DBStar: An Open-Source Tool Kit for Imaging Analysis with Patient-Customized Deep Brain Stimulation Platforms

Lee

Amaya

et al. 2022

Preprint

Self Cite

Parkinson's Disease (PD) is characterized by distinct motor phenomena that are expressed asynchronously. Understanding the neurophysiological correlates of these different motor states could facilitate monitoring of disease progression and allow improved assessments of therapeutic efficacy, as well as enable optimal closed-loop neuromodulation. We examined neural activity in the basal ganglia and cortex of subjects with PD during a quantitative motor task to decode tremor and bradykinesia - two cardinal motor signs of this disease - and relatively asymptomatic periods of behavior. Analysis of subcortical and cortical signals revealed that tremor and bradykinesia had distinct, nearly opposite neural signatures, while effective motor control displayed unique, differentiating features. The neurophysiological signatures of these motor states depended on the type of signal recorded as well as the location; cortical decoding accuracy generally outperformed subcortical decoding, while tremor and bradykinesia were better decoded from different portions of the subthalamic nucleus (STN). These results provide a roadmap to leverage real-time neurophysiology to understand and treat PD.

Section: Methodsmentioning

confidence: 99%

Decoding Dynamically Shifting States of Parkinson’s Disease: Tremor, Bradykinesia, and Effective Motor Control

Lee

Amaya

et al. 2022

Preprint

Self Cite

“…The raw DICOM images and the linear transform matrices were exported and applied to reconstructed image volumes using the AFNI command ‘3dAllineate,’ bringing them into a common coordinate space (Cox, 1996). Depths were calculated by combining intraoperative recording depth information with electrode reconstructions obtained from intra- or postoperative images using methods described previously (Lauro et al, 2016; Lauro et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Rapid Motor Fluctuations Reveal Short-Timescale Neurophysiological Biomarkers of Parkinson’s Disease

Ahn

Lee

et al. 2020

Preprint

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Identifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson's Disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. Here, we recorded neural activity from the subthalamic nucleus (STN) of patients with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment. This approach leveraged natural motor performance variations as a basis to identify corresponding neurophysiological biomarkers. Using machine learning techniques, we show it was possible to use neural signals from the STN to decode the level of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic "beta" oscillations, contributed to this decoding. While signals providing significant information about the quality of motor performance were found throughout the STN, the most informative signals tended to arise from locations in or near the dorsolateral, sensorimotor portion. Importantly, the informative patterns of neural oscillations were not fully generalizable across subjects, suggesting a patient-specific approach will be critical for optimal disease tracking and closed-loop neuromodulation.

“…The raw DICOM images and the linear transform matrices were exported and applied to reconstructed image volumes using the AFNI command "3dAllineate," bringing them into a common coordinate space (Cox, 1996;Li et al, 2016). Microelectrode depths were calculated by combining intraoperative recording depth information with electrode reconstructions obtained from postoperative images using methods described previously (Lauro et al, 2015(Lauro et al, , 2018. To determine the anatomical distribution of microelectrode recording sites across patients, preoperative T1-weighted MR images were registered to a T1-weighted MNI reference volume (MNI152 T1 2009c) using the AFNI command "3dQwarp" (Fonov et al, 2009).…”

Section: Anatomical Reconstruction Of Recording Sitesmentioning

confidence: 99%

Subthalamic-Cortical Network Reorganization during Parkinson’s Tremor

Lee²,

Asaad³

2021

Preprint

Self Cite

Tremor, a common and often primary symptom of Parkinson's disease, has been modeled with distinct onset and maintenance dynamics. To identify the neurophysiologic correlates of each state, we acquired intraoperative cortical and subthalamic nucleus recordings from ten patients performing a naturalistic visual-motor task. From this task we isolated short epochs of tremor onset and sustained tremor. Comparing these epochs, we found that the subthalamic nucleus was central to tremor onset, as it drove both motor cortical activity and tremor output. Once tremor became sustained, control of tremor shifted to cortex. At the same time, changes in directed functional connectivity across sensorimotor cortex further distinguished the tremor state.