PUBLICATION
A Visual Interface for Exploring Hypotheses about Neural Circuits
- Authors
- Vohra, S.K., Harth, P., Isoe, Y., Bahl, A., Fotowat, H., Engert, F., Hege, H.C., Baum, D.
- ID
- ZDB-PUB-230407-47
- Date
- 2023
- Source
- IEEE transactions on visualization and computer graphics 30(7): 3945-3958 (Journal)
- Registered Authors
- Engert, Florian
- Keywords
- none
- MeSH Terms
-
- Animals
- Brain/diagnostic imaging
- Brain/physiology
- Computer Graphics*
- Models, Neurological
- Nerve Net/diagnostic imaging
- Nerve Net/physiology
- Neurons/physiology
- User-Computer Interface
- Zebrafish/physiology
- PubMed
- 37022819 Full text @ IEEE Trans. Vis. Comput. Graph.
Citation
Vohra, S.K., Harth, P., Isoe, Y., Bahl, A., Fotowat, H., Engert, F., Hege, H.C., Baum, D. (2023) A Visual Interface for Exploring Hypotheses about Neural Circuits. IEEE transactions on visualization and computer graphics. 30(7):3945-3958.
Abstract
One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping