Transcript Slide 1

Conjunct COST B27 and SAN
Scientific Meeting,
Swansea, UK, 16-18 September 2006
BRAIN-RATE AS A COMPLEMENTARY
DIAGNOSTIC INDICATOR AND
BIOFEEDBACK PARAMETER
Nada Pop-Jordanova, MD, DSc (1)
and Jordan Pop-Jordanov, DSc (2)
(1) Faculty of Medicine, University of Skopje, Macedonia
(2) Macedonian Academy of Sciences and Arts
N. Pop-Jordanova, J. Pop-Jordanov, COST/SAN, Swansea, UK, 2006
Contents:
1.Empirical EEG-arousal correlation
2.Theoretical consideration
3.Brain-rate as arousal indicator
4.Some clinical results
5.Conclusions
N. Pop-Jordanova, J. Pop-Jordanov, COST/SAN, Swansea, UK, 2006
1. Empirical EEG–arousal correlation
1.1 Textbook classification of EEG activity
and the level of arousal
(Pritchard, Alloway, 1999)
Classification
Beta waves
Frequency
14−30 Hz
Alpha waves
8−14 Hz
Theta waves
4−8 Hz
Delta waves
0.5−4 Hz
Level of Arousal
Alert, eyes open
Quiet waking, eyes closed
Drowsy, sleep stages 1 and 2
Deep sleep, stages 3 and 4
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1.2 Extended classification of EEG activity
and the mental states
(Bendorfer, 2001)
Brainwaves
Gamma
High Beta
Mid Beta
SMR
Alpha
Theta
Delta
Frequency
(Hz)
(35Hz+):
(22−35 Hz):
(15−20 Hz):
(12−15 Hz):
(8−12 Hz):
(4−7 Hz):
(0.5−3 Hz):
States
association with peak performance
high correlation with anxiety
active, external attention
relaxed state, body stillness
relaxed, passive attention
very relaxed, inwardly focused
sleep, deep mediation
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1.3 Clinical EEG-arousal correlation
Slow waves – Underarousal (depression, autism,
etc.)
Fast waves – Overarousal (caffeine, anxiety,
alcoholism, etc.)
“Mixed”: Slow or fast waves – UA or OA
(ADHD, OCD, headache, etc.) → subgroups
(clusters)
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1.4 From “how” to “why” questions
Why is the arousal correlated with EEG
frequency?
Why in this pattern?
Why in this frequency interval?
Why alpha band corresponds to “relaxed” state?
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2. Theoretical consideration
2.1 Basic mental-neural mechanism
Hypotheses:
Eccles (1986) - microsite probabilities
Jibu and Yashue (1995) - photon-corticon
dynamics
Penrose/Hameroff (1998) - microtubular
proteins
Romijn (2002) - virtual photons
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General scheme
Synaptic activity
activity
e/m fields
mental
Solutions: conceptual, lacking analytical
expressions and numerical results.
Common elements: electric field and dipole
molecules on nano level (nanomedicine).
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Basic “nanometric” scale
New domain BMBS includes nanomedicine
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2.2 Field – dipole interaction
J. Pop-Jordanov, E. Solov’ev, N. Pop-Jordanova,
N. Markovska, D. Dimitrovski (1998)
D. Dimitrovski, J. Pop-Jordanov, N. PopJordanova, E. Solov’ev (2004)
Analytical solution for transition probabilities:
Pab  e
2Cab

N
 Pab ( f )
N = 1012 dipole molecules per neuron
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2.3 Transition probabilities and mental arousal
J. Pop-Jordanov and N. Pop-Jordanova (2004)
Readiness to change the state → mental arousal
(alertness)
Pab  A  e
fe
 ln 2
f
fe

f
2
(S = Smax for f = fe)
A = is capacity, not entity!
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2.4 A = A (f) empirical results
Arousal
1
0.9
Peak perf.
Anxiety
0.8
Alert
0.7
Still
0.6
Relax
0.5
0.4
Drowsy
0.3
0.2
Deep sleep
0.1
0
1
Delta
4
8
Theta
12
10
16
20
SMR Mid High
Beta Beta
Alpha
30
50
Gama
100
Frequency [Hz]
EEG activity and the mental states i.e. arousal (adapted
from Tables 1.1&1.2).
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A (f )
2.5 A = A (f) theoretical formula
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
ch
ad
1
10
100
f [Hz]
Derived theoretical diagram for mental arousal
ch – children (fe = 6 Hz), ad – adults (fe = 10 Hz)
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1.4 From “how” to “why” questions
Why is the arousal correlated with EEG
frequency?
Why in this pattern?
Why in this frequency interval?
Why alpha band corresponds to “relaxed” state?
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3. Brain-rate as arousal indicator
3.1 Definition of arousal
- General activation of the mind (Kahnemann
1973)
- General operation of consciousness (Thacher
and John 1978)
- General drive state of the brain and mind
(Watkins 1997)
- Simple increases in activity, lying at the
bottom of homeostasis (Damasio 2003)
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3.2 Integrality of arousal and EEG spectrum
By definition arousal is a general, integral
characteristic of mental state.
Simultaneously, it is correlated with the integral
(polyrhythmic, polychromatic) EEG spectrum.
The main characteristic of such a spectrum is its
mean frequency weighted over the whole
spectrum. We named it brain-rate (fb).
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3.3 Brain-rate formula
N. Pop-Jordanova and J. Pop-Jordanov (2005)
,
Vi
f b   f i Pi  f i
V
i
i
,
V  Vi
i
or
1
fb 
V

fV  f  df , V  V  f  df

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Analogies:
Ai
Xc   Xi
  X i Pi
A
i
i
2
T
Ei Pi

