BrainInformationFlow | Principles underlying information flow across the entire brain of the zebrafish

Summary
How information is processed and flows through the brain to generate motor behaviours and cognitive functions is a paramount question in neurosciences. Donald Hebb proposed that individual neurons cooperate to form larger functional structures (neuronal assemblies) that communicate between them through phase sequences. Recent experiments support the existence of assemblies but how does the information flow between these neuronal assemblies, across the entire brain, remains elusive.
I propose to use the zebrafish larva as the experimental model that in combination with optogenetics and light-sheet microscopy, enables monitoring whole-brain dynamics, with single-neuron resolution in an intact behaving vertebrate. Taking advantage of a multidisciplinary approach involving cutting-edge optical techniques, genetics, optogenetics, and mathematical methods from graph theory and statistical mutual information, I intend to shed light on basic principles underlying the flow of information across the entire brain. Specifically, I will study the following aims:
* Description of the connectivity structure and organization across the whole brain.
* Testing the existence of bottlenecks and surrogate connectivity between neuronal assemblies.
* Network connectivity robustness: circuit and physiological compensations following flow of information interruption.
In recent years, zebrafish became an important model for human diseases (e.g. Parkinson's, Rett's syndrome, or autism). Thus, my findings may contribute to the understanding of information flow anomalies associated with neurological disorders, and therefore open new doors for the design of novel treatments, still impossible to envision using more complex animal models.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/845631
Start date: 01-01-2020
End date: 31-12-2021
Total budget - Public funding: 196 707,84 Euro - 196 707,00 Euro
Cordis data

Original description

How information is processed and flows through the brain to generate motor behaviours and cognitive functions is a paramount question in neurosciences. Donald Hebb proposed that individual neurons cooperate to form larger functional structures (neuronal assemblies) that communicate between them through phase sequences. Recent experiments support the existence of assemblies but how does the information flow between these neuronal assemblies, across the entire brain, remains elusive.
I propose to use the zebrafish larva as the experimental model that in combination with optogenetics and light-sheet microscopy, enables monitoring whole-brain dynamics, with single-neuron resolution in an intact behaving vertebrate. Taking advantage of a multidisciplinary approach involving cutting-edge optical techniques, genetics, optogenetics, and mathematical methods from graph theory and statistical mutual information, I intend to shed light on basic principles underlying the flow of information across the entire brain. Specifically, I will study the following aims:
* Description of the connectivity structure and organization across the whole brain.
* Testing the existence of bottlenecks and surrogate connectivity between neuronal assemblies.
* Network connectivity robustness: circuit and physiological compensations following flow of information interruption.
In recent years, zebrafish became an important model for human diseases (e.g. Parkinson's, Rett's syndrome, or autism). Thus, my findings may contribute to the understanding of information flow anomalies associated with neurological disorders, and therefore open new doors for the design of novel treatments, still impossible to envision using more complex animal models.

Status

CLOSED

Call topic

MSCA-IF-2018

Update Date

28-04-2024
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Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.3. EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions (MSCA)
H2020-EU.1.3.2. Nurturing excellence by means of cross-border and cross-sector mobility
H2020-MSCA-IF-2018
MSCA-IF-2018