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Digital twins for model-driven non-invasive electrical brain stimulation

Project description

A digital brain twin for neuroscience research

'Digital twins' is a term used in industry to describe the development of almost exact replicas of different products for monitoring purposes and add-ons testing. The EU-funded Neurotwin project borrows the concept of digital twins for developing personalised hybrid brain models. Using neuroimaging data from Alzheimer's disease, scientists will build a model that recapitulates the networks and the dynamical landscape of the individual brain. The objective is to employ this model to design and test personalised neuromodulation protocols capable of restoring healthy dynamics. With research at the intersecting frontier of nonlinear dynamics, network theory, biophysics, engineering, neuroscience, clinical research, and ethics, Neurotwin will deliver model-driven breakthroughs in basic and clinical neuroscience, with patients ultimately benefiting from safe, individualised therapy solutions.

Objective

Neuropsychiatric disorders are a leading cause of global disability-adjusted life years, and solutions are lacking. Can digital twins be useful? At least in some cases, we hold they will be central to progress. Recent findings suggest that non-invasive brain stimulation may be a valuable option in conditions such as epilepsy or Alzheimer's (AD). Still, a better understanding of mechanisms and patient-specific factors is needed. Personalized hybrid brain models uniting the physics of electromagnetism with physiology – neurotwins or NeTs – are poised to play a fundamental role in understanding and optimizing the effects of stimulation at the individual level. We ambition to deliver disruptive solutions through model-driven, individualized therapy. We will build a computational framework – weaved and validated across scales and levels of detail– to represent the mechanisms of interaction of electric fields with brain networks and assimilate neuroimaging data. This will allow us to characterize the dynamical landscape of the individual brain and define strategies to restore healthy dynamics. Benefitting from existing databases of healthy and AD individuals, we will deliver the first human and rodent NeTs predicting the effects of stimulation on dynamics. We will then collect detailed multimodal measurements in mice and humans to improve the predictive power of local and whole-brain models under the effects of electrical stimulation, and translate these findings into a technology pipeline for the design of new personalized neuromodulation protocols which we will test in a cohort of AD patients and healthy controls in randomized double-blinded studies. With research at the intersecting frontier of nonlinear dynamics, network theory, biophysics, engineering, neuroscience, clinical research, and ethics, Neurotwin will deliver model-driven breakthroughs in basic and clinical neuroscience, with patients ultimately benefiting from safe, individualized therapy solutions.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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RIA - Research and Innovation action

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Call for proposal

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(opens in new window) H2020-FETPROACT-2018-2020

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Coordinator

NEUROELECTRICS BARCELONA SL
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 844 750,00
Address
AV. TIBIDABO 47 BIS
08035 BARCELONA
Spain

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Este Cataluña Barcelona
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 844 750,00

Participants (7)

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