covered is higher
47:53and that the range of the Z values is better.
47:56You can do all sorts of other things,
47:57but it's just better.
47:59So, OK, now we have something right
48:03now criterion 5 is,
48:05is this thing generalizable?
48:07And what I mean by that is if I were to.
48:11Say Mark or Chris,
48:13can you guys use the weights,
48:15the actual thing that I found
48:18here and reproduce the exact map?
48:21Using a split half cross validation
48:24of the model,
48:25can you get the same picture again?
48:28That's that's what we care about.
48:29Not just that you can point to five,
48:31reject the null and publish,
48:33but that the picture of the neural
48:36topography is reproducible.
48:37And this is what we get in this case,
48:39right?
48:40So and we did this 10,000 times,
48:43but half and various ways and tried to
48:46break it. You know, pretty robust.
48:48Both dense level and the parcel level, right?
48:52And this is only 219 people, right?
48:54We're not talking gargantuan samples here,
48:56right? It's just that you have the
48:59right slice through the geometry and
49:01then all of a sudden you're getting
49:03maps that reproduce even in patients.
49:05So now this is the engine that we
49:08submit this to in order to select
49:11the most stable features, right.
49:13And I'm not going to walk through
49:15this because it's certainly dense,
49:16but it's, it's simply, you know,
49:19under the hood it's some basic
49:21linear algebra of optimizing each
49:23feature in relation to stability
49:25criteria from the out of sample.
49:28Generalization.
49:28And so then you can ask the question of which
49:32parcels of the map should we trust the most?
49:35And those are the parcels that
49:36then we can use as a, you know,
49:38for further feature engineering.
49:40Interestingly though,
49:41what you can also do is then once
49:43you've done this and you find
49:45your trustworthy parcels right,
49:46the ones that truly generalize,
49:48you can then ask how do they covary in
49:51relation to the behavioral feature selection?
49:54Turns out there's a nonlinear relationship,
49:56right?
49:56Which means that the more extreme the
49:58person is on their behavioral loading,
50:01the more you trust their neural net.
50:03That makes sense, right?
50:05That's intuitive.
50:06And so when you then do this and purely
50:09filter people based on behavior,
50:11just take the 10th.
50:13And the 90th percentiles of the
50:16behavioral scores and segment that way.
50:19Then you can begin to segment
50:22based on neurobehavioral similarity
50:25of the map until you get.
50:27To the very peak of this patient
50:29selection and then you ask how accurate
50:31is this model and then you can see that
50:34it's pretty damn accurate at a sample,
50:36right.
50:36So in other words it's classifying people as
50:38plus or minus and in terms of their range.
50:41And then you can repeat this on
50:43a completely independent sample
50:44and again show that it works.
50:46In terms of segmentation by the way,
50:48Chris,
50:49this is the OCD and and skids data
50:53set which is cross diagnostic that
50:55we tried this with, right so.
50:57This isn't even patients schizophrenia
50:59anymore right.
50:59It's just saying does your brain
51:01map look like some norm that we
51:04can behaviorally incur right.
51:06So it's really about symptom
51:08configurations right.
51:09No longer about our you know,
51:11do you have a diagnostic category
51:13in it anyway,
51:14how can we leverage gene expression
51:16out to molecularly benchmark this
51:18and link it back to pharmacology.
51:20So again I'm going to just remind you
51:21of this framework Gemini dot, right.
51:23So now we can take this PC three
51:26map that is trustworthy.
51:28And again,
51:29correlated with gene expression patterns
51:31in the same way that we've done with MSD.
51:33And this is just proof of principle,
51:35right?
51:35Again, we can show some relationships,
51:37I'm not going to get into this too much,
51:39but for instance,
51:40you can show that the interneuron markers
51:43or GABA subunits or serotonin
51:45receptor subunits have
51:47correspondence with this map.
51:48It's I'm not claiming mechanism or anything,
51:52I'm just saying you could do this,
51:53right. This is doable.
