Bradford Martins, MD, PhD. May 2024

May 13, 2024

Information

Title: Human brain effects of DMT assessed via EEG-fMRI

https://doi.org/10.1073/pnas.2218949120

ID11665

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00:00We're going to need to do this.
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00:23I'm trying trouble finding
00:24the little share screen guy.
00:28It is share the screen but you mean
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00:35So I think there should be bottom that.
00:39I think the first thing when you
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00:45share and then you can share your
00:48extended desktop desktop with us,
00:50right. It that's you.
00:52So if you stop sharing and share again,
00:55well the problem
00:56is I can't find the actual share
01:00button now it like vanished.
01:03Oh put escape, put escape,
01:05here we go, hit escape.
01:08And so if I
01:15put some presenter about now,
01:21here we go. OK, here we go.
01:24Now you can see the presentation
01:26in our presenter mode.
01:27Yeah, that's good now excellent.
01:30OK. So yeah, human brain effects
01:33of DMT assessed via EEG and F MRI.
01:36And so I think I said,
01:39you know they were in the F MRI,
01:40but they additionally one
01:42thing that they did is that's,
01:45you know not super common is
01:46to do both at the same time.
01:48So we have both,
01:51we'll get into it but the beautiful
01:54like spatial resolution with the FM,
01:56RI and then temporal resolution with the EEG.
01:59So
02:02just to I'm gonna kind of give some
02:04like background on the study and
02:05then we'll go into the results and
02:07all the the cool stuff they found.
02:08So participants for the study,
02:12basically people that were older than 18,
02:16you know, they couldn't have any
02:18of the MRI contraindications.
02:20It being a giant magnet,
02:21you can't have like any pacemakers
02:23or any metal and can't be pregnant.
02:29No psychedelic experience was
02:31it exclusion criteria.
02:33So these were had to be people
02:35who had at least one psychedelic
02:37experience in their lifetime.
02:39They also couldn't have had an
02:41adverse reaction to a psychedelic.
02:43And I tried to figure out what they
02:44used to define an adverse reaction.
02:46I didn't. I don't know if it was just a,
02:49you know,
02:50bad experience or if it meant like an
02:53allergic reaction or like, you know,
02:56they it triggered psychosis or something,
02:58something like that.
02:59But I don't know.
03:00It couldn't have an adverse reaction.
03:03They also couldn't have a history
03:05of psychiatric or physical illness,
03:07so they were all healthy controls.
03:10They also couldn't have a
03:12family history of psychosis,
03:14and they couldn't have excessive
03:16alcohol or drug abuse, which again,
03:18I tried to figure out what excessive meant,
03:20but I don't know, so whatever.
03:24Excessive it ended up being a 20 people,
03:29seven women mean age 33.5 and the standard
03:33deviation was around I think 7 or 8 years.
03:37So for the dosing they did two
03:40separate 28 minute scans and
03:43we'll get into why in a second.
03:45Like I said, they measured F,
03:47MRI and EEG concurrently.
03:51So they what they would do is
03:54they would put them in the scan.
03:57I might have this below,
03:58but yeah, so they scan.
04:00They would start 8 minutes prior to dosing.
04:03Then they would dose with 20
04:06milligrams of IVDMT in 10 milliliters
04:08of saline or a placebo of just
04:1110 milliliters of saline injected
04:14over 30 seconds in the scanner.
04:16They had eyes closed and they
04:18put an eye mask on to ensure that
04:20their eyes remained closed during
04:22the duration of the scan.
04:23And the reason they did two scans was
04:26because during the first scan they
04:29simple the participants simply lied
04:30in the scanner and then afterwards
04:33they did the visual analog scale
04:36to kind of write the intensity
04:38of the experience that they had
04:41during the second scan.
04:43Every minute they had the
04:47participant respond and give
04:49a real time intensity rating.
04:52So they were able to use,
04:55they predominantly used the first
04:58scan data and these data analysis,
05:01but they were able then to use in
05:03some of the analysis where they're
05:06specifically looking at intensity the
05:08the intensity ratings from when they
05:10were in the scan during the second scan.
05:15So some of the neuroimaging,
05:18I'm just specific like mechanics
05:23here the for the FM RI they used a a
05:27three Tesla Simmons Magnetum brewery,
05:30whatever it's a scanner.
05:33They used a 12 channel head coil
05:36which if you do any F MRI imaging
05:39is actually a relatively it.
05:41It's not a very precise head coil,
05:43but they had to use that in order
05:46to capture the EEG simultaneously.
05:48So sometimes you have up to
05:49like a 64 channel head coil.
05:51So 12 is is actually again
05:53kind of a poor resolution, but
05:58we'll talk about this a lot
06:00more on one of the slides.
06:03But in data processing of fMRI data,
06:06the decision to go with regressing
06:11the global signal or to not regress
06:17the global signal and use anatomical
06:20signals and replace replacement
06:23of that regressor, it's important.
