"Network Physiology and Pathology of Sleep" Robert Thomas, MD (09/14/2022)

November 22, 2022

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00:00Okeydoke. Let's see.
00:04Good afternoon, everybody.
00:06Can you guys hear me? Oh, excellent.
00:09Nice to see all the familiar faces.
00:13Welcome back from the spring break
00:15and my name is Andrea Zinchuk and
00:18I wanted to welcome all of you
00:21to our Joint Sleep Conference,
00:23which is a seminar that's held
00:25between the Yale Beth Israel,
00:27Brigham Women's MGH, BMC Tough sleep centers.
00:29And so we have grown over the last couple
00:32of years and they will continue to grow and.
00:35Excited to talk to you guys today about our.
00:40Great Speaker Doctor Robert Thomas.
00:41But before I do that, I just want
00:43to make a couple of announcements.
00:441st, Please take a moment to
00:46make sure that you're muted.
00:48And to receive CME credit,
00:50please see the chat room for instructions.
00:52And if you're not already
00:54registered with the CME,
00:55you will need to do that first.
00:57That there will be a code in there
01:00that you can text to the phone
01:02number for CME to get credit.
01:04And so the recording of the session
01:06will usually be available online
01:08within two weeks and there will be a
01:10link provided in the chat for that.
01:11And if you have questions during the talk,
01:13please make sure to make use of the
01:17chat chat room throughout the hour.
01:18I will moderate it as well.
01:21And so it is a without further ado,
01:24it is a great pleasure for me
01:26to introduce our next speaker,
01:27which is Doctor Robert Thomas from
01:30Beth Israel Deaconess Medical Center.
01:33And Harvard School of Medicine.
01:36And it's a real pleasure to have
01:40Robert speak here.
01:41Because Robert is turns out to be
01:43a mentor of mine and he is the one
01:45who got me interested in sleep,
01:46medicine and.
01:47Kept encouraging me to stay
01:50and stick with research,
01:51which has been a wonderful endeavor
01:54for me and I've collaborated with
01:56Robert over the last ten years or so.
01:58And so Robert is one of the foremost
02:01experts in sleep to sort of breathing
02:03in the United States and worldwide.
02:06And he had coined the term
02:09treatment emergent central sleep
02:11apnea or complex sleep apnea and
02:13describe it first in the mid 2000s.
02:16He has published.
02:17You know nearly 100 articles and
02:20many books and is editor of the.
02:23Landmarks book by Doctor Krieger and others
02:26principles and practice of Sleep Medicine.
02:30He's edited sleep disorder breathing
02:32section and is a recipient of
02:34many grants over the past several
02:36years from various foundations,
02:38including the NIH.
02:40And so without further ado,
02:43I'm going to let Robert take
02:44over since he has some very
02:46fascinating things to share with
02:47us on his thoughts about network
02:50Physiology and pathology of sleep.
02:52So thank you again everyone for attending.
02:54Welcome Robert and looking
02:56forward to this wonderful talk.
02:59Thank you, Andre.
03:00And I'm of course very happy to be
03:03here to share with you some of my.
03:06More recent thinking and learning.
03:10In the area of sleep
03:13Physiology and pathology.
03:14So it turns out that anybody
03:17who deals with sleep signs,
03:20Sleep Medicine, sleep pathology,
03:23sleep treatments,
03:24we are actually network scientists,
03:28physiologists, therapists,
03:29whether we like it or not.
03:33Of all these specialties of
03:36specialties of healthcare.
03:39Sleep is the closest which comes to almost
03:42automatically being a network science.
03:45Much of network science and
03:46network medicine has focused on
03:48things like metabolic networks,
03:50gene networks, epigenetic networks,
03:52seemingly things which are far
03:55away from touching the patient.
03:57But it turns out that we have been
04:00touching the network of sleep.
04:02All along. When we look at sleep.
04:05From the network point of view,
04:08it becomes actually quite
04:10interesting and fascinating,
04:11and I hope I can share
04:14that excitement with you.
04:15And let me move my slide.
04:17Here we go.
04:19So my main disclosure really for this is.
04:23I'll be showing you a few slides,
04:24uh,
04:25using technology I was involved
04:27in developing cardio pulmonary
04:29coupling to track.
04:30In an ambulatory way,
04:32the network biology of sleep.
04:34But other than that, much of this is just.
04:38Good stuff. OK.
04:41So I will start off by saying that
04:43sleep is a unique network state.
04:46Just think of the.
04:48Number of different functions which have to.
04:52Function.
04:54Together with some degree of
04:56tolerance and harmony of each other.
04:58To give us what is sleep?
05:01Network activity may be intrinsic
05:04to a subsystem.
05:05Integrated or communicator?
05:07In the context of sleep.
05:10So as an example,
05:11the respiratory network is
05:13a subsystem of sleep.
05:15Integrated networks are seen when you
05:17have say sinus arrhythmia where you
05:20have cardio pulmonary integration.
05:22There can be wide scale
05:24communication with spindles.
05:26Long range communication from
05:28the cortex to the brainstem.
05:30The non R.E.M slow oscillation
05:32which permeates.
05:33Pretty much the whole brain.
05:36These are all forms of
05:38network and coupling activity.
05:40Now couple Physiologies are,
05:42of course,
05:43networked.
05:43So one could to some extent
05:47interchangeably use coupling and network,
05:50but networks go far beyond just coupling.
05:55There are minimal.
05:57Overlaps between fundamental
05:59oscillatory outputs of sleep.
06:02So you take the spindle frequency.
06:04You take heart rate,
06:06you take slow oscillation,
06:08you take the cyclic alternating pattern.
06:11You have oscillations in
06:13the range of subsecond to.
06:1630 to 40 seconds and all of these have
06:19to actually occur in some coordinated
06:22interactive way during sleep.
06:25The components of course
06:26in sleep are dispersed.
06:28In biological space, right,
06:30the brainstem is somewhat
06:31away from the cortex.
06:33There is a necessity to travel in time.
06:36We sleep over time.
06:39And individual subsystems have
06:42different driving mechanisms.
06:44So the respiratory driver is different,
06:47the homeostatic driver is different,
06:48the circadian driver is
06:50substantially different,
06:51but all of these somehow had to
06:53work together like a happy family.
06:58One of the most important components of
07:00the brain which enables networks to work,
07:03of course, is the white matter tracks which.
07:07Essentially connect.
07:07Everything which requires to be connected.
07:10So this is just a DTI fibre track just
07:13to just to remind you that without
07:15a good white matter connectivity we
07:18can't do networking for nuts. OK.
07:21Just listing some sleep networks.
07:24You know the cortical network,
07:26there are more cortical loops.
07:28Interthalamic network,
07:30brainstem has numerous networks.