51:55But finally to conclude, you can then.
51:59Benchmark this against our
52:01pharmacological targets.
52:02So this is actually an in vivo ketamine map,
52:06same GBC measure,
52:07healthies versus healthy people,
52:09placebo versus.
52:13Infusion, right?
52:14And then we can select people along the
52:17axis that presumably varies in relation
52:20to that work without ever optimizing it.
52:23We're not optimizing it yet.
52:24And then you take two people
52:25on the extreme ends, right,
52:27and these are their actual brain maps.
52:29These are two people diagnosed
52:30with schizophrenia, right?
52:31They both have the same diagnosis.
52:32Yet I'd like to argue that their symptom
52:35configurations are completely different
52:36and their brains don't look the same.
52:38Right. Yet we're treating them the same.
52:40We're giving D2 blockers to both of these
52:42people as the initial line of defense,
52:44when in fact, who knows,
52:46maybe one person would respond
52:47way better to close the people.
52:48And we have no idea that that
52:50is true or not true, right?
52:52But you can then quantify that using.
52:56This.
52:56Framework that Lisa has advanced in
52:59relation to a given target and you can
53:01say which person is more similar, right?
53:04So this person looks like PC-3,
53:06which looks like ketamine.
53:08So presumably this person will get
53:09worse if you give them ketamine and this
53:11person would maybe even get better, right?
53:13I don't know.
53:14But you could do the same thing with
53:17LSD now and repeat this for another
53:19access and recapitulate this principle.
53:22Now these maps can be iteratively optimized.
53:27This is a.
53:28Feature selection problem.
53:29Now we can use the LSD,
53:31psychedelic or ketamine target
53:33maps to find people who may
53:35benefit the most quantitatively,
53:37rationally,
53:38iteratively in a fast fail algorithm that
53:41says these two patient populations ought
53:44to show the opposite effects of this drug.
53:47And that's a strong inference
53:48rational framework, right?
53:49And so just to summarize,
53:52like I do think we need
53:54informatics and scalability.
53:55I do think we need to first and foremost.
53:58Their behavior,
53:58right?
53:59Select the right combination we need to
54:02achieve criteria for trustworthiness
54:04of those behavioral models, right?
54:07That's that's a must.
54:09Then and only then do we go to brain imaging,
54:12and then we need an interpretable.
54:14Robust and generalizable solution of
54:17the brain back which in turn then we
54:20can cross validate with pharmacological
54:23and gene expression and other metrics
54:26that that the field is bringing to bear.
54:28So, so in summary,
54:29I I do think that we have an
54:31opportunity here, right.
54:32I think that what we're doing in our
54:34department is truly transformative.
54:35I'm just one out of many people who
54:37are doing this work and we have I
54:40think an iterative framework right for
54:42really dissecting heterogeneity with.
54:44Imaging and behavior.
54:45And this can be optimized actually
54:47again for patient selection and
54:49precise delivery of of psychedelic
54:51compounds to the right patient.
54:53So I'll stop there and think
54:55I'm actually in time.
54:56Remarkable.
54:58Impressive. Thank you, Alan.
55:02That was a remarkably lucid presentation
55:04of some very complicated material.
55:09Questions. Comments. We have just a
55:12couple minutes before official end time.
55:25Ellen, I wonder if you could
55:27swing back to speculate.
55:29Since sort of the motivating you,
55:32you've spent a lot of time talking
55:33about the framework, the technology,
55:34the analytics and the potential and and
55:37just a couple slides on the psychedelics.
55:39In the middle,
55:40which I think is fine because, you know,
55:43it's important for us to recognize this.
55:46What what what you're working on,
55:47but I wonder if you could project OK.
55:50This group is motivated primarily by an
55:52interest in how do the psychedelics work?
55:54Who can they help? You know,
55:56how would you imagine over the coming years?
55:58And I know you've thought about
56:00this a lot because you're doing
56:01it and planning on doing it.