06:25But again, I'm going to talk about
06:27it a little bit more coming up.
06:28I just want to mention it because
06:31I do think that's very crucial
06:33to all fMRI analysis
06:37after motion artifacts.
06:40There were a, there were as you can
06:43imagine being on DMT and an fMRI scanner,
06:46there was a a significant amount of
06:48motion and so they did find that motion
06:51correlated with some of the findings.
06:54And So what they did to kind of
06:57revalidate those finding was they tested
06:59on a sub sample of eight participants
07:01that did not have head motion and
07:03were able to validate that their
07:05findings in the larger sample held up.
07:09And I'm not I don't show those analysis,
07:13but that I just wanted to mention too.
07:15So for the EEG they did 31 scalp
07:18sites following a 10 to 20 convention
07:21and then I've shown that here just
07:24so you can see that the how the
07:26EEG leads are broken up to capture,
07:29you know for the forebrain lobes.
07:34They also had two leads for heart rate,
07:36artifact minimization and then
07:40anything outside of the 250
07:42window they regressed out.
07:46They also regressed out movements
07:48relate to jaw clenches and
07:56which I'm not. It was another signal
07:59that is I guess typically regressed
08:04out and they had two individuals who
08:10also had excessive data artifacts who
08:13could not be included in the analysis.
08:16And so in all of the data analysis that
08:22will show involving both EEG and fMRI,
08:26it's actually 12 of the 20 participants
08:28they had to get rid of eight of them.
08:34Some background on data analysis
08:35and FM R I I just because I'm
08:37going to be using these terms.
08:39So when we're looking at F MRI data,
08:42you know you get these nice pictures
08:45of brain regions that are activated
08:47and what one of the main measures that
08:50we use is functional connectivity.
08:52And so functional connectivity is
08:53essentially when you're looking
08:55at two regions of the brain,
08:56how In Sync is the BOLD signal.
09:01And so here you can see that they
09:05showed and the intraparietal sulcus,
09:07the posterior cingulate cortex
09:09and the medial prefrontal cortex.
09:12And in this example,
09:14the posterior cingulate and the medial
09:16prefrontal are very highly correlated.
09:18So they have a pretty high
09:20functional connectivity.
09:21And then the intraparietal sulcus
09:25or infraparietal sulcus is a
09:28much less correlated.
09:30So it actually is,
09:32I think it's actually pretty
09:33negatively correlated here.
09:34So they would have a a very negatively a
09:37negative functional connectivity score.
09:42So this can be done.
09:45Functional connectivity can be measured
09:47both in terms of the individual brain
09:49regions as well as whole networks.
09:52And so they're going to do both.
09:55And in terms of the whole networks,
09:59there are pretty at this point pretty
10:02well established resting state networks
10:07around. I usually see eight,
10:08but seven is also pretty common and
10:11so I'm showing five of them here.
10:14The default mode which for
10:18anybody not familiar is involved
10:19in a lot of internal reflection,
10:21kind of a lot of thinking about
10:25the self dorsal attention,
10:27a lot of working memory,
10:32some language salience is you know
10:36the just attending to information
10:39frontal parietal does a lot of
10:43executive functioning, visual,
10:45self-explanatory and then what's
10:47not shown is the somatomotor
10:50cortex and the limbic cortex.
10:52And so they included both of
10:54those as networks as well.
10:56And what they're going to do with
11:00these is you can measure both the
11:03connectivity of the regions that
11:05are within the network and you can
11:08measure the connectivity between the
11:10entire networks with other networks.
11:13And so the words that they use
11:15throughout the paper for the connectivity
11:20within any individual network,
11:22they call integrity,
11:24the network integrity and then the
11:28connectivity between any two networks
11:31they call the degree of segregation.
11:33So networks with that are not commuting,
11:36communicating with each each other are said
11:38to have a higher degree of segregation.
11:41As you'll see,
11:43when they start to communicate
11:44with each other,
11:45they decrease in segregation.
11:50They like I said, they did both a network
11:54level analysis and then they used this
11:57anatomical brain Atlas as A to create
12:00RO is and they did 112 regions with
12:05every other region in the brain as a
12:09global functional connectivity analysis.
12:12And so that'll be in a lot of the results.
12:16One other concept that is kind of there's
12:19not a great way to visualize this,
12:21but I think we all know conceptually
12:24how it works is something they call the
12:27principal gradient was essentially that as
12:30you know the the human brain developed,
12:33the more ancestral parts of our brains
12:35are located towards the middle and then
12:37as you move outwards in the cortex,
12:40you get to the higher level
12:42thinking cognitive functions.
12:44And so they frequently refer to
12:46moving up the gradient or being
12:49at the top of the gradient.
12:51And so that essentially is referring
12:53to the out, you know,
12:55the outer layers of the cortex
12:56involved in our higher order thinking.
