Functional categories in macaque frontal eye field

Abstract

Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual-motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and cluster outcomes vary with analysis assumptions, consensus clustering aggregates over multiple cluster analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report ten functional categories, expanding on the traditional three. Categories were distinguished by latency, magnitude, and sign of visual response, presence of sustained activity, and dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provides better information guiding microcircuit models of cortical function.\n\nSIGNIFICANCE STATEMENTThe contribution of a brain region cannot be understood without knowing the diversity, arrangement, and circuitry of constituent neurons. Traditional descriptions of frontal eye field include visual, visuo-saccadic, and saccadic categories. Here, we employ a novel consensus clustering method to identify more reliably functional categories in neural data. While confirming the traditional categories, consensus clustering distinguishes additional, previously unappreciated diversity in neural activity patterns. Such information is necessary to formulate correct microcircuit models of cortical function.