3k i
 r   i Pi
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3.4 Spectral gravity and the state of a system
The parameter characterizing the state and the
changes (shifts) of any system with spectral
properties is the spectrum gravity (Xc, T, σr, …),
i.e. the mean value which comprises weighted
contributions of all spectral components (bands).
So e.g. the state transition (solid ↔ liquid ↔
gas) depends on the integral parameter T, the
stability of a boat – on the center of gravity Xc,
the criticality of a reactor cell - on the mean
reaction rate, etc.
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Based on the presented theoretical results and
empirical evidence we suppose that the same
principle is applicable for the arousal level
correlated to EEG spectrum gravity fb.
Consequently, the brain-rate fb can indicate the
states of underarousal or overarousal, in the
same way as the other mentioned indicators (Xc,
T, σr, …) differentiate the levels of activation of
corresponding systems.
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4. Some clinical results
4.1 NF training of ADHD children
N. Pop-Jordanova (2006)
Parameter
Beta brain
waves
Before NF (µV) After NF (µV)
4.86 ± 1.6
8.0 ± 1.38
t-test Significance
5.23
p<0.01
15.29 ±1.38 8.47
p<0.01
Theta Brain
waves
20.95 ± 1.38
Theta/beta
4.7 ± 1.38
2.0 ± 1.6
4.5
p<0.01
Brain-rate
7.86 ± 0.56
8.22 ± 0.63
6.6
p<0.01
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4.2 Brain-rate as indicator of inner arousal
Anxiety
PTSD
Panic attack
Anxiety
Stuttering
OCD
Ticks
ADHD
Pavor nocturnus
N
Mean age
EC
EO
2
1
4
8
1
2
3
22
2
45
12
13.5
14.6
11
14.5
12.5
9
7.5
10.54
7.54
8.21
8.19
8.50
8.28
8.47
7.60
8.13
8.56
6.27
7.58
7.57
8.27
8.25
8.48
7.86
8.48
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4.3 Blood lead level and brain-rate
Characteristic correlations
IQ (Pb)
r = – 0.24

( Pb)


( IQ )

r = 0.25
r = – 0.31
fb ( Pb)
fb ( Pb)
r = 0.08
r = – 0.12
 
fb  
 
r =0.30
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Scatterplot: 2/$
vs. fb [Hz] (Casewise MD deletion)
fb [Hz] = 7,2468 + ,18796 *2/$
Correlation: r = ,29545
9.4
9.2
9.0
8.8
8.6
fb [Hz]
8.4
8.2
8.0
7.8
7.6
7.4
7.2
7.0
6.8
6.6
1.5
2.0
2.5
3.0
3.5
4.0
2/β
4.5
5.0
5.5
6.0
6.5
95% confidence
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4.4 Brain-rate and reference databases
Theta-beta ratio
Brain-rate
NeuroRep
1.49
9.73
SKIL
1.70
8.51
EureKa3!
1.46
8.94
Calculated using the spectral data from White 2003
for ten adults with ADHD
(NeuroRep: Hudspeth 1999; SKIL: Sterman & Kaiser 2000;
EureKa3!: Congedo 2002)
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Being in the range of low alpha, the results for fb
suggest that in this case ADHD Cluster 3 (Müller
2006) prevails. This is not visible from the /β
ratio.
Consequently, the “internally directed attention”
(Cooper, Croft, Dominey, Burgess, & Gruzelier,
2003) can be considered as a characteristic of
psycho-dynamical equilibrium (i.e. max entropy
of an isolated system), as well.
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5. Conclusions
1. Brain-rate can be considered as an integral
brain state attribute, correlated to its electric,
mental and metabolic activity.
2. In preliminary assessment, brain-rate may
serve as an indicator of general mental arousal
level, similar to heart-rate, blood pressure and
temperature as standard indicators of general
bodily activation.
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3. By comparing EC and EO brain-rate values
the diagnoses of inner arousal can simply be
achieved.
4. As a measure of arousal level, brain-rate can
be applied to discriminate between subgroups
(clusters) of “mixed” disorders (e.g. ADHD,
OCD, headache).
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5. Brain-rate can be used as a multiband
biofeedback parameter in mediating the
underarousal
or
overarousal
states,
complementary to few-band parameters and the
skin conduction.
6. Brain-rate training is especially suitable to
reveal the patterns of sensitivity/rigidity of EEG
spectrum and its frequency bands, related to
permeability of corresponding neuronal circuits.
Based on this information, individually adapted
NF protocols can be elaborated.
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7. It is recommended to include the brain-rate
values in the standard EEG and NF software's
and databases, along with the frequency band
values.
8. Further studies of advantages and limitations
of brain rate concept for diagnostics and
treatment are needed.
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