MINECO (PID2021-123087OB-I00)

Finished

Research line:

Health informatics

Description:

In the developed world, the prevalence and incidence of numerous nervous system diseases have increased considerably in recent decades, especially those related to the ageing process. Nervous system disorders are entities with a strong disease burden, understood as disability, premature mortality, need for care and treatment, and impact on social, economic and family support environments. The project locates in this context and expects to contribute to improving the detection and prognosis in diverse nervous system conditions using minimally invasive methods and taking into account sex and gender differences. It will particularly focus on: (a) early diagnostic and prognosis of neurodegenerative diseases (Parkinsons disease and Alzheimers disease) and (b) early detection of adverse episodes of dysreflexia or stress in persons with cognitive impairments.

Data used in this project will derive from different sources, always carrying gender information. In the case of neurodegenerative pathologies, we will use previously collected data in observational studies of collaborating entities (Instituto de Investigaciones Sanitarias Biocruces Bizkaia (IISBB) and CITA-Alzheimer) or public repositories (Parkinsons Progression Marker Initiative). In the other context, we will use autonomic testing data recorded in IISBB from patients with Spinal Cord Injury and new data related to stress created in own-designed experiments. For these experiments, the team will collaborate with MJN Neuroserveis, PLUX and the Instituto de Telecomincaçoes of Lisbon to design wearable devices for capturing physiological signals in the least intrusive way and make subjects feel comfortable while being monitored. Finally, having collected data from all the sources, we will fuse, organise and curate them. Then, we will combine machine learning (ML) algorithms and transfer learning to learn from the data available, build models with prediction and prognosis abilities and unveil the most relevant biomarkers. In all cases, undesirable biases will be identified and different techniques will be used to face these problems, such as resampling techniques, decoupled classifiers or adversarial learning. When necessary, ad hoc models will be generated instead of one-size-fits-all type models. In addition, the use of explainable ML methods will help to find potential mistaken conclusions and discover sex and gender differences. Undoubtedly, the achievement of minimally invasive early detection systems will lead to effective screening health programs from which the whole population can benefit.

The project encompasses several disciplines. Consequently, the research team is formed by experts from different backgrounds coming from diverse institutions and research centres. The main goal of this multidisciplinary team is to design systems that can be moved from laboratory contexts onto daily life use in health-related areas. Hence, the contributions of the project will be easily transferred to the medical sector or the sector related to the rehabilitation of people with cognitive disabilities, for instance. Moreover, the project aligns with the Health thematic priority of the State Plan 2021-2023, and the Health cluster of the Horizon Europe 2021-2027 program and, given its gender vision and focus on older persons and disabilities, also with the United Nations' Sustainable Development Goals.

Web site: https://bishsibs.wordpress.com/

Financing Entity:

MINECO (PID2021-123087OB-I00)

Lead researcher:

Olatz Arbelaitz

Participants:

Ainhoa Yera
Andoni Arruti
Asier Salazar
Iñigo Perona
Ibai Gurrutxaga
Javier Muguerza
Jesús M. Pérez
José I. Martín
José Luis Jodra
Olatz Arbelaitz
Raquel Martínez

No. of researchers:

11

Amount:

119.669

Start date:

2022-09-01

Final date:

2025-08-31

Scope:

National

Comments:

Investigador responsable: Olatz Arbelaitz / Javier Muguerza
Par.(not ALDAPA): Ane Murueta-Goyena, Marta Ruiz Lopez, María de Arriba Sánchez
Par.(Doctores del equipo de trabajo): Asier Arrizabalaga Otaegi, Markel Vigo, Hugo Placido da Silva

Director(s):