Summary
One of the predominant riddles of sensory biology is the diversity in fish auditory systems. It is widely
accepted that fishes are well adapted to utilising underwater sounds as sensory cues in key life-history
events. However, the functional significance and the driving force leading to the differences in fish
inner ear sizes and structures are unknown. A complex interplay of physical, evolutionary, functional
and ecological factors may shape the different elements: a multiscale environment too complicated
for human conceptualisation. I propose to address this question by applying novel bioimaging and
computational tools to investigate elasmobranch fish ears. Firstly, diffusible iodine-based contrast enhanced
computed tomography (diceCT) will be used, co-registered with MRI data, to build 3D high
resolution models of the inner ears. Secondly, a Finite Element (FE) model will be created to digitally
replicate a fish ear and understand the biomechanics of its structure. Finally, a statistical framework
will be developed to incorporate the factors that may shape the hearing system of elasmobranch
fishes, including the collected data, together with the available physiological, ecological and
biogeographical information on each species, as well as species’ acoustic environmental parameters. A
Machine Learning algorithm will be applied to infer patterns and relationships between the factors, to
perform both cluster and prediction analyses. Thus, a reliable model will be developed, which can
predict the hearing capability of any elasmobranch species based on the ear morphology and the first
evidence of the function of fish ear diversity.
accepted that fishes are well adapted to utilising underwater sounds as sensory cues in key life-history
events. However, the functional significance and the driving force leading to the differences in fish
inner ear sizes and structures are unknown. A complex interplay of physical, evolutionary, functional
and ecological factors may shape the different elements: a multiscale environment too complicated
for human conceptualisation. I propose to address this question by applying novel bioimaging and
computational tools to investigate elasmobranch fish ears. Firstly, diffusible iodine-based contrast enhanced
computed tomography (diceCT) will be used, co-registered with MRI data, to build 3D high
resolution models of the inner ears. Secondly, a Finite Element (FE) model will be created to digitally
replicate a fish ear and understand the biomechanics of its structure. Finally, a statistical framework
will be developed to incorporate the factors that may shape the hearing system of elasmobranch
fishes, including the collected data, together with the available physiological, ecological and
biogeographical information on each species, as well as species’ acoustic environmental parameters. A
Machine Learning algorithm will be applied to infer patterns and relationships between the factors, to
perform both cluster and prediction analyses. Thus, a reliable model will be developed, which can
predict the hearing capability of any elasmobranch species based on the ear morphology and the first
evidence of the function of fish ear diversity.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/897218 |
| Start date: | 01-06-2022 |
| End date: | 31-05-2025 |
| Total budget - Public funding: | 272 084,16 Euro - 272 084,00 Euro |
Cordis data
Original description
One of the predominant riddles of sensory biology is the diversity in fish auditory systems. It is widelyaccepted that fishes are well adapted to utilising underwater sounds as sensory cues in key life-history
events. However, the functional significance and the driving force leading to the differences in fish
inner ear sizes and structures are unknown. A complex interplay of physical, evolutionary, functional
and ecological factors may shape the different elements: a multiscale environment too complicated
for human conceptualisation. I propose to address this question by applying novel bioimaging and
computational tools to investigate elasmobranch fish ears. Firstly, diffusible iodine-based contrast enhanced
computed tomography (diceCT) will be used, co-registered with MRI data, to build 3D high
resolution models of the inner ears. Secondly, a Finite Element (FE) model will be created to digitally
replicate a fish ear and understand the biomechanics of its structure. Finally, a statistical framework
will be developed to incorporate the factors that may shape the hearing system of elasmobranch
fishes, including the collected data, together with the available physiological, ecological and
biogeographical information on each species, as well as species’ acoustic environmental parameters. A
Machine Learning algorithm will be applied to infer patterns and relationships between the factors, to
perform both cluster and prediction analyses. Thus, a reliable model will be developed, which can
predict the hearing capability of any elasmobranch species based on the ear morphology and the first
evidence of the function of fish ear diversity.
Status
SIGNEDCall topic
MSCA-IF-2019Update Date
28-04-2024
Images
No images available.
Geographical location(s)
