Connectal Coding: Discovering the Structures Linking Cognitive Phenotypes to Individual Histories

Abstract

Cognitive phenotypes characterize our memories, beliefs, skills, and preferences, and arise from our ancestral, developmental, and experiential histories. These histories are written into our brain structure through the building and modification of various brain circuits. Connectal coding, by way of analogy with neural coding, is the art, study, and practice of identifying the network structures that link cognitive phenomena to individual histories. We propose a formal statistical framework for connectal coding and demonstrate its utility in several applications spanning experimental modalities and phylogeny.Scientific explanations are characterized by modeling mechanisms of phenomena that describe (i) the constituent parts, (ii) the properties of those parts, and (iii) the interactions among them [1]. What we measure and model is often limited by technology.In brain sciences, 20 th century innovations enabled studying the parts and their properties, while studying interactions was limited [2] and laborious [3]. For example, nanoscale electron microscopy (EM) enabled studies of ultrastructure [4], microscale light microscopy and physiology enabled studies of single cells [5], and macroscale magnetic resonance imaging (MRI) enabling studies of brain regions [6,7]. 21 st century innovations include serial EM for measuring subcellular interactions [8], improved LM [9][10][11][12][13][14], and MRI [15-18] to estimate interactions within and across brain regions.These 21 st technologies now enable modeling brain connectivity, in a complementary fashion to models of brain activity that were developed in the 20th century [19,20]. Models of brain activity, typically referred to as neural coding, link patterns of brain activity to past, ongoing, and future events. By contrast, models of brain connectivity, which we refer to as connectal coding, link patterns of brain connectivity to past, ongoing, and future events. The nature of the patterns, events, and links change by virtue of switching focus from activity to connectivity. Moreover, the statistical models one can leverage to learn those links from the data must also change.The goal of this manuscript is to introduce in clear terms, motivate from first principles, and formalize this emerging approach to studying the brain. While neural activity coding is well established and widely accepted as a (possibly the) legitimate framework for studying the brain, connectal coding remains in its infancy. Below we outline our rationale for why connectal codes are not just valuable, but required to unify 20th and 21st century mentalities to model the parts, their properties, and interactions among them together to infer improved scientific explanations of cognitive phenomena. Modeling Brains as Networks

Graph Matching via Optimal Transport

“…Graphs are widely used in many fields within the scientific community where data is relational, including social networks, computer vision, and neuroscience [5,9,28]. In many of these settings, we often work simultaneously with multiple graphs, and want to quantify how they relate to each other.…”