Evan Gordon “A mind-body network alternates with effector-specific regions in primary motor cortex”

Transcript

• 00:06Hey, everybody.
• 00:06Thank you very much for coming today.
• 00:08I'm going to talk a little bit
• 00:10about some very interesting,
• 00:11very surprising findings we have
• 00:14observed recently using functional
• 00:16connectivity in the primary motor plants.
• 00:19So primary motor cortex,
• 00:21everybody's just favorite part of the brain,
• 00:24really most interesting part of the brain.
• 00:29No, it's it's not right is kind of boring.
• 00:33We all care about frontal cortex,
• 00:35we care about lateral parietal cortex.
• 00:38And the reason is that we kind of already
• 00:40know how the motor cortex is organized.
• 00:43How can we hide this in some way?
• 00:53Already it's already
• 00:54minimized by this. Right, so.
• 00:59Yeah.
• 01:03Yeah, we already know how the
• 01:05motor cortex is organized.
• 01:06We know that the motor cortex
• 01:08is organized as a homunculus,
• 01:11and we've known this for a long time.
• 01:13The homunculus was first described
• 01:15by Wilder Penfield and then 1930s.
• 01:17And the basic idea that.
• 01:20Almost everybody here probably knows is
• 01:22that their motor representations organized
• 01:24as a linear progression of body parts.
• 01:27They just kind of run up your body
• 01:29from your foot on the dorsal medial
• 01:32portion of your primary motor cortex,
• 01:34wrapping around to your your your body,
• 01:39your shoulder, your elbow, your hand,
• 01:41and then going to your eyes,
• 01:44your face down in the lateral ventral
• 01:48lateral portion of the primary motor cortex.
• 01:51You know this is the first thing
• 01:52that we if this isn't the first
• 01:54thing that we all learn in first
• 01:55year introductory neuroscience class.
• 01:57It's it's the first thing we all remember.
• 01:59Right.
• 01:59And the reason this is the first
• 02:01thing we all remember is that this is
• 02:03incredibly cool and salient right.
• 02:04Like I mean some things you learn
• 02:07in intro it's like Oh well neurons
• 02:10have like these dendrites and axons.
• 02:12But this little man in my brain that is
• 02:15cool and I can remember that it's salient.
• 02:17It's it's really sticky concept, right.
• 02:20Like and so.
• 02:21This idea has really stuck it.
• 02:24It's been really influential,
• 02:26and it's lasted since Penfield
• 02:27first described this in the 1930s.
• 02:29Of course,
• 02:30Penfield techniques were really crude.
• 02:32You know,
• 02:33he was doing intraoperative
• 02:36direct cortical stimulation.
• 02:37Is electrodes were particularly precise.
• 02:40And you know how he marked out,
• 02:42you know which motion he got when he
• 02:44stimulated which part of the body?
• 02:46He had little pieces of paper
• 02:48that he stuck onto the cortex.
• 02:49And that's how he sort of kept track, right?
• 02:51And then he would write it
• 02:53all down in a notebook.
• 02:54So it's pretty crude by today's standards,
• 02:57but he got it right.
• 02:59Did he?
• 03:02We obviously have, I think more
• 03:04advanced ways to map the brain now,
• 03:07especially using F MRI and
• 03:11F MRI basically has.
• 03:14Ratified the homunculus,
• 03:16although that's been more viewed as,
• 03:19oh, you know,
• 03:19F MRI is showing us the right thing,
• 03:21then you know,
• 03:22the homunculus is clearly right because
• 03:24MRI says musculus is true, obviously.
• 03:28So you know,
• 03:28like if you do motor tasks,
• 03:30you can clearly see this organization
• 03:32where there's foot representation
• 03:33in the dorsomedial part,
• 03:35there's hand representation
• 03:35in the dorsal lateral part,
• 03:37there's face representation in
• 03:39the ventrolateral part and resting
• 03:41state shows us this as well.
• 03:43Thomas's networks back in 2011 had
• 03:46this clear segregation between at
• 03:48least the face and the the arm slash foot.
• 03:51We've shown that you can actually
• 03:53segregate foot from arm and this is,
• 03:55this is all really consistent with the phone.
• 04:02Of course, the idea that there is the human,
• 04:04let's say, get it like all of
• 04:06these different body parts are
• 04:07represented in slightly different
• 04:08places in primary motor cortex.
• 04:09That suggests that there's probably
• 04:11these functional subdivisions within
• 04:13primary motor cortex within these big,
• 04:15you know, so far we've mostly seen,
• 04:16you know, this is the big hand arm area,
• 04:19this is the big leg foot area,
• 04:21but there's probably subdivisions
• 04:22within that, right?
• 04:25We and others, especially Rodrigo,
• 04:27have had a lot of success using what
• 04:29we call precision functional mapping,
• 04:31where we collect tons of data in individuals
• 04:33to get really good representation of
• 04:35brain organization in each person,
• 04:37and we've used this to describe
• 04:40subdivisions of large scale brain networks
• 04:43in these individuals. And, you know,
• 04:45there's been a lot of great work.
• 04:46You know, we, we've shown subdivisions in
• 04:48cool networks like the default network
• 04:50and we've shown subdivisions in really
• 04:52interesting portions of the brain,
• 04:54like like how the striatum is
• 04:56connected to the medial frontal cortex.
• 04:58So we said, OK, you know,
• 05:00this is an easy next step.
