Motor Task Detection From Human STN Using Interhemispheric Connectivity

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson's disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS sy...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2018-01, Vol.26 (1), p.216-223
Main Authors: Niketeghad, Soroush, Hebb, Adam O., Nedrud, Joshua, Hanrahan, Sara J., Mahoor, Mohammad H.
Format: Article
Language:English
Subjects:
Aged
Algorithms
Brain - physiology
Brain stimulation
Correlation
Deep brain stimulation
Deep Brain Stimulation - methods
detection
Electronic mail
Evoked Potentials
Feedback
Female
Fingers - physiology
Functional Laterality
Humans
Lead
local field potential (LFP)
Male
Middle Aged
Movement
Neural Pathways
Nonlinear Dynamics
nonlinear regression
Parkinson Disease - rehabilitation
Parkinson's disease
ROC Curve
Satellite broadcasting
Subthalamic Nucleus - anatomy & histology
Subthalamic Nucleus - physiopathology
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Summary:Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson's disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient's behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%).
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2017.2754879