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we're still waiting well I'm going to be

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talking about nested rhythms and and

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nested rhythms are a bit of a different

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approach to thinking of EEG instead of

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looking at one peak and where it's going

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or another peak and where it's going

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we're literally looking across the

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frequency spectrum for coupling between

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rhythms and and this can be viewed in a

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couple of different ways one is called a

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joint time-frequency analysis or jtf a

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and we'll show you some of that data

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assuming the computer comes back on and

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the other one is looking at it with the

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by spectral index and the by spectrum

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literally is a the x axis as a frequency

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from DC to 100 Hertz and the y-axis is

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the frequencies from zero to entered

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hurts and anything that's off the

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45-degree angle or not immediately on

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the axis ends up being across spectral

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coupling so if 10 and 20 are interacting

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you end up seeing off angle dots that

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correspond with with 10 on one axis and

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20 on the other so you'll end up seeing

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mirror image flares coming off the 45

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degree angle line this is important

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because we literally can predict

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consciousness based on this the DC field

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potentials since I've used the term

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nested rhythms the DC field potentials

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when they go negative which in EEG terms

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is up all of you that are electronic

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engineers go well that's wrong negative

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is down on the oscilloscope isn't it but

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eg people are upside down and backwards

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anyway so that this not that that

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difficult to understand let's look at

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nesting this is a set of recordings in

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the brain you see the spike train up at

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the top this is in the limbic system and

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it's generating a proximate ly six cycle

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per second excuse me about a five cycle

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per second wave a 200 millisecond

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periodicity and the other frequency is

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at a hundred cycles a second it's gamma

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and you'll notice that the gamma is

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literally as a wave shape being

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modulated by the theta the gamma sits in

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the theta nest when theta goes negative

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gamma can happen when theta goes

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positive gamma can't interestingly your

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digit span how many numbers you can

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remember in a number string is

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determined by how many gamma wavelets

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fit in a theta nest it's like a register

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on a computer how many little numbers

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can you hold in this register well if

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your gamma is nested well you end up

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with approximately 7 gamma wavelets in

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your theta frequency nest but the entire

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oscillatory EEG is in the DC nest the

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direct current field potential nest

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modulates on and off the entire

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alternating current EEG which is how the

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calc ami turned off his somatosensory

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strip he pulled the plug on the DC posit

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electronegativity it went positive and

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the alternating current EEG of the brain

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working literally was turned off in a

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somatosensory strip so the DC fields are

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the base nest which modulates the entire

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EEG if you look to the literature this

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is called infra slow EEG it's below a

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third of a Hertz or a half of hurts so

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it's it's extremely slow activity you

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might say well DC that's zero not like

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point three or point2 yeah but it drifts

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up and then down so it has an apparent

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frequency but it's really not a

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frequency it's simply the on and off of

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the DC signal DC and gamma are the basic

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heart of the biz index their secret

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yeah here we are the secret formula here

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for the biz index which is the surgical

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depth of anesthesia monitor is the

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mathematical relationship between point

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three eight and thirty eight Hertz well

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that's slow cortical potentials in gamma

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and that measures from conscious to

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unconscious their index has a few other

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tricks for measuring the too deep like a

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burst suppression detector telling you

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that your brain is going flat and then

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bursting it's not a good sign you're too

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deep and then also a flatline detector

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and if the anesthesiologist is created a

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flat EEG you're probably a bit too deep

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so theta nephs gamma again that controls

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digit span we've mentioned that let's

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take an example of a jtf a display here

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we have a normal high functioning and

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low functioning a DD here you go from

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slow to fast this is 74 cycles a second

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so gamma occurs in the generally from

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about 35 or so up that's the red sheets

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that are going up that you see there and

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this is a one thousand one hundred

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milliseconds this is an event-related

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synchronization d synchronization the

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first hundred milliseconds our baseline

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the next one the next thousand

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milliseconds end up being the one second

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of the brain responding to us to a

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stimulus a go/no-go stimulus and

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basically you can see here from the

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first center milliseconds you get a

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burst of gamma 1 2 3 4 5 just hit 6

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gamma is chirping it's nested in theta

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you can see in the healthy normal

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functioning brain gamma is occurring in

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brief bursts called chirps and those

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little chirps imagine that a nest and

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chirps but they called them chirps

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before they thought about nesting it

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just happened and now in the high

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functioning a DD you can see that we

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still have one two three four five six

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of them but boy are they weak and in low

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functioning a DD he's missing some nests

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there's some trooping not going on there

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so literally

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what we have here is conscious less

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conscious less conscious and literally

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on the biz index people that have a DD

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look like they're in stage one sleep

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there they're not fully conscious by the

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way a DD is very common to have sleep

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disturbances as well so the the actual

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original slide was looking only at the

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beta synchrony the beta synchrony takes

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about five hundred milliseconds although

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they called it here at 620 it actually

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occurs at about 500 and here in the high

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functioning a DD it's at 750

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milliseconds and here it just barely

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starts to happen at nine hundred

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milliseconds so the beta synchronization

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which is event related to the task it

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ends up being delayed further and

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further by being less conscious this is

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a by spectral index you can see on the

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upper right this is a Parkinson's

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patient when neural networks are bound

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and don't unlock gamma becomes

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persistent it doesn't occur in a dynamic

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chirp it's on so gamma is not

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necessarily a good thing it in fact

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occurs in pathology as well the off OOP

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let's go back if you go back here the

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non 45 degree angle line flares here

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this is a control a patient this is a

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Parkinson's patient with a bound neural

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network that's locked not just bound but

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locked and it loses the dynamic gammas

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on it just stays on that's pathological

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and as you can see if you gather not

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with respect to parkinsonism but all

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patients and you end up displaying all

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patients of all categories here the

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average of all patients you get off 45

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degree angle line flaring here the

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average of all controls you end up

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mimicking this display so when you have

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pathology it's extraordinarily common to

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end up seeing the lack of neural network

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dynamic shown as a by spectral index

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flare that's not appropriate so

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consciousness ends up being spawned by a

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cross spectral interaction between the

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slow cortical potentials or DC field

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potentials and neural network dynamics

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of gamma and when those two are related

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you're conscious the implication here

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for our next speaker is we actually did

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a recording on bill doing some healings

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and the implication for the model here

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is if you're looking for stuff that

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might be happening during these trance

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personal interactions look for cross

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spectral events that are associated with

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whatever these changes are that are

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happening during the healing and in fact

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using the by spectral approach we looked

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for harmonics suggesting that the neural

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networks were activated in a different