ReviewNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026
The potential of utilizing Local Field Potential (LFP) in diagnosis and management of Parkinsonism's hallmark symptoms: a comprehensive review.
Review in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly impacts motor functions. Despite advances in diagnostic imaging, accurately diagnosing and understanding PD remains challenging. Local field potentials (LFPs), which represent the collective electrical activity of neuron populations, can provide insights into the pathophysiology of PD, particularly in symptoms such as tremors, dystonia, gait disturbances, sleep disorders, and cognitive dysfunction. Tremors, a hallmark symptom of PD, have been closely studied through LFPs recorded from the subthalamic nucleus (STN). LFPs have revealed abnormal oscillatory activity linked to tremor generation and have been used to enhance deep brain stimulation (DBS) outcomes. Similarly, dystonia and gait disturbances in PD have been associated with specific LFP patterns, offering potential biomarkers for treatment modulation. In the context of sleep disorders, studies have demonstrated that LFPs can accurately classify sleep stages in PD patients, suggesting a role for LFP-guided closed-loop DBS systems in improving sleep quality. Furthermore, cognitive deficits in PD have been examined through LFP recordings during tasks that involve motivational incentives, revealing alterations in theta oscillations associated with cognitive processing. This comprehensive review highlights the diverse applications of LFPs in understanding and managing PD symptoms, emphasizing their potential in personalized medicine, while also pointing to key future directions, such as expanding datasets, refining machine learning models for LFP analysis, and investigating the long-term stability and efficacy of LFP-guided therapies.
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