07:33The respiratory network,
07:35the blood pressure regulatory network,
07:38the cardiac output and autonomic
07:41Dr Networks you have networks for.
07:45Feeding control, of course, right?
07:47Extraordinary network.
07:50Which is involved in sleep regulation too.
07:52You have the better reflects the chemo
07:55reflex, the central autonomic network.
07:56You are the R.E.M non R.E.M network.
07:59Sleep. To weight transition,
08:01how does that occur?
08:03Clearly it's a network change.
08:04You have the arousal network,
08:05you have respiratory,
08:07generative and respiratory control networks.
08:09Of course you have the motor network which
08:12can cause periodic and aperiodic outputs.
08:15Yeah, cardio,
08:16autonomic interactions.
08:17They're probably more networks,
08:19but you get the point.
08:20Sleep is 1 big networked family of subnets.
08:28Just a very quick reminder of the
08:31classic networks which we normally
08:33think about when. Talk about sleep.
08:35So you have. The wake network.
08:39You have the orexin which is. Sort of.
08:46A kind of orchestrator of sorts
08:48of how these networks interact.
08:50You have the non R.E.M network and
08:53every several months there is some new.
08:56Cell group which has been discovered
08:59which regulates non REM sleep.
09:02Ohh, you have the R.E.M network.
09:05So you get a point.
09:07Now, these networks,
09:07of course you're familiar with.
09:09This audience is reasonably familiar with.
09:11But if you're not?
09:13The movement between Wake R.E.M
09:15and non R.E.M is dependent on
09:18very different network behaviors.
09:21And different networks which
09:24are mostly non overlapping.
09:27A quick word about the
09:29central autonomic network.
09:30It's all the way to the top of the
09:33neuraxis with the entry singlet,
09:34insular cortex, amygdala, hypothalamus,
09:37the periaqueductal Gray matter,
09:39the parabrachial complex,
09:41the nucleus tractus solitarius,
09:43the ventrolateral medulla.
09:44These are all part of the central
09:47autonomic network and it's
09:49deeply engaged in numerous sleep
09:52physiologies and pathologies.
09:53There are multiple inputs including.
09:57A visceral information which
10:00is topographically arranged.
10:02In the tractor Solitarius,
10:04as well as chemo reflex and
10:06bioflex inflammation going in,
10:08as well as humoral inputs through
10:11the circumventricular organs.
10:12The insula and the amygdala
10:15are quite high order.
10:17Also the ventromedial prefrontal cortex,
10:19and of course these paraventricular nucleus
10:21controls specific subsets of preganglionic,
10:23sympathetic and parasympathetic neurons.
10:25So central Autonomic network is
10:28something which doesn't get much
10:30attention in clinical medicine,
10:32but it's clearly extremely important
10:35and certainly those who deal with
10:38pain and psychotic disorders clearly
10:41have more interest in this network.
10:47I think I'll model it said this OK.
10:50Just a quick shout out to
10:53the respiratory network.
10:54Today will not be a talk on the respiratory
10:57network that probably deserves an entire
10:58half day symposium in its own right.
11:01But you have central pattern generators,
11:02you have the motor output,
11:04you have the breathing plant.
11:05You have. Very specific.
11:09Neuronal firing patterns,
11:11inspiratory neuron post inspiratory firing
11:14during inspiration and active expiration.
11:18Post inspiration and activist exploration.
11:22And of course you have
11:24the read reploid nucleus,
11:25the paraphasias nuclear complex,
11:30the new ataxic center.
11:32Again, all of these have to work as a very
11:35fine network while awake and wirelessly.
11:38A good example of a respiratory network
11:40gone bad, of course, is opiates.
11:41The opiates are not the only one you know.
11:43Opiates, Baclofen, all types of
11:46pathologies in the brainstem area,
11:48whether to stroke, degeneration,
11:51multiple sclerosis.
11:53Sitting balbia and such will of
11:56course disrupt the respiratory
11:57network with direct impact. AI.
12:00Traffic, little sclerosis, polio.
12:02We don't see polio much.
12:03Hopefully we will not,
12:05even though there are some murmurs.
12:07Now one very interesting.
12:10Kind of relearned the feature is that.
12:14The respiratory signal actually
12:16permeates the whole brain.
12:19So it turns out you can find the
12:21as you breathe in and out you can
12:25find the respiratory oscillation.
12:27Encoded in hippocampal oscillations
12:29in the olfactory bulb.
12:32In the no forget what VMS.
12:36In fact, I don't forget what PC is,
12:38but this is the dentate.
12:41Dentate nucleus.
12:42So essentially when you breathe in
12:44and out and the ancient side it,
12:47right?
12:49You can control and influence
12:50the entire brain,
12:51which of course influenced than
12:53the body by paste breathing.
12:56It would make sense that evolution decided
12:58to Co opt signal as strong as respiration,
13:02as fundamental as respiration
13:04for other functions.
13:05So respiration actually
13:07provides a network signal,
13:09a network coordination
13:11signal throughout the brain.
13:14As to sleep spindles,
13:15as an example it provides.
13:17A fast oscillatory network
13:18information throughout the brain.
13:22Hypercapnia has somewhat specific
13:25network going through the lateral
13:27parabrachial nucleus and this was
13:29worked out by Cliff Zapus group and.
13:33Uh. Directly targeting it,
13:36you know, may have a role in
13:39reducing arousals during sleep,
13:41but nevertheless the hypercapnia
13:43network seems to be separate
13:45from the hypoxic network and the
13:48respiratory mechanoreceptor network.
13:53The Telemo cortical network, of course we.
13:57No. Well, there's not need much need to spend
14:00time on this in a Sleep Medicine audience.
14:03But it's a very complex interaction of.
14:07Tell him cortical cell conductance.
14:11The hyperpolarization activated spike
14:14theoretical thymic nucleus spindles,
14:17as well as interaction with the one
14:19to four Hertz Delta. You can think of
14:22the spindles as a 5G cell network.
14:25It's a carrier wave which
14:27allows information to travel.
14:29And allows way to see scale short
14:33short time range synchronization.
14:36The solation. To describe hysteria in 1991.
14:42It's less than one Hertz and
14:44it's on off state of the cortex,
14:46which I will briefly mention later on.
14:49It enables large rail, large,
14:51large range synchrony of.
14:54Cortical activity subcortical activity.
14:58It aggregates spindles.
14:59And spindles, almost certainly.
15:03It's part of the biological glue of sleep.
15:06So just think of the effect of a
15:09benzodiazepine on sleep. Fragmented sleep.
15:11You take a benzodiazepine,
15:13your enormous amount of spindling,
15:15but you also have. Almost a bland.
15:20Uh. Cortical architecture.
15:22You have ample spindling.
15:25You have reduction in slow wave sleep.
15:27Every epic moralist looks like the other.
