In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called “Texera.” The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.
The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.
more » « less- NSF-PAR ID:
- 10406071
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Computer Graphics Forum
- Volume:
- 41
- Issue:
- 3
- ISSN:
- 0167-7055
- Format(s):
- Medium: X Size: p. 573-607
- Size(s):
- p. 573-607
- Sponsoring Org:
- National Science Foundation
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Whole body plethysmography in rodents represents a high face‐value method for measuring respiratory outcomes in rodent models of these diseases and disorders.
Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data.
Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication‐worthy graphs with statistics.
We validate Breathe Easy with a terabyte‐scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.
