Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter

Autor(es):Mostrar menos -

Godinho, Fabio ^{[1, 2, 3]} ; Fim Neto, Arnaldo ^{[2, 4, 5]} ; Bianqueti, Bruno Leonardo ^{[2, 4]} ; de Luccas, Julia Baldi ^{[2, 4]} ; Varjao, Eduardo ^[3] ; Terzian Filho, Paulo Roberto ^[3] ; Figueiredo, Eberval Gadelha ^[6] ; Almeida, Tiago Paggi ^{[7, 2, 8]} ; Yoneyama, Takashi ^[8] ; Takahata, Andre Kazuo ^{[2, 4]} ; Rocha, Maria Sheila ^[9] ; Soriano, Diogo Coutinho ^{[2, 4]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sch Med, Dept Neurol, Div Funct Neurosurg, Inst Psychiat, Sao Paulo - Brazil

^[2] Fed Univ ABC, Ctr Engn Modeling & Appl Social Sci, Alameda Univ, BR-09606045 Sao Bernardo Do Campo, SP - Brazil

^[3] Santa Marcelina Hosp, Dept Funct Neurosurg, 177 Rua Santa Marcelina, Sao Paulo, SP - Brazil

^[4] Brazilian Inst Neurosci & Neurotechnol, Campinas - Brazil

^[5] Univ Estadual Campinas, Inst Phys, Dept Cosm Rays & Chronol, Campinas - Brazil

^[6] Univ Sao Paulo, Sch Med, Sao Paulo - Brazil

^[7] Univ Leicester, Dept Cardiovasc Sci, Leicester, Leics - England

^[8] Technol Inst Aeronaut, Div Elect Engn, Sao Jose Dos Campos - Brazil

^[9] Santa Marcelina Hosp, Dept Neurol, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: European Journal of Neuroscience; v. 53, n. 8 FEB 2021.
• Citações Web of Science: 1

Resumo

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. (AU)

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