Digital atlasing and standardization in the mouse brain.

Abstract

Digital brain atlases are used in neuroscience to characterize the spatial organization of neuronal structures [1]–[3], for planning and guidance during neurosurgery [4], [5], and as a reference for interpreting other modalities such as gene expression or proteomic data [6]–[9]. The field of digital atlasing is extensive, and includes high quality brain atlases of the mouse [10], rat [11], rhesus macaque [12], human [13], [14], and several other model organisms. In addition to atlases based on histology, [11], [15], [16], magnetic resonance imaging [10], [17], and positron emission tomography [11], modern digital atlases often use probabilistic and multimodal techniques [18], [19], as well as sophisticated visualization software [20], [21]. Whether atlases involve detailed visualization of structures of a single or small group of specimens [6], [22], [23] or averages over larger populations [18], [24], much of the work in developing digital brain atlases is from the perspective of the user of a single resource. This is often due largely to the challenges of data generation, maintenance, and resources management [25], [26]. A more recent goal of many neuroscientists is to connect multiple and diverse resources to work in a collaborative manner using an atlas based framework [2], [19]. This vision is appealing as, ideally, researchers would be able to share their data and analyses with others, regardless of where they or the data are located. An important step in this direction is the specification of a common frame of reference across specimens and resources (either as coordinate, ontology, or region of interest) that is adopted by the community. In this perspective, we propose a collaborative digital atlasing framework for coordinating mouse brain research that allows access to data, tools, and analyses from multiple sources.