13:01So, so on the data analysis for the EEG
13:05side and as Full disclosure, yeah, yeah.
13:12Oh, did somebody have a question? Oh,
13:15I think somebody's mic or something. OK.
13:19Sorry Brad, can I ask a quick
13:21question about global connectivity?
13:23Is it different between,
13:25so it's all the RO is with
13:28all of the other RO is.
13:30So it's you get them one of the
13:33each of them as C and then yeah and
13:37I'll, I will I'll show you in the results.
13:39But essentially they have a 112 by
13:43112 grid and it is all the RO is
13:46with all the other RO is and that's
13:49the global functional connectivity.
13:53So yeah, Full disclosure,
13:57I am not as burst in the EEG,
13:59so I know basics.
14:01If there's any questions regarding
14:03EEG methodology, let me know.
14:06But the EEGS was, you know,
14:08divided into I think the normal band.
14:10So you have the delta, the Theta,
14:12the alpha, the beta and the gamma.
14:14And just as a a review since we'll
14:16be talking about them a lot.
14:17So working from the kind of the bottom up,
14:21the delta waves are usually
14:23associated with dreaming,
14:25sleep, loss of bodily awareness.
14:28Theta is often creativity, insight.
14:31Again, some like deep meditation, dreaming,
14:34and quote UN quote reduced consciousness.
14:37Alpha is our, you know,
14:38our our usual like right
14:41after you close your eyes,
14:43physically and mentally relaxed waves.
14:46Beta is our awake, alert consciousness.
14:48This is how we are usually living our lives.
14:49And then gamma is heightened perception,
14:52learning and kind of your more
14:55intense cognitive processing.
14:58Something that they refer to a lot
15:03is the diversity of the signal.
15:06And So what they did in order to
15:08determine kind of the diversity of
15:11the EEG signal is they did this.
15:13Lempel, Zev, Ziv, Maybe analysis,
15:18which essentially takes a signal.
15:20Like you can see there's a little bit of a
15:23curve to it and it's converts it into it,
15:27gives it a mean and then gives it basically
15:30a binomial distribution over the mean.
15:33And then by doing so,
15:35they're better able to compare the
15:39different brain waves from different
15:41areas of the brain and say that,
15:43you know, with this standardized
15:45mean that there's a lot more
15:46diversity in the brain wave.
15:50And then the last thing that I'm
15:52gonna mention in terms of just the
15:55EEG data analysis is that they talk
15:58about forward and backward waves.
16:01And so I'll admit this is probably the
16:04one that I am least familiar with.
16:06But my understanding is that essentially
16:10along the midline you have these EEG nodes.
16:13You get the signal from those nodes
16:17that is put into A2 dimensional space.
16:21You can see the the waves here,
16:24those wave, that two-dimensional
16:26space is then transformed to be from
16:29like time space into frequency.
16:31And so you can see kind of along
16:34in the Gray there the waves along
16:36again like this midline axis.
16:39And what they do is they pull out
16:42the maximum values of the waves in
16:45both the upper and lower quadrants.
16:48And then the upper quadrant is
16:50considered this Max value which
16:52represents the forwardness of the
16:54power of the wave and the lower
16:57quadrant is the Max value represents
16:59the backward power of the wave.
17:02And they log transform these so
17:04that you get this final score.
17:06And if the final score is positive
17:08then the wave is said to be more
17:10of a wave with forward power and
17:13if it's negative then the wave is
17:16said to have more backward power.
17:19That's really my extent of my understanding.
17:21So if anybody who has a lot of EEG wants
17:22to jump in and tell me I'm completely wrong,
17:24feel free to do so.
17:26But that's the gist of the
17:29forward and backwardness.
17:31So getting into some results.
17:33So the first thing we have here
17:37are just the subjective scores
17:39of the participants via the three
17:42measures that they did.
17:43So on the left under A is
17:46the visual analog scores.
17:48And you can see completely as expected
17:52that these individuals had all sorts
17:55of changes in their visual perception
17:59significantly more than placebo.
18:01You know, geometric patterns,
18:03sense of space and time is altered,
18:06rich visual experience felt
18:08completely immersed in the visuals,
18:11different reality or dimension,
18:12sense of time is altered, etcetera,
18:14etcetera.
18:14And then if you look at the bottom,
18:17the the one in which the placebo is
18:20significantly greater than DMT is,
18:21I felt completely normal.
18:23So not feeling normal under the drug B.
18:27Here we have the altered states of
18:30consciousness questionnaire which
18:31breaks it down into I believe it's 12
18:35into these like categories and you
18:38can see disembodiment and elementary
18:40imagery are as well as complex imagery
18:43actually are kind of the three most
18:46sided areas of altered consciousness.
18:50And then you have C which is the
18:52mystical experience questionnaire.