• 05:02Let's just use the same approach,
• 05:04use this, you know,
• 05:06precision functional mapping and
• 05:07individuals to look for the homuncular
• 05:09subdivisions in motor cortex.
• 05:10And this will be, you know,
• 05:11an easy straightforward result and,
• 05:13you know, just.
• 05:15Again,
• 05:15ratifying that you know mapping
• 05:17that you can really get precise
• 05:20network connections when you map
• 05:22stuff really well in individuals.
• 05:24Well, we didn't find that molecules,
• 05:27surprisingly.
• 05:28Here's what we did find when we ran a
• 05:31series of functional connectivity seeds
• 05:34down the precentral gyrus and here.
• 05:37So here's 6 seeds right running
• 05:39from #1 and you can barely see it.
• 05:41Endorsing Media Group or yeah,
• 05:43dorsomedial and one running down
• 05:47to SEAT 6 invention lateral M1.
• 05:50We can pretty easily pick out exactly
• 05:53the foot and hand and face areas here.
• 05:56Number one is clearly foot #2 is hand,
• 05:59#5 is face, and if you do this
• 06:01is in the single individual.
• 06:02If you do,
• 06:03you know the classic HP motor
• 06:05task in this individual,
• 06:07which we've done,
• 06:08it lines up with these divisions
• 06:09just perfect.
• 06:12But what about seeds 2, four, and six?
• 06:15They look completely different,
• 06:17and in fact they don't look
• 06:19like 3 separate things.
• 06:20Instead, these three areas,
• 06:21seeds 2, four, and six,
• 06:23are all strongly connected with each other.
• 06:26There's this.
• 06:28We're distributing network
• 06:30inside primary motor cortex.
• 06:34These three seats.
• 06:37And if you don't really believe me,
• 06:39here is sort of all of the
• 06:41seeds in primary motor cortex.
• 06:42And as we run the seed from
• 06:45dorsal medial to ventral lateral,
• 06:47you can see it describe clearly what we
• 06:50call the effector specific regions, foot,
• 06:52hand, face as well as these three weird,
• 06:55we don't know what to call
• 06:57them inter effector regions.
• 06:58They're between the effector
• 07:00specific regions and they're there.
• 07:02Yeah there there's just sort of
• 07:04they're linked to each other,
• 07:05positioned between this is a weird thing.
• 07:11So that was in one person that I showed you,
• 07:13but we can see this these three intra
• 07:16effector regions strongly connected to
• 07:18each other in precentral gyrus in every
• 07:21single person we look at and I think
• 07:23participant 5 here participant 5 is the
• 07:26unusual because they also have strong
• 07:28connections with the motor face area,
• 07:30but they they still have this
• 07:32distributed thing in sort of the
• 07:34more dorsal part that should be like
• 07:36torso wish and the more ventral.
• 07:38That the lateral part that
• 07:40should be below the face area,
• 07:42it shows up really consistently.
• 07:44We have so much data in these people
• 07:46that we can split it out and we
• 07:48can do sort of within individual
• 07:50replications and the, you know,
• 07:52it replicates within individual and
• 07:55it is in all of the big group average
• 07:57datasets that we've ever looked at.
• 08:00So on the top we have the UK B,
• 08:024000 people from UK B second we have
• 08:054000 people from the ABC D third we have.
• 08:08Um, 800 people from the HCP.
• 08:10And on the bottom we have our old
• 08:13Washington University 120 data set.
• 08:14That's really old data,
• 08:15but even in this really old data,
• 08:17it was honestly,
• 08:18this data is not even field mapped.
• 08:22We didn't even realize that we
• 08:23should be field mapping it before
• 08:25we mapped it to the cortical surface
• 08:27and even this data you can see these
• 08:29three interdigitated intra effector
• 08:31regions inside primary motor board.
• 08:33So it's there.
• 08:36So you know we also have a good
• 08:38amount of pediatric data and we're
• 08:40starting to look at position functional
• 08:42mapping in pediatric populations.
• 08:44So we said how when does this emerge,
• 08:48when does this,
• 08:49these three inter affected regions,
• 08:50when do they emerge,
• 08:51it's just just in adults.
• 08:53Well it's in adults.
• 08:54We found it in nine year olds,
• 08:57you know we knew it was in nine year
• 08:59olds because it's so clear in a CD data.
• 09:02He found it in on 11 month old.
• 09:05Really easily,
• 09:05really easy to find it in an 11
• 09:07month old is not quite as strong
• 09:08in this 11 month old,
• 09:09but it's it's there on the top there.
• 09:12Not present in any unit in a neonate.
• 09:15We see all of these motor
• 09:18regions very broad smeared.
• 09:19There's not hard borders between any of them,
• 09:21and most critically there's not
• 09:24any distributed connectivity within
• 09:26precentral gyrus in this NEO.
• 09:28So it seems to this seems is
• 09:30not present at birth,
• 09:31but it seems to emerge as these
• 09:32cortical areas start forming
• 09:33in the first year of life.
• 09:37Here's a weird thing.
• 09:38These three inter effective regions,
• 09:40this network really strongly connected with
• 09:43the singular opercular control network.
• 09:45A singular perculiar network
• 09:46has a million different names.
• 09:48We call it single circular.
• 09:49It's also called the salience
• 09:50network in I think Thomas masks the
• 09:53ventral attention network, right?
• 09:55It's a it's an attention network.
• 09:57It's a it's a network that does top
• 10:00down control, it maintains goals,
• 10:01it helps set goals.