56:03So how, how would you envision
56:05applying this framework to a deep,
56:07to developing a deeper
56:09understanding of how psychedelics?
56:11Affect the brain both in terms
56:13of their acute, you know,
56:15psychotomimetic, dissociative,
56:16whatever effects and in terms of
56:19their longer term therapeutic effects.
56:21Yeah, so.
56:24So there
56:25there's two pieces of work
56:26that I didn't have the time to
56:27highlight and I was wrestling with.
56:29Do I want to go into them or not?
56:30And so, so one paper that Katrin
56:33published is looking at time
56:35dependent effects on the brain of
56:37silybin and the same imaging session.
56:39So one thing that she's done that I
56:42think is really impressive as shown
56:44the evolving neural neural effect
56:46of these compounds in the same
56:49person and showing how these maps,
56:51these topographies evolve as we.
56:54Selecting data overtime and that
56:56gives us confidence of the, the, the.
57:01Basically neural targeting engagement.
57:02So that's one thing that I think
57:04we really need more of these neural
57:07targeting management and so then then
57:08what Josh has actually done cleverly
57:10in in a in a sister paper to Lisa's paper,
57:12which is a whole nother beast
57:14that I didn't want to get into.
57:15John senior author on that,
57:17he's actually taken the the observation
57:19from Katrina was the observation
57:21and then fit gene expression to the
57:24computational models out of John's labs.
57:26And I,
57:27I I really didn't want to get into that
57:29because the the technical detail behind it.
57:31This is maybe beyond our time scope today,
57:34but you guys should invite
57:35him to talk about that.
57:37And So what he's done is put in
57:40gradients of 5H2A pharmacology
57:42into the biophysical models,
57:44right,
57:45simulated surrogate models and then
57:47fit them to individual people given
57:49LSD and found that actually explains
57:52the data way better in relation to
57:54their symptoms that they get acutely.
57:56So that's that's another paper.
57:58So these two pieces of work are all
58:00about neural target engagement.
58:01Confidence.
58:01And then Chris,
58:02your question is how do we apply this?
58:04What do we do with this in relation
58:07to helping people who may benefit
58:09from the the administration of
58:11these psychedelics and that has
58:13to do with finding individuals in
58:15the general population whose?
58:18Purported or potential neural
58:21system disturbance alteration,
58:24whatever term you want to use
58:25in relation to their behavior.
58:27In this case mood maps onto that
58:31neural target engagement profile right.
58:34And so the question becomes there
58:36are two questions right that we're
58:39after is the effect of LSD and or
58:42silicide been uniform across people.
58:44In other words if you give it
58:46to me and you and mark and.
58:48Anita across.
58:49Doses.
58:50Is our brain topography gonna look the same?
58:54Turns out not,
58:56that's not true and that matters.
58:59So for patient precision
59:01delivery that matters, right,
59:02because if say you are particularly
59:05amenable to respond to that compound,
59:07but I'm not right,
59:08then you wouldn't give it to me.
59:10And that has nothing to do with
59:12my behavioral alteration per se,
59:14but it may have a lot to do with the
59:17receptor occupancy and the the nature of the,
59:20you know, individual.
59:21Creation.
59:21Turns out this is unpublished work,
59:23but ketamine is even more high than.
59:25Functional,
59:26right?
59:26Turns out that there is no one axis
59:28of the average effect
59:29Academy on the human brain.
59:31It's actually highly dimensional,
59:33which obscures paradoxically
59:35the average effect, right,
59:37if you have multiple dimensions.
59:38And so this is what we're after.
59:39We're after mapping variation of
59:42psychopharmacology within and
59:43across people in order to then
59:45informed precision of how it
59:47relates to circuit disturbance.
59:53I don't thank you very much for your call.
59:54I have a question about the study
59:57that you show about the functional
01:00:01connectivity after LSD and was it in the
01:00:05acute phase or like post acute or like,
01:00:08I just want to know how long after
01:00:10those of like any psychedelic,
01:00:12we have a question. And two scans,
01:00:16one at 75 minutes, one at 300 minutes.