18:53And I I always love when we kind of group
18:56the mystical experience questionnaires,
18:58because the one that always comes
19:00up as the most powerful or the
19:03most prevalent is ineffability.
19:05And so, OK, well, we can't describe it.
19:08So there it is, it's ineffable.
19:13So that's the breakdown
19:15of the questionnaires.
19:16So here is the what I was describing
19:19the kind of within network and
19:23between network comparisons.
19:25And So what you can see is that
19:29in a you have the seven networks,
19:32so visual, somatomotor, limbic,
19:34dorsal, anterior salience,
19:36frontal parietal, default mode.
19:39And all of the networks with the
19:43exception of the visual and the somato
19:47motor saw significant changes in the
19:51within network connectivity between
19:55the from placebo and DMT.
19:59And what they found really was,
20:01if you looked at the DMT, is often
20:09the coherence of the network.
20:13Is decreased. It is communicating
20:19less In Sync with itself
20:24and it's one of the results
20:28that they're going to kind of
20:29repeatedly talk about here.
20:30Is that the in terms of like this
20:34concept of principal gradient is that
20:37all of these networks that show the
20:40highest differences in their integrity
20:43are these higher order networks,
20:45whereas the what we consider lower
20:48on the principal gradient are
20:50more basic brain functions or the
20:53networks coinciding with more and
20:55more basic basic brain functions
20:57are actually relatively intact.
21:02B here shows this is a plot of
21:07the network connectivity with
21:09all the other networks. And so
21:14on the left you can see the placebo,
21:16on the right you can see the DMT.
21:18And then of course here's the difference.
21:20And you can see again there are these
21:24significant changes occurring in the
21:27higher order networks where they're
21:30becoming more connected with each other
21:33in the DMT than during the placebo.
21:38Here's where I want to talk about global
21:40signal just for a second because again,
21:42I think it is something that when
21:44you're reading any fMRI paper,
21:45you need to take into consideration.
21:47And so in the main results of the paper,
21:51they did not regress out the global signal.
21:53Instead they regressed out.
21:55These three signals that they claim will
21:58take will correspond to the parts of the
22:01signal that are not neuronal in nature,
22:04and so they regressed out a ventricle signal,
22:08a draining veins signal,
22:11and a white matter signal.
22:14But then they did do this supplementary
22:16analysis shown here in B,
22:17where they did regress out the global signal,
22:20and you do see some differences.
22:21And this is pretty typical what that
22:24although a lot of the results ultimately
22:26end up still being significant,
22:28the degree to which they're
22:30significant is decreased.
22:32And then even the if you look at
22:34some of these lower order areas,
22:37the visual and the somato motor
22:39cortex in the main results,
22:40there's no difference.
22:42But then actually what you see in
22:44when you regress out the global
22:47signal is that they are actually
22:49less in tune with each other.
22:52They're,
22:52they're or I should say they're
22:55more negatively correlated and so
22:58they are going to again report
23:00predominantly these results up top here.
23:03But just to note that if they do
23:07regress out the global signal,
23:08there are some significant differences
23:11and so to take any interpretability
23:13in this with a small grain of salt.
23:19So here is a picture showing increases in
23:22the kind of again the global functional
23:26connectivity as mapped onto a a 3D brain.
23:30And so you see that with the placebo
23:37there is you know a lot of either
23:41very minimally correlated network
23:44connectivity or so even some like
23:47negatively correlated or actually
23:51I guess this one's not negative,
23:52it's just minimal.
23:54With the DMT you see things become a lot
23:58more correlated between the the networks.
24:03D is showing you the breakdown
24:05of the networks in the Atlas.
24:06And so you can kind of see that
24:10especially in this this anterior
24:14cingulate corresponding to kind of the
24:18default mode salience network area,
24:21it becomes very highly correlated with the
24:25rest of the brain during the DMT experience.
24:31This I thought was interesting and I
24:34wanted to potentially get out of the
24:36PowerPoint for a second, but so they,
24:38so here's the global functional
24:40connectivity map again showing you
24:42kind of the regions of the brain
24:45that are are very like function are
24:49correlated with the rest of the brain.
24:51And these are now broken down into regions,
24:53they're not broken down into network.
24:54So that's why they look a
24:56little bit different.
24:57And so this, that's this MAP.
25:01And then what they did was they took
25:03a PET imaging study and they showed
25:07the serotonin 2A receptor density
25:10and the kind of comparison of where
25:13the serotonin 2A receptors are the
25:16most dense versus you know where
25:19these regions of global function
25:21activity are the most changed.
25:24And, you know,
25:27they do seem to be fairly consistent regions.
25:30You see a lot of of receptor density,
25:33you know,
25:34in the temporal pole and you
25:36see a lot change.
25:37You see a lot of receptor density
25:38in the in the frontal cortex,
25:40a lot of change in the frontal cortex.
