EEG Signals in Psychiatry: Biomarkers for Depression Management - Quantitative EEG Analysis Methods and Clinical Applications - page 291

11.1.3 Interpretive Frameworks to Relate qEEG to Other Neurobiological
Measures
Cerebral glucose uptake and blood flow were hypothesized long ago to be reflec-
tions of the brain's energy utilization [17]. In healthy subjects, cerebral glucose
uptake and blood flow generally are accepted as tightly coupled measures of cere-
bral energy utilization [18-20]. Indeed, the mainstays of functional neuroimaging
methodologies—positron emission tomography (PET), single photon emission
computed tomography (SPECT), and fMRI—have contributed much to our under-
standing of the physiology of the CNS by providing a window into regional metabo-
lism or blood flow. Given that the brain's electrical activity represents the single
greatest demand on cerebral metabolism [21], the measurement of electrical energy
also should be coupled to cerebral metabolism and perfusion, an idea that traces
back to Berger [22].
To overcome issues about inconsistencies in the methods and results encoun-
tered in previous studies, the UCLA Laboratory of Brain, Behavior, and Pharmacol-
ogy examined the relationship between surface-recorded EEGs in different
frequency bands and the perfusion of underlying brain tissue by performing simul-
taneous qEEG recordings during sessions measuring regional cerebral perfusion
with

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O-PET in healthy adults, at rest and while performing a motor activation
task. We established that the relationship between qEEG measures (i.e., absolute
and relative power) and regional blood flow was influenced by the recording mon-
tage being used [8], with the best correlations being obtained through a
“reattributional” montage. With this approach, qEEG power values for each elec-
trode location were computed by taking power values from bipolar pairs of elec-
trodes that share a common electrode and averaging them together to yield the
reattributed power (Figure 11.1) [8]. For example, to determine a power value for
the brain region underlying the F4 electrode, we first compute power spectra for the
neighboring bipolar channels that include the F4 electrode (i.e., F4-F8, F4-AF2,
F4-FC2, and F4-FC6) and then average the absolute power values from those chan-
nels to obtain the reattributed power for the F4 electrode. This is somewhat similar
to the single source method of Hjorth [8, 23, 24], but this approach recombines the
power values, whereas Hjorth's method recombines voltage signals by averaging
signal amplitudes from pairs of electrodes. The reattributional montage provides a
higher association between EEG measures and regional cortical perfusion than does
the Hjorth method [8] and so offers an advantage if a researcher's scientific
objective focuses on understanding findings within the general functional

neuroimaging conceptual framework.
We developed the cordance method to incorporate this re-referencing approach
and then to employ normalization and integration of absolute and relative power
values from all electrode sites for a given EEG recording in three steps [10]. First, the
reattributed absolute power values are calculated at each electrode site, and
reattributed relative power is calculated in the conventional manner at each elec-
trode site, as the percentage of reattributed power in each band, relative to the total
spectrum considered (in that work, 0.5 to 20 Hz) [25].
Second, these absolute and relative power values for each individual EEG
recording are normalized across electrode sites, using a z -transformation statistic to
assign a value to each electrode site s in each frequency band f [yielding A norm ( s,f )