15:31And almost certainly it has glued the
15:33sleep in a way which is fairly unique
15:36through the spindling mechanisms.
15:37Now if you take a sodium oxybate,
15:39you're gluing it through non
15:42spindling mechanisms,
15:43probably direct cortical network mechanisms.
15:48Oh, a spindling is not necessary
15:50to increase cohesion,
15:51as we know from anyone who uses an
15:53oxybate or atypical antipsychotics,
15:56because.
15:58Network strengthening at the cortical level.
16:03I will be spending a bit of time
16:06on restless legs and periodic
16:07limb movements as we go along.
16:09It's the ultimate networked
16:12Physiology and pathology.
16:14And there's this really
16:17elegant paper from John Liu.
16:20And Patrick Fuller's group here where
16:23they targeted multiple sites of ablation.
16:28Across the striatum, globus pallidus.
16:32And even the cortex,
16:33motor cortex and showed that at all
16:36of these levels you actually had.
16:38The induction of theoretical movements,
16:41the kind which you see with restless legs,
16:44as well as as iron deficiency in rodents.
16:49A classic example of a network
16:53regulatory dysfunction.
16:57Now the. Periodically movement network.
17:00The restless legs periodic limb
17:02movement network is tightly
17:04linked to autonomic activation.
17:05You all know that.
17:07Whenever there is a periodical improvement.
17:11Usually there is at least a
17:13little bit of cardio activation.
17:15Sometimes they're truly blind.
17:17You see nothing happening.
17:18But more often than not, you see.
17:21Blood pressure surges.
17:23You see arousals,
17:24and the degree of arousal
17:26correlates reasonably well with the
17:28amount of blood pressure surge.
17:30There's increasing evidence.
17:32Suggestive evidence that adverse
17:34cardiovascular outcomes are
17:36epidemiologically linked to restless legs.
17:39Periodically,
17:39movements are really quite severe
17:40in heart failure and renal failure
17:42patients and likely contributes to
17:44pathological and nocturnal hemodynamics.
17:45Because these people really kick.
17:47I mean they kick.
17:48You can even without measuring
17:49blood pressure,
17:50you can see the applet signal,
17:52you know, squeeze,
17:53you can see the heart rate
17:55bump up and and so on.
17:56That can't be a good thing.
17:59So let's spend a little bit of time on.
18:04PLM's. Just a bit now, but more to come.
18:08So this is this of course is an
18:09example of periodic limb movements,
18:11but notice how there's really
18:13not much autonomic activation.
18:15There's very little change in cardiac rate.
18:19In fact, any change in RR.
18:23Is not related to the
18:26leg movements themselves.
18:28You can see the plate signal
18:32showing essentially very. What?
18:34Actually no reduction in amplitude,
18:37suggesting there is not recurrent
18:39sympathetic activation with
18:40these periodic limb movements.
18:42So these are what I call dumb
18:44periodic limb movements.
18:45Dumb meaning they don't do anything.
18:48I will show you later on not very
18:50dumb periodical improvements.
18:54This is the only classic slide
18:56I will show you and this of
18:58course is the two process model.
19:00If that is not the ultimate
19:02network interaction, what is right,
19:03you have the whole sleep system now
19:05interacting with the circadian system
19:07which has its own down network.
19:09I'm not going to be talking much about
19:12circadian networks today because
19:13obviously I'm not the best person for
19:15that and we will not have enough time.
19:17But the circadian network of course
19:20interacts with the sleep network and
19:22is its own entity in its own right,
19:24having his own networks.
19:27Uh, in the brain, in the liver,
19:29in the body, you name it.
19:30The circadian system is probably.
19:33And even more impressive network
19:35than the Sleep network.
19:37But this is remind you that everybody has
19:39a sleep system and a circadian system,
19:41and both these network states
19:43do interact immensely.
19:47So how can you measure the
19:48network health of sleep?
19:51So it turns out that we are
19:53doing this a lot of the time.
19:56Classic polysomnography of course measures.
20:00The Sleep network,
20:01but the scoring of course,
20:02does not think network.
20:05The closest to network would be arousal after
20:10respiratory event or desaturation linked to.
20:13A respiratory event, perhaps?
20:16But the standard manual very very
20:19precisely tries to steer us away from
20:23thinking of the brain and sleep as
20:26a large scale integrated network.
20:28But from classic polysomnography
20:30you can certainly extract all
20:32kinds of network behaviors.
20:34Functional MRI or sleep very clearly
20:36shows the network behavior of sleep.
20:39And I'll give you a small
20:40sample high density,
20:41EG it's superficial cortical networks.
20:46But you can do high density EEG
20:49polysomnography with including,
20:50you know, autonomic Dr and so on,
20:52and map out how the cortical
20:55activity changes with the breathing,
20:57with arousals, with muscle,
20:59sympathetic nerve activity and so on.
21:02Death recordings, uh,
21:04where they also is hemodynamics,
21:06respiration, ECG can again map out.
21:10Networks,
21:10I'm not going to be killing you
21:13with intensely mathematical.
21:15Uh, displays on this talk.
21:20I'm trying to keep it as user
21:22friendly as possible, but there are.
21:23If you go to pub Med and put
21:25these keywords in,
21:26you will get ample sophisticated papers,
21:30which honestly many of them are over my head.
21:34Analysis of coupled oscillations.
21:35I will talk a little bit about that.
21:37I will talk a little bit
21:39about time delay stability,
21:40because this is a method.
21:43Described by Plaman Ave.
21:46And his group from Boston University.
21:49Which talks about the kind.
21:52It's a measure of network strength and.
21:56We look at sleep studies where there's
21:58pathology across multiple systems.
21:59So as an example you have.
22:02Periodically movement coinciding with
22:04the respiratory arousal coinciding with.
22:07Cardio acceleration coinciding with
22:08the cortical arousal coinciding
22:10with the whole body movement.
22:14The strength of that connectivity
22:16can be described in different ways,
22:19but one of them is called
22:21the time delay stability.
22:23Of course there is a graph based analysis,
22:25network strength measures like path length,
22:27etcetera. I will not be going into that.
22:30Similarly, the integrated analytics such
22:32as I'm just loosely calling the sense,
22:35Wellman, Azerbaijan, Scotty,
22:38Andrew and Alice computations and algorithms,
22:41I will not be discussing that today,
22:44but they are very dependent
22:46on network connectivity.
22:47The heart rate response to arousal is
22:50an example that's a good example of.
22:52Not calling it network,
22:54but believe me it is as network as they come.
22:59Just to remind you that we of course
23:02measure the network all the time.
23:08This is an example of.
23:11The network of sleep at work.
23:13So you have air flow and then you have
23:15the peripheral arterial tonometry signal
23:17and your arterial blood pressure.
