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Well, Gustavo, I've been

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reading his work
since I was an undergraduate.

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I think of my Ph.D.

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student and a great expert in network
connectivity in the brain

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where he was to apply
this knowledge of neuroscience

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and physics together.

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So he's currently full professor

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catedrático at the University of Pompeu Fabra
in Barcelona, where he's based,

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and he also runs 
the Center for Brain Cognition there

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and is an accredited
professor and has had an EOC.

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Advance
Grant is now part of the new Synergy

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Grant in neuroscience
to be awarded in Spain and to work on

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basically stroke recovery in networks
and in stroke recovery.

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And I was in Hamburg yesterday,
and I just found out that Gustavo

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is a member
of the Science Academy of Hamburg.

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So the list is very long.

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There also worked with Siemens
and has an award from Siemens in terms

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of innovation for patents as well
when we were talking about this idea.

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So yeah,

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and for those of you that know

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e-brains and so the Human Brain
Project legacy and Gustavo was part

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of the Human Brain project is now a member
of the scientific committee

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of e-brains,
which there are two people per country.

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So I think it's pretty fair to say that,
you know, he's not the not maybe.

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Well, we don't know.

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I would say the top neuroscientist
in Spain at the moment.

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Yeah.

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So we're very happy to have him.

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So over to you. Thanks.

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Yeah.

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Thank you so much for the exaggerated
and kind introduction, Brian.

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And I think especially for for inviting me
and for having the possibility

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to present part of my interest nowadays

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from the scientific point of view.

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So the title of my talk is a little bit

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provocative as you realize
the thermodynamics of mine.

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I mean, I hope that 
I think I have never found a paper

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on thermodynamics and neuroscience before.

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So from that point of view, it seems to be
really a little bit provocative.

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But at the end of the day,
you will see that the content

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is not provocative at all.

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As I mean, it's a very, very, very robust

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framework, very natural
and very intuitive framework that

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we think can be used in neuroscience

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for answering some particular questions.

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And this is how I will start

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it just motivating the questions.

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So what we want to achieve, 
which kind of question we want to answer

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in this cognitive neuroscience field,

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the main question that we are

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trying to solve is a about

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the brain

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dynamics
and who is running the brain dynamics?

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Who is orchestrating the brain dynamics?

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And this is what we call more
technically the data

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organization in the brain dynamics.

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So just to give you a feeling,
we want to know very,

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very fundamental things.

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For example, if I am doing nothing,
what we call our data resting state,

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how is my hierarchical organization
at the spatial temporal level?

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So they are all regions

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equally important.

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So we have a kind of democracy,
we have a flat organization

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or there is a kind of in the other extreme
dictatorship, there are some regions

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we are really running the shows
and giving and driving.

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The rest of the brain is one question
And then how would this theoretical

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organization change and differentiate
to other, for example, cognition?

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So instead of doing nothing,
I start to do a working memory task

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or decision making social task, whatever.

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So is something changing or not?

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I mean, from the hierarchical
point of view and of course in different

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brain states, if I am sleeping,
if I am under anesthesia, if I take drugs

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or in the biomedical context,

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if I am healthy, or I have

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some kind of neuropsychiatric

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disease, is this Iraqi colonization
reflecting that?

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And if it is reflecting that is perhaps
the causes of my disease or not?

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And if I know how,
how is the right hierarchical organization

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and how is the brain,
the irregular indecision

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in a particular disease perhaps, that
give me some hints to not only predict

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and and and make some diagnosis on that,
but even to design

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some possible therapies.

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So the the most natural way

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of starting to talk about the hierarchy
actually is nothing new.

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I mean, people

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in artificial intelligence, in
computer science, they know that it seems

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well, not centuries, but decades at least.

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And is it?

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Well, then just do it
and measure your functionality and

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direction your interactions

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because you are interesting dynamics.

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So it should be
a special temporal interactions.

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Just measure with your favorite measure

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of function and interactions
and then put that in a graph and do it.

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You don't kill analysis, nothing else.

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It's nothing to to complex.

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I mean, all the tools are there.

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The problem is that's the type of data
that we have

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in neuroscience
are not always allowing this thesis.

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Try forward, detect the solution

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sometime is possible
and I will show in the next slide.

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One example that is
this is what they call the the detect

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this the most simple solution.

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For example,
you can use Granger causality.

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We were talking about that
at a couple of minutes ago and

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undefined your functional interaction
in term of Granger causality

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and then you build your graphs
under all the different situation

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and they start to analyze.

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Look, I mean in this conditions,
that particular region is really

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running the culture.

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So what the whole talk is about,

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what we can do if we don't want to measure
directly the functional interaction

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because we don't have enough data,
that is the main problem.

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And we are using the three
coming from the thermodynamics,

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which is sounds very technical,
but you will see it's very, very simple.

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It's the breaking of the detail balance.

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So we know in the thermodynamic.

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But I will describe that with some details
that in the talk

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that when you break the symmetry
in the interactions,

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meaning if you break the symmetries,
you start

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to build up hierarchical organization,

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then this is what we call
the breaking of the detail balance.

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And that has consequences

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that could be reflected
in other measurements

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which are not the functional interaction.

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For example, one very well-known
measure is the arrow of time,

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and that is what they will use

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just by concentrated
on the arrow of time of brain signals.

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Indirectly, I can say something

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about the hierarchy organization
and that is fantastic

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because as we will see,
the collective decision of that of time

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is a much more simple business
to measure with the

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with a low amount, 
a small amount of data.

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And that is the talk.

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I mean, basically to
to see how we can measure the theoretical

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or any session indirectly through this
feature of the done while in time, namely

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that what we call out of time or non
reversibility or non-equilibrium.

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So we we practically equalize
all these words out of time,

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not of activity.

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The non-equilibrium and what is important
for us is, you know, to get organization.

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I don't care if the brain is in article
by like because I physicist but

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but I theoretical immunoassay
I don't care if that is a narrow of time

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or it's just Barcelona with it

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but they care about the target
organization and that is what we do.

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So in the first part that I,
I will do this in a model three way

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and then at the end of the talk,
I will say a couple of what about that

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we can use now these new measurements
instead of the classical measure

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like function
are going activity to job correlations

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or even

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sophisticated measure in cases
that we can use that

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as I mentioned, greater causality
before to construct a model.

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And then we will have
some causal mechanistic explanation

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of this particular type
of hierarchical organization.

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So that is the motivation.

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I'm I'm glad of that though,
first very rapidly,

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because if you are in neuroscience,
I mean, people were interested

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in the local organization,
the whole brain level

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also since many decades.

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One typical example that we know is 
the standard.

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Can that together with the champion

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sans your use,
it is this concept from bars

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that was a global workspace idea and it's
very well reflected in this cartoon.

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I mean where would you see all these
nodes, which are the brain regions

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and and the imagine it

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still strange regional hierarchy you know
organize it where you have very physical

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brain regions probably more associated
with sensory information processing

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and some central regions
which are this global workspace

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which they are in the central circle
which are regular 18 hour

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driving at orchestrate,
orchestrating the the other regions.

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And in fact, as you know,
they use this theory as an example.

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It's not necessarily
the the main use of that

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for plane in a different brain
instead like consciousness.

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So if the global workspace those of you
the region has the top of the Iraqi

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allow the ignition of information
so the transfer of information

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from the periphery
to another part of the brain to another

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part the in the in the periphery than

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they claim, then there are some evidences

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that is associated
with what we call consciousness.

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And if you are not allowing this
this ignition that you see,

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they are in this read
the example called 60 on the right

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called subliminal that you see
are some activation

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but is not enough to

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to to to a start

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to this huge integration that you see
for example in the green curve.

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Okay as I said, the most simple and direct

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way is really to measure
functional interaction.

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We took an example
where we were able to do that

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because we had a lot of data
and that is the case of the Human

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Connection Project, as you know, is
a large collection of a thousand people,

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a thousand participants,
many different condition.

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Neuroimaging is
if I mainly the result is mean,

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but I will concentrate
only on them if MRI today

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and there are many conditions,

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resident state and many different
cognitive condition that you see here.

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The Tauber like working memory,
socio relational task model task

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language gambling. Imagine at this.

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And then if you take your measure
of Granger causality,

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I took a transfer entropy normalize it
with silvergate blah blah blah.

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I don't go into the details.

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Then you can measure
directly the interactions

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and then you select
who are at the top of the hierarchy.

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And those are the red regions in the top
renderings.

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In each different conditions,
you see that there are different regions

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running the Joe They are the
I don't remember, I think I took a

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they are like five
or 10% of the top regions

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and that is and do have more if you do.

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Now the intersection is a little bit
more sophisticated at the intersection,

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but there is

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just roughly to give you a flavor is
and it kind of intersection

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of all these red regions
all the top regions in

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in different cognitive situations
than what you get is

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what you see in B in this rendering this
yellow and orange regions

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which are basically quantification
of the global workers space.

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What the regions with and all possible

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cognitive demands
are always running the shows

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now and the folk
would be associated with that.

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So from that point of view, in that case,
because we have so many

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and so high quality data, remember 
not only

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the large number of participants,
but also two sessions, in some cases

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they are four session of resting state,
long sessions, 22 minutes,

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and with the fabulous year of 0.7 seconds.

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So it's fantastic data.

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So with that you are

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you managed to really calculate
the convention causality.

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Go now to another context
which is also equally relevant

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psychiatric application,
biomedical obligation.

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You are lucky
if you get the resting state of 7 minutes

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with the bacteria
from the hospital, 2 seconds

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and 20 subjects.

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You cannot calculate anything down.

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So therefore we were motivated
and I want to apply all these ideas

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to to psychiatry.

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So I am obliged to develop a framework,
a theoretical framework

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that allows me with this low
amount of data to characterize

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and this is what we call
that the more dynamic state of mind

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and now I will go a little bit
more into the details.

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Before that,
I suddenly started to have a result.

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Originally, I am coming
from quantum mechanics, I mean actually

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from electrodynamics, quantum mechanics
and a I and I was always even nowadays

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very interested in history of science
and what you see

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there is the the,
the image of a very well-known

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scientist, Edwin Schrodinger,
a Nobel Prize in quantum mechanics.

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And of course, I mean he
he had he ran very beautiful

244
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and very relevant ideas
in quantum mechanics.

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And I got a question.

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For example,
I was not aware that he was working also

247
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at the end of his life in in life science.

248
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And he had a great idea
that he wrote in this book what this life.

249
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And it's
a tiny book, very beautiful, written.

250
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And there he puts an idea,
namely that we should use

251
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non-equilibrium thermodynamics
in the context of biology

252
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because he associate
basically life with non-equilibrium.

253
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But that's open a whole field
in system biology.

254
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So at the molecular level where they
they started to apply really

255
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those ideas of non-equilibrium
thermodynamics in protein

256
00:16:10,800 --> 00:16:13,800
synthesis
and so on, and continue even nowadays.

257
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So what I am doing today
here is to try to apply exactly

258
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the same kind of ideas
but in the macroscopic context.

