Summary
Studying the brain mechanisms behind consciousness is a major challenge for neuroscience and medicine. Yet so far, there is no such thing as a unique biomarker that can precisely define the state of consciousness of a disorders of consciousness (DOC) patient. All the biomarkers proposed so far are theory-based but empirically defined (EBM; empirical biomarkers): the thresholds that separate categories are set in a data-driven way. In this project, we propose a novel approach using model-based biomarkers (MBM). This new family of biomarkers (MBMs) will not only complement the EBMs but mainly will naturally address the knowledge gaps associated with the understanding of the underlying causal mechanisms behind the different states of consciousness. The modelling of the structural and functional connectivity will be combined with novel, systematic perturbational approaches that can provide new insights into the human brain’s ability to integrate and segregate information over time. In particular, with this approach we will address the hypothesis that MBMs provide functional fingerprinting of conscious states and insights into the underlying necessary and sufficient brain networks as well as their neural mechanisms. To address the development of these biomarkers, we propose a highly interdisciplinary project that combines basic and clinical neuroscience with whole-brain computational modelling proposing a synergy between two groups with large expertise in each area to address a common question
We will develop computational whole-brain models based on single-patient neuroimaging data. We will extract MBM from the adjusted model parameters and from in-silico simulations. We will test the utility of these biomarkers for the diagnosis of patients with chronic DOC. Then, we will contrast the MBM with a set of previously developed EBM. Finally, we will analyze the diagnostic and prognostic capacity of these biomarkers in DOC patients in both chronic and acute stages.
We will develop computational whole-brain models based on single-patient neuroimaging data. We will extract MBM from the adjusted model parameters and from in-silico simulations. We will test the utility of these biomarkers for the diagnosis of patients with chronic DOC. Then, we will contrast the MBM with a set of previously developed EBM. Finally, we will analyze the diagnostic and prognostic capacity of these biomarkers in DOC patients in both chronic and acute stages.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/896354 |
| Start date: | 01-09-2021 |
| End date: | 31-08-2023 |
| Total budget - Public funding: | 196 707,84 Euro - 196 707,00 Euro |
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Original description
Studying the brain mechanisms behind consciousness is a major challenge for neuroscience and medicine. Yet so far, there is no such thing as a unique biomarker that can precisely define the state of consciousness of a disorders of consciousness (DOC) patient. All the biomarkers proposed so far are theory-based but empirically defined (EBM; empirical biomarkers): the thresholds that separate categories are set in a data-driven way. In this project, we propose a novel approach using model-based biomarkers (MBM). This new family of biomarkers (MBMs) will not only complement the EBMs but mainly will naturally address the knowledge gaps associated with the understanding of the underlying causal mechanisms behind the different states of consciousness. The modelling of the structural and functional connectivity will be combined with novel, systematic perturbational approaches that can provide new insights into the human brain’s ability to integrate and segregate information over time. In particular, with this approach we will address the hypothesis that MBMs provide functional fingerprinting of conscious states and insights into the underlying necessary and sufficient brain networks as well as their neural mechanisms. To address the development of these biomarkers, we propose a highly interdisciplinary project that combines basic and clinical neuroscience with whole-brain computational modelling proposing a synergy between two groups with large expertise in each area to address a common questionWe will develop computational whole-brain models based on single-patient neuroimaging data. We will extract MBM from the adjusted model parameters and from in-silico simulations. We will test the utility of these biomarkers for the diagnosis of patients with chronic DOC. Then, we will contrast the MBM with a set of previously developed EBM. Finally, we will analyze the diagnostic and prognostic capacity of these biomarkers in DOC patients in both chronic and acute stages.
Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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