Transcript OHBM'02, Sendai EEG and fMRI Walter J. Freeman

Spatial patterns of EEG gamma and fMRI
Using the neurodynamics of local mean fields (EEG)
manifested in electromagnetic potentials to interpret
spatial patterns of cerebral blood flow in behavior.
Walter J Freeman
Department of Molecular & Cell Biology
University of California at Berkeley
http://sulcus.berkeley.edu
Organization for Human Brain Mapping
Sendai, Japan 5 June 2002
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Abstract: Dendrites vs. Axons: Energy requirements
Dendrites take 95% of the energy that brains use to process
information, whereas axons take only 5%. Their activity
is the main determinant of patterns in fMRI.
Dendrites are also the main source of the electric current
that generates the EEG in passing across brain tissue.
The EEG has optimal temporal and spatial resolution for
imaging neural activity in cognition, in order to relate it to
patterns of metabolic activity by means of fMRI.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Questions to be raised
1. What is the dependence of metabolic energy usage on
the temporal spectral ranges of the EEG?
1 - 7 Hz — delta, theta?
8 - 25 Hz — alpha, beta?
25 — 100 Hz - gamma, higher?
2. What spatial structures of the EEG are best correlated
with patterns of metabolic energy utilization?
Localization of specific psychological functions?
Global patterns of cognitive operations?
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Rabbit EEG, Temporal PSD
Temporal Power Spectral Density, PSDt:
• Spectral peaks of power indicate limit cycle attractors,
that are characteristic of band pass filters
operating at single frequencies.
• Spectral distributions of power indicate chaotic attractors,
that indicate nonconvergent, creative neurodynamics.
• The more revealing spectral displays are done in log-log
coordinates: log power versus log frequency.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spectral peaks vs. “1/f”
A. EEGs from olfactory
bulb and visual cortex
of rabbit superimposed
on respiratory cycles.
B. Spectra show “1/f” fall
in log-log coordinates,
but with peaks in theta
and gamma ranges for
bulb but not so clearly
for neocortex.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Rabbit EEG, Spatial PSDx
Left: Olfactory bulb. The upper curve is the spectrum of a
point dipole. The dots show the spectrum of the 8x8 array.
Right: Visual cortex. The Nyquist frequency is estimated
to be 0.5 cycles/mm; sampling rate should exceed 1/mm.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Human intracranial EEG under anesthesia
EEG from the superior
temporal gyrus recorded
with a 1x64 linear array
of electrodes spaced at
0.5 mm and 3.2 mm in
length, fitting onto the
gyrus without crossing
sulci. These 15 adjacent
EEGs are representative
of the set. Note the finegrain spatial differences.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Awake intracranial EEG
Another patient was
recorded under local
anesthesia, showing
the emergence of
gamma oscillations.
The 1x64 linear array
was held on the pia
of the precentral
gyrus for several
seconds of recording.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Human temporal PSD
PSDs are compared from the EEGs in anesthetized and
awake neurosurgical patients. Both reveal “1/f”.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Human pial spatial spectrum
Spatial spectra of the human epipial EEG. These curves
provide the basis for fixing the spatial sampling interval.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Calculation of spatial Nyquist
pat ient int ercept , a slope, b
max, c
min, d
inflect ion, x
inflect ion, y c - d
1
4.13
-1.54
3.50
1.74
0.040
0.56
1.76
2
5.12
-2.27
4.24
1.90
0.038
0.41
2.34
3
4.42
-1.88
3.61
1.82
0.042
0.38
1.79
4
4.38
-2.20
3.70
1.48
0.032
0.33
2.22
5
4.16
-1.95
3.32
1.43
0.042
0.39
1.89
Average 4.44
-1.97
3.67
1.67
0.039
0.41
2.00
0.14
0.15
0.09
0.002
0.03
0.12
± SD
0.18
Tabl e 1. Eval u ati on of spati al spe ctra by li n e ar re gre s s ion u s in g 3 li n e s egme n ts.
log p = c
f< x c/m m,
log p = a + b log f
log p = d
OHBM’02.Sendai
x Š f Š y c/mm,
f > y c/mm,
EEG and fMRI
Walter J Freeman
Design of an optimized epipial intracranial array
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Breakdown by temporal band, 5 Hz bands
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Human scalp EEG and EMG
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Log-log display, EEG and EMG, temporal PSDt
Green: EEG from
frontal scalp.
Red: Frontal EMG
from scalp
Blue: EEG from
parietal scalp
Black: EMG from
parietal scalp
OHBM’02.Sendai
L
O
G
P
O
W
E
R
Log Frequency, Hz
EEG and fMRI
Walter J Freeman
Human scalp EEG, EMG, Spatial PSDx
An example is
shown of the
human spatial
spectrum from
the frontal area of
the scalp, with
and without
deliberate EMG.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Temporal band breakdown, 10 Hz bands
No significant dependence was found of the spatial spectra
on temporal band width, except that for theta vs. gamma.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatial band breakdown, cycles/mm
Temporal spectra are shown for narrow spatial pass bands
in search for significant wave numbers. None were seen.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Derivative of PSD
Left: Power as a function of frequency in linear coordinates.