• 10:03It it does error processing,
• 10:05it has insular components that are linked to.
• 10:08That sort of do like pain registration
• 10:10and it's really strongly connected here.
• 10:13You can see in in these dorsal medial
• 10:15aspect that that's the SMA and the
• 10:18dorsal anterior singulate cortex.
• 10:20These are portions of the single
• 10:22particular network really strongly
• 10:23connected to these intra effector regions.
• 10:26And if we say OK,
• 10:28which parts of the brain are more
• 10:29connected to the interior of the
• 10:31three interactor regions than
• 10:32any of our hand foot mounters.
• 10:34So,
• 10:35so how are these interfactory
• 10:37regions different?
• 10:38From the effector specific regions
• 10:40we get basically exactly the
• 10:41singular curricular network.
• 10:43It traces the outlines of the single
• 10:45curricular network very closely,
• 10:47even with strong connectivity
• 10:49in anterior prefrontal cortex.
• 10:51In primary motor cortex being
• 10:53connected really strongly to
• 10:55that's that's lateral area 10.
• 10:57That's weird.
• 10:59But here we see the quantification.
• 11:01We look across all the different brain nodes.
• 11:03Singular is the one that has the
• 11:05biggest difference between the inter
• 11:08effectors and the effector specific networks.
• 11:10If we sort of visualize these
• 11:13relationships as a graph,
• 11:14we can see that where the single
• 11:16particular network is in purple
• 11:18down there on the left,
• 11:19the affector specific areas are in orange,
• 11:23blue and green on the right.
• 11:25These these intra effector regions
• 11:28are positioned between the single
• 11:30particular control and these
• 11:32classic hands like mountains.
• 11:34So is this.
• 11:36This is how control gets into motor systems.
• 11:41So here's a visualization of this,
• 11:44another visualization of the singular effect.
• 11:46Right here we have these are all
• 11:49of the very highly sampled high,
• 11:51high data individuals we have.
• 11:53Each one of them is represented as one dot.
• 11:56This sort of purplish represents
• 11:58the connectivity with the single
• 12:00particular network in each individuals
• 12:03interactor network and each individual.
• 12:05Also on the right you can see their foot,
• 12:08hand and mouth and there's you.
• 12:10So you can see this sort of paired effect.
• 12:12There's lines between each
• 12:13individual's interfactory network
• 12:15and their foot handed out,
• 12:17and you can see that the interactor in
• 12:19every individual is higher than every foot,
• 12:21hand mouth area.
• 12:23Because all lines go down,
• 12:25but it's not just singular particular
• 12:26network that we're seeing this
• 12:28difference and there's actually a
• 12:29lot of different ways that these
• 12:31interfactory regions are different
• 12:32from the foot hand mountains, so.
• 12:34Yeah.
• 12:35The Intersector network has stronger
• 12:38connectivity to the middle insulin
• 12:40than the foot, hand and mouth areas.
• 12:43The inner Effector network has stronger
• 12:45connectivity with the cerebellar vermis.
• 12:47The Interfactory network has stronger
• 12:49connectivity with the dorsal posterior
• 12:50containment, which is supposed to be
• 12:52the motor portion of the containment.
• 12:55The Interfactory network has
• 12:56stronger connectivity with a
• 12:57number of thalamic regions.
• 12:58Basically, most of motors values
• 13:00the central medial values,
• 13:02the ventral intermediate nucleus
• 13:04and the ventral posterior
• 13:06medial nucleus of the thalamus.
• 13:09The Interfactory network has much
• 13:11weaker connectivity with adjacent S1.
• 13:13This is a classic finding that M1
• 13:16is really strongly functionally
• 13:18connected to adjacent S1,
• 13:20but it's it's not really true with the
• 13:23inter effectors and you can go back well.
• 13:27If we go back and and sort of look at them,
• 13:30you can see that they're really in.
• 13:33The precentral gyrus they don't really.
• 13:35The top one extends a bit
• 13:37to post central gyrus,
• 13:38but the the the middle one and the
• 13:40inferior one really don't extend
• 13:42into post central gyrus very much.
• 13:44So they seem to be divorced
• 13:48somewhat from semantic sensation.
• 13:51We can move away from
• 13:52functional connectivity.
• 13:53We can look at structure.
• 13:54The inner effectors have lower cortical
• 13:56thickness than any of the foot,
• 13:57hand,
• 13:58mouth reactions.
• 13:58In three of our subjects we
• 14:00had diffusion imaging,
• 14:01really high amounts of diffusion imaging.
• 14:03We looked at fractional
• 14:05anisotropy right under cortex.
• 14:07The Interfactory networks has
• 14:09higher FA right under Cortex.
• 14:11This one I don't understand.
• 14:12I haven't figured out what this means yet,
• 14:14but it means something I don't know.
• 14:17And the inner effector network,
• 14:18we also looked at at Mylan
• 14:20mapping the T1T2 ratio,
• 14:22the intersector networks have
• 14:24lower myelin intracortical myelin
• 14:26content than the hand area,
• 14:28higher than the foot area,
• 14:29about the same as the back there, so.
• 14:31Overall, there's just,
• 14:32this is a completely different system.
• 14:35There's just a ton of different ways,
• 14:37ton of different connections kind
• 14:38of structure that suggest that
• 14:40this is a completely different
• 14:42system from our classic foot,
• 14:43hand, mouth things.
• 14:46So how does this make sense in the context
• 14:48of the homunculus that we've all learned?
