NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity

Ali K. Al-Awami, Johanna Beyer, Hendrik Strobelt, Narayanan Kasthuri, Jeff W. Lichtman, Hanspeter Pfister and Markus Hadwiger

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity
IEEE Transactions on Visualization and Computer Graphics, Vol.20, No.12 (Proceedings IEEE Information Visualization 2014), pp. 2369-2378 , 2014

NeuroLines is a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial to understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. In our paper we describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

@article{Awami2014Neurolines,
 title = {NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity},
 author = {Al-Awami, Ali K. and Beyer, Johanna and Strobelt, Hendrik and Kasthuri, Narayanan and Lichtman, Jeff W. and Pfister, Hanspeter and Hadwiger, Markus},
 journal = {IEEE Transactions on Visualization and Computer Graphics (Proceedings IEEE Information Visualization 2014)},
 year = {2014},
 volume = {20},
 number = {12},
 pages = {2369--2378},
}