Natural Grouping of Neural Responses Reveals Spatially Segregated Clusters in Prearcuate Cortex

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 fro...

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Bibliographic Details
Published in:	Neuron (Cambridge, Mass.) Mass.), 2015-03, Vol.85 (6), p.1359-1373
Main Authors:	Kiani, Roozbeh, Cueva, Christopher J., Reppas, John B., Peixoto, Diogo, Ryu, Stephen I., Newsome, William T.
Format:	Article
Language:	eng
Subjects:	Algorithms Animals Arrays Behavior, Animal - physiology Boundaries Brain Mapping - methods Cluster Analysis Electrodes Frontal Lobe - physiology Haplorhini Image Processing, Computer-Assisted Magnetic Resonance Imaging - methods Neural Pathways - physiology Neurons Physiology Population Visual Cortex - physiology
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Summary:	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 at least two spatially segregated subnetworks that differentially represent behavioral choice and reaction time. Importantly, these subnetworks are detectable during different 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 bears strong resemblance to the identification of “resting-state” networks in fMRI data sets. Our results demonstrate a powerful new tool for identifying cortical subnetworks by objective classification of simultaneously recorded electrophysiological activity. •Unsupervised algorithms identify natural functional modules in prearcuate cortex•Modules are consistent across animals, tasks, and temporal epochs of a task•Modules segregate more on the basis of “common noise” than task-driven activity•The “noise” signal is temporally broad-band across several orders of magnitude Kiani et al. apply unsupervised clustering algorithms to multielectrode recordings from monkey cortex, revealing spatially segregated subnetworks that are stable across behavioral contexts. They can be detected from spontaneous activity alone, resembling fMRI “resting state” networks, but at cellular scale.
ISSN:	0896-6273 1097-4199