The intrinsic white matter connections of the frontal cortex are highly complex, and the organization of these connections is not fully understood. Quantitative graph-theoretical methods, which are not solely reliant on human observation and interpretation, can be powerful tools for describing the organizing network principles of frontal cortex. Here, we examined the network structure of frontal cortical subregions by applying graph-theoretical community detection analyses to a graph of frontal cortex compiled from over 400+ macaque white-matter tracing studies. We find evidence that the lateral frontal cortex can be partitioned into distinct modules roughly organized along the dorsoventral and rostrocaudal axis.

Neuroanatomical tracer studies in the nonhuman primate macaque monkey are a valuable resource for cognitive neuroscience research. These data ground theories of cognitive function in anatomy, and with the emergence of graph theoretical analyses in neuroscience, there is high demand for these data to be consolidated into large-scale connection matrices (“macroconnectomes”). Because manual review of the anatomical literature is time consuming and error prone, computational solutions are needed to accomplish this task. Here we describe the “CoCoTools” open-source Python library, which automates collection and integration of macaque connectivity data for visualization and graph theory analysis. CoCoTools both interfaces with the CoCoMac database, which houses a vast amount of annotated tracer results from 100 years (1905–2005) of neuroanatomical research, and implements coordinate-free registration algorithms, which allow studies that use different parcellations of the brain to be translated into a single graph. We show that using CoCoTools to translate all of the data stored in CoCoMac produces graphs with properties consistent with what is known about global brain organization. Moreover, in addition to describing CoCoTools' processing pipeline, we provide worked examples, tutorials, links to on-line documentation, and detailed appendices to aid scientists interested in using CoCoTools to gather and analyze CoCoMac data.

Results from fMRI have strongly supported the idea that the ventrolateral PFC (VLPFC) contributes to successful memory formation, but the role the dorsolateral PFC (DLPFC) in memory encoding is more controversial. Some findings suggest that the DLPFC is recruited when one is processing relationships between items in working memory, and this processing specifically promotes subsequent memory for these relationships. However, previous studies could not rule out the possibility that DLPFC promotes memory during all elaborative encoding conditions and contributes to memory on all subsequent associative memory tests. To address this question directly, we used fMRI to examine activity during two encoding tasks that prompted participants to encode either relational or item-specific information. On relational trials, participants imagined pairs of items interacting, whereas on item-specific trials, participants imagined the items spatially separated and in different sizes. After scanning, we examined memory for relational information and item-specific information. fMRI results showed that DLPFC activity specifically promoted memory for relational information during relational encoding and not memory for item-specific information during item-specific encoding. In contrast, activity in the VLPFC predicted memory for both relational and item-specific information. The present results are consistent with the idea that the DLPFC specifically contributes to successful memory formation through its role in building relationships among items.