Digital Health: From the Development of Neural Probes to Intelligent Clinical Data Management

• Grant number: 25/05650-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: June 01, 2025
• End date: May 31, 2027
• Field of knowledge: Engineering - Electrical Engineering - Telecommunications
• Principal Investigator: Hugo Enrique Hernández Figueroa
• Grantee: Ursula Fernanda Salazar Roggero
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 21/11380-5 - CPTEn - São Paulo Center for the Study of Energy Transition, AP.CCD

Abstract

Neurological disorders, such as Alzheimer's and Parkinson's diseases, multiple sclerosis, and strokes, are the leading cause of disability and the second leading cause of death worldwide. Unfortunately, due to the aging population, these numbers have been increasing over the past three decades. In this context, the development of tools that assist in the study of the nervous system is absolutely necessary. On the other hand, edge computing is expected to play a central role in the implementation of smart cities, overcoming the disadvantages of approaches centralized in large data processing centers, such as network delays or data protection issues within the framework of the LGPD. This project aims to integrate advanced technologies in the field of digital health, addressing everything from the development of neural probes to intelligent clinical data management. The project involves creating a photonic platform using nanoantennas, which are nanometric metallic devices. An appropriate incident light excites in these nanoantennas the so-called localized plasmonic modes, which are electromagnetic waves initially confined to their surface and subsequently radiated back into free space. The proposed probes consist of a substrate with nanoantennas, where neurons are cultured. When the neurons are stimulated, they generate an electric field that affects the plasmonic resonance of the nanoantennas, modulating the transmitted or reflected fields. In this way, by temporally monitoring the optical signal of the nanoantennas, it is possible to indirectly measure the electrical signal of the cells. In parallel, the developed platform will be based on a software solution in an edge computing environment for managing the data collected during experiments, such as neural signals, images, and videos of excited neurons. This decentralized approach ensures that data is processed and analyzed locally, reducing latency and increasing the security of sensitive data. In a digital health scenario, edge computing enhances service by enabling local analysis of information, optimizing the computational resources of end devices, and thus reducing costs, in addition to decreasing the need for distant communications with centralized clouds. Its effective implementation requires research on distributed data management, including acquisition, processing, routing, and storage. Therefore, virtualization technologies and tools must be improved to support edge-native applications capable of having their functionalities divided to run securely and distributed across different computing infrastructures: from the device itself to the cloud, passing through the edge cloud. The distributed computing and network infrastructure currently being deployed for 5G networks will evolve over time to B5G and 6G. Along this path, the distributed computing infrastructure must support the needs of vertical customer services in the context of cloud network slicing and private 5G networks. In conclusion, this platform integrates sensing and connectivity concepts and techniques applied to the study of the brain within the scope of digital health. (AU)