259
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So I am not applying this to molecules,
but I will apply to the whole way

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that is the

261
00:16:30,360 --> 00:16:33,600
joy attached to this claim
that the idea is not coming from me.

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It's coming from him. And

263
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so here is a

264
00:16:38,720 --> 00:16:42,000
cartoon session of this idea
now with a little bit more of that there.

265
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What you see on the top is a brain
with just a cartoon.

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I said four regions.

267
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Of course, we will take hundred of regions
most of the time.

268
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And then you if you study the the
I introduction,

269
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assuming that we can measure that

270
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and if we know that the functional
interaction are absolutely symmetrical,

271
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then automatically we know
that the system is nonhierarchical.

272
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This is a democratic flat system.

273
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They have all the same problem.

274
00:17:12,320 --> 00:17:14,920
There is no a leader there.

275
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A From the thermodynamic point of view,
this is called

276
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detail balance,
this symmetry in the information flow.

277
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And then we know
automatically two consequences.

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One that's that is no entropy production.

279
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And second, which is actually

280
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a theology of the first,
that is an arrow of time.

281
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So that is that there is no out of time.

282
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Sorry, that is reversibility.

283
00:17:43,280 --> 00:17:47,280
If you go now to the bottom
part of the slide

284
00:17:47,360 --> 00:17:49,680
and then you break the order, assumed that

285
00:17:49,680 --> 00:17:52,960
you are breaking the symmetry

286
00:17:53,040 --> 00:17:56,760
that you start to generate those rings
like in the global workers base.

287
00:17:56,840 --> 00:18:01,800
So some of the central
guys are more important because they're

288
00:18:01,880 --> 00:18:04,360
they could be driving the other guys.

289
00:18:04,360 --> 00:18:10,440
And then we know that
if we break the symmetry

290
00:18:10,520 --> 00:18:13,520
in the functional interaction
we have, yet our key

291
00:18:13,560 --> 00:18:16,320
and we have a breaking
of the little violence

292
00:18:16,320 --> 00:18:19,280
and then exactly the control
that we assumed before,

293
00:18:19,280 --> 00:18:23,440
we have non-equilibrium
and that means that we have production

294
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entropy different from zero
and we have non reversibility.

295
00:18:28,520 --> 00:18:31,920
Another of that
and that is the good new because

296
00:18:32,040 --> 00:18:35,040
the production entropy
is something that is very beautiful.

297
00:18:35,200 --> 00:18:38,200
But we can also not measure per say

298
00:18:38,280 --> 00:18:41,120
that paper is measured
in that even from my lab.

299
00:18:41,120 --> 00:18:45,960
But we do dirty tricks that I am ashamed
to talk about that today.

300
00:18:46,040 --> 00:18:48,960
We reduce the space,
I mean, and using technique.

301
00:18:48,960 --> 00:18:50,520
And so

302
00:18:50,600 --> 00:18:52,160
but these are high dimensional systems.

303
00:18:52,160 --> 00:18:56,400
So to to estimate probability and entropy
in the high dimensional system,

304
00:18:56,400 --> 00:18:59,400
forget it. But the idea

305
00:18:59,600 --> 00:19:04,480
but as I say, they were of
that is the arrow of time.

306
00:19:04,560 --> 00:19:07,200
And this is what we measure.

307
00:19:07,200 --> 00:19:12,360
We just look, if there is a distinction
in the in the time evolution of our

308
00:19:12,360 --> 00:19:15,920
but in signals,
most of the time could be bold signals.

309
00:19:16,000 --> 00:19:19,240
I will show some example
with the local field but then Jensen

310
00:19:19,240 --> 00:19:24,360
so that you get the feeling that it's not
depending on the other measurement.

311
00:19:24,440 --> 00:19:28,480
So this is just the Hollywood version
of the concept of not reversibility

312
00:19:28,640 --> 00:19:30,080
and reversibility.

313
00:19:30,080 --> 00:19:32,360
Just to give you a feeling
that what you see

314
00:19:32,360 --> 00:19:35,680
in these two movies on the left
is the non reversible system.

315
00:19:35,680 --> 00:19:38,680
Non-Equilibrium So a glass of wine

316
00:19:38,680 --> 00:19:42,680
which is destroyed by the 
impinging bullets

317
00:19:42,760 --> 00:19:46,360
that you see the for one movie
and the same movie, but backwards

318
00:19:46,440 --> 00:19:49,280
and it's very clear
what is for What about this fog one.

319
00:19:49,280 --> 00:19:50,400
Yeah.

320
00:19:50,400 --> 00:19:54,760
A On the other side
you have a typical classical

321
00:19:54,840 --> 00:19:59,320
reversible system
colliding consisting of volleyballs

322
00:19:59,400 --> 00:20:02,800
that would be perfectly
elastic is perfectly reversible.

323
00:20:02,880 --> 00:20:05,720
In fact, what you see
is the moving forward backward.

324
00:20:05,720 --> 00:20:09,520
And you cannot distinguish,
you cannot say which one is the reality.

325
00:20:09,560 --> 00:20:12,160
Both that, okay,

326
00:20:12,240 --> 00:20:15,600
the problem is that it's not always
the case.

327
00:20:15,680 --> 00:20:19,560
That is so easy to distinguish
reversibility from the possibility.

328
00:20:19,760 --> 00:20:22,960
And this is the reason
why I put here on the left.

329
00:20:23,040 --> 00:20:27,200
Actually, I have to confess
I am a fan of Christopher Nolan,

330
00:20:27,280 --> 00:20:31,360
but Tenet is the worst movie, of course.

331
00:20:31,440 --> 00:20:34,760
But for me it was a good example
because are people traveling forward

332
00:20:34,760 --> 00:20:39,440
and backward in time producing entropy
or or the increase in entropy?

333
00:20:39,520 --> 00:20:43,160
You see in this excerpt of the movie
that there are some people

334
00:20:43,160 --> 00:20:46,480
there, for example,
traveling forward and backwards.

335
00:20:46,480 --> 00:20:47,360
It's very clear.

336
00:20:47,360 --> 00:20:51,600
It's like the glass of wine is
is absolutely clear.

337
00:20:51,680 --> 00:20:52,880
What you see on the right.

338
00:20:52,880 --> 00:20:55,040
It is a tricky system.

339
00:20:55,040 --> 00:20:57,720
It's a physical system.
It's not important in the detail.

340
00:20:57,720 --> 00:21:02,160
It is an expensive too,
but that by construction,

341
00:21:02,240 --> 00:21:06,160
just skiing 
in that nature of physics paper,

342
00:21:06,240 --> 00:21:09,120
it generates a system that he knows

343
00:21:09,120 --> 00:21:12,440
because he simulated is by definition

344
00:21:12,440 --> 00:21:16,440
non reversible, is in non-equilibrium.

345
00:21:16,520 --> 00:21:19,520
And then you measure something,
it's the magnetization

346
00:21:19,720 --> 00:21:23,680
and you see how the magnetization evolve
in time is one of this kind of actually,

347
00:21:23,880 --> 00:21:28,400
I run the simulation, I run the simulation
on the proposed budget game

348
00:21:28,480 --> 00:21:32,840
and I don't remember which one
is the forward on the backward ration.

349
00:21:32,840 --> 00:21:36,840
So one caller is for
when I'm the other college smuggler,

350
00:21:36,920 --> 00:21:40,880
I don't know and I cannot recognize.

351
00:21:40,960 --> 00:21:43,400
So meaning, even if I know

352
00:21:43,400 --> 00:21:46,680
that I should be able to recognize

353
00:21:46,760 --> 00:21:50,360
is not this is not intuitive A

354
00:21:50,520 --> 00:21:55,160
and the first year
since kicking with a fabulous idea

355
00:21:55,160 --> 00:21:58,360
so basically this is assuming
all this concept of

356
00:21:58,440 --> 00:22:01,440
out of time
entropy in an non-equilibrium or so on.

357
00:22:01,680 --> 00:22:05,480
But the important part of this slide
is the the

358
00:22:05,560 --> 00:22:08,560
the panel on the on the right.

359
00:22:08,720 --> 00:22:12,240
What this is reflecting
is the in some cases

360
00:22:12,240 --> 00:22:17,720
it's very easy to distinguish
like in the top four from backward

361
00:22:17,800 --> 00:22:19,640
A you don't need nothing.

362
00:22:19,640 --> 00:22:25,520
I mean you just need your eyes
and that is in other cases like in this

363
00:22:25,600 --> 00:22:30,400
physical example of you are seeing,
she is difficult,

364
00:22:30,480 --> 00:22:33,440
but we know how to distinguish things

365
00:22:33,440 --> 00:22:36,440
in difficult cases, especially nowadays.

366
00:22:36,440 --> 00:22:39,720
It is actually the field of

367
00:22:39,800 --> 00:22:42,360
of machine learning.

368
00:22:42,360 --> 00:22:44,520
A And therefore the idea

369
00:22:44,520 --> 00:22:47,920
was to use a machine learning network
that we call on purpose.

370
00:22:47,920 --> 00:22:50,920
Then it but means a temporal

371
00:22:50,920 --> 00:22:53,920
evolution deep learning network

372
00:22:54,080 --> 00:22:56,680
in order to distinguish
in the case of the brain,

373
00:22:56,680 --> 00:23:01,720
which is not like the case of the movie
of Tenet it or the class of one,

374
00:23:01,720 --> 00:23:06,200
but more the difficult case
and to use this machine learning technique

375
00:23:06,440 --> 00:23:11,800
in order to just distinguish
the direction of the time

376
00:23:11,880 --> 00:23:14,880
to determine
if there is a matter of time or not.

377
00:23:15,040 --> 00:23:17,240
So we apply this in all possible contexts.

378
00:23:17,240 --> 00:23:20,480
One can apply this
to at the whole brain level,

379
00:23:20,480 --> 00:23:24,120
at the network level, at the local level,

380
00:23:24,200 --> 00:23:27,200
with different type of signals,
different type of species.

381
00:23:27,200 --> 00:23:30,320
Of course, at the beginning
they just concentrate on ball

382
00:23:30,360 --> 00:23:34,440
signals, neuroimaging in humans
and the different conditions.

383
00:23:34,520 --> 00:23:35,240
And what you see in

384
00:23:35,240 --> 00:23:39,240
the middle panel is just in one dimension
so that we can plot something

385
00:23:39,320 --> 00:23:44,160
is just your ball signal
as you measured it in one region.

386
00:23:44,240 --> 00:23:46,800
A And then by hand

387
00:23:46,800 --> 00:23:51,560
you generate the backward motion,
you flip it. Yes.

388
00:23:51,640 --> 00:23:56,160
And then you know which one you flip it
and which one is the real measurement.

389
00:23:56,160 --> 00:24:00,880
And then you can train a deep
learning classifier black box

390
00:24:00,960 --> 00:24:02,720
in a supervised way

391
00:24:02,720 --> 00:24:06,440
because you know who is or whose role
or who is of output.

