Data integration through brain atlasing: Human Brain Project tools and strategies

Abstract

The Human Brain Project (HBP), an EU Flagship Initiative, is currently building an infrastructure that will allow integration of large amounts of heterogeneous neuroscience data. The ultimate goal of the project is to develop a unified multi-level understanding of the brain and its diseases, and beyond this to emulate the computational capabilities of the brain. Reference atlases of the brain are one of the key components in this infrastructure. Based on a new generation of three-dimensional (3D) reference atlases, new solutions for analyzing and integrating brain data are being developed. HBP will build services for spatial query and analysis of brain data comparable to current online services for geospatial data. The services will provide interactive access to a wide range of data types that have information about anatomical location tied to them. The 3D volumetric nature of the brain, however, introduces a new level of complexity that requires a range of tools for making use of and interacting with the atlases. With such new tools, neuroscience research groups will be able to connect their data to atlas space, share their data through online data systems, and search and find other relevant data through the same systems. This new approach partly replaces earlier attempts to organize research data based only on a set of semantic terminologies describing the brain and its subdivisions.

Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies

“…However, our survey of the current literature above revealed that the location of data is often poorly described and documented, making reported anatomical positions hard to replicate. Combining the findings summarized in Table 1 and accumulated experiences with interpretation and validation of anatomical locations in a wide range of materials measurements collected in context of the Human Brain Project (see e.g., Bjerke et al, 2018 ), we identified some key documentation elements that we found to be of particular importance for our ability to unequivocally specify anatomical locations for different data sets. We also formulate a set of method-independent recommendations for a minimum documentation practice that could alleviate the ambiguity observed in many research papers (see above), and facilitate interpretation of anatomical positions and comparison of research findings.…”

“…A considerable challenge for efforts toward integration of different types of neuroscience data is the heterogeneity in the spatial scale and modality of data. In context of the ambition of the Human Brain Project to make heterogeneous brain data accessible for integrative analyses and computational modeling ( Bjerke et al, 2018 ), we have explored ways to assign location to disparate categories of murine neuroscience data. Using the minimum requirements for documentation of anatomical location proposed above as a starting point, and having the ambition to optimize anatomical descriptions of different types of neuroscience data, we established workflows to relate data sets acquired by in vivo electrophysiology, immunohistochemistry, in situ hybridization, transmission electron microscopy and in vitro electrophysiology with cell reconstruction to a common spatial atlas framework.…”

“…In our example, a non-standard oblique section plane was used to identify electrode tracks, which is very difficult to compare with a traditional 2-D atlas framework. Using the QuickNII tool, the section image could nevertheless be mapped to atlas space, thus allowing the location of electrode tracks to be annotated and visualized (Figures 3B1 – 3B3 ; Puchades et al, 2017 ; see also similar example shown in Bjerke et al, 2018 ).…”

“…With increasingly efficient data production pipelines the number of scientific reports and amount of available data is steadily growing ( Hey and Trefethen, 2003 ). To organize, compare and integrate such large amounts of data into new knowledge and understanding about the brain, new computational approaches have emerged ( Amari et al, 2002 ; Bjaalie et al, 2005 ; Koslow and Subramaniam, 2005 ; Bjaalie, 2008 ; Tiesinga et al, 2015 ; Bjerke et al, 2018 ) to make data discoverable, accessible, interpretable and re-usable, as outlined in the widely endorsed FAIR Guiding Principles (Findability, Accessibility, Interoperability, and Re-usability; Wilkinson et al, 2016 ). However, these integration efforts face the challenge that neuroscience data span multiple spatial and temporal scales (see, e.g., Amunts et al, 2016 ), and that results are commonly reported in journal articles as narratives supported with documentation in selected figures and tables that are difficult to compare.…”

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

“…As the SWC data does not directly reflect the morphological features of the neuron, the raw data need to be preprocessed. L-Measure is one of the subprojects of the computational neuroanatomy group in the Human Brain Project [22,23]. L-Measure can calculate a large number of morphological features from the 3D data of neurons such as the surface area of soma, the number of stems, the characteristics of compartments and branches, the properties of bifurcation points, and so on.…”