Spatial patterns of EEG gamma and fMRI

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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 Organizati
on for Human Brain Mapping Sendai, Japan 5 June
2002
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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?
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Spectral peaks vs. 1/f

• 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.

OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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
fine-grain spatial differences.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Human temporal PSD
PSDs are compared from the EEGs in anesthetized
and awake neurosurgical patients. Both reveal
1/f.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Human pial spatial spectrum
Spatial spectra of the human epipial EEG. These
curves provide the basis for fixing the spatial
sampling interval.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Calculation of spatial Nyquist
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Design of an optimized epipial intracranial array
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Breakdown by temporal band, 5 Hz bands
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Human scalp EEG and EMG
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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
L O G P O W E R
Log Frequency, Hz
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Spatial band breakdown, cycles/mm
Temporal spectra are shown for narrow spatial
pass bands in search for significant wave
numbers. None were seen.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Spatial AM patterns in the olfactory bulb
OHBM02.Sendai EEG and fMRI
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Clustering of AM patterns with CS vs. CS-
A 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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
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Spatially distributed CS information
Deletion of a subset of channels that was
selected randomly degraded the goodness of
classification. Information is uniformly
distributed.
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Spatial patterns, vision
A single set of 64 EEG traces amplitude is in
upper right frame.
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Classification of AM patterns CS versus CS-
OHBM02.Sendai EEG and fMRI
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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.
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EEGs simultaneously from limbic and sensory
cortices
OHBM02.Sendai EEG and fMRI
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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Multiple cortices - deletions of selected areas
OHBM02.Sendai EEG and fMRI
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Auditory cortex Ohl, Scheich Freeman (2001)
OHBM02.Sendai EEG and fMRI
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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.
OHBM02.Sendai EEG and fMRI
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman
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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.
OHBM02.Sendai EEG and fMRI
Walter J Freeman