Connectome verification: inter-rater and connection reliability of tract-tracing-based intrinsic hypothalamic connectivity.

Motivation: Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example.

Results: A workflow is proposed that allows detecting variability of connectivity at different steps of data processing in connectome metastudies. Variability between three blinded experts was found by comparing the connection information in a sample of 16 publications that describe tract-tracing-based neuronal connections in the hypothalamus. Furthermore, observation scores, matrix visualizations of discrepant connections and weight variations in adjacency matrices are analyzed.

Availability: The resulting data and software are available at https://neuroviisas.med.uni-rostock.de/neuroviisas.shtml.