25:43And then what they did.
25:44And I thought this was cool because
25:46it's just so simple and so easy.
25:49And can you see my Internet screen
25:52or are you still in the PowerPoint?
25:55No, we see your Internet screen.
25:57Oh, perfect. OK, great.
26:00So the the if you've never used neurosynth,
26:03it's a very cool website and
26:06what you can do is you can go on
26:09and if you have a region of the
26:12brain that you're interested in.
26:13So here I just put in the posterior
26:16cingulate and it's one of my
26:18favorite areas of the brain.
26:20And you can go to studies that
26:23you know reference this or list.
26:26They pull from studies words that
26:30are associated with the specific
26:35voxels that you're
26:37clicking on in the picture.
26:39And so like here's a list of
26:41like the top 30 terms, right,
26:43of the that were associated
26:45with the posterior cingulate,
26:46obviously a lot of kind of anatomical.
26:49And then you have some resting state.
26:51Autobiographical memory goal,
26:53directed default mode, recollection.
26:55And so then what they did.
26:58Now let's see if my PowerPoint comes back.
27:02Maybe PowerPoint. Why is
27:08it? Oh, I know what it's.
27:10I know what's happening.
27:15There we go,
27:21right. So then they found that
27:23these like they created these word
27:26clouds where the regions that were
27:28them showed the greatest functional
27:31connectivity as well as the highest
27:33level of serotonin receptor density.
27:35And you see that again these are regions
27:39that are involved in tasks that are we
27:43would consider higher order processing.
27:45And so I just again I thought it was
27:47very simple to like prove the point of
27:49these are regions that are higher order
27:51and just pull on a you know thousands
27:53of studies in order to show that with
27:56almost you know very minimal work.
28:00Here are the some of the
28:05subjective measurements along
28:07with the neuroimaging results.
28:09So further to the left you have the EEG
28:14bandwidths you see some correlations with.
28:19I thought this was interesting,
28:20the alpha waves corresponding or
28:23being significantly related to entity
28:26experience and different dimensions.
28:29The diversity of the waves being kind of
28:31related to more of the bodily effects,
28:33what's considered a real or rich experience,
28:37very little of the network level analysis,
28:42the integrity that of the networks was
28:45related to any of the subjective measures.
28:49Some of the more global connectivity
28:52though was related to being in
28:55this like synesthesia and dreamlike
28:58state which is which is interesting.
29:02There's this measure that we'll
29:03talk about when we get to it,
29:04but there's also this measure of
29:06gradient which wasn't really court,
29:09didn't really correspond with anything.
29:11And again you know these regions
29:14down here the the forward and the
29:17backward waves and then somato
29:19motor limbic corresponding with
29:21ineffability and some of the more you
29:25know mystical experience categories.
29:27So this is a lot and I unfortunately I
29:30just couldn't find a way to break this
29:32down into being a a simpler picture.
29:34So just bear with me as we move
29:35through all these,
29:36But so first in A we have essentially the
29:42intensity of the subject of the experience,
29:46so the intensity during that's that
29:48those ratings during the second scan
29:51with changes in functional connectivity,
29:551st just global function functional
29:58connectivity mapped out in
30:00three-dimensional space.
30:01Here again you see that
30:03frontal parietal network,
30:04I mean the frontal lobe is has a lot of
30:12global functional connectivity and is also
30:16very highly correlated with the intent,
30:20the degree of intensity of the scores when
30:23you break it down into actual networks,
30:26the salience frontal parietal and
30:29default mode network with the other
30:33networks is very highly correlated
30:36with the degree of intensity.
30:38And also,
30:38I guess the limbic system is
30:40a little bit too.
30:41And then here is that global connectivity
30:44map I was talking about where you
30:47can see every single brain region
30:50mapped out in terms of the degree to
30:52which it's associated with intensity.
30:55And you see the same thing as in the others,
30:57but by region that there's just
30:59very significant correlations
31:01with intensity with the salience,
31:03frontal,
31:03parietal and default mood network regions.
31:06They did
31:10A&amp;B. Here we have a very similar design,
31:13except this time intense
31:14in instead of intensity.
31:16What we have is the degree
31:19of DMT in the plasma.
31:21So again very similar to the above
31:24findings where the level of DMT
31:27in the plasma was very strongly
31:29correlated with the frontal network
31:31as well as the OR the frontal lobe
31:34and then the salines network,
31:36frontal parietal default mode
31:38and again those brain regions
31:41making up those three networks.
31:45See here is the regional
31:49functional connectivity kind of
31:51mapped out in a different way.
31:55This is across time and so you can
31:57it's basically just showing you
31:59the changes in functional a global
32:01functional connectivity over time.
32:03And what's significant about it is
32:06when you compare the DMT to the
32:09placebo about set for the 1st 7
32:12minutes after that the DMT dose,
32:15you have these very,
32:17very high changes in the global
32:21connectivity that then slowly
32:22decrease over time.