• 14:51Well, the only way to answer that
• 14:53question is to go map the homunculus.
• 14:55So in two of our really
• 14:58highly sampled individuals,
• 14:59we conducted a an extensive series
• 15:01of homuncular mapping tasks.
• 15:02This is basically modeled after
• 15:04the HP motor task, which does,
• 15:05you know, move your hand, move your,
• 15:07move your collector foot, wiggle your tongue.
• 15:10Except we didn't just do those.
• 15:12We did toes, ankle, knee,
• 15:13which is dominant shoulder,
• 15:14elbow, wrist, hand, eyelid.
• 15:16High round nostrils jaw swallowing
• 15:18tongue and we had each subject
• 15:20run 64 different 5 minute tests.
• 15:23So this is the map of motor preference.
• 15:27This is placed onto a flat map.
• 15:29The green area is the lower limb area.
• 15:33That what we were talking
• 15:34before about this the foot area.
• 15:36The blue area is generally the hand
• 15:39area and the yellow, the orange.
• 15:42Yellow is generally the face area
• 15:45and what you can see here is that
• 15:47it actually does not look linear.
• 15:49It does not look like there is a linear
• 15:52progression from foot to face instead.
• 15:54Within each of these three
• 15:56separate fields, the foot field,
• 15:58the hand field in the face field,
• 16:01it looks like there's this
• 16:02concentric organization where in
• 16:04the middle of this field we have
• 16:06the most distal body part, right.
• 16:08So that would be the toes in the foot area,
• 16:10the hand in the in the upper arm
• 16:13area and the tongue in the face area.
• 16:16And then we have sort of concentric
• 16:18they it's a little messy,
• 16:19but you can see it most clearly in
• 16:21sort of the say the foot area over on.
• 16:24On the the.
• 16:27Left hemisphere,
• 16:28we have these concentric bands
• 16:30of representation where you
• 16:32have the most distal area,
• 16:33then proximal area around that,
• 16:35proximal area around that and
• 16:37then oh and you can see here's a
• 16:39representation for instance I've
• 16:41fit some curves to and I'll show
• 16:43you these curves in more detail.
• 16:45I've sort of fit some curves
• 16:48along along M1 extending from
• 16:50the dorsal portion down to the
• 16:52ventral portion on the X axis.
• 16:54Here you can see activation
• 16:56on the the Y axis.
• 16:57Position along M1 and what I'm
• 16:59doing is I am looking at activation
• 17:02strength at each position along
• 17:05this dorsal ventral axis and I fit.
• 17:08I fit one or two peak Gaussian
• 17:12curves here to represent like is
• 17:14there one peak is there to beacon?
• 17:15Whichever whichever fit is better,
• 17:17I represent and you can see that there
• 17:20are these dual peak curves for every
• 17:22motion for every one of these hand motion,
• 17:25wrist motion,
• 17:26elbow, shoulder.
• 17:28Abdominal and they are progressively
• 17:30farther and farther away from
• 17:32the hand from the hand peak,
• 17:34the most distal body part area.
• 17:39So yeah,
• 17:39these distal party body parts are in
• 17:41the center of these of the three fields,
• 17:43the proximal body parts are
• 17:45progressively surrounding them.
• 17:46What about the inner effectors?
• 17:47These fields intersect in the inner effects.
• 17:50It's like where the as the fields expand,
• 17:53they run into each other in
• 17:54the inner effectors.
• 17:57The cool part of this is this is completely
• 18:00violating pennfields among this, right?
• 18:01But it's not completely violating
• 18:03what's been shown in the macaque and
• 18:05then ignored because it's not a good
• 18:07as good a story as though molecules.
• 18:09So back in 1978, Juan showed basically
• 18:12this exact same thing in macaques,
• 18:14where you have the shoulder
• 18:16representation that's outside the elbow,
• 18:18the elbow representation
• 18:18is outside the wrist,
• 18:20the wrist representation is
• 18:22outside the fingers.
• 18:23You get these dank house 2006,
• 18:26dumb strict 2005.
• 18:27The park you can date you know they
• 18:30even say explicitly look this is
• 18:33distal versus proximal representations
• 18:35here concentrically in the in the
• 18:38this is in the hand area here.
• 18:40So this isn't a new observation
• 18:43but it's certainly new to anybody
• 18:45like me who thought that there
• 18:47was a monoculus and M1.
• 18:51So what are the intra
• 18:52factors doing in all this?
• 18:54Well, the interactor regions,
• 18:55you know, I showed you before that
• 18:57they have a preference, they do,
• 18:59they have a motion preference,
• 19:00they have to the winner take
• 19:02all approach is required to make
• 19:04them have a motion preference.
• 19:05But actually if you look in more detail,
• 19:08they have integrated activation
• 19:10during movement of many different
• 19:13body parts and their activation tends
• 19:16to coincide with strong activation
• 19:18in the single opercular network and.
• 19:21Most especially in this SMA dorsland tier
• 19:23singular portion of the singular network,
• 19:25although other portions as well.
• 19:28So here for instance,
• 19:30this is what looks like it's the
• 19:33winner of body motion in many
• 19:35parts of the interceptor network,
• 19:36the abdominals, right?
• 19:37And you can see that these abdominal
• 19:39activations really nicely traced
• 19:40out the inner effector network.
• 19:42They're strongest in the most superior
• 19:44region of the inner effector network,
• 19:46but they are clearly present
• 19:48in the other regions as well,
• 19:50but they're also.
