text: 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. claims and remarkably are defined better by ‘common noise’ than task-evoked reactions. Our parcellation process works well on ‘spontaneous’ neural activity and thus bears strong resemblance to the recognition of ‘resting state’ networks in fMRI datasets. Our results demonstrate a powerful new tool for identifying cortical sub-networks by objective classification of simultaneously recorded electrophysiological activity. Intro Sensory and engine cortices of the primate mind are often characterized by the presence of topographic maps. For example main visual cortex (V1) consists of maps of retinotopic space orientation preference and ocular dominance (Engel et al. 1994 Katz et al. 1989 LeVay et al. 1975 Vehicle Essen et al. 1984 Wiesel and Hubel 1974 The boundaries of V1 defined by each of these maps coincide exactly with each other and with architectonic borders as well reinforcing the notion that V1 is definitely a distinct cortical area with a specific set of functions. Historically topographies of this nature have been important in improving our understanding of the organization and function of the cerebral cortex (e.g. Felleman and Vehicle Essen 1991 Hubel and Livingstone 1987 Mishkin et al. 1983 Zeki and Shipp 1988 In contrast parcellation of the cortex into practical modules is more challenging in association areas where spatial topography may be indistinct or missing altogether. Some areas of the prefrontal cortex (PFC) can be broadly defined by zones of anatomical projections (Carmichael and Price 1994 Petrides and Pandya 1999 Preuss 2007 or general styles in physiological properties. For example studies in monkeys and humans suggest localization styles within PFC based on sensory input AZD4017 modality (Romanski and Goldman-Rakic 2002 reactions to incentive vs. consequence (Monosov and Hikosaka 2012 actual vs. hypothetical incentive results (Abe and Lee 2011 and a hierarchy of cognitive control (Badre and D’Esposito 2009 But outside the frontal eye fields (Bruce et al. 1985 and possibly the frontal lobe ‘face patches’ (O. Scalaidhe et al. 1997 Tsao et al. 2008 razor-sharp boundaries and salient physiological distinctions are rare in PFC. In general single units recorded in PFC show multiplexed signals of great variety and neighboring neurons display little evidence of common physiological features that are characteristic of columnar corporation in more main sensory and engine areas. Here we take a fundamentally different approach to detecting topographic boundaries in prefrontal cortex. We hypothesized that our limited knowledge concerning topographic corporation in frontal cortex may arise from several related limitations of traditional methods for characterizing neuronal activity. First previous studies possess mainly Rabbit Polyclonal to LY6E. relied on a small number of electrodes (usually one) leading investigators to focus on the response properties of individual AZD4017 neurons rather than the population. Second neural reactions are usually characterized by their mean-the 1st statistical instant of a distribution. Higher moments especially trial-to-trial fluctuations and response correlations across the population are frequently not studied primarily due to lack of simultaneous recordings. And third neural reactions are typically characterized only with respect to task events that are of interest to the experimenter. By breaking these standard boundaries it may be possible to discover organizational principles and topographies that have been unfamiliar heretofore. We approached this problem from a somewhat agnostic perspective. We bypassed some fundamental limitations of solitary unit recording by employing multi-electrode AZD4017 (Utah) arrays to record simultaneously from tens-to-hundreds of devices at regularly spaced intervals across a specific region of prefrontal cortex. Second we used unsupervised algorithms to identify natural groupings of neurons based on their response covariation both task-driven and task-independent. Finally we projected the objectively recognized groupings of neurons back onto the AZD4017 arrays to determine whether they were spatially segregated inside a topographic manner. We statement recordings from your prearcuate gyrus a region of prefrontal cortex that bears visual cognitive and attention movement related signals in a variety of behavioral jobs (Constantinidis and Goldman-Rakic 2002.