23:20Typically, when any individual
23:22signal shows a major deviation,
23:24a major perturbation, a major transient,
23:27these others will have it at
23:29the same time because they are.
23:31Tightly linked. And network.
23:33Now whether you call them coupled or
23:35network is the same thing here anyway.
23:37But what it tells you is that you can
23:39use any of these individual signals in
23:42the right context to predict the other.
23:44So if you're sleeping and your blood
23:45pressure is showing the profile,
23:46which is seen here.
23:48Either you have sleep apnea or
23:50you have periodically movements,
23:52or or someone has slapped headphones on
23:54you and I'm beeping you every 35 seconds.
23:58If respiration of course looks like that,
24:00you know there is apnea and you
24:03will almost certainly have blood
24:05pressure tracking along with it.
24:09A cyclic alternating pattern or
24:12cap is the best. Day-to-day.
24:15Example of the cortical network
24:18stability or instability.
24:20Remember networks can be vertical.
24:22You know across different levels
24:23of the brain it can be horizontal.
24:26Across the same level.
24:27So the slow oscillation is
24:29a cortical network thing.
24:33Cyclic alternating pattern is
24:35both horizontally integrated as
24:37well as vertically integrated.
24:40So when you have. The A phase and B
24:43phase alternating you have immense
24:45changes in the cortical network.
24:48Uh activity, and this is a period
24:51of cap of cyclical training pattern.
24:54Which is markedly amplified by
24:56disease and here you have non cap
24:58or non cyclic alternating pattern.
25:00You will notice that I'm sort of
25:02repackaging a lot of things that
25:04you already know in a network
25:06in kind of network thing.
25:09Now, it's very important to note that
25:11when there is a cortical perturbation,
25:13there is usually a downstream perturbation.
25:15So it turns out whenever you have K
25:18complexes and phasic egg activity,
25:20you will have an increase in blood pressure,
25:21increase in heart rate,
25:23increase in tidal volume,
25:25the flat signal amplitude will drop.
25:28You will have a blood pressure surge.
25:31Maybe a little blimp,
25:33maybe not necessarily a surge,
25:34but these will occur very reliably as the
25:38reflection of the integrated network.
25:40Just to remind you that you may
25:42have deep sleep which is unstable.
25:44So this is M3 showing a lot of
25:47intermittent phasic activity.
25:49So deep and stable is now this is
25:51so you can be deep and stable,
25:53deep and light, light and stable and such.
25:56So all all the combinations are actually
25:58possible. And this is an example of.
26:01The network of sleep being the cortical
26:04network at least at a minimum,
26:05being unstable despite sleep
26:07being quite deep.
26:10Network switching.
26:11We see this on every polysomnogram.
26:13If you choose to look at it,
26:14well, almost every.
26:15So on the top left you have
26:17ongoing respiratory events,
26:19fragmented sleep.
26:20In the middle you see the abrupt switch.
26:24Ohh there's no change in body position.
26:26This is not a therapy study.
26:28We did nothing spontaneous
26:29switched to stable state.
26:31This is how non REM sleep works.
26:33Not N 1, N 2, N 3, N 4, N 5, N 6.
26:35This is how non REM sleep works.
26:37You have two fundamental network behaviors,
26:40one where you have low frequency
26:43oscillations dominating.
26:44The other way you have essentially everything
26:48synchronized around respiratory frequency,
26:50individual breath frequency.
26:51So this is an example of network switching.
26:55Sleep onset is another great
26:56example of network switching,
26:58which we'll go into a little bit of detail.
27:01So delving a little bit more into the,
27:03you know,
27:04horizontal network of non REM sleep,
27:06the glue of non REM sleep.
27:09Up here you have cortical network.
27:13Local field potentials.
27:14This is what we see on the brain.
27:16You know a slow oscillations but
27:18if you measure multi unit activity
27:20you will find firing silence,
27:22firing silence.
27:23So this is the on off state of non REM
27:26sleep which is a fundamental building
27:28block of non REM sleep on top of which
27:32everything else essentially rides.
27:33And there's some examples
27:35where the cortical network,
27:36the non R.E.M slow oscillation breaks down.
27:39And the best example would be
27:41perhaps Alzheimer's disease.
27:43You know, as your cortex breaks down,
27:44your slow oscillation will also start
27:47disappearing and start fragmenting.
27:51The small isolation builds up
27:54in frequency and spatial extent
27:56as sleep starts and deepens.
27:58And essentially, as we fall asleep, we have.
28:01The network, uh, is.
28:04The Sleep network is quite unstable,
28:06so we have microsleeps.
28:07You have a little bursts of sleep,
28:09K complexes, some spindles alpha in and out.
28:12And as we go further and further
28:14and further you have coalescence
28:15of the slow oscillation resulting
28:17in you know in three or stage
28:20four or stable non REM sleep.
28:21And if you count the number
28:23of slow oscillations per unit
28:25time it progressively increase.
28:27So essentially the cortical network can
28:30be vulnerable when the cycles per minute
28:33of slow oscillation is at the lower end.
28:35And it becomes very stable.
28:37It's like a very good self sealing tire.
28:40You book the the sleeping brain.
28:44When the slow oscillation density is high,
28:46the network is very stable,
28:48while if you poke it when it is not
28:51so stable you will have an awakening
28:53or you will have a arousal with
28:56respiratory transient and so on.
28:59OK.
28:59So the slow oscillation and
29:01and slow waves you know,
29:03for for a while it was thought
29:04to be fairly passive things,
29:06but it turns out they're not passive.
29:08And using simultaneous EEG and fMRI.
29:11Uh,
29:12this is very elegant paper in PNAS in 2008
29:15actually showing that many cortical areas,
29:19including inferior frontal,
29:21middle prefrontal precuneus,
29:22the posterior singlet, all actually
29:25activate during slow oscillation.
29:27So slow oscillation.
29:28What you see on the surface,
29:30there's a slow wave, it's an inhibitory wave.
29:32That is true.
29:33That's the off state.
29:34And then you have the on
29:36state with activation.
29:37So it turns out that.
29:39The driver of this on,
29:41off, on,
29:41off is not some passive Zen
29:43kind of state of the brain,
29:45but very active engagement by
29:47a fairly widespread network of
29:49very specific areas in the brain.
29:51And this is just the pictorial representation
29:53of the areas which I laid out to you.
29:56And of course,
29:58this breaks down when you have insomnia.
30:00Uh, you know, Parkinson's disease.
30:02Uh, I think of other,
30:03you know, bad brain states,
30:05traumatic brain injury.
30:07A word on time delay, stability.
30:09So basically what it means is that you have,
30:12you know, your EKG, heart rate,
30:14respiration, eye movements.
30:15If all of them are very tightly synchronized,
30:19it means you have short time delay stability.
30:23The DPO sleep the less
30:25synchronized these are.