32:23And these during these first 7 minutes,
32:26these changes in global functional
32:28connectivity are significantly
32:31correlated with the the intensity
32:35scores and the plasma DMT.
32:39This is when those these correlations
32:41that we talked about just now are
32:44that they're at their strongest.
32:45D is just MAP is mapping this
32:48out minute by minute and so let
32:52me see on time. So just for times sake,
32:55I'm not going to go too much into this,
32:57but essentially you're what they're
32:59able to do is break down the BOLD
33:01signal in the FM RI and do into little
33:05chunks and then they measure at the
33:07each individual minute the signal.
33:10And so you can see that here's minute zero
33:14and then at minute two there are these
33:17significantly significant changes where
33:20those three networks default mode frontal,
33:24parietal and salience network become just
33:27very highly integrated with each other
33:30and that's that's shown there in the red.
33:32And then over time you know that
33:34they slowly dissipate back out,
33:37although at at least at minute 7,
33:39you know some of those correlations
33:41are still persisting.
33:42But that those initial between
33:44minute two and minute three,
33:46you see these just the the whole brain kind
33:49of becomes just integrated with itself.
33:51And then that's essentially E is
33:55showing for each and each little dot
33:57here is an individual brain region.
33:59This is the degree of the serotonin
34:01receptor density at that brain region.
34:03And then the X axis is the global functional
34:08connectivity and the intensity scores.
34:10And there's this positive correlation
34:11as we'd expect where the more
34:14serotonin receptors you have,
34:15the more those regions are contributing
34:17to the global functional connectivity
34:18and the intensity
34:22on this figure.
34:24So this is the kind of gradient
34:27mapping that I was talking about.
34:31Again, without getting into it too much,
34:33you know they they wanted to prove
34:36this this principal gradient that
34:37they've that they've come up with.
34:39And So what they're showing here is these
34:44regions that are sort of segregated and
34:48working independently during the placebo
34:51all become kind of meshed together.
34:54And you can see that the two extremes,
34:56the two, the degrees of segregation
34:59of the regions all converge
35:01towards a middle ground here in B.
35:05This is showing the degree
35:10of integrity of the regions.
35:14And So what kind of along
35:16what we said earlier,
35:17the here's the somatosensory cortex,
35:19here's, you know,
35:21some of the visual cortex information
35:23and those remain pretty segregated and
35:28the integrity of the networks remains
35:31intact throughout the DMT experience.
35:34Whereas other these like higher order
35:38regions corresponding to the default mode,
35:40the frontal,
35:42parietal and the salience network are all
35:48decreasing in their network integrity.
35:52And you know,
35:53they kind of show this a bunch
35:54of different ways,
35:55but here's another way they show it.
35:56And here's the integrity on
35:59this nice little plot.
36:01And then here they are showing
36:04that again these are regions
36:07that are sort of acting in this,
36:12this gradient where the two opposite ends
36:15represent higher degrees of segregation.
36:17And as you converge towards 0,
36:19the the networks are more
36:21integrated with each other.
36:23And so during the DMT experience,
36:25you have all these networks converge
36:27towards 0 where they again decreased
36:31segregation increased or yeah,
36:35decreased segregation,
36:37decreased internal integrity.
36:42Here's some of the EEG data.
36:43And so in a just showing the
36:50kind of the different waves
36:56in terms of the changes with DMT.
37:00And so the the most significant
37:02ones here are the alpha.
37:04There was the significant decrease in
37:08alpha waves and then there was this
37:11big increase in signal diversity.
37:15B shows this in terms of the whole brain.
37:18Here's the frequency or the power
37:21during placebo of essentially a
37:24wave representing the whole brain.
37:26Here it is during DMT.
37:28Again just breaking down signal
37:31diversity you can see very clearly.
37:33Here's a a different
37:38brain signals and you can just
37:41see how condensed they are in the
37:44placebo and how how widespread
37:45they are in terms of their this
37:48measure of diversity in DMT.
37:52Here they're looking at the E&amp;C,
37:55they're looking at the changes
37:57in the EEG with intensity.
38:00And so the delta scores are
38:04very highly correlated with the
38:06degree of intensity as well
38:08as the the diversity. Measure
38:13D is showing basically that same thing
38:18over time where the intensity score is the
38:21green line and then the red line is the
38:23DMT measures for each for the delta waves,
38:26alpha waves and the diversity scores.
38:29And during these Gray boxes is when
38:32there's a significant correlation between
38:34those waves and the intensity measures.
38:37And so you can see delta waves have this
38:40increase from until about 5 minutes.
38:42That corresponds with increase in intensity.
38:44Alpha waves have this decrease
38:47that work goes for about 8 minutes.
38:48That corresponds with intensity.
