Bradford Martins, MD, PhD. May 2024

Transcript

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• 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&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&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.