30:26So the lighter your sleep,
30:28the more synchronized in
30:30time these signals are.
30:32And the deeper you sleep,
30:34the more apart.
30:35So it becomes a way to
30:36measure the integrated step,
30:38the integrated.
30:39A network behavior of sleep and if
30:42you spend all your night being in
30:45short range time delay stability,
30:48it means that you have these
30:49ongoing transients all night long,
30:51which of course would be a bad thing.
30:54OK. Why bother? Well, it turns out that.
31:01The Sleep network is dysfunctional
31:04in epilepsy, dementia, stroke,
31:06itel fibrillation, heart failure,
31:09neuromuscular disorders.
31:12Parkinson's disease?
31:13Disneya syndromes.
31:14That should be reason enough, right?
31:17But it turns out that a
31:18lot of our sleep disorders,
31:20things which bother us a lot,
31:22are classic network disorders.
31:23Insomnia is a network disorder.
31:25I think hypersomnia is also
31:27a network disorder. Now.
31:29Eclipse, of course, is a network disorder.
31:32Brain injury and sleep apnea
31:34certainly affects the white matter.
31:36So it should make the network weaker.
31:39Of course we have enormous resilience.
31:41We have lots of white matter
31:42and lots of redundancy,
31:43but still white matter injury is,
31:46you know,
31:47the most tightly linked brain
31:49pathology to sleep apnea.
31:54Just checking my time.
31:56So what can network Physiology do for
31:58sleep science and Sleep Medicine?
32:00If you think network,
32:01remember we only do network,
32:02but we don't think network.
32:05What is this glue of sleep which holds?
32:09Disparate oscillations and synchrony.
32:11You know, wakeful cognition has always
32:14had this, you know, binding problem.
32:18How does? Different parts of
32:20the brain hold things together.
32:22The gamma Oscillation is one of the binders,
32:24but. It's always been, uh,
32:26the issue of consciousness.
32:27When we are conscious, how do we bind?
32:31Brain information networks together.
32:33So let's flip to sleep.
32:36Clearly there is a glue of sleep which holds
32:38all these crazy oscillations and synchrony.
32:41So we have a binding problem in sleep
32:43and how does this inform consciousness?
32:46What is the minimum unit of
32:48sleep to perform function?
32:50There must be some universal
32:51law of tolerance of sleep,
32:53fragmentation and arousals.
32:54A few decades ago there was a paper.
32:58I think it's Mark Bonnet.
33:01Ohh, where they woke up healthy
33:03volunteers every so often,
33:05starting at once an hour,
33:06every half an hour, etcetera,
33:08to see the effect the next day on,
33:09I believe there were MSLT,
33:11so at least there was, you know,
33:14some measurement of alertness.
33:16So it turned out that once
33:17you came to 10 minutes,
33:18all hell broke loose at about 30 minutes.
33:21You had an irritated person,
33:23but their data and performance
33:24the next day was reasonable.
33:26So that's an example of trying to figure
33:29out what is the minimum unit of sleep.
33:31Has to be left alone to do his thing.
33:34And of course, how many such
33:35units do we need to string along?
33:38To figure out what is adequate
33:40sleep versus not,
33:41why are certain individuals
33:43incredibly fragmented sleep?
33:45Who seem asymptomatic and vice versa.
33:48Of sleep apnea is an example.
33:50All of you know that substantial
33:52minority of sleep apnea patients,
33:54especially in epidemiological studies.
33:57Really. They said they feel fine.
34:00Don't bother me.
34:00If they brought to the sleep link,
34:02they're sulking there while they're partners,
34:04you know?
34:06Snitching on them about
34:07snoring and gasping and so on.
34:09Do a sleep study. They look terrible.
34:11They say they feel fine.
34:13You convince them to somehow try CPAP.
34:15They're using it.
34:16They don't feel any different.
34:18How do you explain that?
34:21Can the disruption grade of
34:24pathology be quantified better
34:26than just counting arousals or
34:28counting brief transience?
34:30And is a kind of network map of
34:32sleep useful in clinical practice?
34:36Some examples of of course.
34:39You know breakdown syndromes?
34:41Heart failure? It will.
34:43Fibrillation, severe TBI treatment,
34:46resistant depression,
34:47mania, neurodegeneration.
34:48The Sleep network breaks down.
34:51There's no shortage of
34:52evidence that it breaks down.
34:54And the binding mechanisms
34:56like the slow oscillation,
34:57the cyclic alternating pattern,
34:59the PGO waves,
35:00these are all vulnerable as
35:02the brain starts breaking down.
35:03The worst degrees of sleep
35:05fragmentation we will see in the
35:06sleep clinic is heart failure.
35:08Parkinson's, it'll fibrillation.
35:11Probably the worst,
35:12and sometimes patients with
35:14irregular sleep wake cycle
35:15disorder or very severe kind of
35:18complex psychiatric comorbidities,
35:19PTSD plus psychosis and such.
35:22You can have incredibly fragmented
35:23sleep where they look like rats,
35:24and I'll show you a sample anyway.
35:30Network breakdown so.
35:32Look at the cortical level.
35:35It's normally fairly resilient and redundant.
35:38As an example, there is virtually no
35:41description of loss of sleep with a whole
35:44range of strokes of any kind of any severity.
35:48And distribution.
35:50Traumatic brain injury, of course,
35:52badly affects the sleep network.
35:54But a stroke is another good example
35:56where the sleep network starts getting
35:58unstable but does not disappear.
36:00As almost Parkinson's epilepsy,
36:02not your typical you know, average epilepsy.
36:05But once you go to epilepsy needing
36:08multidrug therapy treatment resistant
36:10by whatever definition, thinking about,
36:13you know, epilepsy surgery and such,
36:16there's ample data there that sleep
36:18is poor even by conventional metrics.
36:20You know, in one and two, etcetera.
36:22Sleep is more fragmented,
36:24never mind going deeper into it.
36:26The Thermo Cortical network
36:28of course is disabled.
36:29Terribly by fatal familial
36:31insomnia and other prion disorders.
36:34That immediate till I make stroke
36:36has hypersomnia as a network output.
36:39And of course, the tumors in the
36:42area can disrupt sleep state.
36:43The sleep Wake Transition Network
36:45is an amazing network, isn't.
36:46Just think of it.
36:48We are moving from this.
36:50You know,
36:51hopefully most of us on this call are,
36:53you know,
36:54pretty good sleepers and we just
36:56switch on the system and switch off
36:58the system and these systems are
37:01completely different and we make
37:03this transition almost effortlessly.
37:07But those who don't make it effortlessly,
37:09of course, have insomnia with
37:13various driver mechanisms.
37:15Amygdala Bay syndrome.
37:16So anxiety, fear, PTSD makes the
37:18sleepy transition network unstable.