38:50And then the signal diversity kind of
38:53lasts most of the trip where they're they
38:55have this increased diverse signal that
38:58corresponds with increased intensity.
39:01Again, here's kind of the
39:03forward and backward for time.
39:04I'm just going to kind of move forward
39:06because I think these are the next
39:07things are a little bit more interesting.
39:09So here they break it down by
39:12wavelength and wave, I mean wave type.
39:14And so here you have the frontal,
39:16parietal, delta regions.
39:19These course, the changes in
39:22these correspond with regional,
39:24the changes in the region,
39:26global functional connectivity.
39:27Of most of the networks the only one
39:30that's not significant is the limbic
39:32network and so that's shown here.
39:34These increase or the change, yeah,
39:38the increase in the delta signal
39:40changes along with the increase in
39:43the global functional connectivity.
39:44The alpha waves are the opposite where
39:47the decrease in the alpha signal
39:49corresponds with the increase in
39:51the global functional connectivity.
39:53These were measured in the parietal,
39:55the largely the parietal lobe.
39:59Here you have the gamma and the gamma.
40:03Waves corresponded with only the
40:06limbic and the frontal parietal
40:10networks and then here's the measure
40:12of again the signal diversity.
40:15And so signal diversity as it
40:18increased corresponded with the global
40:19functional connectivity increases
40:20of the limbic network frontal,
40:22parietal and default mode and so some
40:25kind of summary and discussion points.
40:27So you know they they talk about the
40:31transmodal association cortex pull or top.
40:33You know one thing with this group is I
40:35don't think they understand acronyms,
40:37but the essentially the transmodal
40:39association cortex pull is like the top
40:42of the brain, the top of the cortex.
40:44And So what they're saying is that
40:46the increased communication they have
40:48this increased communication at high
40:50level associations in this decreased
40:52at communication at the lower level
40:54sensory motor as well as again the D,
40:57this decrease in segregation of the top
41:00of the brain from the rest of the cortex.
41:03And ultimately this dysregulation
41:05of brain activity which is evidenced
41:07by the reductions in alpha,
41:08the increases in gamma and the
41:10signal diversity was associated with
41:12this increased global functional
41:14connectivity at the brain's outer cortex.
41:17The increased delta may serve,
41:19and I thought this was interesting
41:21as a marker for a significant
41:23alteration of consciousness,
41:24whereas typically it's been linked you know,
41:25to the sleep,
41:27to those like reduced consciousness level.
41:29But they argue that maybe that
41:31instead it is actually a transition
41:34or this altered consciousness rather
41:36than just reduced consciousness.
41:38And they actually did a whole analysis
41:42looking at the degree of sleepiness
41:45and found that there was no correlation
41:48between sleepiness and the delta waves.
41:53The DMT induced serotonin
41:565 HT 2A receptor agonism.
41:59This regulates the high level cortical
42:01activity and limbic activity.
42:03The densest expression of these receptors
42:05is found in the top of the cortex,
42:07and as the present study have shown,
42:09this is also where psychedelics have
42:11their initial and most robust effects.
42:13The receptors are also densely expressed
42:15in the primary visual cortex and DMT is
42:18known for inducing vivid visual imagery.
42:20And they did find this significant
42:22relationship between changes and
42:24global functional connectivity
42:25induced by DMT and the ratings of
42:28intense subjective experiences.
42:30So the two big conclusions from
42:31this study where that there's this
42:33increased communication between the
42:34top of the cortex and the rest of the
42:36brain and this could be interpreted
42:38as evidence of expanded information
42:40processing in the sort of hyper
42:43associated style of cognition.
42:45And where they proposed taking this
42:48as is that asking the question of do
42:51the magnitude of these global brain
42:53changes relate to kind of increased
42:55plasticity in the brain in terms of
42:58a both neuronal and behavioral sense
42:59And is that why we are seeing that
43:02these drugs are so beneficial in terms
43:04of changing behavior in people with
43:07psychiatric and substance use disorders.
43:09So that's what I've got.
43:11I like to end with a little joke so
43:14there and I can answer any questions.
43:21Yeah.
43:23Thank you so much, Brad.
43:24Very good presentation and interesting paper.
43:29So I think the last point that they made,
43:33the neuroplasticity,
43:34if we really believe that like
43:37neuroplasticity is one of the leading
43:40mechanisms of the air therapeutic effects,
43:43it's actually that's a very
43:45good question to see how these
43:48functional changes in functional
43:50connectivity really translates
43:53into neuroplasticity changes.
43:55I don't think that we know any
43:57evidence to answer that question.
43:59I don't know. Yeah, I don't know any.
44:02But I thought I did think that was
44:04a very cool framing for the kind of
44:05where we want to take it as a field.
44:08Mm hmm. Right. Let's see if there is
44:11any question or discussions, comments.
44:18Hi Brad, thanks.
44:19That was really interesting.
