A novel automated Parkinson's disease identification approach using deep learning and EEG

In vitro electrophysiological characterization of Parkinson's disease: challenges, advances, and future directions

Parkinson's disease is the second most common neurodegenerative disorder, characterized by the progressive loss of dopaminergic neurons in the substantia nigra and the accumulation of α-synuclein aggregates. While significant progress has been made in understanding the genetic and biological aspects of Parkinson's disease, its complex pathophysiology remains poorly understood, and current therapeutic approaches are largely symptomatic. Advanced in vitro models have emerged as essential tools for studying Parkinson's disease related mechanisms and developing new therapeutic strategies. However, the electrophysiological characterization of neurons in these models remains underexplored. This review highlights the importance of employing electrophysiological techniques, such as patch-clamp recordings and microelectrode arrays, in providing critical insights into neuronal dysfunction, synaptic impairments, and network disruptions in Parkinson's disease. The aim is to summarize the key discoveries in the electrophysiological characterization of the pathology and the related progress made in recent years, underlying the main challenges, including the lack of standardized protocols, and the heterogeneity of cellular sources and culture systems. Addressing these limitations is crucial for improving reproducibility and facilitating cross-study comparisons, allowing for a deeper understanding of Parkinson's disease pathophysiology. By refining and standardizing electrophysiological approaches, these efforts will enhance our understanding of Parkinson's disease's underlying mechanisms, ultimately accelerating the discovery of robust biomarkers and the development of more effective therapeutic strategies.

Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.