P284 Subthalamic LFP Spectral Decay Captures Movement-Related Differences Between Parkinson’s Disease Phenotypes
Luiz Ricardo Trajano da Silva1, Maria Sheila Guimarães Rocha2, Slawomir Nasuto3, Bradley Voytek4, Fabio Godinho5,Diogo Coutinho Soriano*1
1Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC (UFABC), São Bernardo do Campo, Brazil
2Department of Neurology, Santa Marcelina Hospital, São Paulo, Brazil
3University of Reading, Berkshire, United Kingdom
4Department of Cognitive Science, Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
5Division of Neurosurgery, Department of Neurology, Hospital das Clínicas, University of São Paulo Medical School, São Paulo, Brazil
*Email: diogo.soriano@ufabc.edu.br
Introduction
Parkinson’s Disease (PD) is a heterogeneous neurodegenerative disorder characterized by a wide range of motor and non-motor symptoms [1]. Movement disorder specialists classify PD into subtypes, including tremor dominant (TD) and postural instability and gait disorder (PIGD) [2, 3, 4]. One promising robust biomarker for deep brain stimulation (DBS) therapy is the 1/f^chi spectral decay observed in local field potentials (LFPs). This decay has been linked to the excitatory/inhibitory synaptic balance, providing valuable insights into neuronal circuit dynamics [5, 6, 7, 8]. Therefore, this study explores changes in the spectral decay across rest and movement conditions in different PD phenotypes, aiming to advance personalized DBS strategies.
Methods
STN-LFP recordings from 35 hemispheres (15 TD, 20 PIGD) during rest and movement (elbow extension and flexion) conditions (1 minute each) were acquired during the intraoperative procedure for implanting DBS electrodes. Welch periodogram and spectral parametrization, as proposed in [5], were used to estimate the LFP adjusted low beta (13–22 Hz), high beta (22–35 Hz) rhythms bandpower (i.e., corrected by 1/f^chi background), and the spectral decay parameter chi. Mixed-ANOVA was used to evaluate differences between subtypes and rest/movement conditions. The procedure was approved by the ethical committee for research in human beings(CAAE: 62418316.9.2004.0066).
Results
(Fig. 1) shows the parametrized spectral decay for TD (A) and PIGD (B) phenotypes, the respective PSDs adjusted by 1/f^chi (panels C and D), and the box plots for the bandpower rhythms and spectral decay (E, F, G). Lower beta power showed an interaction between phenotype and motor condition (F(1,33) = 6.67, p = 0.014), with a significant decrease during movement (p = 0.003) for the TD group. High beta bandpower showed a marginal effect for phenotype during rest (F(1,33) = 3.39, p = 0.07). The spectral decay exponent also indicates an interaction between phenotype and the motor condition (F(1,33) = 5.67, p = 0.02), with a post-hoc analysis unveiling a marginal phenotype difference during movement (p = 0.088).
Discussion
Spectral parameterization revealed significant differences between the TD and PIGD subtypes, highlighting distinct neuronal dynamics in the subthalamic nucleus (STN) during movement (elbow flexion). Our findings indicate that beta-band suppression during movement, as documented in previous studies [9–12], is predominantly driven by TD patients. Conversely, the PIGD group showed increased high-beta activity, which has been linked to motor rigidity symptoms [13], along with a steeper aperiodic exponential decay, suggesting a more inhibited synaptic balance in the STN during movement. These results highlight the potential of spectral decay components as biomarkers for personalized DBS strategies for PD patients.
Figure 1. Figure 1 – Aperiodic-adjusted and Aperiodic Component PSDs and Grouped Boxplot for subtype and rest/movement conditions. A and B, aperiodic component PSD for TD and PIGD groups, respectively. C and D, Aperiodic-adjusted PSDs for TD and PIGD groups, respectively. E, F, and G, Boxplot for subtype and rest/movement conditions exhibiting mixed-ANOVA results. (.) 0.05 < 𝘱 < 0.1 ;*𝘱 < 0.05;**𝘱 < 0.01
Acknowledgements
Authors acknowledge the financial support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)- Finance Code 001 and CNPq (grant number 313970/2023-8).
References
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