44:22I I I'm not a neuroimaging specialist
44:25I I'm starting up some studies with
44:29neuroimaging and migraine after
44:31psilocybin I and I'm looking at the
44:34at the literature as it relates to
44:37migraine and I I appreciate how
44:39psychedelics can be used as a tool
44:41to understand consciousness as well.
44:43Are there comparable conditions that
44:46that we can compare these these these
44:49functional changes to like certain
44:52disease states or something else
44:54because we're we're not just starting
44:56to study consciousness right now like
44:58we've been doing it for some time.
45:00So is there something and that,
45:02you know,
45:02we can compare DMT or psilocybin too,
45:04to kind of get a sense
45:06of what they're doing?
45:08I
45:08mean, the classic one right is,
45:10is psychosis and schizophrenia,
45:13and there are very key
45:16differences between those.
45:18But the the thing that is so
45:22different about psychedelics than,
45:25you know of most other kind of
45:30psychotropic drugs including like
45:31MDMAI mean if we're talking purely
45:34like the classical psychedelics
45:35is the degree to which these this
45:38changes in perception occur without
45:41changes in level of consciousness.
45:44And so again like the sort of
45:48visual hallucinations that we
45:50can sometimes see or you know,
45:54auditory,
45:54there's less auditory hallucinations
45:56during these substances,
45:57but those are like really
45:59the closest that I know of in
46:02terms of kind of non substance
46:06induced biological processes.
46:15OK, I have a question as well.
46:17First, thank you Brad for
46:19this clear presentation.
46:20There was a lot of data and thank you
46:23for explaining the basic concepts.
46:26So my first question was about
46:30visual cortex or visual network.
46:32And although we see a lot of visual
46:35hallucinations with psychedelics,
46:37the connectivity was actually
46:40decreased compared to placebo.
46:42When you interview this visual
46:45when you use psychedelics,
46:48yeah. So it it's decreased to
46:52other networks is what they found,
46:55but it remains intact in terms of
46:59the communication within itself.
47:01So you know it may be that the
47:05reason that the visual components
47:07of the hallucinations are so strong
47:10is because compared to the rest
47:13of these higher order cognitions
47:15which are kind of just the visual
47:18cortex is still maintaining its.
47:21It's like normal it's heightened but
47:24the integrity within the network
47:27itself is remaining is remaining into.
47:32I'm trying not to use the same word
47:35twice but is remaining intact and is
47:37not becoming kind of in meshed with
47:39all these higher order cognitions.
47:42That makes sense completely.
47:44And one question beyond this study.
47:46If we think that these changes are the,
47:49you know the mediator or the neural
47:52substrate of behavioral plasticity,
47:54do we have or cognition plasticity?
47:58Do we have any studies that
47:59show because you know,
48:00we say that psychedelics
48:02have log lasting effects.
48:03Do we have any data that
48:05shows that these changes,
48:06these increase decrease in
48:08integrity and increase in global
48:11functional connectivity lasts,
48:13I don't know one week at least later,
48:152 weeks later
48:18There's definitely a psilocybin study.
48:20I'm trying to remember who did
48:22it because I don't think it
48:24was the Carhartt Harris Group.
48:26But they did a study and found
48:30that essentially for like the week
48:33following a psilocybin dosing,
48:36the changes remained like the
48:41kind of like the, you know,
48:42if we go back to the entropic brain
48:44hypothesis and everything's mashed together,
48:45there's increased entropy that those
48:48increased entropy changes persisted
48:50in like the one week follow up.
48:52And then I think there was either
48:54an 8 week or a three month follow up
48:58and they found that the brain had
49:01shifted in terms of how the like
49:04the entropic changes had occurred.
49:07They still existed,
49:08but they were decreased in the degree
49:11to which they existed and there were
49:13some new changes that seems like they
49:15had kind of come about as a result
49:19of those initial entropic changes.
49:21But you know,
49:23anecdotally,
49:24what we're seeing from of the
49:27other studies is that it's about
49:296 months that these seem to cause
49:31changes in the brain that persist
49:34before kind of reverting back to
49:36some of the old behaviors.
49:41But that's all. Again, that's
49:42that last piece is anecdotal
49:45interesting. Thank you so
49:47much analysis. Now
49:51I'm sort of following up
49:52on that last question.
49:53Some studies have found that the acute
49:57psychedelic experience correlates with
49:59lasting clinical effects and others don't.
50:01And because I study headache disorders,
50:03it's largely found not to be relevant
50:05and for headache we use lower doses,
50:08even even some sub psychedelic doses.
50:10So that's kind of an open-ended question.
50:13You know, what do you think's going on there?
50:14Does it really matter what
50:16happens during the the dosing?
50:20Or are we, you know,
50:21looking just at the psychic experience
50:23and not thinking about some other
50:25changes that might be happening,
50:27happening on like the physiological
50:29level or something else?
50:31Yeah.