Back

A Novel, Open-Source Virtual Reality Platform for Dendritic Spine Analysis

Reimer, M. L.; Kauer, S. D.; Benson, C. A.; King, J. F.; Patwa, S.; Feng, S.; Estacion, M. A.; Bangalore, L.; Waxman, S.; Tan, A. M.

2024-02-07 bioinformatics
10.1101/2024.02.02.578597 bioRxiv
Show abstract

Neuroanatomy is fundamental to understanding the nervous system, particularly dendritic spines, which are vital for synaptic transmission and change in response to injury or disease. Advancements in imaging have allowed for detailed 3D visualization of these structures. However, existing tools for analyzing dendritic spine morphology are limited. To address this, we developed an open-source, virtual reality (VR) Structural Analysis Software Ecosystem (coined "VR-SASE") that offers a powerful, intuitive approach for analyzing dendritic spines. Our validation process confirmed the methods superior accuracy, outperforming recognized gold standard neural reconstruction techniques. Importantly, the VR-SASE workflow automatically calculates key morphological metrics such as dendritic spine length, volume, and surface area, and reliably replicates established datasets from published dendritic spine studies. By integrating the Neurodata Without Borders (NWB) data standard and DataJoint, VR-SASE also aligns with FAIR principles--guidelines aimed at improving the findability, accessibility, interoperability, and reusability of digital assets--enhancing data usability and longevity in neuroscience research. MotivationTechnological limitations of available image-analysis tools for analyzing 3D fine-structure hinders effective research and is often costly. An accessible and efficient solution is crucial to overcome these research challenges. We addressed this by integrating the NWB data standard and DataJoint technology into an open-source, virtual reality workflow, enhancing dendritic spine analysis.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.