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Abstract A prominent aspect of primate lateral prefrontal cortex organization is its division into several cytoarchitecturally distinct subregions. Neurophysiological investigations in macaques have provided evidence for the functional specialization of these subregions, but an understanding of the relative representational topography of sensory, social, and cognitive processes within them remains elusive. One explanatory factor is that evidence for functional specialization has been compiled largely from a patchwork of findings across studies, in many animals, and with considerable variation in stimulus sets and tasks. Here, we addressed this by leveraging the common marmoset (Callithrix jacchus) to carry out large-scale neurophysiological mapping of the lateral prefrontal cortex using high-density microelectrode arrays, and a diverse suite of test stimuli including faces, marmoset calls, and spatial working memory task. Task-modulated units and units responsive to visual and auditory stimuli were distributed throughout the lateral prefrontal cortex, while those with saccade-related activity or face-selective responses were restricted to 8aV, 8aD, 10, 46 V, and 47. Neurons with contralateral visual receptive fields were limited to areas 8aV and 8aD. These data reveal a mixed pattern of functional specialization in the lateral prefrontal cortex, in which responses to some stimuli and tasks are distributed broadly across lateral prefrontal cortex subregions, while others are more limited in their representation. 

Abstract The prevalence of data collected on the same set of samples from multiple sources (i.e., multi-view data) has prompted significant development of data integration methods based on low-rank matrix factorizations. These methods decompose signal matrices from each view into the sum of shared and individual structures, which are further used for dimension reduction, exploratory analyses, and quantifying associations across views. However, existing methods have limitations in modeling partially-shared structures due to either too restrictive models, or restrictive identifiability conditions. To address these challenges, we propose a new formulation for signal structures that include partially-shared signals based on grouping the views into so-called hierarchical levels with identifiable guarantees under suitable conditions. The proposed hierarchy leads us to introduce a new penalty, hierarchical nuclear norm (HNN), for signal estimation. In contrast to existing methods, HNN penalization avoids scores and loadings factorization of the signals and leads to a convex optimization problem, which we solve using a dual forward–backward algorithm. We propose a simple refitting procedure to adjust the penalization bias and develop an adapted version of bi-cross-validation for selecting tuning parameters. Extensive simulation studies and analysis of the genotype-tissue expression data demonstrate the advantages of our method over existing alternatives.