01:00:18So you could argue because the ketanserin
01:00:21and the LSD half lives have slightly
01:00:24overlapping and distinct curves.
01:00:26So we wanted one early and one late,
01:00:28and it turns out that that matters.
01:00:31So like do you know of any?
01:00:33Like is the Hyperconnectivity continues
01:00:35after for example after one week?
01:00:39I don't know. We don't know.
01:00:41That's a wide open question, right.
01:00:42So. So I don't know that anybody's
01:00:44looked at these sustained effects.
01:00:46What we have some stuff from Arena
01:00:48Australia's data set, right.
01:00:49Which again I it's it's really her
01:00:52story to report but but it turns
01:00:53out also when we give ketamine
01:00:55and look at people a day later,
01:00:57right, with F MRI and behavior,
01:00:59there's this and you guys know this, right.
01:01:01There's this crazy inverted V relationship.
01:01:03Some people have a sustained effect of
01:01:06the antidepressant phenomenon and other
01:01:08people go right back to where they were.
01:01:10And we don't know why this is.
01:01:12This is unexplored neurobehavioral effects.
01:01:14We don't know,
01:01:15but we know that everybody
01:01:17acutely shows some kind of.
01:01:19Clinical efficacy,
01:01:20but then a day later you have this rebound
01:01:23and who is rebounding and who is not why?
01:01:26You know there's ideas about synaptic
01:01:28plasticity and LTP like phenomena
01:01:30and and you know which people have
01:01:32that dendritic proliferation would
01:01:34then stabilizes and who is most
01:01:36likely to benefit from that kind
01:01:39of and you know psychedelics and
01:01:40ketamine very different from ecology,
01:01:42very,
01:01:42very different but maybe converging on
01:01:44some endpoint of exciting and driving
01:01:46the circuits into an LTP like phenomena,
01:01:48we don't know.
01:01:49And
01:01:50is there any relationship,
01:01:52any association between the
01:01:53degree of this hyperconnectivity
01:01:56and response to treatment?
01:01:58We don't know that's nobody has
01:02:00that data set again nobody's
01:02:03done MD either you know major,
01:02:06major depression or you know severe mood
01:02:08disturbance data set or experiment in
01:02:10which people were given either ketamine,
01:02:12indoor silybin or randomized to one of
01:02:16these arms scanned prior at baseline.
01:02:19Scanned acutely scanned post
01:02:21and then scanned later when they
01:02:23either sustain their recovered and
01:02:25understood what predicts it right.
01:02:26Like wide open question.
01:02:27And I think our department is unique
01:02:29position to go after this right.
01:02:31I think there's plenty of
01:02:33people who have the the means,
01:02:35expertise and talent to go after
01:02:36this and it's a fascinating question
01:02:38like this is what we need to know.
01:02:41Thank you. No, my pleasure.
01:02:45Anahita stole my question.
01:02:46I I also had the question about can
01:02:48we predict who was going to respond
01:02:50if they have a certain a certain
01:02:52pattern that that that that shows up.
01:02:55But I also wonder whether there have
01:02:57been other neuroimaging studies,
01:02:58not with psychedelics but with
01:03:00other treatments that showed
01:03:02changes in the connectivity such
01:03:05as T or presence. Totally, totally.
01:03:09So actually I forgot.
01:03:10So charity of Donna did a nice study
01:03:12where he looked at meta analysis of of.
01:03:15Uh, effects of ketamine. I don't wanna,
01:03:17I actually don't wanna forget that I
01:03:19think John's on the paper, John Christow.
01:03:21So there is some evidence of this in
01:03:23the literature that people have done,
01:03:24but just not the experiments that
01:03:26you guys were asking about that.
01:03:28But to your point, yes.
01:03:29In fact, we worked with Anil Malhotra and
01:03:32to look at the effect of clozapine, right.