• 19:51Extensively present in the single
• 19:53Percoll network,
• 19:53outlined in purple.
• 19:57But if we look across many
• 20:00different movements and we look
• 20:02across the whole extent of M1,
• 20:04and this is a little bit busy,
• 20:06but this is basically the same
• 20:08idea as what I showed before,
• 20:10where we're tracing these curves of
• 20:13activation from dorsal to ventral,
• 20:16dorsal on the left, ventral on the right.
• 20:19And for each of these different motions,
• 20:21we say, OK, at what, how active is this?
• 20:25A portion of Cortex during this motion,
• 20:29and what you can see is that in the effector
• 20:32specific areas here in the dark green,
• 20:34in the blue and in the
• 20:37orange we have this effect,
• 20:39where their preferred body part
• 20:41motions are of course very strong.
• 20:43And everything else is very suppressed,
• 20:45right by contrasting the intra
• 20:47effector regions in the purple
• 20:49surrounded by the dotted right,
• 20:51everything is kind of act.
• 20:54There's nothing that's really
• 20:55being strongly suppressed.
• 20:56Everything is.
• 20:57Medium activated and there's
• 20:58there's some that are stronger
• 21:00and there's some that are weaker,
• 21:02but across all body parts,
• 21:03they're all exhibiting some
• 21:04degree of activation here.
• 21:09The other thing that we were thinking
• 21:11about when we're trying to figure
• 21:13out what this network is doing is,
• 21:15well, let's think about it.
• 21:16It has these strong connections
• 21:17with the single regular network,
• 21:19single circular network is,
• 21:20is this goal oriented network,
• 21:22it maintains task goals.
• 21:25Could these regions have anything
• 21:28to do with with goals or planning?
• 21:32So what we did is we conducted 10 different
• 21:34in in our two highly sample subjects,
• 21:36we conducted 10 different runs of a task
• 21:39that required very complex coordinated
• 21:41movement across the hands and feet.
• 21:43And critically,
• 21:44this task had separable phases where
• 21:47they executed the complex motion
• 21:49and before that they had a little
• 21:51bit of time to plan how they were
• 21:52going to execute the complex motion.
• 21:54This actually helped them execute
• 21:56it because the motions were like
• 21:58like rotate your your your hand
• 22:00counterclockwise while your foot is.
• 22:02You know,
• 22:02bending forward and backward
• 22:04students both at the same time.
• 22:05So there was this planning phase and
• 22:07there was this execution phase and what
• 22:09we found is that when we contrasted
• 22:10the plan versus execution phase,
• 22:12the effect is a little weak,
• 22:14but it was clearly stronger overall in
• 22:16the inner effector regions than in the
• 22:19effector specific regions that were
• 22:21actually going to execute the test.
• 22:24So it looks like these regions
• 22:26are doing some sort of.
• 22:28Action planning as well.
• 22:31And then the final thing that we
• 22:33wanted to do when we're thinking
• 22:35about what these regions might
• 22:36be doing is we wanted to see if
• 22:38they were present in mechanics.
• 22:40So we got a bunch of data from
• 22:44the the Prime DE data set this
• 22:47was from kindly sent to us by.
• 22:50By Mike Williams Group and we were we
• 22:53were poking around in these macaque
• 22:57resting state functional connectivity
• 22:59matrices, and interestingly,
• 23:00we found that this is a this
• 23:02is group average.
• 23:03The individual mechanics are much noisier
• 23:05than we can get in individual humans,
• 23:07but this is group average data.
• 23:08Interestingly,
• 23:08we found that if we place a seed
• 23:10in anterior singular cortex,
• 23:12this is about in in monkey terms,
• 23:16it's the singular motor air.
• 23:18The which singular motor area is it?
• 23:20It's the.
• 23:21Hoddle single Motor area if the anterior
• 23:24portion of the caudal singular motor area,
• 23:27we get something that really does kind of
• 23:29look like the single opercular network here,
• 23:31our classic single circular slash
• 23:33salience slash ventral attention network.
• 23:34It has this extension into the farms margin.
• 23:36All this of the cingulate gyrus back here,
• 23:38it has its representation
• 23:40in supramarginal gyrus,
• 23:41it has it has strong representation
• 23:44and critically it has distributed
• 23:46representations within primary
• 23:47motor cortex when we seed the
• 23:50macaque anterior cingulate cortex.
• 23:52And we think that this is as good a
• 23:56candidate as any for a homologue of this
• 23:58intro effector network that we're seeing.
• 24:00The cool thing is that if you
• 24:02look at the mechanic literature,
• 24:04if you look at the,
• 24:06the radio graphic tracing
• 24:08literature in the Cacs,
• 24:10Peter Strick has actually done a lot
• 24:12of work recently where he showed that
• 24:14there are distributed portions of
• 24:16primary motor cortex that project down to
• 24:18internal organs like the adrenal medula,
• 24:20like the stomach and the kidney here.
• 24:23And frankly these adrenal medula protections,
• 24:26to my eyes at least,
• 24:27they pretty much exactly line up with
• 24:30the locations where we are finding enter
• 24:33where we're finding distributed functional
• 24:35connectivity with anterior singular cortex.
• 24:37And it, it kind of makes sense
• 24:39because the you also have this sort of
• 24:42anterior cingulate projections down
• 24:43into atrium which medula as well.
• 24:45Now straight characterize these as a well.
• 24:49He has a paper called the Mind Body
• 24:51Connection and it's not crazy to think that.