392
00:24:06,520 --> 00:24:10,680
And then see if you can separate that
than using all the tricks

393
00:24:10,760 --> 00:24:14,360
of machine learning in a cross-validation,
blah blah, blah blah.

394
00:24:14,440 --> 00:24:18,280
You check the performance
if you see that the performance is high.

395
00:24:18,280 --> 00:24:22,960
So you can distinguish a signal
that you have never seen before,

396
00:24:23,000 --> 00:24:26,280
that means that that is an arrow of time

397
00:24:26,360 --> 00:24:29,000
that means that the system
is not reversible.

398
00:24:29,000 --> 00:24:34,080
That means that this is a non-equilibrium
and the most important consequence

399
00:24:34,080 --> 00:24:39,080
is to see them easier to kick out
and you have a quantification.

400
00:24:39,080 --> 00:24:44,640
So the performance of this network
quantified the level of hierarchy.

401
00:24:44,720 --> 00:24:50,560
On the contrary, if it is low, you have
more reversibility, more akin, leave, less

402
00:24:50,640 --> 00:24:52,280
less theoretical organization.

403
00:24:52,280 --> 00:24:56,120
But as I said,
you can use that in different context.

404
00:24:56,120 --> 00:24:58,280
I am not going into the details.

405
00:24:58,280 --> 00:25:03,360
What you see on the right as a whole
brain level network, labor or local level.

406
00:25:03,360 --> 00:25:06,520
I think I will give you an example of the

407
00:25:06,520 --> 00:25:10,680
of the two extremes
global and local level.

408
00:25:10,760 --> 00:25:14,680
So this is one case

409
00:25:14,760 --> 00:25:16,400
again, HDP,

410
00:25:16,400 --> 00:25:20,920
so that thousand people neuroimaging here,
but the resting state

411
00:25:20,920 --> 00:25:25,440
and all the different conditions
I concentrate on the on the left panel,

412
00:25:25,520 --> 00:25:26,520
it's the important one.

413
00:25:26,520 --> 00:25:31,280
And you see on the x axis,
all the conditions rest in first

414
00:25:31,360 --> 00:25:34,040
and then all the cognitive task.

415
00:25:34,040 --> 00:25:38,440
And on the y axis
you see for the cross-validation

416
00:25:38,440 --> 00:25:42,880
set, therefore
you see more or less hundred dots.

417
00:25:42,960 --> 00:25:44,600
I trained it the ten and

418
00:25:44,600 --> 00:25:47,960
with the more or less 900 participants

419
00:25:47,960 --> 00:25:50,960
and then a 200

420
00:25:51,000 --> 00:25:53,960
random participant for cross-validation.

421
00:25:53,960 --> 00:25:58,920
And you check the the label of non CBT

422
00:25:59,000 --> 00:26:02,520
as a performance of the net
for each single subset.

423
00:26:02,600 --> 00:26:05,720
And what you see is somehow expected
they are or the reading

424
00:26:05,720 --> 00:26:08,720
increase in order of larger organization

425
00:26:08,760 --> 00:26:13,200
resting is the less dark color,
but this hierarchical is not zero.

426
00:26:13,280 --> 00:26:14,800
You see, it's pretty guy.

427
00:26:14,800 --> 00:26:17,200
It's of one five, five, whatever.

428
00:26:17,200 --> 00:26:21,560
So there is a radical realization in your
brain, even when you are doing nothing,

429
00:26:21,680 --> 00:26:25,000
which is not cytology, because we knew
about the existence of networks.

430
00:26:25,000 --> 00:26:28,760
And so but it's nice to quantify that.

431
00:26:28,760 --> 00:26:33,600
And then what is expected, of course,
that when you start to use your brain

432
00:26:33,600 --> 00:26:37,400
for doing something, of course
you break even more the symmetry,

433
00:26:37,480 --> 00:26:41,560
even more the functioning director,
because you need to promote the cross-talk

434
00:26:41,560 --> 00:26:45,520
very specifically between
different members of your brains

435
00:26:45,600 --> 00:26:49,360
and therefore
the organization is increasing.

436
00:26:49,400 --> 00:26:52,800
The beauty is that
you are quantifying this and then

437
00:26:52,800 --> 00:26:56,360
you can see that
there are different target organization

438
00:26:56,440 --> 00:27:00,400
in the social task
compared to whatever emotion.

439
00:27:00,400 --> 00:27:04,360
And this is totally different.

440
00:27:04,440 --> 00:27:06,920
I don't comment on the other plots.

441
00:27:06,920 --> 00:27:13,080
This is just to give very rapidly to give
you a flavor of biomedical application.

442
00:27:13,160 --> 00:27:15,560
This it is also a public dataset

443
00:27:15,560 --> 00:27:18,560
from UCLA, a

444
00:27:18,640 --> 00:27:20,880
It's very small,
but what you have to see on

445
00:27:20,880 --> 00:27:24,320
the top left is exactly
the same as we have seen before.

446
00:27:24,320 --> 00:27:29,480
But now for four groups
of participants in resting state,

447
00:27:29,720 --> 00:27:35,520
the first group, the Gray is Healthy
Group control, and then the other ones

448
00:27:35,520 --> 00:27:40,720
which are significantly below the
the control group.

449
00:27:40,800 --> 00:27:43,680
So they have less
hierarchical organization

450
00:27:43,680 --> 00:27:47,040
under the same condition reasons
they are schizophrenic,

451
00:27:47,040 --> 00:27:50,040
bipolar and attentional deficit.

452
00:27:50,240 --> 00:27:54,080
And then you can go into the more specific
description of that.

453
00:27:54,080 --> 00:27:57,080
This is a global measure,
of course, of out of time.

454
00:27:57,160 --> 00:28:02,920
If you start to apply exactly the same
philosophy at the local brain area level

455
00:28:03,000 --> 00:28:06,080
and then you rendered
the differences is what you see.

456
00:28:06,080 --> 00:28:10,560
For example, at the bottom, just the
difference between bipolar and controls,

457
00:28:10,560 --> 00:28:14,280
between attentional deficit and controls
or schizophrenia and controls.

458
00:28:14,360 --> 00:28:18,440
And then you can start to interpret
in which way

459
00:28:18,440 --> 00:28:22,200
that you darker organization
change in all of them.

460
00:28:22,200 --> 00:28:26,960
I mean, in all of them globally goes
down is flatter but in different ways.

461
00:28:26,960 --> 00:28:30,240
For example,
I don't want to go into the details.

462
00:28:30,320 --> 00:28:34,040
You see, schizophrenia in general
is going down

463
00:28:34,120 --> 00:28:37,320
all the regions in attentional deficit
is mainly

464
00:28:37,320 --> 00:28:41,120
somatosensory or occipital areas
going down, which makes sense.

465
00:28:41,120 --> 00:28:44,120
And one can interpret that that
because they are more

466
00:28:44,280 --> 00:28:48,320
driven by the vitamin and therefore that
then you don't get the gaze

467
00:28:48,320 --> 00:28:50,120
and things like that.

468
00:28:50,200 --> 00:28:51,560
Okay.

469
00:28:51,560 --> 00:28:54,880
Just very rapidly to give you a flavor,

470
00:28:54,880 --> 00:29:01,160
we can simplify this things
and instead of using machine learning,

471
00:29:01,240 --> 00:29:04,800
we can use a measure which is even a

472
00:29:04,800 --> 00:29:08,520
much simpler to compute

473
00:29:08,600 --> 00:29:11,600
exactly the same concept out of time.

474
00:29:11,800 --> 00:29:15,120
The idea is following on the top line.

475
00:29:15,120 --> 00:29:21,120
You see how to calculate that
for a pair of regions.

476
00:29:21,360 --> 00:29:25,920
And then in the bottom part is
you can do this for all pairs

477
00:29:26,000 --> 00:29:29,520
in a matrix and then get the exact
information at the whole brain level.

478
00:29:29,640 --> 00:29:31,080
The idea is very simple.

479
00:29:31,080 --> 00:29:33,160
Don't don't see that slide is too complex.

480
00:29:33,160 --> 00:29:35,960
I realize

481
00:29:35,960 --> 00:29:39,960
you measure the correlation
but in a shifted way.

482
00:29:40,040 --> 00:29:44,680
So you break the symmetry in time
on purpose

483
00:29:44,760 --> 00:29:47,120
of the duration of the two signals.

484
00:29:47,120 --> 00:29:48,160
So instead of calculating

485
00:29:48,160 --> 00:29:51,480
the correlation, as always
you do the shifted correlations.

486
00:29:51,560 --> 00:29:54,880
Usually you move one to,
I mean, some methodology

487
00:29:54,920 --> 00:30:00,320
for designing the optimal shifting,
and then you do this with the forward

488
00:30:00,320 --> 00:30:04,520
version and get the number 
and then you do the backward maps.

489
00:30:04,520 --> 00:30:07,240
You and do exactly the same.

490
00:30:07,240 --> 00:30:11,120
If the system has an arrow of time,
that will be different.

491
00:30:11,200 --> 00:30:15,480
So if you do, the difference
of the two shifted correlation,

492
00:30:15,560 --> 00:30:18,000
this is what we call inside out

493
00:30:18,080 --> 00:30:22,320
for some strange reason, but

494
00:30:22,400 --> 00:30:25,400
it's a number
and that we characterize the label

495
00:30:25,400 --> 00:30:29,520
of asymmetry in this in this case.

496
00:30:29,600 --> 00:30:32,840
And as I said,
you can do that for all possible person

497
00:30:32,840 --> 00:30:35,840
that you have the whole brand label.

498
00:30:35,840 --> 00:30:38,840
Just again, another example in this case,

499
00:30:38,840 --> 00:30:42,800
another species, they are monkeys,
another type of measurements.

500
00:30:42,800 --> 00:30:47,400
They are a and

501
00:30:47,480 --> 00:30:51,480
actually intra cortical EEG.

502
00:30:51,560 --> 00:30:56,000
So phase cortical EEG is all high quality

503
00:30:56,200 --> 00:30:59,200
electrical signals for monkeys.

504
00:30:59,360 --> 00:31:03,520
A difference in this case is different
brain isolate conditions.

505
00:31:03,600 --> 00:31:05,480
They have manipulation.

506
00:31:05,480 --> 00:31:10,320
Whether you see the difference
between awake and asleep or awake

507
00:31:10,320 --> 00:31:15,840
and the action of different anesthetic
all under poverty.

508
00:31:15,920 --> 00:31:18,720
And what you see on the right
path in all these books,

509
00:31:18,720 --> 00:31:24,440
blood is always the same is that measure
of non reversibility meaning

510
00:31:24,520 --> 00:31:26,960
of hierarchy

511
00:31:26,960 --> 00:31:29,880
in all this condition
and what you see is in conscious state,

512
00:31:29,880 --> 00:31:33,040
so in awake is always higher than asleep

513
00:31:33,120 --> 00:31:35,520
or higher than in unconscious state,

514
00:31:35,520 --> 00:31:39,400
like in under the effect of anesthetic

515
00:31:39,400 --> 00:31:44,000
or like propofol
and D or ketamine and empty.

