Physiological Control and Monitoring

IPs: Miguel Valencia 

Description:

Deep brain stimulation (DBS) therapy is a highly effective treatment for motor symptoms in people with Parkinson's disease (PD). However, customization to the needs of each patient is a challenge that requires time and expertise to adjust the stimulation parameters for each individual. 

Our goal is to develop quantitative methods to improve the programming of DBS systems based on the electrophysiological signals recorded by the device itself. To this end, we will coordinate the work of teams belonging to 4 health institutions (5 hospitals) and implement protocols that allow the inclusion of patients from different institutions.. 

Objectives:

• To implement a multicenter data collection protocol that allows the inclusion in the study of PD patients operated on in different hospitals.
• To design and implement the necessary infrastructure to carry out data collection, management and processing, guaranteeing the ethical and legal requirements, and at the same time ensuring an effective and efficient use of the data.
•  Develop data analysis tools and algorithms that allow suggestions to be made about the efficacy of different pacing electrode contacts based on the analysis of the electrophysiological signal collected from them.
•  To develop data analysis tools and algorithms that, with the optimal stimulation contact identified, allow suggestions to be made about the most appropriate stimulation amplitude. 

IP: Miguel Valencia 

Description:

There is a general consensus on the importance of complementing the development of new pharmacological therapies with the implementation of intelligent and customizable stimulation systems that improve the performance and broaden the applicability of the procedures and technologies currently in use. Among the different approaches proposed, the possibility of implementing systems based on adaptive or closed-loop stimulation schemes stands out. This type of approach incorporates tools from engineering control theory, artificial intelligence (AI) and machine learning (ML), resulting in responsive, dynamic and adaptive systems with the capacity to self-regulate. In the case of disorders affecting the brain, this approach offers the possibility of developing neurostimulation systems capable of personalizing the stimulation patterns depending on the patient, the brain state and the progression of the disease, thus breaking with the traditionally used open-loop neurostimulation systems and most of their limitations.

Objectives:

• Development of tools for real-time biomedical signal processing with application in neurological and neurodegenerative diseases.
• Development of computational models for in-silico evaluation of approaches based on dynamical systems control theory.
• Development of analytical methods (signal processing and AI/ML) for the identification of non-invasive markers of parkinsonian state with application in real-time monitoring systems.
• Development of new adaptive deep brain stimulation (aDBS) strategies with application to gait disorders in Parkinson's disease.

IP: Miguel Valencia 

Description:

The monitoring and diagnostic devices and technologies currently used to record biophysical parameters in patients in or out of hospital have clear limitations or are inadequate when used in poorly controlled environments or by poorly qualified personnel. 

However, recent advances in the field of signal processing, machine learning and microelectronics, together with new technologies such as functional printing or nanotechnology, make it possible to create expert systems capable of improving many of the capabilities of current systems, addressing their limitations and expanding their scope of application..

Objectives:

• Development of sensors based on functional printing technologies and hydrogels to capture biological parameters on the skin.
• Development of brain interfaces based on flexible nanostructured electrodes that allow a better integration with brain tissue, minimize adverse reactions and make it possible to carry out brain monitoring and stimulation in a more stable and lasting way.
• Development of medical devices that, by capturing biomedical signals, make it possible to determine the presence/absence of neurological alterations automatically and in real time, being suitable for use by healthcare personnel without specific training in neurology or neurophysiology.

IP: Miguel Valencia 

Description:

Neurological diseases are a major burden on both the healthcare system and society as a whole. Worldwide, they are one of the leading causes of disability and premature death. Therefore, there is an imperative need to develop therapies that cure or at least halt their progression. However, the failure rate in the development of these therapies is tremendously high. 

And although the reasons behind this failure are numerous and complex, they can essentially be summarized in three: a) the lack of knowledge of the pathophysiological mechanisms responsible for the disease hinders the process of identifying and validating a therapeutic target; b) the complex nature of neurological diseases, with their intricate symptomatology, is not easy to understand and is difficult to replicate in animal models; and finally, c) we do not have translational biomarkers that allow us to evaluate the efficacy of new treatments, facilitating the development of new therapies. 

In our case, we consider electrophysiological signals as a technique that provides objective clinical endpoints that can be translated into preclinical studies and vice versa. Using this capability, we develop quantitative functional markers of brain state and evaluate their ability to measure the effect on brain activity of pathological processes or possible therapeutic interventions designed to address them.

Objectives:

• Development of new analytical approaches to quantify brain activity and its translational potential as non-invasive biomarkers useful in the development of therapies with application in neurological diseases.
• Translational biomarkers based on electromyography for the development of therapies in neuromuscular diseases (ALS).
• Quantification of the therapeutic effect of strategies based on gene therapy.