• 24:54Because why would you have direct projections
• 24:58between these medial frontal control regions?
• 25:01Even in the cat they probably do some sort
• 25:04of pop down control and the adrenal medula,
• 25:06well I mean to me the and to to strike
• 25:09the most likely explanation is that they
• 25:12are helping this sort of allostatic.
• 25:15If you know the word this sort of allostatic
• 25:18regulation pre regular anticipatory
• 25:20regulation of your internal body states,
• 25:23you have to give them.
• 25:24Is required to do something
• 25:26dangerous or stressful,
• 25:27even when they're planning it
• 25:28before they start doing it.
• 25:30Even when they're planning it,
• 25:31they you have this ramping up
• 25:34of adrenal projections that is
• 25:36actually absolutely critical,
• 25:38so that when they start doing it,
• 25:40their adrenaline levels are already high.
• 25:43So.
• 25:43In summary,
• 25:45we have found this weird inter effective
• 25:49integrated interdigitated INTERFACTORY
• 25:50network in primary motor cortex
• 25:53is strongly connects to prefrontal
• 25:55single particular regions that we know
• 25:58initiate and maintain task goals.
• 26:00It Co activates with the single particular
• 26:02network across many different movements,
• 26:04most strongly to axial body muscles.
• 26:07But not only it seems to activate
• 26:10at least to some degree across
• 26:12many different movements.
• 26:14It's very strongly segregated from,
• 26:17and it functions completely different
• 26:19from these systems that we know that
• 26:22are critical for complex, precise,
• 26:24isolated motion of your effectors,
• 26:26of especially of your hands and
• 26:28your feet and your tongue.
• 26:30And we it seems to be to some degree
• 26:32involved in action planning and
• 26:34it may also project to internal
• 26:36organs such as the adrenal medula,
• 26:38the stomach and the kidneys.
• 26:40So what is it? What?
• 26:42How do we put all this together?
• 26:44What is all this?
• 26:45I I don't.
• 26:45I'm not going to claim that I have
• 26:47the final answer.
• 26:48But our best guess,
• 26:49what makes the most sense right
• 26:51now to us is that this is a system
• 26:53for integrated whole body action
• 26:55in the service of goals.
• 26:57You have goals that are generated
• 26:59in the single opercular network.
• 27:02Somehow your body needs to know
• 27:04about those goals so that when
• 27:06you start going to execute them,
• 27:08your body is ready to do that.
• 27:11This may this may involve complex
• 27:13goal directed actions that require
• 27:14whole body integration.
• 27:16This may be in particular the
• 27:18kind of action that
• 27:19this interfactory network
• 27:21is particularly good at.
• 27:23This is my guess.
• 27:24We can't do this in in F MRI.
• 27:26Of course we can't have somebody
• 27:27dance or play football in F MRI.
• 27:29But I think it's it's potentially
• 27:31reasonable to think that in that
• 27:33whole body actions may be more
• 27:35strongly represented in the inner
• 27:37effector network than they are in
• 27:39represented in these other inspectors.
• 27:41Specific regions that we know are really,
• 27:43really good.
• 27:43If what you need to do is play piano,
• 27:45you need to only move your hands.
• 27:47Great. Perfect for the motor hand area.
• 27:49But what if your hand movement
• 27:51has to be totally integrated with
• 27:53your mouth and your face movement?
• 27:55What if it needs to be integrated
• 27:57with your foot movements?
• 27:59Maybe you need a distributed
• 28:01integrated system,
• 28:02but we also think that maybe this
• 28:05system is performing anticipatory
• 28:07physiological changes so that planned
• 28:10actions can be appropriately executed.
• 28:12I have to give a big talk
• 28:14my my hands are sweating,
• 28:16I have butterflies in my stomach.
• 28:17These are somatic responses not to
• 28:20anything currently happening to you,
• 28:22but to your plans.
• 28:23I have to go to the bathroom.
• 28:26Why do I have to go to the bathroom when
• 28:28I shouldn't have to go to the bathroom?
• 28:30Except I have to give a talk in 5
• 28:32minutes and suddenly my body has sort
• 28:33of started ramping everything up.
• 28:35How does that happen?
• 28:36This is 1 potential mechanism.
• 28:39So we were calling this the
• 28:41mind body network for a while,
• 28:42but I I don't love that name.
• 28:44So we've moved to calling this a
• 28:47somatic cognitive Action Network.
• 28:49I don't love names.
• 28:50I don't love this name,
• 28:51but I think this is the name that
• 28:54captures the most of what we have here.
• 28:57So we do think, but regardless of the name,
• 29:00we do think that it's I think appropriate
• 29:02now to rethink Penfield homunculus.
• 29:05Instead of this sort of linear
• 29:07representation running from foot to face,
• 29:09instead we have these three
• 29:11separate effector specific fields
• 29:12that are not organized linearly.
• 29:15They are organized as concentric
• 29:17fields where they meet.
• 29:19You have this separate system
• 29:21where top down control is coming
• 29:23in not for isolated movements,
• 29:26but for control of your whole body.
• 29:29And we're really excited.
• 29:30We just found out last week this
• 29:32has been accepted at nature.
• 29:34It's now in press.
• 29:36So we're super excited about that.
• 29:39So that's all I have.
• 29:42Unless people I I also have
• 29:43so much stuff in this paper.
• 29:45You can go read the paper.
• 29:46There's a bunch of findings
• 29:47I had to leave out.
• 29:48But this is all I really
• 29:49have time for right now.
• 29:50So I'd like to acknowledge the
• 29:52huge number of people who put a
• 29:54ton of effort in a ton of data.
• 29:56You may have noticed it was like it was
• 29:58like 12 different datasets in this project.
• 30:01And so everybody who contributed data,
• 30:03everybody contributed,
• 30:04contributed effort,
• 30:05but especially Nico Dosenbach,
• 30:07Tim Laman,
• 30:07and Russell and Chavin did the bulk.
• 30:09Of the the work here.
• 30:12Thank you very much. Any questions?
• 30:19That's typical stuff.
• 30:20I really like this sort of
• 30:22curiosity driven science.
• 30:24We try to figure out the whole story we had.
• 30:26But my question is, are very curious,
• 30:29curiosity driven 1A couple years ago,
• 30:31my wife tried to get me to Pilates, yeah,
• 30:33which didn't go as well as obviously,
• 30:37but one of things you have to do is
• 30:39learn how to control parts of your body.
• 30:41And I'm just thinking about 11 and I wonder
• 30:43if you got a Pilates expert in there.
• 30:45About myself in this kind of you
• 30:47think that you'd see this kind of more
• 30:49sort of precise representations that
• 30:51were of the abdomen and the sort of
• 30:54abdominal system as you have to learn
• 30:56how to control that volitionally.
• 30:57I I think that's very plausible.
• 31:02And So what would you,
• 31:03what would you predict,
• 31:04I think you would predict that that
• 31:06the abdominal representations might
• 31:07be you might be able to start ice,
• 31:10they might start isolating a little bit
• 31:12more from this network potentially.
• 31:13Yeah, I think it's I think it's
• 31:15great hypothesis.
• 31:19Great talk. You know,
• 31:22given that the brain is sort
• 31:23of like billions of neurons,
• 31:25finally all different frequencies
• 31:26and different networks,
• 31:27and the bold signals,
• 31:28the bold signals.
• 31:29So it's not actually sensitive
• 31:31to those types of.
• 31:33Could it be that you've found a
• 31:35FMR bold signal specific enough
• 31:37place that doesn't necessarily
• 31:39refute the traditional funds
• 31:41based on their likelihood?
• 31:45So we've certainly thought about this,
• 31:48you know, how much of this
• 31:50is a weird F MRI artifact?
• 31:52So we're really heartened by
• 31:53the fact that there's these
• 31:54structural differences, yes.
• 32:00Right. But that's different.
• 32:04But that's that's a great point.
• 32:06It's, it's certainly the interpretation
• 32:08becomes a lot harder if you're
• 32:10saying that the bold signal is
• 32:12giving us something that is.
• 32:14Totally valid,
• 32:15but completely independent from.
• 32:17Also correct. You know,
• 32:21electric cortical stimulation things.
• 32:24That would be, I think that would be tough.
• 32:26It's, I'm not saying it's wrong,
• 32:27but I think that would be tough.
• 32:28However, we are certainly
• 32:29heartened by the fact that a,
• 32:30we have this structural data that
• 32:32also suggests there's something
• 32:33fundamentally different about these areas.
• 32:35BI did, I did not have a
• 32:37chance to put this in the talk.
• 32:39There was an interesting paper in
• 32:412020 by a French group that was
• 32:45basically redoing pending, right.
• 32:47And so they were doing,
• 32:48they were doing homuncular mapping
• 32:50with direct electrical stimulation
• 32:51and they had a nice big data set.
• 32:53It was like 100.
• 32:55People and they had multiple
• 32:57simulation sites per person and
• 32:59and they put everything in and
• 33:01I space it looks very beautiful.
• 33:03The interesting thing was they had a gap.
• 33:08When they sort of plotted things from
• 33:09the dorsal portion to the ventral portion,
• 33:11there was this gap where they
• 33:14didn't get any responses at all.
• 33:16And we went and plotted their responses.
• 33:19On to our break.
• 33:20We have this might have this slide.
• 33:21Hang on.
• 33:28Yeah, here we go. All right.
• 33:31So here is the figure from
• 33:33Rue at all on the lower left.
• 33:36So they have all of these different motions.
• 33:39And on the the Y axis
• 33:43here is distance along M1.
• 33:45And you can I have this little
• 33:47arrow pointing to the gap.
• 33:49They even point out this gap in their paper.
• 33:51They have this gap between
• 33:53the the hands in the face.
• 33:55And we took all of their simulations and
• 33:57we plotted them onto the cortical surface
• 33:59and that's what's being represented.
• 34:01In the middle portion here and under
• 34:03that we're showing that the HP
• 34:05Group average version of the Inter
• 34:07effector regions and you can see
• 34:09that this gap exactly lines up with
• 34:12the middle inter effector region.
• 34:14So there may be something about
• 34:16these interactive regions that don't
• 34:19respond to stimulation, right?
• 34:20And then there was also a recent
• 34:23paper that actually just it came
• 34:25out after our free print.
• 34:27This is a cool paper.
• 34:28No, I have this miss labeled.
• 34:31Sorry,
• 34:32this is not Courier.
• 34:40There we go. Yeah, this is a really
• 34:42cool paper by Jensen at all.
• 34:44And what they did is they,
• 34:46they actually heard about our,
• 34:48our results a little bit ahead
• 34:51of time and they went and had
• 34:53electrodes in approximately the
• 34:55same locations as our superior and
• 34:58our middle inner vector regions.