37:20Of course, pain and stress
37:21will do the same thing too.
37:26More on bad networks,
37:27the REM sleep network breakdown
37:29and the disorders you know well.
37:31Non REM sleep network and sleepwalking as
37:34well as depression, the arousal network.
37:37It's unstable inclined Levine syndrome
37:39and bipolar disease on a long on a long
37:43on an intermediate to long time scale.
37:45A hypoactive arousal network,
37:46of course, anesthesia, comma, etcetera.
37:49And hyperactive arousal network is seen
37:52in PTSD stress, abnormal respiration.
37:57So what are the consequences of
37:59the time of night distribution
38:01of the slow oscillation glue?
38:03Our network is more vulnerable later in the
38:05night than earlier in the night, clearly.
38:08Uh, and they're usability.
38:09You know, changes across the night.
38:12Successful insomnia treatment almost
38:15certainly improves effective.
38:17Glumness of the slow oscillation,
38:19although as far as I know this has not been
38:22explicitly studied and analyzed and computed.
38:26I've got sleep goes in cycles,
38:28so you have critical points of weakness
38:30occurring regularly across the night.
38:31We always have light periods,
38:33we always have unstable periods.
38:35We always have sleep cycles.
38:37So if you're a light sleeper,
38:40genetically predisposed
38:41to have not great glue.
38:43These critical points of weakness
38:45is where you have breakdown of the
38:48slow oscillation and this blueness.
38:50And of course,
38:51then you respond to stressors and
38:53environmental noise and so on.
38:54And of course the slow oscillation
38:56breaks down with a whole range of
38:59abnormal cortical health conditions.
39:01Almost certainly genetic factors are
39:03associated with sleep resilience and
39:05likely impact the slow oscillation.
39:07Uh, we don't.
39:08We don't have great individual genes.
39:10Um. At the individual level.
39:15But certainly at the jeevas level you
39:17have neiss an example which covaries
39:19with insomnia as well as the restless legs.
39:22That's probably not just by chance.
39:25And of course, in insomnia,
39:26pharmacotherapy is,
39:27from one view, illogical.
39:29You need the greatest help in
39:32the second-half of the night,
39:34when the slow oscillation is the weakest.
39:36But guess what?
39:37We get most of our help in the
39:38first half of the night.
39:40Of course,
39:40if a sleep onset problem is just
39:41a dominant problem,
39:42it's probably circadian.
39:46A quick word on respiratory
39:47network dysfunction.
39:48You know, all of this,
39:49I don't have to, you know,
39:50really go through any of these.
39:54OK. Sleep onset. Let's spend a few
39:56minutes talking about sleep onset,
39:58because it's a big deal for insomnia.
40:01For sleep apnea. For vigilance.
40:05This is probably the best single paper
40:08I come across looking at sleep onset.
40:10I don't know if Mike is on this call,
40:12but if he is shut out, it's a great paper.
40:16Really worthwhile reading.
40:18So essentially.
40:21This is a very geeky kind of paper.
40:24So you have this ball which you squeeze.
40:26You're measuring respiration.
40:29You're measuring the.
40:32The long flexor?
40:34The flexor historeum profundus.
40:37You are. Tracking.
40:42Responses. This is a breathing.
40:46There's a, there's a metronome.
40:48Essentially you're breathing
40:49in relation to that.
40:51And you have the squeeze amplitudes.
40:55Dropping overtime as we
40:56transition into sleep.
40:57OK, so this is just. The base.
41:00And how they compute the squeeze amplitude,
41:04the alpha power, the Theta and delta power.
41:07Behavioral responses gone.
41:09Weak probability curves.
41:12And of course the ospital spectrogram.
41:13You see the alpha dying out and
41:16Theta and delta building up.
41:22Any further, you have response
41:23probability in the squeeze amplitude.
41:25Just another depiction of essentially
41:27the same thing. But just think,
41:29every time you're falling asleep,
41:30there's this whole book
41:32on sleep onset by Ogilvy.
41:34It's yo thick and really great reading.
41:39It's. At the end of my fellowship,
41:41so it's over 20 years old,
41:43but there's a lot of good data there listing
41:45all the different kinds of physiological
41:47changes which occur at sleep onset,
41:49evoke responses, and so on.
41:52So just think of the importance of
41:54sleep onset and the incredible network
41:56change which occurs during sleep onset,
41:59respiration, EG behaviors.
42:02And even in the brain.
42:06Multi level changes which occur and
42:08we have to navigate this and we do
42:10this effortlessly most of the time,
42:11unless of course you have insomnia
42:13and heart failure and such.
42:14Then of course the sleep onset has to.
42:18You have to redo your sleep onset
42:21multiple multiple times across the night.
42:24Obviously that will be hostile,
42:26and blood pressure of course
42:28fluctuates up and down and rises if
42:31you keep having these transients.
42:33Now what's the clinical equivalent
42:35of that what I call?
42:37Of state amplified wake sleep,
42:39transitional instability.
42:40If you have a better acronym, send it to me.
42:43So this is a 10 minute.
42:46Yes, a 10 minute snapshot.
42:49And technically scored mostly as weak.
42:52I hope I don't have to convince
42:54you that this is serious pathology,
42:56where you have central apneas,
42:57you have some periodical movements and such.
43:00This is another example of amplified
43:02wake sleep transitional instability.
43:04The conventional scoring may
43:06exclude a lot of this.
43:07Don't do that,
43:08because this is clearly very
43:10pathological and these patients will
43:12always complain of onset insomnia,
43:13at least a lot of insomnia.
43:15And of course it's more common with heart
43:18failure and it'll fibrillation and such.
43:21So. Some examples of interesting
43:25coupling and networking.
43:27Of various physiologies which you see.
43:30So periodically movements
43:32these are alternating.
43:34And.
43:37One lot is roughly synchronizing
43:39with the respiratory recovery,
43:41it seems, but apparently it's
43:43just following his own rhythm.
43:45Now here it is in the center, respiration.
43:46Here it's.
43:47You know in the middle of it,
43:50while these less frequently
43:53occurring periodically movements are
43:55right in the middle of the event.
43:58So. There is variable coupling.
44:01And and sleep is basically slow wave sleep,
44:05so each one is pretty much doing his thing.
44:09Without really caring too much
44:10what the others do.
44:11Now there are some fundamental
44:13frequencies in sleep which occur and
44:15they are expecting that, but overall.
44:17The various networks now, uh,
44:19you know, somewhat uncoupled.
44:23Thinking more about the respiratory
44:24motor complex, so here you have.
44:28Well, these are not typical
44:29periodic limb movements.
44:29You will agree. These are.
44:34We are having these clusters of limb
44:38movements occurring periodically.
44:40With the right periodic sequence,
44:42except that it's clusters and when you
44:45go and take a close look you see that.
