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Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter
Author(s) -
Godinho Fabio,
Fim Neto Arnaldo,
Bianqueti Bruno Leonardo,
Luccas Julia Baldi,
Varjão Eduardo,
Terzian Filho Paulo Roberto,
Figueiredo Eberval Gadelha,
Almeida Tiago Paggi,
Yoneyama Takashi,
Takahata André Kazuo,
Rocha Maria Sheila,
Soriano Diogo Coutinho
Publication year - 2021
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.15103
Subject(s) - subthalamic nucleus , local field potential , deep brain stimulation , neuroscience , movement disorders , parkinson's disease , psychology , dystonia , physical medicine and rehabilitation , medicine , disease
Parkinson's disease (PD) is clinically heterogeneous across patients and may be classified in three motor phenotypes: tremor dominant (TD), postural instability and gait disorder (PIGD), and undetermined. Despite the significant clinical characterization of motor phenotypes, little is known about how electrophysiological data, particularly subthalamic nucleus local field potentials (STN‐LFP), differ between TD and PIGD patients. This is relevant since increased STN‐LFP bandpower at α–β range (8–35 Hz) is considered a potential PD biomarker and, therefore, a critical setpoint to drive adaptive deep brain stimulation. Acknowledging STN‐LFP differences between phenotypes, mainly in rest and movement states, would better fit DBS to clinical and motor demands. We studied this issue through spectral analyses on 35 STN‐LFP in TD and PIGD patients during rest and movement. We demonstrated that higher β 2 activity (22–35 Hz) was observed in PIGD only during rest. Additionally, bandpower differences between rest and movement occurred at the α–β range, but with different patterns as per phenotypes: movement‐induced desynchronization concerned lower frequencies in TD (10–20 Hz) and higher frequencies in PIGD patients (21–28 Hz). Finally, when supervised learning algorithms were employed aiming to discriminate PD phenotypes based on STN‐LFP bandpower features, movement information had improved the classification accuracy, achieving peak performances when TD and PIGD movement‐induced desynchronization ranges were considered. These results suggest that STN‐LFP β‐band encodes phenotype‐movement dependent information in PD patients.