A fundamental challenge in studying the frontal lobe is to parcellate this cortex into "natural" functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated subnetworks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes atleast two spatially segregated subnetworks that differentially represent behavioral choice and reaction time. Importantly, these subnetworks are detectable duringdifferent behavioral states and, surprisingly, are defined better by "common noise" than task-evoked responses. Our parcellation process works well on "spontaneous" neural activity, and thus bearsstrong resemblance to the identification of "resting-state" networks in fMRI data sets. Our results demonstrate a powerful new tool for identifyingcortical subnetworks by objective classification ofsimultaneously recorded electrophysiological activity.
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