01:03:35We're actually writing this up
01:03:36for publication as we speak.
01:03:37And so, yes,
01:03:38in fact you can predict and and
01:03:39turns out that these are very strong
01:03:42effects actually when you get
01:03:43people who are responding, right.
01:03:45The neural maps of predicting who responds
01:03:48are actually quite nice and clean.
01:03:50It's just that these are small samples
01:03:53like we're talking 141520 people, right.
01:03:54So it's just the first wave of
01:03:56work that's coming out, right?
01:03:58Like this is the next generation,
01:03:59right.
01:04:00They don't like precision pharmacology
01:04:02to dissect individual variation in
01:04:04relation to neurobehavioral effects.
01:04:06Like, I I'm just super excited,
01:04:08right,
01:04:08because I think this is actually happening.
01:04:10Like we actually see this now like
01:04:11the next 5 to 10 years as possible.
01:04:16Great. Thanks.
01:04:20Very excited. I'm sorry, go ahead.
01:04:24I was just going to say we're
01:04:25overtime and I wonder if
01:04:27we should wrap up that if.
01:04:28Well, I was just going to say,
01:04:30I'll make it quick then.
01:04:31So very exciting
01:04:32work, great presentation and the
01:04:37several questions that were just
01:04:40asked made me think about some of our
01:04:43work in the psychotherapy Development
01:04:45Center and the data that we've been
01:04:47collecting over the past 15 years
01:04:49by integrating F MRI measures into
01:04:51randomized clinical trials and the
01:04:53potential for these approaches we've
01:04:55been using things like connectome based.
01:04:58Predictive modeling, but there
01:04:59are many different approaches to understand
01:05:03better how people may respond to treatment.
01:05:08So I think again very exciting work
01:05:12would be great to to speak further.
01:05:15Because I think that we are at a
01:05:17stage and as you mentioned uniquely
01:05:20positioned within our department to
01:05:22make significant contributions to the
01:05:25understanding of how we might best
01:05:27advance psychiatric care for people.
01:05:29I couldn't agree more mark.
01:05:31And I think that your point there's many
01:05:34different ways that we'll all be in some
01:05:36family of general linear models, right.
01:05:39So and and people can approach
01:05:40this from various angles.
01:05:42I think that you know to your
01:05:44point it's going to be the data.
01:05:45That you guys have and we're going to
01:05:47continue to collect and it's going to
01:05:49be about the right behavioral response
01:05:51mapping which all of you are alluding to.
01:05:53So I I couldn't agree more and I think
01:05:56it's just it's it's just kind of you know.
01:05:59Maybe it's the sunny day,
01:06:00so I feel optimistic,
01:06:02but but I I I genuinely think that this
01:06:06was not possible 15 years ago, right?
01:06:08Like we didn't have the tech to do this
01:06:11and we actually now not only have the tech,
01:06:13but the information to do it.
01:06:14And so I want to just leave
01:06:16people with that idea,
01:06:17right, that that you know.
01:06:21And the fact that Yale is really
01:06:23stepping up into psychedelic
01:06:24medicine and and that you guys are
01:06:26doing this work and I couldn't
01:06:28be more supportive and whatever,
01:06:30whatever you need on on,
01:06:31happy to help.
01:06:36So with that, I think we
01:06:38should close for today. Alan.
01:06:39Thank you again for being here with us
01:06:43today and for covering this material.
01:06:46I believe next month, tentatively,
01:06:48Cyril has agreed to present either he
01:06:50or someone from his group or about some
01:06:52of the work that they've been doing,
01:06:54perhaps about DMT,
01:06:55where they've done some of the first work,
01:06:58both in health and in individuals
01:07:00with depressions. That'd be exciting.
01:07:01That's not confirmed yet,
01:07:02but we'll send out emails once we
01:07:06have a confirmation and a title
01:07:08and hope to see you all then.
01:07:10Take care, everybody.