Journal
EUROPEAN JOURNAL OF NEUROSCIENCE
Volume 53, Issue 8, Pages 2804-2818Publisher
WILEY
DOI: 10.1111/ejn.15103
Keywords
biomarkers; brain waves; neurology; neurosurgery; physiology
Categories
Funding
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [117659/2018-5]
- British Heart Foundation [PG/18/33/33780]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior [2019/1814368, 33144010015P8]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo [2013/07559-3, 2017/00319-8, 2018/02251-4, 2018/14283-8, 2018/14285-0, 2019/09512-0]
- International Association for the Study of Pain
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Parkinson's disease patients exhibit differences in STN-LFP beta-band activity between tremor dominant (TD) and postural instability and gait disorder (PIGD) motor phenotypes, especially during rest and movement states. Movement-induced desynchronization patterns in alpha-beta range vary in TD (10-20 Hz) and PIGD patients (21-28 Hz), leading to improved classification accuracy when considering movement information. These findings suggest that STN-LFP beta-band encodes phenotype-movement dependent information in PD patients.
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 alpha-beta 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 beta(2) activity (22-35 Hz) was observed in PIGD only during rest. Additionally, bandpower differences between rest and movement occurred at the alpha-beta 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 beta-band encodes phenotype-movement dependent information in PD patients.
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