Right upper curve: log-log coordinates. Right lower curve:
log of the derivative of the power vs. frequency, which may
approximate the energy required for generating gamma.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
The Answer to Question One
1. What is the dependence of metabolic energy usage on the
temporal spectral ranges of the EEG?
1 - 7 Hz — delta, theta?
8 - 25 Hz — alpha, beta?
25 — 100 Hz - gamma, higher?
The answer is unknown. Insufficient data.
Studies are needed in which the fMRI patterns are carefully
correlated with scalp recordings while power in the spectral
bands of the EEG and EMG is enhanced or diminished.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatial patterns of gamma EEG
A common approach to derive the behavioral correlates of
gamma activity is to localize the ‘hot spots’ with high
amplitude, fit them with an equivalent dipole, and find the
phase relations between spots to infer causal relations.
An alternative approach is to combine both the high and
the low amplitudes into a spatial pattern that resembles an
interference pattern in fluids, and to follow sequences of
these global patterns like frames in a movie film.
In patterns, dark spots are equal in value to light spots.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatial pattern measurement
Measurement of spatial patterns of gamma activity is made
easy by the fact that neuron populations in local areas such
as sensory cortices by cooperative synaptic interaction form
wave packets (Freeman, 1975), that share a common wave
form in domains 10 - 30 mm in diameter (Freeman, 2002).
The textures of the wave packets are given by amplitude
modulation (AM) of the gamma carrier wave. The phase
modulation (PM) is useful to measure the size, duration, and
location of wave packets, but it carries no information that
relates to perception and cognition, and can be neglected in
initial cognitive studies of global gamma activity.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatial AM patterns in the olfactory bulb
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Clustering of AM patterns with CS+ vs. CSA rabbit was trained to
discriminate two odors
from the background air
(control, •), one that was
reinforced (+), the other
not (-), Each symbol
shows a single pattern of
AM modulation of the
gamma carrier, which was
projected from 64-space
by stepwise discriminant
analysis into 2-space.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatial filter tuning
The classification
assay was used to
find the optimal
values for low and
high pass spatial
filters. The high
cut-off was at the
upper inflection in
the spatial spectra.
The low cut-off
was fixed by the
array window size.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatially distributed CS information
Deletion of a
subset of
channels that
was selected
randomly
degraded the
goodness of
classification.
Information
is uniformly
distributed.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Spatial patterns, vision
A single set of 64 EEG
traces; amplitude is in
upper right frame.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Classification of AM patterns: CS+ versus CS-
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Effect of channel deletion on visual CS classification
Deletion of
subsets of
channels that
were selected
randomly
degraded the
goodness of
classification.
Information is
uniformly
distributed.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
EEGs simultaneously from limbic and sensory cortices
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Determining gamma range
The classification assay
was used to find the
optimal values for low and
high pass temporal filters.
The high cut-off was at the
inflection to the noise
plateau in the spectra. The
low cut-off was determined
by the intrinsic nonlinear
cortical dynamics. Gamma
was species-specific: cat:
35-60 Hz; rabbit 20-80 Hz.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Multiple cortices - deletions of selected areas
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Auditory cortex: Ohl, Scheich & Freeman (2001)
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Intracranial gamma activity
Four sets of data from epidural electrode arrays have shown
that the spatial AM patterns of gamma oscillations relating to
perception contain information that is spatially distributed
and graded, and not localizable to point sources.
• Olfactory bulb in rabbit
• Sensory neocortices in rabbit
• Auditory cortex of Mongolian gerbil
• Multiple sensory and limbic cortices of cat
Analysis of scalp EEG by others* indicates they are global.
Scalp gamma in perception is likely to be non-localizable.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
The Answer to Question 2
2. What spatial structures of the EEG are best correlated with
patterns of metabolic energy utilization?
Localization of specific psychological functions?
Global patterns of cognitive operations?
The answer is unknown. Insufficient data.
Studies are needed in which the fMRI patterns are carefully
correlated with gamma EEG patterns by means of multivariate
statistics in very high dimensional state spaces, while the
cognitive contents are manipulated by psychophysical designs.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman
Acknowledgments
I am grateful for contributions from my students and postdocs
over 45 years of research on intracranial EEG in animals and
humans, and now on scalp EEG from normal volunteers.
Their names are listed in my books and in our numerous
publications that we have co-authored in refereed journals.
Support for this research has come from the National Institute
of Mental Health, grant MH 06686, and the National
Aeronautics and Space Agency, grant NCC 2-1244.
OHBM’02.Sendai
EEG and fMRI
Walter J Freeman