Biology subjects

King, J. F.

Publications and source records attributed to King, J. F..

2 recordsLinked to original sources

bioRxiv2024

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

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.

bioinformatics↗

bioRxiv2020

Sniff-synchronized, gradient-guided olfactory search by freely-moving mice

For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in micro-organisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely-moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To investigate the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms.

neuroscience↗