Summary
A key step towards untangling the complexity of the human brain is to understand how functionally specialised subunits are interconnected in the brain’s network to influence each other and produce experiences and behaviour. Magnetic resonance imaging uniquely allows to explore this systems-level view of neural connections and to probe brain organisation.
Despite great promise, conventional approaches have experienced difficulties in delivering robust and tangible applications, particularly for the individual, either for neuroscience or clinical practice. The connectome, the comprehensive map of brain connections, is unique in every person; yet there are fundamental limitations in its personalised mapping. Lack of standardised, accurate measures of brain connections and absence of objective references introduce errors and reduce interpretability and reproducibility.
I will develop a novel algorithmic platform for brain connectivity mapping, which will establish measurement principles to allow, for the first time, quantitative and objective characterisation of the brain connectome and its individual variability. Through a mixture of highly-interdisciplinary computational and experimental research, I propose to tackle unmet challenges and shift the paradigm from ad-hoc image processing pipelines to a comprehensive framework governed by principles of metrology. I will develop platforms that a) integrate cross-modal information for accurate standardised measurements of brain connections and b) link these measurements to reference standards, reflecting the population, as well as the individual.
I will subsequently tackle important representative questions that rely on the ability to capture personalised signatures of the brain architecture: in basic neuroscience, the ability to predict the neural connectivity that underpins behavioural traits; in clinical neuroscience, the ability to use normative models of connections to improve subject-specific diagnosis in depression.
Despite great promise, conventional approaches have experienced difficulties in delivering robust and tangible applications, particularly for the individual, either for neuroscience or clinical practice. The connectome, the comprehensive map of brain connections, is unique in every person; yet there are fundamental limitations in its personalised mapping. Lack of standardised, accurate measures of brain connections and absence of objective references introduce errors and reduce interpretability and reproducibility.
I will develop a novel algorithmic platform for brain connectivity mapping, which will establish measurement principles to allow, for the first time, quantitative and objective characterisation of the brain connectome and its individual variability. Through a mixture of highly-interdisciplinary computational and experimental research, I propose to tackle unmet challenges and shift the paradigm from ad-hoc image processing pipelines to a comprehensive framework governed by principles of metrology. I will develop platforms that a) integrate cross-modal information for accurate standardised measurements of brain connections and b) link these measurements to reference standards, reflecting the population, as well as the individual.
I will subsequently tackle important representative questions that rely on the ability to capture personalised signatures of the brain architecture: in basic neuroscience, the ability to predict the neural connectivity that underpins behavioural traits; in clinical neuroscience, the ability to use normative models of connections to improve subject-specific diagnosis in depression.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101000969 |
| Start date: | 01-10-2021 |
| End date: | 30-09-2026 |
| Total budget - Public funding: | 1 995 269,00 Euro - 1 995 269,00 Euro |
Cordis data
Original description
A key step towards untangling the complexity of the human brain is to understand how functionally specialised subunits are interconnected in the brain’s network to influence each other and produce experiences and behaviour. Magnetic resonance imaging uniquely allows to explore this systems-level view of neural connections and to probe brain organisation.Despite great promise, conventional approaches have experienced difficulties in delivering robust and tangible applications, particularly for the individual, either for neuroscience or clinical practice. The connectome, the comprehensive map of brain connections, is unique in every person; yet there are fundamental limitations in its personalised mapping. Lack of standardised, accurate measures of brain connections and absence of objective references introduce errors and reduce interpretability and reproducibility.
I will develop a novel algorithmic platform for brain connectivity mapping, which will establish measurement principles to allow, for the first time, quantitative and objective characterisation of the brain connectome and its individual variability. Through a mixture of highly-interdisciplinary computational and experimental research, I propose to tackle unmet challenges and shift the paradigm from ad-hoc image processing pipelines to a comprehensive framework governed by principles of metrology. I will develop platforms that a) integrate cross-modal information for accurate standardised measurements of brain connections and b) link these measurements to reference standards, reflecting the population, as well as the individual.
I will subsequently tackle important representative questions that rely on the ability to capture personalised signatures of the brain architecture: in basic neuroscience, the ability to predict the neural connectivity that underpins behavioural traits; in clinical neuroscience, the ability to use normative models of connections to improve subject-specific diagnosis in depression.
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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