516
00:31:44,080 --> 00:31:47,520
And then when the effect of the anesthetic
on disappear is the third

517
00:31:47,520 --> 00:31:51,880
part of the of the blood
that you start to recover

518
00:31:51,880 --> 00:31:55,200
and the organization
you start to build up again,

519
00:31:55,280 --> 00:31:59,680
there is only one exception, which is
ketamine is going in the other way.

520
00:31:59,760 --> 00:32:01,440
And you can interpret that.

521
00:32:01,440 --> 00:32:04,880
The state reason
why ketamine is not only an anesthetic

522
00:32:05,040 --> 00:32:10,120
but is nowadays
also use it as a fun drug and

523
00:32:10,200 --> 00:32:13,120
is increasing the hierarchy

524
00:32:13,120 --> 00:32:14,400
in a funny way.

525
00:32:14,400 --> 00:32:17,760
This is a biomedical application
of the same framework.

526
00:32:17,760 --> 00:32:20,480
These are data from years and berries.

527
00:32:20,480 --> 00:32:22,640
Coma patients

528
00:32:22,720 --> 00:32:24,600
controls minimal consciousness

529
00:32:24,600 --> 00:32:28,560
and deep coma,
and you go in the right direction.

530
00:32:28,560 --> 00:32:30,800
A Of course

531
00:32:30,800 --> 00:32:35,240
controls have a certain level
of about you not to give you any session

532
00:32:35,240 --> 00:32:39,320
a little bit less in minimal consciousness
and much less significantly,

533
00:32:39,480 --> 00:32:43,480
much less in in the deep learning case

534
00:32:43,560 --> 00:32:46,200
shows

535
00:32:46,200 --> 00:32:49,760
now that we have a good measure
that characterized the

536
00:32:49,960 --> 00:32:54,760
the hierarchical organization in

537
00:32:54,840 --> 00:32:58,280
let's see if we can explain that
mechanistically.

538
00:32:58,280 --> 00:33:03,040
So what this means with this is a model
for the measurement quantification

539
00:33:03,040 --> 00:33:08,200
and we can try now to build a model
which explain exactly that measure.

540
00:33:08,200 --> 00:33:13,600
So the the get out of key to the
quantification of the Iraqi organization.

541
00:33:13,680 --> 00:33:17,400
And so the

542
00:33:17,480 --> 00:33:21,560
the sense of the model
I think is relatively well-known nowadays.

543
00:33:21,560 --> 00:33:25,280
But just to cartoon, is that

544
00:33:25,360 --> 00:33:27,520
what we call whole brain model

545
00:33:27,520 --> 00:33:31,080
and nowadays is mainly the integration

546
00:33:31,080 --> 00:33:35,280
of the coupling coming from the anatomy.

547
00:33:35,360 --> 00:33:39,800
For example, in humans, most of the cases
coming through the stratigraphy.

548
00:33:39,800 --> 00:33:43,080
But of course, if you have better ways
of describing the anatomy,

549
00:33:43,080 --> 00:33:46,080
you should to use that

550
00:33:46,280 --> 00:33:47,640
is the case of animals.

551
00:33:47,640 --> 00:33:49,520
Most of the cases are structural.

552
00:33:49,520 --> 00:33:53,360
The so-called connectome,
the structure are in terms of the fibers,

553
00:33:53,440 --> 00:33:55,520
existing couplings.

554
00:33:55,520 --> 00:33:59,720
And then you put your favorite way
of describing the local dynamics

555
00:33:59,960 --> 00:34:03,680
and you try then to explain
the global dynamics, nothing else.

556
00:34:03,680 --> 00:34:05,880
This is called hybrid model it, of course.

557
00:34:05,880 --> 00:34:10,840
And you you have to define okay,
but which aspect of the global dynamics.

558
00:34:11,000 --> 00:34:14,880
Traditionally, we were always obsessed
with the function of connectivity,

559
00:34:14,960 --> 00:34:18,040
functional connectivity dynamics. And so

560
00:34:18,120 --> 00:34:21,120
a what I will use today is

561
00:34:21,280 --> 00:34:25,160
get out again when I say, for example,
the inside out matrix.

562
00:34:25,240 --> 00:34:31,600
So the the,
the label of of of asymmetry

563
00:34:31,680 --> 00:34:36,040
in the in the forward or backward version
of the shifted functional connection.

564
00:34:36,040 --> 00:34:38,840
And I will try to explain that

565
00:34:38,840 --> 00:34:39,920
from the empirical data.

566
00:34:39,920 --> 00:34:43,640
So I will try to fit the model
so that they explain the

567
00:34:43,680 --> 00:34:47,960
the so the top 
matrix is the biblical one and they will

568
00:34:47,960 --> 00:34:52,160
try to generate an explanation of that
with the model for that.

569
00:34:52,160 --> 00:34:56,520
They will try in many parameters, namely
I will associate to each existing

570
00:34:56,520 --> 00:35:02,120
fiber value and this is what we call
effective connectivity.

571
00:35:02,200 --> 00:35:04,840
A And I added just because

572
00:35:04,840 --> 00:35:09,120
the term effective connectivity
was used in many different contexts,

573
00:35:09,200 --> 00:35:12,440
I put generative affective connectivity

574
00:35:12,520 --> 00:35:15,680
because it's the one really coming from
the from the model.

575
00:35:15,800 --> 00:35:18,000
So and, and we optimized that.

576
00:35:18,000 --> 00:35:20,920
And in this case we use our favorite

577
00:35:20,920 --> 00:35:24,280
the local linear, local 
nonlinear dynamic,

578
00:35:24,280 --> 00:35:30,640
which is the two Orlando oscillator
and does some good reason for using that.

579
00:35:30,720 --> 00:35:34,600
An example first left of again

580
00:35:34,600 --> 00:35:38,640
model three HCB

581
00:35:38,720 --> 00:35:41,960
What you see in
green is the box plot of all ties together

582
00:35:42,080 --> 00:35:45,800
now and not distinguishing them
but calculated with the inside out.

583
00:35:45,920 --> 00:35:50,000
So they are at the top
has the maximal hierarchy

584
00:35:50,080 --> 00:35:54,240
in the middle and blue light blue
you see resting state.

585
00:35:54,400 --> 00:35:58,520
The difference that one is with the seven
Tesla, the others with a three Tesla.

586
00:35:58,520 --> 00:36:00,160
So but very similar.

587
00:36:00,160 --> 00:36:04,560
And what is important
that is they are a below

588
00:36:04,560 --> 00:36:07,840
the hierarchical organization
of cognitive tasks

589
00:36:07,880 --> 00:36:12,160
as we found before with with Tenet,
but now with the inside out.

590
00:36:12,200 --> 00:36:15,240
So that is is good that meaning that

591
00:36:15,240 --> 00:36:19,600
the methodology on the same data
is achieved in exactly the same.

592
00:36:19,680 --> 00:36:22,320
But the most interesting case
is the yellow box plot.

593
00:36:22,320 --> 00:36:24,400
The yellow plot is

594
00:36:24,480 --> 00:36:28,800
a condition
where they are watching a movie

595
00:36:28,880 --> 00:36:33,760
which is very interesting
and use a lot nowadays in neuroscience.

596
00:36:33,840 --> 00:36:37,400
And what we see
is something that is unexpected.

597
00:36:37,480 --> 00:36:40,160
The theoretical organization goes down

598
00:36:40,160 --> 00:36:43,920
that caused me 
a couple of months in my life

599
00:36:44,000 --> 00:36:48,720
because I was absolutely convinced
that is an error and I was doing the

600
00:36:48,720 --> 00:36:53,480
MATLAB and so on, say,
come on, I am getting older.

601
00:36:53,480 --> 00:36:57,720
I mean, I'm not used to do

602
00:36:57,800 --> 00:36:59,840
I calculated within it.

603
00:36:59,840 --> 00:37:01,640
Also down

604
00:37:01,640 --> 00:37:06,920
below, I calculated with the dataset from,
from Lausanne, from Switzerland,

605
00:37:06,920 --> 00:37:10,920
with young people
watching movie all the time.

606
00:37:11,000 --> 00:37:15,320
So I started slowly to be
convinced is really smaller.

607
00:37:15,400 --> 00:37:17,920
And then I got the interpretation.

608
00:37:17,920 --> 00:37:20,960
I mean, well,
the first interpretation is,

609
00:37:20,960 --> 00:37:25,480
finally demonstrate
why movies are so relaxing

610
00:37:25,560 --> 00:37:29,040
is because
they are disconnecting the brain.

611
00:37:29,120 --> 00:37:31,520
But actually the truest nature.

612
00:37:31,520 --> 00:37:35,400
Of course, movies are relaxing,
I think is the word

613
00:37:35,520 --> 00:37:38,720
resting state is absolutely misleading.

614
00:37:38,800 --> 00:37:43,320
Of course, during resting state
we are doing much more things that we

615
00:37:43,400 --> 00:37:46,560
that that would that

616
00:37:46,680 --> 00:37:51,520
we are executing in movie watching.

617
00:37:51,600 --> 00:37:56,400
And it's confusing the word resting
I mean that implicitly.

618
00:37:56,480 --> 00:37:56,960
Okay.

619
00:37:56,960 --> 00:38:00,200
So we have very very clear
quantitative results.

620
00:38:00,200 --> 00:38:03,680
So we create a model in
this case is one model

621
00:38:03,680 --> 00:38:07,280
for each single participant
in each single condition.

622
00:38:07,280 --> 00:38:14,040
So one model for participant one
watch in a movie or resting state.

623
00:38:14,120 --> 00:38:16,400
And then we take the GIC.

624
00:38:16,400 --> 00:38:19,600
So the generative affective connectivity
and we try to prove,

625
00:38:19,600 --> 00:38:22,600
okay, this generative effect,
the connectivity,

626
00:38:22,800 --> 00:38:26,400
the generators of the Iraqi,
is the mechanistic explanation

627
00:38:26,400 --> 00:38:29,560
of the generation of the,
you know, should be useful.

628
00:38:29,720 --> 00:38:34,440
How I showed that while the easiest way
is let's try to classify

629
00:38:34,640 --> 00:38:39,960
so we use standard classifiers
in this case support vector machine,

630
00:38:40,040 --> 00:38:44,600
for example,
we try to classify the conditions

631
00:38:44,600 --> 00:38:49,720
if one particular person is watching
a movie or is in rest.

632
00:38:49,800 --> 00:38:51,200
And as you see

633
00:38:51,200 --> 00:38:55,160
on the right, I mean,
or just get the feeling by the box plot,

634
00:38:55,160 --> 00:39:00,600
which are very high, the level of accuracy
of these classifiers is pretty high.

635
00:39:00,680 --> 00:39:04,320
So over 90% if you do
with the functional connectivity.

636
00:39:04,320 --> 00:39:09,440
So forget thermodynamics, forget modeling,
forget everything, just

637
00:39:09,520 --> 00:39:13,240
the absolutely stupid
functional connectivity of your data.