44:47This oscillatory there's reverberatory
44:50interaction between respiratory
44:52output and the motor systems output.
44:56Is alternating too.
44:59Of course, you have huge arousal here.
45:00Isn't that fascinating?
45:05What drives and what follows
45:06is a never ending question.
45:07And when someone has periodical
45:10movements and has apnea here,
45:12it looks like well,
45:13maybe the respiration is driving.
45:17The periodic elements.
45:19The same person slightly later,
45:21where the limb movements are
45:24occurring with the recovery
45:25breath in the middle of an apnea.
45:28It's doing his own thing.
45:31This is an example of dumb
45:34periodic limb movements.
45:35And now you have not done
45:37periodic limb movements.
45:38This is blood pressure.
45:39You know here is blood pressure.
45:40So you have repeated surges of
45:43blood pressure occurring with
45:44every single leg movement.
45:46This is not dumb.
45:47Periodically movements
45:48and a great example of a
45:50pathological sleep network.
45:57You can. Now there is some scoring
46:00guidelines say nothing about the chin
46:03EMG during non REM sleep basically.
46:06Yeah, it may be low, you know,
46:08but there's an enormous amount
46:10of information in the chin EMG.
46:12You can see how this coupling between
46:15cortical transients and chin transients,
46:17cortical chin, cortical chin cortical thin,
46:20and you can extract the quality
46:22of sleep by EMG analysis.
46:25I'm sure not done it yet.
46:27Hopefully.
46:27Hopefully you can another example
46:29of cortical motor network connection
46:32while breathing is reasonably good.
46:34Breathing is reasonably good,
46:35but all hell is breaking loose
46:37when it comes to sleep quality.
46:39So now you have a dissociation of.
46:41So the cortical motor system is
46:42doing its thing in a different way,
46:44while respiration says I don't really care,
46:47I'm going to do my own little thing.
46:50This person is on a postulator pressure.
46:55On the left, you have network
46:57success where you have good
47:00behavior while there's some R.E.M,
47:01dominant apnea, etcetera.
47:03On the right, you have network failure.
47:06This looks like a poorly sleeping rat.
47:09Not only a good rat, but a poor rat.
47:12And below. Essentially the same
47:15person getting a titration.
47:17Another night and sleep remains very,
47:20very poor. It's a network failure.
47:26Let's see. OK, just do three more minutes.
47:29So. Once you start thinking network,
47:33you start seeing a lot of really
47:35interesting kind of features.
47:37So this is a hypnogram
47:39from the home sleep study.
47:41Showing craziness of the heart rate, right?
47:45Going down and then OK,
47:47then down and OK.
47:48And there is some respiratory stuff,
47:50there's some you know oxygenation stuff,
47:53some snoring now take a close up look.
47:57So you have stable breathing.
48:01Sorry, stable breathing,
48:02a little bit of snore and the
48:06plate signal behaves well.
48:08You have on the other end on the right.
48:11You have abnormal respiration.
48:14And you have bradycardia return to normal,
48:18bradycardia return to normal.
48:19And I just put a line around
48:22the respiratory recovery starts.
48:24It's not synchronized.
48:26However, when state is stable.
48:30This cardiac arrhythmia is not occurring.
48:33So it is not entrained,
48:34but perhaps stable,
48:36unstable sleep networks enable.
48:38Certain pathologies to come through.
48:41So this is a transition where breathing
48:43is starting to look a little unstable.
48:45A little bit of snoring coming
48:47in and you can start seeing this
48:50intermittent bradyarrhythmia coming.
48:54You do better than hypersomnia.
48:56Can be thought of as a network of failure.
48:59We got to get up in the morning.
49:01We need a wake network to take
49:03over as an example of a patient
49:05who keeps sleeping, sleeping,
49:06sleeping, not very fragmented.
49:08This one is much more sleep,
49:09sleeping, sleeping, sleeping.
49:11But it's very fragmented and
49:13sleeps I think 22 hours, 22 hours.
49:19So it's a speculative hypothesis, but.
49:23Perhaps hypersomnia can be
49:24considered a failure of a switch
49:27from the sleep to awake network.
49:33Uh. I should probably stop here.
49:43So. Stabilizing networks
49:46to target sleep disorders.
49:48We do it all the time.
49:49Here are examples.
49:50We don't think network,
49:51but we're doing it all the time.
49:53Successful sleep treatment
49:54of any kind requires.
49:57Stabilizing sleep networks.
50:01And I think I will stop with that if there's
50:04you know if there's some time I can go back
50:06and show you cardio pulmonary coupling,
50:08how it can be used to track network
50:10stability in an ambulatory way,
50:12but it's not necessary to make my point.
50:14So in summary, sleep is a unique
50:16network state, it's multi Physiology,
50:18it has multiple dimensions including time,
50:21it's dynamic,
50:22it's morphing the phase transitions,
50:25predictable changes in disease,
50:26predictable effects of treatment
50:28which works or doesn't work.
50:30And network analysis is severely underused.
50:32And sleep research and
50:34nonexistent and sleep practice.
50:36Network science is alive and well.
50:39There are.
50:40In so many different metrics which can
50:42be generated and potentially useful
50:44and track disease and quantify beyond.
50:47You know we keep talking beyond the
50:49HI beyond the HI yeah this is one of
50:52those beyond the HI kinds of things
50:54which almost certainly can be useful.
50:56A call out for frontiers
50:59and network Physiology.
51:00It's a new subsection of frontiers.
51:02The new journal within Frontiers
51:05and Ivanov is the. Is the edit.
51:09El France chief overall editor.
51:12It has a section on sleep
51:13and circadian systems,
51:14networks and sleep and circadian systems.
51:17And you know,
51:18if you have a network key kinds
51:20of things you like to publish,
51:22we are open and happy.
51:25I don't know if Flammen is on this call.
51:28I hope he could join,
51:29but we're very excited about the
51:33potential of rapid translation.
51:35Uh, not just uh, you know,
51:39interesting theoretical physics,
51:40see kind of papers,
51:42but really trying to better
51:44understand disease and tracking
51:46and pathophysiology by looking
51:48at multi system integration and
51:50breakdown in the sleep state.
51:52So with that I shall stop and happy
51:55to take questions and similar things.
51:58Thank you.
52:00Great. Well, thank you very much
52:02Robert for this tour, tour of the.
52:05Sure. Of the sleep network.
52:07So I will moderate the questions
52:09that you guys have them,
52:10please put them in the chat
52:11and if you have a question,
52:12just raise your hand and I will unmute you.
52:15So that you can go ahead and and do that.
52:17And so it looks like Mayor Krieger,
52:19you have a question.
52:19Go ahead Mayor
52:21Robert, I didn't see oxygen on your slide
52:24where you talked about potential therapies
52:28for some of these network problems.
