Back

Comparative analysis of the neural and muscle systems in the subumbrella of hydrozoan jellyfish.

Norekian, T. P.; Moroz, L. L.

2026-08-31 zoology
10.64898/2026.08.30.748097 bioRxiv
Show abstract

Hydrozoa is a group of relatively simple animals with a well-developed nervous system. The nervous system in all hydrozoan medusae is highly conserved and includes outer and inner nerve rings at the bell margin, a neural network in the manubrium, and radial neural pathways that connect them. However, one element of the nervous system shows substantial variability among species: the subumbrella neural network. We examined the structure of the nervous and muscular systems in the subumbrella of 14 species of hydrozoan medusae. The main conclusion of this study is that the distribution of neural networks in the subumbrella strongly correlates with the distribution of smooth radial muscles. This correlation suggests that smooth radial muscles are the primary target of the subumbrella nervous system. Most species in the order Anthoathecata show a trend toward secondary loss of the neural networks and radial smooth muscle fibers in the subumbrella region, concentrating neural elements and smooth muscles only in the radial pathways along the radial canals. By contrast, all studied species in the order Leptothecata have neural networks in the subumbrella area, as well as radial smooth muscle fibers spread throughout the entire subumbrella region. The correlation between radial smooth muscles and the nervous system is also observed in the radial pathways along the radial canals. All species with thick bundles of smooth radial muscles along the radial canals have clearly defined, dense neural pathways running along or even embedded within the smooth muscle bundles.

Matching journals

The top 2 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.