638
00:39:13,440 --> 00:39:16,800
So just a correlation you doing
also good job

639
00:39:16,880 --> 00:39:20,840
is to is a blue box blood a
but not always.

640
00:39:21,000 --> 00:39:24,720
The conditions where you are
practically just labor in particular

641
00:39:24,720 --> 00:39:29,160
is what you see right bottom.

642
00:39:29,240 --> 00:39:31,800
We classify there are two type of movies

643
00:39:31,800 --> 00:39:34,520
Hollywood movies and creative common

644
00:39:34,520 --> 00:39:38,600
movies, Creative Commons movies,

645
00:39:38,680 --> 00:39:40,640
YouTubers, things like that.

646
00:39:40,640 --> 00:39:43,040
And Hollywood
are the Hollywood movies are

647
00:39:43,040 --> 00:39:48,600
Mission
Impossible, James Bond, as all the

648
00:39:48,680 --> 00:39:53,400
A F sees, is very bad in classifying that.

649
00:39:53,480 --> 00:39:55,040
But the Jeep.

650
00:39:55,040 --> 00:39:58,240
So the generators of the
you don't go when decision is over

651
00:39:58,240 --> 00:40:02,320
90%, you can distinguish
between the different type of movies.

652
00:40:02,400 --> 00:40:06,160
I would love to repeat that
between Hollywood and European movies.

653
00:40:06,160 --> 00:40:09,160
But then I don't have to do it.

654
00:40:09,320 --> 00:40:11,440
I am sure that that defense

655
00:40:11,440 --> 00:40:15,360
same technology model base

656
00:40:15,440 --> 00:40:17,640
of the inside, out of the
you are going imitation

657
00:40:17,640 --> 00:40:19,760
for the same dataset
that I was showing before.

658
00:40:19,760 --> 00:40:24,360
We then it the biomedical dataset,
the coma cases

659
00:40:24,440 --> 00:40:29,280
and doing the same of course in the box
lot the is not shown here.

660
00:40:29,280 --> 00:40:32,320
The difference is like

661
00:40:32,320 --> 00:40:35,400
internet in the level of unionization.

662
00:40:35,400 --> 00:40:39,240
But when you do the model
and you try to classify the model

663
00:40:39,320 --> 00:40:42,840
in controls
minimal consciousness or uncommunicative

664
00:40:43,000 --> 00:40:46,720
wakefulness in drones
or deep, deep coma,

665
00:40:46,800 --> 00:40:51,320
then you have a very good level
of of separation.

666
00:40:51,400 --> 00:40:55,360
And if you want of of diagnosing

667
00:40:55,400 --> 00:41:01,600
really the type of stage of coma
that that you have in that condition,

668
00:41:01,600 --> 00:41:06,160
which is of course
a hot topic in in in coma research.

669
00:41:06,240 --> 00:41:09,480
I just want to finish with some

670
00:41:09,480 --> 00:41:13,160
speculations about the future,

671
00:41:13,240 --> 00:41:16,240
how we can far that apply

672
00:41:16,400 --> 00:41:20,360
really this these ideas of thermodynamics

673
00:41:20,360 --> 00:41:26,440
and now in a much more sophisticated
way to get extra information

674
00:41:26,520 --> 00:41:29,800
in just based on the same conceptual idea.

675
00:41:29,800 --> 00:41:31,720
I mean in equilibrium

676
00:41:31,720 --> 00:41:37,560
we have no knowledge of time ever seen,
not reversibility and so on.

677
00:41:37,640 --> 00:41:40,080
And in non-equilibrium
we have something different.

678
00:41:40,080 --> 00:41:43,080
We have to give quantization to

679
00:41:43,160 --> 00:41:47,280
A and that is a theorem
which if you are not from physics,

680
00:41:47,280 --> 00:41:52,360
even if you are from physics,
I forgot that theorem.

681
00:41:52,360 --> 00:41:54,800
I mean, to be honest with you,
when I started to look that again,

682
00:41:54,800 --> 00:41:56,840
it's a fluctuation dissipation theorem.

683
00:41:56,840 --> 00:42:00,640
Scardoelli mean, when you say flotation
of what this equation of what?

684
00:42:00,720 --> 00:42:03,400
Well, that was the first

685
00:42:03,400 --> 00:42:08,000
important contribution of Albert Einstein

686
00:42:08,080 --> 00:42:10,880
when he was in Ban at the patent office.

687
00:42:10,880 --> 00:42:15,200
Doing bureaucratic work
seems to be that he was extremely wor

688
00:42:15,280 --> 00:42:18,040
and he started to look at the

689
00:42:18,040 --> 00:42:21,480
what we call nowadays
the Brownian movement.

690
00:42:21,560 --> 00:42:25,680
So you put in a glass of water,
little the particles,

691
00:42:25,760 --> 00:42:30,840
and then he was really just looking at
how they fluctuating fluctuation.

692
00:42:30,920 --> 00:42:32,800
And he explained fact that

693
00:42:32,800 --> 00:42:37,200
what happens as he was really

694
00:42:37,280 --> 00:42:40,760
very bored and then say what
do what should they do now?

695
00:42:40,760 --> 00:42:42,840
Okay,
let's put some charge on the particle.

696
00:42:42,840 --> 00:42:44,360
And I put an electric field.

697
00:42:44,360 --> 00:42:46,360
So I produced this system.

698
00:42:46,360 --> 00:42:49,000
And what he found is fabulous

699
00:42:49,000 --> 00:42:52,000
and is the
and this is the dissipation part.

700
00:42:52,160 --> 00:42:57,320
Namely, that's a
if the system is in equilibrium,

701
00:42:57,400 --> 00:43:01,400
the fluctuation,
but addict hundred percent,

702
00:43:01,480 --> 00:43:06,440
the dissipation that is
magic is not magic, of course.

703
00:43:06,520 --> 00:43:08,280
But but it sounds like magic.

704
00:43:08,280 --> 00:43:14,080
I mean, I just look how they fluctuate
when I not produce the system.

705
00:43:14,160 --> 00:43:16,600
And that is enough.

706
00:43:16,600 --> 00:43:17,920
I can predict Perfect.

707
00:43:17,920 --> 00:43:18,880
I don't need to measure.

708
00:43:18,880 --> 00:43:22,480
I can predict what would happen
when you party of the system.

709
00:43:22,560 --> 00:43:24,960
Fantastic

710
00:43:25,040 --> 00:43:26,120
parenthesis.

711
00:43:26,120 --> 00:43:28,520
Let's go to neuroscience,

712
00:43:28,520 --> 00:43:31,040
one of my essays in energy Partners.

713
00:43:31,040 --> 00:43:32,320
But it's not because of that.

714
00:43:32,320 --> 00:43:35,520
I discuss all that they got into that.

715
00:43:35,600 --> 00:43:39,160
And with this Marcelo, my cimini

716
00:43:39,240 --> 00:43:43,880
he had is a medical doctor, had a fabulous
intuition and a fabulous idea.

717
00:43:43,880 --> 00:43:49,320
He has no idea about Albert Einstein,
has no idea about the this patient.

718
00:43:49,400 --> 00:43:53,080
And many years ago
they have this great idea,

719
00:43:53,080 --> 00:43:56,840
we will produce the brain
with DMEs, for example.

720
00:43:56,880 --> 00:43:59,960
That's the easiest way
I will characterize the brain

721
00:43:59,960 --> 00:44:02,960
in a in a very cheap way, e.g.

722
00:44:03,080 --> 00:44:05,320
so really cheap.

723
00:44:05,320 --> 00:44:08,000
And he had this intuition.

724
00:44:08,000 --> 00:44:11,720
That's when a part of the brain
and they have different

725
00:44:11,720 --> 00:44:14,080
underlying dynamics
because it's associated

726
00:44:14,080 --> 00:44:19,440
with different brain states,
sleep, anesthesia, coma, whatever.

727
00:44:19,520 --> 00:44:21,240
Then the effect of

728
00:44:21,240 --> 00:44:24,240
the perturbation is different

729
00:44:24,280 --> 00:44:27,800
and then say how a measure
that I have no idea.

730
00:44:27,800 --> 00:44:29,920
I never studied the physics or I have.

731
00:44:29,920 --> 00:44:34,360
So I used a more simple thing,
which is fantastic.

732
00:44:34,440 --> 00:44:36,120
And this is what he called the PCI.

733
00:44:36,120 --> 00:44:40,080
The perturbation Complexity
index are used to compress the ability.

734
00:44:40,080 --> 00:44:43,760
So the sleep that you have on your laptop,
you don't need to understand

735
00:44:43,760 --> 00:44:47,360
even celibacy for complexity.

736
00:44:47,440 --> 00:44:49,280
So I use impulsive complexity.

737
00:44:49,280 --> 00:44:52,280
So I think how much I can compress

738
00:44:52,480 --> 00:44:56,520
the signal that I evoke
after the perturbation

739
00:44:56,600 --> 00:44:59,840
and they managed to publish

740
00:44:59,920 --> 00:45:06,040
very relevant papers, nature signs
and in many other journals

741
00:45:06,120 --> 00:45:09,320
showing basically what is the nice,
what is not gotten.

742
00:45:09,320 --> 00:45:14,720
I too, from one of the early papers
that's under different conditions like

743
00:45:14,800 --> 00:45:18,000
resting wakefulness or sleep

744
00:45:18,080 --> 00:45:21,640
and non-REM sleep or REM sleep or coma.

745
00:45:21,640 --> 00:45:25,520
What you see at the bottom right again,
deep coma,

746
00:45:25,520 --> 00:45:29,600
meaning out of consciousness
or anesthesia is different here.

747
00:45:29,640 --> 00:45:33,400
You see, even without the PCI,
I am not the blood in the box,

748
00:45:33,400 --> 00:45:36,880
blood of the PCI
hip. Lots to that, of course.

749
00:45:36,960 --> 00:45:37,280
But you

750
00:45:37,280 --> 00:45:41,000
see here just by the cartoon
that the signal is simple

751
00:45:41,000 --> 00:45:46,360
and therefore is more compressible,
the more unconscious you are.

752
00:45:46,440 --> 00:45:48,320
A fantastic idea.

753
00:45:48,320 --> 00:45:51,480
Really fantastic idea.
Fantastic intuition.

754
00:45:51,560 --> 00:45:54,840
And they,
they and they, of course, develop it

755
00:45:54,840 --> 00:45:57,800
that in the context
of not only of of biomedical application,

756
00:45:57,800 --> 00:46:02,280
but in the context of consciousness
research.

757
00:46:02,360 --> 00:46:04,520
Now I try to link and of the parenthesis

758
00:46:04,520 --> 00:46:08,240
I try to link the fluctuation
dissipates in theorem

759
00:46:08,320 --> 00:46:12,880
with this and with the more dynamics
and this is how I will finish.

760
00:46:12,960 --> 00:46:15,440
The idea is very simple and

761
00:46:15,440 --> 00:46:19,280
I hope that you already came to the idea

762
00:46:19,360 --> 00:46:22,360
that works
because the system is non-equilibrium.

763
00:46:22,600 --> 00:46:25,080
If it would be equilibrium,

764
00:46:25,080 --> 00:46:28,080
then a massive mini

765
00:46:28,200 --> 00:46:32,640
would have discovered
that I don't learn anything

766
00:46:32,720 --> 00:46:35,120
because I could distinguish them
of course,

767
00:46:35,120 --> 00:46:36,800
but I do not need the perturbation.

768
00:46:36,800 --> 00:46:40,640
I just look at the fluctuation level
and that should be enough.

769
00:46:40,720 --> 00:46:42,440
But it's not enough. Why?

770
00:46:42,440 --> 00:46:45,440
Because all those brains
are in Non-Equilibrium

771
00:46:45,440 --> 00:46:50,160
and therefore the fluctuation dissipation
theorem does not hold.

772
00:46:50,240 --> 00:46:55,000
And that means that the degree
of violation of the fluctuation

773
00:46:55,000 --> 00:46:59,680
dissipation theorem
or the degree of hierarchy, realization

774
00:46:59,680 --> 00:47:06,480
of the degree of arrow time,
or the degree of non-equilibrium

775
00:47:06,560 --> 00:47:09,720
is a measure of the brain of states.

776
00:47:09,800 --> 00:47:14,880
And instead now of characterizing
this brain state with data of time,

777
00:47:14,880 --> 00:47:18,680
as we have done during the whole talk,
in all the different styles, model,

778
00:47:18,680 --> 00:47:22,800
free model based,
I would use perturbation,

779
00:47:22,880 --> 00:47:27,000
and that is a beautiful way of doing that.

780
00:47:27,080 --> 00:47:30,720
So if the brain would be in equilibrium,
what your is

781
00:47:30,800 --> 00:47:34,680
and I say to you that the spontaneous
state, the resting state,

782
00:47:34,760 --> 00:47:38,360
and this mathematically
that I see it in a very simple way.

783
00:47:38,360 --> 00:47:41,480
I mean, I don't go into the details,
but it's just correlations.

784
00:47:41,480 --> 00:47:44,960
It's like the vanishing connectivity
and the dissipation

785
00:47:44,960 --> 00:47:49,320
which is basically what we call
susceptibility is the effect of the.

786
00:47:49,400 --> 00:47:53,200
So it's a more sophisticated
version of the level of

787
00:47:53,280 --> 00:47:54,480
they should be equal.

788
00:47:54,480 --> 00:47:57,240
And that is the fluctuation
dissipation theorem.

789
00:47:57,240 --> 00:48:00,760
But that is not the case
because the brain is in non-equilibrium.

790
00:48:00,840 --> 00:48:04,040
And therefore what we want to see is

791
00:48:04,120 --> 00:48:07,440
how much we are
contradicting this equality.

792
00:48:07,560 --> 00:48:10,960
So we define the measure,
which is basically the difference

793
00:48:11,120 --> 00:48:16,080
of the left
and right hand side that normalize it.

794
00:48:16,160 --> 00:48:21,480
And this is the degree of deviation
from the fluctuation dissipation theorem.

795
00:48:21,560 --> 00:48:24,320
And this is a measure, another measure,

796
00:48:24,320 --> 00:48:29,080
a clever measure, because it's used in
something that is not the in in the

797
00:48:29,080 --> 00:48:30,200
in the pure fluctuations,

798
00:48:30,200 --> 00:48:34,600
you are adding the defect
of the dissipation of the perturbation.

799
00:48:34,680 --> 00:48:37,440
And of course, one can do this

800
00:48:37,440 --> 00:48:41,040
with the model
exactly the same as before.

801
00:48:41,120 --> 00:48:45,640
And you produced the model in Silico
in all these different way

802
00:48:45,640 --> 00:48:49,440
and just measure different
anticipation deviation.

803
00:48:49,520 --> 00:48:52,320
And what you see here,
just look at the box below.

804
00:48:52,320 --> 00:48:56,480
I mean, the renderings is that
this is just about the we did,

805
00:48:56,560 --> 00:48:59,960
but just in the box plot,
you see two cases

806
00:48:59,960 --> 00:49:03,720
sleep against Awake human neuroimaging

807
00:49:03,800 --> 00:49:07,400
small set 18 participant
at the date of hand was love

808
00:49:07,600 --> 00:49:11,200
very well known
and the data that we use it all the time?

809
00:49:11,200 --> 00:49:16,360
HCB More or less thousand participants in
all this condition repressing cognition.

810
00:49:16,360 --> 00:49:20,360
So and the degree of violation
of the voltage anticipation theorem

811
00:49:20,400 --> 00:49:24,720
is a good quantitative measure
of the degree of non nicotine,

812
00:49:24,800 --> 00:49:27,560
so that you are justifying

813
00:49:27,560 --> 00:49:32,640
why the PCI works and how you

814
00:49:32,720 --> 00:49:34,640
can define the.

815
00:49:34,640 --> 00:49:39,640
It sounds a little bit arrogant,
but it's not that the right version of PCI

816
00:49:39,640 --> 00:49:43,440
because it's not intuitive.

817
00:49:43,440 --> 00:49:46,440
It's the one that came in
from first principles,

818
00:49:46,440 --> 00:49:49,840
namely the fluctuation, dissipation, delay
and in that direction

819
00:49:49,920 --> 00:49:51,200
that we are trying to go.

820
00:49:51,200 --> 00:49:56,000
Now, they say I think they go
very rapidly on the conclusion

821
00:49:56,000 --> 00:50:00,880
their main objective was hierarchical
organization,

822
00:50:00,960 --> 00:50:04,760
corporate level,
using whatever you use e.g.

823
00:50:04,760 --> 00:50:08,840
me, G,
I and I, even local field potentials

824
00:50:08,840 --> 00:50:12,680
animals, humans
in all possible conditions.

825
00:50:12,760 --> 00:50:14,800
And the trick was extremely simple.

826
00:50:14,800 --> 00:50:17,880
It just looked out of time
with different methodologies, with machine

827
00:50:17,880 --> 00:50:22,080
learning, with correlations.

828
00:50:22,160 --> 00:50:25,720
so basically that is the main
they commit out of time.

829
00:50:25,720 --> 00:50:29,280
Non-Equilibrium And you don't go
any stage on the word turbulence.

830
00:50:29,280 --> 00:50:31,400
Forget it comes around.
I did talk of that.

831
00:50:31,400 --> 00:50:35,800
I'm not mentioning today,
but this is strongly related with that.

832
00:50:35,880 --> 00:50:39,640
And we have done a model of that
and we proved that the generators know

833
00:50:39,640 --> 00:50:43,440
the parameters of that model,
have efficient interpretation of these

834
00:50:43,440 --> 00:50:48,120
what we call generative
effect. Connectivity is informative.

835
00:50:48,200 --> 00:50:49,440
Is that

836
00:50:49,440 --> 00:50:53,240
then something about the origin of this
hierarchical organization?

837
00:50:53,240 --> 00:50:58,240
And nowadays
we are trying to develop a sophisticated,

838
00:50:58,320 --> 00:51:02,880
sophisticated technique in the framework
of the fluctuation dissipation data.

839
00:51:02,960 --> 00:51:07,840
So I was doing all this with my good
friend from Oxford, Morten Kringle.

840
00:51:07,840 --> 00:51:13,520
Why we develop it, all these ideas,
basically, do we think of it?

841
00:51:13,600 --> 00:51:15,960
We were also born, not invented.

842
00:51:15,960 --> 00:51:17,760
This at home.

843
00:51:17,760 --> 00:51:23,280
Was that really a pleasure to work
with him online on all these things?

844
00:51:23,360 --> 00:51:32,440
So thank you very much for your attention.

845
00:51:32,520 --> 00:51:36,680
But wow, that's a lot of food

846
00:51:36,680 --> 00:51:42,320
for thought, Gustavo, and that 
I have a lot of questions that I want.

847
00:51:42,520 --> 00:51:44,040
One point.

848
00:51:44,040 --> 00:51:48,320
So you derive two indices, one

849
00:51:48,320 --> 00:51:53,000
through the reversibility component
and then another one when you compare

850
00:51:53,080 --> 00:51:55,560
functional conductivity with this more

851
00:51:55,560 --> 00:51:59,480
elegant dissipation equation.

852
00:51:59,560 --> 00:52:02,200
And what
I found interesting is the ranking

853
00:52:02,200 --> 00:52:04,360
of the cognitive states
from the Human Connection

854
00:52:04,360 --> 00:52:07,600
Project is the same
with the both techniques, isn't it?

855
00:52:07,680 --> 00:52:09,320
It's roughly the same. Yeah.

856
00:52:09,320 --> 00:52:13,200
So you said the extremes
are the same social and emotional, such as

857
00:52:13,200 --> 00:52:16,360
is the maximum, the emotion is the minimum
them in the squid.

858
00:52:16,360 --> 00:52:17,440
Interesting to me.

859
00:52:17,440 --> 00:52:20,360
So there's something tapping into

860
00:52:20,360 --> 00:52:22,080
something that you use.

861
00:52:22,080 --> 00:52:26,600
It's yes, I mean the the actual one

862
00:52:26,600 --> 00:52:29,600
can one can look really

863
00:52:29,600 --> 00:52:31,760
when can look the renderings

864
00:52:31,760 --> 00:52:37,160
of the local version of the arrow of time
and then you see that that's really there

865
00:52:37,160 --> 00:52:42,960
much more asymmetry in a complex task
like the social task of the HTP

866
00:52:43,040 --> 00:52:47,080
than in the emotional task,
which is basically just visual perception.

867
00:52:47,080 --> 00:52:50,320
I mean, they call emotion
because they distinguish as angry

868
00:52:50,360 --> 00:52:53,360
nothing but the
that there is an emotional component.

869
00:52:53,360 --> 00:52:56,320
You see really the typical suspects.

870
00:52:56,320 --> 00:52:59,760
I mean that
there are asymmetries in the interactions

871
00:52:59,840 --> 00:53:02,600
but that there is a much more massive

872
00:53:02,600 --> 00:53:06,640
asymmetries, relational
or organized orchestration

873
00:53:06,640 --> 00:53:11,480
of computation in complex task
than in simple task, as I suspect.

874
00:53:11,560 --> 00:53:13,360
And one thing that I didn't

875
00:53:13,360 --> 00:53:17,640
quite understand
is how you do the perturbation

876
00:53:17,720 --> 00:53:20,360
on the last part. Yeah, yeah.

877
00:53:20,360 --> 00:53:22,480
So we take the model,

878
00:53:22,480 --> 00:53:27,480
individual participant,
individual condition,

879
00:53:27,560 --> 00:53:31,800
we take the model in Silico
and then we produce the model.

880
00:53:31,880 --> 00:53:35,720
So each
single note is perturb it in some way.

881
00:53:35,720 --> 00:53:36,960
I inject noise.

882
00:53:36,960 --> 00:53:40,680
All right, Inject noise beginning
right over the air, the beauty

883
00:53:40,720 --> 00:53:44,400
that you can do some analytical tricks
because the model is linear.