52:31I found in Canada was it was easy
52:33to order oxygen for heart failure.
52:36And some of the patients did incredibly well.
52:41That is true.
52:42Oxygen definitely should be there.
52:44In fact, one of my earliest oxygen
52:47successes was a patient who had very
52:49severe he had changed strokes and clinic.
52:52He was that severe.
52:53And on oxygen therapy,
52:55he was back to hunting.
52:56So you know that is true.
52:58I just wish it was more predictable.
53:01But that is true.
53:02Patients can have really
53:04great benefit from oxygen,
53:05but it's just less predictable than ideal.
53:08Oxygen of course changes the sleep
53:10network in many ways because the
53:11current it will suppress curby
53:13body firing to some extent.
53:14And the current body of course
53:17influences the tractor solitarius,
53:18which then of course influences just about
53:21everything in the brain and brainstem.
53:23So.
53:24Yeah, oxygen, definitely.
53:25I should add that.
53:31All right, very good.
53:34I have a question Robert and so.
53:36Is very interesting you pointing out the
53:39differences between stable and unstable
53:41and to sleep and in many cases where
53:44it just seems to occur spontaneously.
53:46Do you have an idea or at least
53:48if you have some hypothesis as
53:50to why might that occur?
53:51So we we do see that in PSG's
53:54quite often where you have.
53:56Stage two sleep, but one is stable,
53:58one is not stable.
53:58Do you have a sense of why that might occur?
54:01Honestly, no. Some ideas as to why,
54:05how, how it could occur? Umm. No.
54:11The Telemac cortical system has a membrane
54:15oscillations which can change over.
54:18You know. Multiple seconds and minutes.
54:23And because they are so central in the, you
54:26know, between the cortex and the brain stem.
54:29It's possible that changes in
54:30telemac cortical conductance,
54:31especially potassium conductance,
54:33could be a switching kind of mechanism.
54:37It's possible that in stable state
54:40there is mild hypercapnia mild.
54:43Even in normal stable state.
54:45That it after a certain point
54:49it triggers a hyper. Well.
54:52Hard to call it hypercapnia,
54:54but the CO2 mediated.
54:57Arousal, which enables the switch to.
55:00Another state.
55:01But in reality, I'm just guessing.
55:03I simply do not know.
55:05I have for many years.
55:07I tried to convince Cliff shaper,
55:10Jerome Siegel,
55:11and a few others to try to study this state.
55:15Which of course occurs in rats and mice.
55:18Called intermediate sleep I believe.
55:21And that's what actually is our enemy.
55:23I mean, our enemy is what the
55:26neurobiologists kind of, sort of ignore.
55:28Uh, we have, uh, less worry about.
55:32Nice, good.
55:33Delta enhanced sleep in the clinic.
55:36Much of what we deal with is unstable,
55:38so it is a mystery and I wish.
55:41Neurobiologists would actually
55:42try to figure it out because
55:44that's what we struggle with.
55:46That's our enemy.
55:49But we see it all the time, right?
55:50I mean, anyone who chooses to
55:51look for it, you'll see it.
55:53And stable breathing periods
55:54have been described, you know,
55:56so many times over the years
55:58in sleep apnea patients.
56:00It's almost certainly a it's not
56:02just a genioglossus activity,
56:04or it's not one thing.
56:05It is the whole state switching.
56:11Gotcha. OK. Anybody else have
56:13any other questions I wanted to
56:16to bring up during this talk?
56:20Want to join in and drop
56:21some words of wisdom?
56:26You too.
56:28Try to try to doctor Evanoff.
56:30Try to meet yourself.
56:37Yes, OK.
56:39So take your other wonderful presentation
56:41and so you paint the big picture and I
56:44think with the big potential for the future,
56:46but basic science and clinical practice.
56:50In general, just West of perhaps the question
56:54about stability and instability and whether
56:57we have a true human studies in sleep,
57:00it seems like, at least from a
57:03certain indication of these dynamics,
57:05whether of individual systems or from
57:07point of view of network interactions,
57:10that sleep has also certain
57:14characteristics of criticality
57:17and this system at criticality.
57:20What this would mean is that
57:23there is certain not local, but.
57:27Global interactions that allow a
57:31transition that are spontaneous.
57:34If we have a system which is truly
57:36in equilibrium then it takes a
57:38lot of energy to drive the system
57:40Congress sleep stage to another or
57:42from one sub state within the sleep
57:45stage to another and having you
57:48know certain critical features from
57:51actually physics point of view of
57:54criticality perhaps has a place.
57:56To better understand the dynamics
57:59and regulation of sleep.
58:01So this in relation to the last
58:04question that was asked about
58:06stable and unstable states.
58:08This transitions across spontaneously
58:10and there could be facilitated by
58:14dynamics which are non equilibrium
58:16non homeostatic dynamics that occur
58:19at small timescales of seconds up
58:21to minutes of course when you speak
58:24about the large window of hours or.
58:27Well, you know the sleep wake cycle
58:29of the 24 hours day then for you have
58:33certain homeostatic control there.
58:34So it's seems that there is some
58:37duality and I think these network
58:39approaches also could perhaps will be
58:42useful in this direction in the future.
58:45But you painted a very really deep and
58:50broad picture and a lot to be investigated,
58:53so obviously this is a visual
58:57one can see traces that.
59:00Visually you can see certain
59:03correspondence in patterns,
59:04but the big challenge is,
59:06as you also pointed out,
59:07is to find reliable metrics.
59:10And in basic science we do not quite
59:15have reliable metrics for systems
59:17which are diverse in nature and that
59:20also work in parallel but work in
59:23parallel across multiple scales.
59:25So simple cross correlation simple
59:28metrics don't quite work or not.
59:31Do not work reliably.
59:32And so in that sense I just would like to,
59:35you know, caution the community.
59:37Of course the horizon is there
59:40and very exciting,
59:41but one also has to be careful
59:43of how to how do we technically
59:46approach the question of extracting
59:49information that is hidden in the
59:51dynamics of the signals and in
59:53their couple. Very good.
59:55Well, thank you.
59:56Thank you very much, Doctor Evanoff.
59:57Thank you very much, Doctor Thomas.
59:59It was a great talk, great conference,
01:00:02lots of stimulation for thought and ideas
01:00:05for future research and collaboration.
01:00:07And thank you all for attending another
01:00:10edition of the joint conference.
01:00:13And we will see you guys back on
01:00:14the 12th of October when Doctor
01:00:16Ali Azar Brazil will join us from
01:00:18pregnant Women's Hospital and talk
01:00:19about his work in examining others,
01:00:23by the way will be networking. Yeah.
01:00:27Okey Doke, very good.
01:00:28So we'll see you guys in October.
01:00:30Have a great week.