Modelling Of Whole-brain Slow oscillatory dynamics in physiology and pathology

Información del proyecto

MoWS

Identificador del acuerdo de subvención: 894032

DOI

10.3030/894032

Proyecto cerrado

Fecha de la firma de la CE 24 Marzo 2020

Fecha de inicio 1 Noviembre 2020

Fecha de finalización 15 Enero 2023

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 183 473,28

Aportación de la UE

€ 183 473,28

Coordinado por

FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Italy

Periodic Reporting for period 1 - MoWS (Modelling Of Whole-brain Slow oscillatory dynamics in physiology and pathology)

Período documentado: 2020-11-01 hasta 2022-10-31

Resumen del contexto y de los objetivos generales del proyecto

The brain is a complex system processing information in a highly distributed, parallel manner. At the microscopic scale, this information is encoded by groups of individual neurons. These local microcircuits are interconnected within larger scale circuits across the brain and the dynamic information exchange across these multiple organization levels is likely to be crucial for brain function.
Despite its importance, very little is known about how neurons exchange information across these scales and it is important, to this extent, to complement the work done by experimental neuroscientists with modelling and data analysis tools.
During MoWS I have worked on the development of an information theoretical toolbox for the analysis of large-scale neural data. The software provides a comprehensive set of tools for researchers to analyze and understand the information processing properties of neural systems at various spatial scales, both in physiological and pathological conditions. This is important because understanding how a healthy brain processes information can help us understand diseases and disorders such as Autism Spectrum Disorders, Alzheimer's, Parkinson's, and Epilepsy.

The overall objectives of this research are to develop a set of tools that can be used to analyze large-scale neural data, and to provide insights into how the brain processes information. By doing so, this research can help researchers to better understand the neural mechanisms underlying brain function and dysfunction, which may ultimately lead to the development of better treatments for neurological diseases and disorders.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

The main result of the project is the development of an open source, fully documented, comprehensive computational suite for information-theoretical analysis of large-scale neural data.
Preliminary results of the project have been presented as a poster at FENS 2023 conference (Paris) and have been submitted in BiorXiv. A manuscript is currently under preparation. Dissemination of the project's results to the general public has been done at the European Researchers Night in Genova (IT) in September 2022.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

The project has considerably improved the availability of high-quality open software for the analysis of neural data. The potential impacts of this research are far-reaching. One key impact is that it can help us to better understand how the brain works, which can inform the development of treatments for neurological diseases and disorders. For example, the toolbox can be used to analyze neural data from patients with epilepsy to identify abnormal patterns of information flow, which can help guide the development of more effective treatments.

Another potential impact of this research is in the field of brain-computer interfaces (BCIs). BCIs are devices that translate neural activity into control signals for external devices, such as prosthetics or computers. The information theoretical toolbox can be used to analyze the neural signals recorded by BCIs, which can help to improve their accuracy and reliability.

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