884
00:53:44,400 --> 00:53:45,200
I see it.

885
00:53:45,200 --> 00:53:49,560
And and, you know, at the end of it,
you have an analytical version

886
00:53:49,640 --> 00:53:51,280
of the dissipation.

887
00:53:51,280 --> 00:53:53,600
So therefore
it is very rapid to calculate.

888
00:53:53,600 --> 00:53:57,360
Okay, You know, I was thinking it had to
be knock out a node or inject something.

889
00:53:57,360 --> 00:53:58,240
Okay.

890
00:53:58,240 --> 00:54:04,120
And just one of the thing I want one point
and with a lot of MRI function

891
00:54:04,120 --> 00:54:08,400
imaging measures is a single patient data

892
00:54:08,400 --> 00:54:13,000
is always very noisy to to classify
a single patient as schizophrenia.

893
00:54:13,000 --> 00:54:14,440
You have one event.

894
00:54:14,440 --> 00:54:18,680
But I just wondering
whether your global measure

895
00:54:18,760 --> 00:54:21,680
should the global measure
of non reversibility

896
00:54:21,680 --> 00:54:25,120
for example
how well that would work on an individual.

897
00:54:25,120 --> 00:54:27,120
Yeah.

898
00:54:27,200 --> 00:54:30,320
Indirectly I saw some results.

899
00:54:30,320 --> 00:54:35,360
So the schizophrenia result bipolar ADHD
or the coma results,

900
00:54:35,600 --> 00:54:38,680
there were single single patients. Yeah.

901
00:54:38,800 --> 00:54:40,120
The dots were single patients.

902
00:54:40,120 --> 00:54:40,480
Yeah.

903
00:54:40,480 --> 00:54:45,800
All, all the analysis, even model
for your model based single patients

904
00:54:45,880 --> 00:54:49,760
and the degree of
and using a global measure you can use.

905
00:54:49,760 --> 00:54:56,280
Well the that is is pretty informative
so the effective connectivity is very good

906
00:54:56,360 --> 00:55:00,200
but just a global measure of
non-equilibrium in some case is very good

907
00:55:00,200 --> 00:55:01,120
to distinguish it.

908
00:55:01,120 --> 00:55:05,440
It's not so good for classifying
of course is to course to block it.

909
00:55:05,520 --> 00:55:08,720
But if you go into the details
and for example,

910
00:55:08,800 --> 00:55:12,120
one example is the cheek,
the affective connectivity

911
00:55:12,200 --> 00:55:17,120
done, done the label of classification
article and really high and really high.

912
00:55:17,120 --> 00:55:21,760
So that so that even with the
with poor data, because the quality

913
00:55:21,760 --> 00:55:26,160
of the data are really not to encompass,
you know, the neuroimaging.

914
00:55:26,160 --> 00:55:30,720
I mean these are next to the factor
and if that's the brain is

915
00:55:30,800 --> 00:55:32,720
pretty distorted,

916
00:55:32,720 --> 00:55:36,160
Yeah,
but is, is another source of noise.

917
00:55:36,200 --> 00:55:40,920
I mean that each individual
is a nightmare.

918
00:55:41,000 --> 00:55:43,480
But in spite of that that you saw

919
00:55:43,480 --> 00:55:47,520
I think was over 80% the classification.

920
00:55:47,600 --> 00:55:47,840
Yeah.

921
00:55:47,840 --> 00:55:49,960
And that's what I was saying
that was doing very well.

922
00:55:49,960 --> 00:55:54,000
I mean, all of these diseases as well,
they share the same genetics

923
00:55:54,080 --> 00:55:57,480
and say schizophrenia
and bipolar disorder and ADHD.

924
00:55:57,480 --> 00:56:02,720
So It would be surprising
that you differentiate well between them

925
00:56:02,760 --> 00:56:04,720
and have the same functional networks
as well.

926
00:56:04,720 --> 00:56:09,560
So that's why it struck me that it does
it does well as a technique.

927
00:56:09,640 --> 00:56:13,960
Yeah, we didn't try to classify it,
to be honest with you.

928
00:56:13,960 --> 00:56:17,400
The the UCLA,

929
00:56:17,480 --> 00:56:19,800
but we classify

930
00:56:19,800 --> 00:56:24,120
other psychiatric diseases
or even smaller, for example,

931
00:56:24,320 --> 00:56:28,920
the depression that I said from robot
Robin Carhart-Harris,

932
00:56:29,000 --> 00:56:32,000
the one that we actually use
psychedelics.

933
00:56:32,000 --> 00:56:37,200
And so it's a beautiful thing
because he uses citalopram that

934
00:56:37,280 --> 00:56:40,480
the traditional they said I

935
00:56:40,560 --> 00:56:44,400
drug for depression or the psychedelics

936
00:56:44,480 --> 00:56:48,600
and then you have both them
and then you can see the effects of loss

937
00:56:48,680 --> 00:56:51,960
and it says model that I said
because they have more

938
00:56:51,960 --> 00:56:55,840
or less 20 patients bad

939
00:56:55,920 --> 00:56:59,000
condition and that works very good.

940
00:56:59,120 --> 00:57:04,000
I mean, the classifier of we do classify
it's not Jake is a little bit different.

941
00:57:04,000 --> 00:57:09,080
Image is another characterization
of the hierarchy but not global.

942
00:57:09,080 --> 00:57:13,680
It's much more detailed
is what we call trophic characterization

943
00:57:13,760 --> 00:57:18,200
and that classify it relatively
well even can predict

944
00:57:18,280 --> 00:57:21,840
the response to the drug,
which is it for me is the Holy Grail.

945
00:57:21,920 --> 00:57:23,760
Yeah.
And so that's going to be looked for.

946
00:57:23,760 --> 00:57:27,760
So a lot of these things we don't have
that much clinical applications

947
00:57:27,760 --> 00:57:31,520
for animal research
despite the huge amount of MRI research

948
00:57:31,520 --> 00:57:35,120
that we had just squeakers
for this one issue that is not so reliable

949
00:57:35,360 --> 00:57:40,760
on an individual
patient basis, except okay,

950
00:57:40,840 --> 00:57:42,640
I have
more questions, but I'm being greedy.

951
00:57:42,640 --> 00:57:46,760
So someone else went or something.

952
00:57:46,840 --> 00:57:49,200
Yeah.

953
00:57:49,200 --> 00:57:50,440
So thank you

954
00:57:50,440 --> 00:57:53,000
for this is an interesting topic

955
00:57:53,000 --> 00:57:55,840
and I would like to know your opinion

956
00:57:55,840 --> 00:57:59,960
about the

957
00:58:00,040 --> 00:58:04,640
the causality
for all these type of molars.

958
00:58:04,640 --> 00:58:08,360
At the beginning you mentioned

959
00:58:08,440 --> 00:58:09,760
causality,

960
00:58:09,760 --> 00:58:12,320
but it seems to be

961
00:58:12,320 --> 00:58:15,320
not enough for several situations.

962
00:58:15,400 --> 00:58:18,360
So I don't know if you are familiar

963
00:58:18,360 --> 00:58:21,640
with the type of alternatives for

964
00:58:21,720 --> 00:58:24,760
so on causality
for measured in the study.

965
00:58:25,000 --> 00:58:27,520
Yeah. Yeah.

966
00:58:27,520 --> 00:58:31,440
I don't know. And

967
00:58:31,520 --> 00:58:36,360
in fact I'm in the second version
of the model for the inside out.

968
00:58:36,440 --> 00:58:41,560
It's a cheap version of the Granger
Causality

969
00:58:41,640 --> 00:58:44,880
A and, and that was enough

970
00:58:44,960 --> 00:58:47,600
but things
because we are not so much interested

971
00:58:47,600 --> 00:58:51,600
in the absolute value I mean perhaps
this body but in characterizing

972
00:58:51,680 --> 00:58:57,360
the degree of interactions,
if you are really interested on the values

973
00:58:57,440 --> 00:59:01,920
but is enough to assess
the degree of asymmetry.

974
00:59:02,000 --> 00:59:05,200
So my my,

975
00:59:05,280 --> 00:59:10,280
my, my belief or my intuition nowadays
is that thanks to this

976
00:59:10,280 --> 00:59:15,880
thermodynamic group, we are allowed
to measure causality in a bad way.

977
00:59:15,960 --> 00:59:18,080
And that's would be enough,

978
00:59:18,080 --> 00:59:22,760
of course,
if I if I would be able to find something

979
00:59:22,760 --> 00:59:28,960
better than the Granger causality
or transfer entropy,

980
00:59:29,040 --> 00:59:30,280
I would use that.

981
00:59:30,280 --> 00:59:33,200
And if I have enough data,
I would use that

982
00:59:33,200 --> 00:59:35,720
and I would
then do the thermodynamic trick.

983
00:59:35,720 --> 00:59:40,800
I just would construct the graph
and analyze in the traditional way.

984
00:59:40,880 --> 00:59:44,720
But I was not able to find that way for

985
00:59:44,880 --> 00:59:49,320
and robust enough for applying this to
to individual patients.

986
00:59:49,400 --> 00:59:53,520
We have a really very bad quality of data,
and this is

987
00:59:53,600 --> 01:00:01,920
that in

988
01:00:02,000 --> 01:00:03,600
that the example

989
01:00:03,600 --> 01:00:07,960
you gave of this reversibility
with the Russian name

990
01:00:08,040 --> 01:00:15,120
at the beginning, what was the name of
the reversibility of movies,

991
01:00:15,200 --> 01:00:20,320
the reversibility of movies in time?

992
01:00:20,400 --> 01:00:22,360
Are the

993
01:00:22,360 --> 01:00:24,880
the movie, you mean? Yeah.

994
01:00:24,880 --> 01:00:26,240
Christopher Nolan No, no, no.

995
01:00:26,240 --> 01:00:28,120
That, that

996
01:00:28,120 --> 01:00:29,720
someone that had shown
that it was reversible,

997
01:00:29,720 --> 01:00:33,120
but over a small timescale
just since, just introducing, you know.

998
01:00:33,120 --> 01:00:37,480
So what sort of timescales
are we thinking of and what,

999
01:00:37,480 --> 01:00:42,320
when when they were,
when they come up with this idea

1000
01:00:42,400 --> 01:00:45,760
that was and it's been actually

1001
01:00:45,840 --> 01:00:51,000
it's artificial timescales,
I mean but the if, if you would apply

1002
01:00:51,000 --> 01:00:56,440
these are exactly the same type
of spin model that we use in zero image.

1003
01:00:56,440 --> 01:00:58,880
And by the way from time to time.

1004
01:00:58,880 --> 01:01:01,880
So you can apply these

1005
01:01:02,000 --> 01:01:05,000
if you want, even at the millisecond scale
if you want.

1006
01:01:05,160 --> 01:01:05,520
Yeah.

1007
01:01:05,520 --> 01:01:06,880
Yeah, I thought so.