• 35:00And they looked at what did they do?
• 35:04Oh, they asked, this is awake.
• 35:07Awake nurse surgery.
• 35:08And so they were recording and they
• 35:11asked participants to move their hand,
• 35:14move their, move their feet,
• 35:16move their tongue.
• 35:17And these electrodes that were
• 35:20placed in the the equivalents
• 35:22of our superior and middle inner
• 35:25effector regions you can see here
• 35:27responded to all of the motions.
• 35:29Whereas the electrodes in the foot region
• 35:32only responded to foot stuff that's purple.
• 35:35The electrodes in the hand region
• 35:36only responded to hand stuff.
• 35:37That's read the electrodes in
• 35:39the inner effector equivalent
• 35:41regions responded to everything.
• 35:42So I think that there is,
• 35:45I think it's not just bold is different.
• 35:48I think it's this has been missed.
• 35:53Some really amazing amount of
• 35:55work and then great results.
• 35:572 to thoughts or comments there.
• 36:00Once, I'm not quite sure I agree
• 36:02on the cognitive part because maybe
• 36:05it's just the difference between
• 36:07literal physiological effort but
• 36:08high coordination in distant
• 36:10body parts whereas anything else
• 36:12where you need your whole body.
• 36:13I mean you mentioned sports,
• 36:14but evolutionary it's probably
• 36:16more like hunting and stuff,
• 36:17you just need a lot more logical ramp up.
• 36:21Maybe that's that's actually the
• 36:22thing not so much the the cognitive
• 36:24part that's what we experience.
• 36:26But in terms of evolution,
• 36:27it's really the overall body ramp up.
• 36:29To to have this demanding whole
• 36:33body movements that's sort of
• 36:34the one thought possibly.
• 36:36I mean this is these are these are.
• 36:40I mean a lot of this.
• 36:43I hope your brain body is actually yeah.
• 36:48A lot of these ideas are are sort of
• 36:51really driven by the idea that we
• 36:53have this very strong connectivity
• 36:54so far anterior and medial medial
• 36:57prefrontal cortex right like this is
• 36:59this dorsal anterior singular cortex.
• 37:01It's it to me it would be really
• 37:03surprising if there was not some
• 37:05cognitive component of this.
• 37:06I'm not saying this is like pure
• 37:08cognition you know like ethereal like
• 37:11you know totally divorced and body movement.
• 37:13But to me there is that is that
• 37:15is a region that is not a really
• 37:18a pure motor region.
• 37:19Right.
• 37:20It's it's a pretty controlled region
• 37:22and maybe control of actions,
• 37:25but it's a pretty controlling region.
• 37:26I think there's a couple
• 37:27of cognitive component,
• 37:28but I agree this needs to be
• 37:30worked out better.
• 37:31Additionally, the trick there,
• 37:32I mean if it yeah control that,
• 37:34it could also be just it's ramp up control,
• 37:37it doesn't minor second point thing
• 37:40all the way back to my work on S2,
• 37:42it may actually be that S1 in fact
• 37:45just three B is the only outlier
• 37:47and everything else is organized
• 37:50by proximal weather system.
• 37:53That's kind of what we didn't
• 37:54make any good big points about
• 37:56this one because it was stupid.
• 37:59All areas of two.
• 38:00It's always the approximate really.
• 38:02I think it's S1 as well.
• 38:05I, I don't,
• 38:05I I we didn't make any claims about this,
• 38:08but I believe it's also as one.
• 38:11We have time. We have one question to chat.
• 38:19Any ideas why they may be separated?
• 38:20Oh my God, this is a wonderful question.
• 38:23No, I think it's this is such
• 38:26an interesting question.
• 38:28Why would they be separated?
• 38:29If you have this system,
• 38:31why does it need to be
• 38:32distributed like this, right?
• 38:33Why don't you just have it all in one place?
• 38:36I've been thinking about this not just
• 38:38in the context of of this system,
• 38:40but also in the context of of like
• 38:43Rodrigo's results showing that that
• 38:46other networks across the cortex.
• 38:48Have this interdigitated property.
• 38:49Why would they have this
• 38:51interdigitated property?
• 38:51What does it do?
• 38:53I think that this is telling us something,
• 38:55but I don't know what yet.
• 38:56I think my best guess is that there
• 38:59are interactions that bold and
• 39:02especially functional connectivity.
• 39:03Bold doesn't really capture between the
• 39:06surrounding areas and the area in between,
• 39:08right?
• 39:09So you've got two infected regions
• 39:11surrounding surrounding the hand area
• 39:13and it to me it seems likely that they
• 39:16may be influencing that hand area.
• 39:18They're not driving its activity,
• 39:20but they're maybe influencing it via
• 39:22short range lateral connections.
• 39:24That's my best guess, but.
• 39:27I don't. I don't know.
• 39:28I think this is something
• 39:29that needs to be worked out.
• 39:31My question only relevance being curious
• 39:34about like is it separately maybe
• 39:36because like it's in the serpents.
• 39:39Have you ever tried looking into the
• 39:41whole space and see whether it's like
• 39:44still connected or like some printed.
• 39:46So I haven't done a lot of looking
• 39:48at these representations in volume,
• 39:51they're not connected with
• 39:52each other in motor cortex.
• 39:54They're too far apart in motor
• 39:55cortex to like actually be connected
• 39:57in volume like they're there,
• 39:58these are like.
• 39:59These are good 2 centimeters apart
• 40:01and Jason in some cases.