Biomedical Engineering Reference
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recorded in human iEEG [93, 94]. Detailed time-frequency analyses of these HGRs
using matching pursuits revealed that they are temporally tightly linked to neuronal
spikes, although their precise generating mechanisms could not be determined [93].
In addition, LFP power in the high-gamma range was strongly correlated, both in its
temporal profile and in its trial-by-trial variation, with the firing rate of the recorded
neural population [94].
Whether the underlying signals giving rise to HGRs are oscillations or tran-
sients, the log power law of electrophysiological recordings would predict that activ-
ity in such a high-frequency range is much more likely to be recorded at the
mesoscale of subdural ECoG if there is some degree of synchronization across a
large population of neural generators. In a recent simulation of the generation of
subdural ECoG HGRs by different firing patterns in the underlying cortical popula-
tion, both an increase in firing rate and an increase in neuronal synchrony increased
high-gamma power. However, ECoG high-gamma power was much more sensitive
to increases in neuronal synchrony than in firing rate [94]. Thus, ECoG HGRs could
index neuronal synchronization even if the underlying firing pattern is not a
band-limited oscillation.
Synchronization across subpopulations of neurons has been hypothesized to
constitute a temporal coding strategy for cortical computation that complements
rate coding and plays a role in higher cortical functions such as attention [95, 96].
Several human iEEG studies have found an augmentation of high-gamma activity in
association with attention [56, 66, 75, 77, 83, 97], as well as long-term memory
[98], and working memory [99]. Regardless of whether HGRs reflect an increase in
firing rate or an increase in neural synchronization, there is ample evidence from the
aforementioned studies that HGRs likely reflect patterns of neural activity that are
relevant to cortical computation, and that HGRs may serve as useful markers for
cortical function mapping.
14.4
Cortical Network Dynamics
Although cortical function mapping has typically concentrated on localizing func-
tional activation in discrete cortical regions, most of the higher cortical functions
that are clinically relevant, for example, expressive and receptive language function,
are understood to depend on the dynamic interplay between multiple cortical
regions that are spatially distributed across different brain regions. For example,
common language tasks may require the activation of Wernicke's area in posterior
superior temporal and inferior parietal regions, Broca's area in inferior prefrontal
regions, and a variety of other regions in frontal, parietal, temporal, and occipital
lobes. To understand the functional role that each individual brain region plays, as
well as the degree to which functions might be shared across networks of cortical
regions, it would be useful to examine the interactions between cortical regions dur-
ing their functional activation, preferably on a time scale that would allow infer-
ences as to whether activation of the network evolves in a serial, parallel, or
cascaded manner. To elucidate the dynamic structure of cortical networks support-
ing cognition, it would also be useful to test whether activity in one component of
the network has a causal influence on activity in other components.
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