Interactive Volumetric Visual Analysis of Glycogen‐derived Energy Absorption in Nanometric Brain Structures

• Published in: Computer graphics forum Vol. 38; no. 3; pp. 427 - 439
• Main Authors: Agus, M., Calì, C., Al‐Awami, A., Gobbetti, E., Magistretti, P., Hadwiger, M.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01-06-2019
• Subjects: Applied computing → Life and medical sciences; Imaging; Brain; CCS Concepts; Energy absorption; Energy consumption; Energy storage; Human‐centered computing → Scientific visualization; Morphology; Rodents; Stacks; Statistical analysis
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.13700

Digital acquisition and processing techniques are changing the way neuroscience investigation is carried out. Emerging applications range from statistical analysis on image stacks to complex connectomics visual analysis tools targeted to develop and test hypotheses of brain development and activity. In this work, we focus on neuroenergetics, a field where neuroscientists analyze nanoscale brain morphology and relate energy consumption to glucose storage in form of glycogen granules. In order to facilitate the understanding of neuroenergetic mechanisms, we propose a novel customized pipeline for the visual analysis of nanometric‐level reconstructions based on electron microscopy image data. Our framework supports analysis tasks by combining i) a scalable volume visualization architecture able to selectively render image stacks and corresponding labelled data, ii) a method for highlighting distance‐based energy absorption probabilities in form of glow maps, and iii) a hybrid connectivitybased and absorption‐based interactive layout representation able to support queries for selective analysis of areas of interest and potential activity within the segmented datasets. This working pipeline is currently used in a variety of studies in the neuroenergetics domain. Here, we discuss a test case in which the framework was successfully used by domain scientists for the analysis of aging effects on glycogen metabolism, extracting knowledge from a series of nanoscale brain stacks of rodents somatosensory cortex.