1008
01:01:06,880 --> 01:01:13,160
But I think what you are trying to,

1009
01:01:13,240 --> 01:01:14,040
to formulate

1010
01:01:14,040 --> 01:01:17,960
is a good question
that I don't know the answer,

1011
01:01:17,960 --> 01:01:22,560
but they will work against me
and is actually is a

1012
01:01:22,600 --> 01:01:26,360
thin is a challenging question
if the level of non-equilibrium

1013
01:01:26,360 --> 01:01:31,320
or if the level of communication
changed with the timescale

1014
01:01:31,400 --> 01:01:35,600
that I think I believe that
of course I don't know.

1015
01:01:35,720 --> 01:01:38,520
We never highlighted one can do that.

1016
01:01:38,520 --> 01:01:39,840
We have not done that.

1017
01:01:39,840 --> 01:01:42,440
For example, in the manga or with imaging.

1018
01:01:42,440 --> 01:01:46,280
And then you really on purpose, you,
you just concentrate

1019
01:01:46,280 --> 01:01:50,320
on different bands or different filters
and then you have the answer.

1020
01:01:50,400 --> 01:01:52,720
Yeah, that is we have never done that.

1021
01:01:52,720 --> 01:01:55,160
It's a good question about that. Yeah,
but there's also a question.

1022
01:01:55,160 --> 01:02:01,360
So we in electrophysiology
we use Granger calls on a team and, and

1023
01:02:01,440 --> 01:02:02,520
sometimes review.

1024
01:02:02,520 --> 01:02:06,120
I sometimes happen to say
have you, have you, have you flip the data

1025
01:02:06,200 --> 01:02:10,240
around to make sure
that there is no Granger causality.

1026
01:02:10,320 --> 01:02:13,320
that should be a good answer goes
I did but the thing so

1027
01:02:13,520 --> 01:02:17,960
yeah the because basically showing
that information flows in one direction

1028
01:02:17,960 --> 01:02:20,360
so the history of area predicts
the history of area

1029
01:02:20,360 --> 01:02:24,240
B better than the history
B itself on its own.

1030
01:02:24,320 --> 01:02:25,640
Yeah I think I got that right.

1031
01:02:25,640 --> 01:02:29,640
Anyway 
so just sort of regressive things and,

1032
01:02:29,720 --> 01:02:33,320
and there should be
and that's what I was going

1033
01:02:33,400 --> 01:02:37,560
I was wondering
okay so maybe, you know, you can be lucky

1034
01:02:37,640 --> 01:02:40,520
and if you're doing with higher
frequencies, you get one result.

1035
01:02:40,520 --> 01:02:43,520
Then if you're dealing with low
frequencies, when you flip the.

1036
01:02:43,600 --> 01:02:44,960
Absolutely.

1037
01:02:44,960 --> 01:02:47,520
Actually we have
that of one of our reviewers also

1038
01:02:47,520 --> 01:02:53,480
was asking, well, you are telling
that thing that in a to cartoonish or it

1039
01:02:53,480 --> 01:02:58,040
depends on causality similar to the cat
you showed that and they have shown that

1040
01:02:58,040 --> 01:03:03,480
I mean I took the ACP I calculated
I had operated like an engine goes I did

1041
01:03:03,560 --> 01:03:07,840
I calculated the degree of asymmetry
of the Granger I was head of the classical

1042
01:03:07,840 --> 01:03:13,760
Granger causality A and that correlates
perfectly with the inside out.

1043
01:03:13,840 --> 01:03:16,280
All right. Okay. Well,
that's good to know.

1044
01:03:16,280 --> 01:03:21,040
Yeah, it's a I was annoyed
because it was extra work, but

1045
01:03:21,120 --> 01:03:21,440
then we

1046
01:03:21,440 --> 01:03:25,240
can refer to it and to review of comments,
please see Decker's

1047
01:03:25,320 --> 01:03:28,000
response on this reversibility of Granger
causality.

1048
01:03:28,000 --> 01:03:32,520
Yeah, there's another point here
and that would then work in your machine.

1049
01:03:32,520 --> 01:03:37,120
But and there's a lot of debate
as to how much the structure,

1050
01:03:37,120 --> 01:03:41,560
the function of connectivity in the brain
matches the structural connectivity.

1051
01:03:41,640 --> 01:03:46,800
So there is some gross component that
and you know, the areas that have bigger

1052
01:03:46,800 --> 01:03:49,920
connections will be fluctuation, the blood

1053
01:03:49,920 --> 01:03:53,120
oxygen level up and down a bit more.

1054
01:03:53,200 --> 01:03:55,280
But you go
one step further with your modeling.

1055
01:03:55,280 --> 01:03:59,680
So you're absolutely you use

1056
01:03:59,760 --> 01:04:02,160
this the generative effect of connectivity

1057
01:04:02,160 --> 01:04:05,160
defined by the structural connectivity.

1058
01:04:05,240 --> 01:04:06,000
Yeah.

1059
01:04:06,000 --> 01:04:08,120
So how how, how do you do that?

1060
01:04:08,120 --> 01:04:09,120
So precisely.

1061
01:04:09,120 --> 01:04:12,120
So it is every single

1062
01:04:12,320 --> 01:04:15,960
node of your functional map have a have

1063
01:04:15,960 --> 01:04:18,960
or have not got to a connection
through fibers?

1064
01:04:19,000 --> 01:04:19,280
No, no.

1065
01:04:19,280 --> 01:04:22,280
The connections
are only the existing connections.

1066
01:04:22,400 --> 01:04:26,360
So therefore I take the the DTI

1067
01:04:26,440 --> 01:04:27,040
with all the

1068
01:04:27,040 --> 01:04:30,840
problems that I have, but
I take these as the ground through for me.

1069
01:04:30,920 --> 01:04:33,200
Okay, this is my template.

1070
01:04:33,200 --> 01:04:38,720
The good news, because in in the cases
where I used the defective connectivity,

1071
01:04:38,800 --> 01:04:42,920
I only take the DTI as a mask,

1072
01:04:43,000 --> 01:04:47,600
so I will only update the connection
that exists.

1073
01:04:47,680 --> 01:04:50,920
But the strain of that connection

1074
01:04:51,000 --> 01:04:54,000
does not need to correlate
with the number of fibers.

1075
01:04:54,000 --> 01:04:55,840
Okay? And that is an advantage.

1076
01:04:55,840 --> 01:04:56,720
And that therefore

1077
01:04:56,720 --> 01:05:00,920
I give the name effective
because I don't care about the anatomy.

1078
01:05:00,960 --> 01:05:05,240
Axes are not axes,
but I decide this strength

1079
01:05:05,400 --> 01:05:09,840
based on the data, of course,
but based on the functional data.

1080
01:05:09,920 --> 01:05:13,880
And they get much more than the If you try
to explain functional connectivity

1081
01:05:13,960 --> 01:05:16,800
with the structure of connectivity, use,
really interesting

1082
01:05:16,800 --> 01:05:20,480
say you get it'll depend on the Barcelona
and blah blah blah blah.

1083
01:05:20,480 --> 01:05:26,360
But then for the standard facilitation,
let's say the hundred, you get 0.3

1084
01:05:26,440 --> 01:05:28,880
a correlation, a model.

1085
01:05:28,880 --> 01:05:31,880
So for example, this tool
and the model in dressing state

1086
01:05:32,000 --> 01:05:35,000
could go to 0.8

1087
01:05:35,040 --> 01:05:37,160
so that means that you are explaining much

1088
01:05:37,160 --> 01:05:40,160
more than the structure,
structural connectivity.

1089
01:05:40,200 --> 01:05:42,560
And this is also known because

1090
01:05:42,560 --> 01:05:45,640
that is a very actually very simple

1091
01:05:45,680 --> 01:05:49,520
manipulation was done by the defend

1092
01:05:49,600 --> 01:05:51,680
with monkeys and by and so Douglas

1093
01:05:51,680 --> 01:05:56,160
look I love with humans one with Anastasia
the other with the

1094
01:05:56,240 --> 01:05:59,400
with the sleep
if you compare just the correlation

1095
01:05:59,400 --> 01:06:04,440
that I mentioned before see with FC
you said 2.3,

1096
01:06:04,520 --> 01:06:09,400
but if you check that in a sleep
or in anesthesia

1097
01:06:09,480 --> 01:06:10,520
goes up.

1098
01:06:10,520 --> 01:06:11,880
All right.

1099
01:06:11,880 --> 01:06:14,880
So I don't remember the number,
I would say instead of point four,

1100
01:06:15,000 --> 01:06:19,200
but significantly, both at

1101
01:06:19,280 --> 01:06:24,160
and that is also entity
and that the means of course the condition

1102
01:06:24,240 --> 01:06:27,920
it's not I always say

1103
01:06:28,000 --> 01:06:29,720
it's beautiful sentence

1104
01:06:29,720 --> 01:06:34,640
from from Aristotle's
that was translated Latin by queen

1105
01:06:34,640 --> 01:06:38,800
and quickly through a GP to that models
which is the GP do

1106
01:06:38,880 --> 01:06:42,400
which means the container shape
the content

1107
01:06:42,480 --> 01:06:46,320
and say yes shape
but is not determine if they can't.

1108
01:06:46,400 --> 01:06:47,000
Right.

1109
01:06:47,000 --> 01:06:51,920
So the see is really shaping
FC but is not defining.

1110
01:06:52,160 --> 01:06:56,240
So the dynamic is defined in the FC

1111
01:06:56,320 --> 01:07:00,040
and therefore is not astonishing that
neither sleep and and in non the sleep

1112
01:07:00,080 --> 01:07:01,560
you have different values for me

1113
01:07:01,560 --> 01:07:04,320
as demonstration
that you see dynamics matters.

1114
01:07:04,320 --> 01:07:05,640
Yeah.

1115
01:07:05,640 --> 01:07:08,720
So the more the more active
your brain is the less it's the less

1116
01:07:08,920 --> 01:07:12,800
the less it, the more independent
Are you from this.

1117
01:07:12,880 --> 01:07:14,960
That is also a very naive

1118
01:07:14,960 --> 01:07:18,680
view of that as a come on
the anatomy is pretty fixed.

1119
01:07:18,760 --> 01:07:19,200
Okay.

1120
01:07:19,200 --> 01:07:22,760
We know that's changed a little bit,
but but it's pretty fixed.

1121
01:07:22,840 --> 01:07:24,600
And we are doing

1122
01:07:24,680 --> 01:07:25,480
in every

1123
01:07:25,480 --> 01:07:29,280
second
of our life totally different things.

1124
01:07:29,360 --> 01:07:31,240
So how how can be

1125
01:07:31,240 --> 01:07:36,560
how could we be
so flexible with the fixed,

1126
01:07:36,640 --> 01:07:41,960
boring anatomy?

1127
01:07:42,040 --> 01:07:43,520
Okay, well, if they.

1128
01:07:43,520 --> 01:07:46,760
No more questions, just think, Gustavo.

1129
01:07:46,760 --> 01:07:48,960
Once again, it is fascinating. Thank you.

1130
01:07:48,960 --> 01:07:50,120
Pleasure. Thank you.