Interactive volumetric visual analysis of glycogen-derived energy absorption in nanometric brain structures
KAUST DepartmentVisual Computing Center (VCC)
Biological and Environmental Sciences and Engineering (BESE) Division
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number2313
Online Publication Date2019-07-10
Print Publication Date2019-06
Embargo End Date2020-07-10
Permanent link to this recordhttp://hdl.handle.net/10754/656807
MetadataShow full item record
AbstractDigital 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 connectivity-based 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.
SponsorsThis work was supported by the CRG Grant No. 2313 from King Abdullah University of Science and Technology KAUST-EPFL Alliance for Integrative Modeling of Brain Energy Metabolism. This work was conducted using resources and services at the Visualization Core Lab at KAUST. We also acknowledge the contribution of Sardinian Regional Authorities (project VIGECLAB). We thank Kalpana Kare and Danyia Boges, for the technical support; Graham Knott and the BioEM Facility at EPFL (Lausanne, Switzerland), for providing the EM stacks.
JournalComputer Graphics Forum