Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Oliveira, AM (Autor) Outra; Coelho, L (Autor) Outra; Carvalho, E (Autor) Outra; Ferreira Pinto, MJ (Autor) FMUP; Vaz, R (Autor) Outra; Paulo Aguiar (Autor) FMUP

Revista

Título: Journal of Neurology

Vol. 270

Páginas: 5313-5326

ISSN: 0340-5354

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 11 Citações

Outras Informações

ID Authenticus: P-00Y-V78

DOI: 10.1007/s00415-023-11873-1

Abstract (EN): Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards smart DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14