Predictability of cortico-cortical connections in the mammalian brain

Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a nonhuman primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here, we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species. Revealed by tract-tracing datasets, communication between the functional areas of the cortex operates via a complex, dense, and weighted network of physical connections with little apparent regularity. Although there are studies showing the existence of nonrandom topological features, their extent has not been clear. Employing a machine learning-based approach, which efficiently extracts structural models from such datasets, here we show that there is a significant amount of regularity embedded in the mammalian connectome. This regularity allows predicting interareal connections and their weights with good accuracy and can be used to infer properties of experimentally untested connections. The structural models are well learned even with small training sets, without overfitting, suggesting the existence of a low-dimensional, universal mechanism for mesoscale cortical network formation and evolution.