Hyve, a compositional visualisation engine for neuroimaging data

COEX 

By embedding data within geometric structures, visualisation provides a gateway to understanding and communicating complex information. In neuroimaging, embedding data in brain geometries improves the tractability of high-dimensional data; visualisation software (e.g., [1-3]) is thus critical in brain mapping. However, the geometries that structure neuroimaging datasets can be highly heterogeneous. MR data, for example, are often reconstructed as image intensity values in a regularly sampled three-dimensional Euclidean volume. By contrast, the convoluted sheet of the mammalian cerebral cortex can be modeled as a two-dimensional Riemannian manifold, which software suites approximate as a polygon mesh[4-5]. Maps of brain connectivity have the intrinsic topology of a graph, with vertices that can be embedded either in physical coordinates or algorithmically in a low-dimensional space[3,6]. Measures of brain function further extend these geometries in the dimension of time.

Existing software has often addressed this heterogeneity by implementing separate plotting routines for different geometries. Here, we introduce the software library hyve (the hypercoil[7] visualisation engine) to implement an alternative compositional approach. Under our approach, users construct a new visualisation protocol by composing an abstract base plotting routine with a chain of functional atoms called primitives (Fig 1). Each functional primitive imbues the base routine with a distinct functionality, forming the basis of a modular, flexible, and extensible system for building reusable plotting protocols.