Adaptive deep brain stimulation in a freely moving Parkinsonian patient.
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ABSTRACT:
SUBMITTER: Rosa M
PROVIDER: S-EPMC5032989 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Deep brain stimulation (DBS) of the basal ganglia can alleviate the motor symptoms of Parkinson's disease although the therapeutic mechanisms are unclear. We hypothesize that DBS relieves symptoms by minimizing pathologically disordered neuronal activity in the basal ganglia. In human participants with parkinsonism and clinically effective deep brain leads, regular (i.e., periodic) high-frequency stimulation was replaced with irregular (i.e., aperiodic) stimulation at the same mean frequency (130 Hz). Bradykinesia, a symptomatic slowness of movement, was quantified via an objective finger tapping protocol in the absence and presence of regular and irregular DBS. Regular DBS relieved bradykinesia more effectively than irregular DBS. A computational model of the relevant neural structures revealed that output from the globus pallidus internus was more disordered and thalamic neurons made more transmission errors in the parkinsonian condition compared with the healthy condition. Clinically therapeutic, regular DBS reduced firing pattern disorder in the computational basal ganglia and minimized model thalamic transmission errors, consistent with symptom alleviation by clinical DBS. However, nontherapeutic, irregular DBS neither reduced disorder in the computational basal ganglia nor lowered model thalamic transmission errors. Thus we show that clinically useful DBS alleviates motor symptoms by regularizing basal ganglia activity and thereby improving thalamic relay fidelity. This work demonstrates that high-frequency stimulation alone is insufficient to alleviate motor symptoms: DBS must be highly regular. Descriptive models of pathophysiology that ignore the fine temporal resolution of neuronal spiking in favor of average neural activity cannot explain the mechanisms of DBS-induced symptom alleviation.
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Project description:BackgroundThe preferable position of Deep Brain Stimulation (DBS) electrodes is proposed to be located in the dorsolateral subthalamic nucleus (STN) to improve general motor performance. The optimal DBS electrode localization for the post-operative improvement of balance and gait is unknown.MethodsIn this single-center, retrospective analyses, 66 Parkinson's disease (PD) patients (24 female, age 63 ± 7 years) were assessed pre- and post-operatively (8.45 ± 4.2 months after surgery) by using MDS-UPDRS, freezing of gait (FoG) score, Giladi's gait and falls questionnaire and Berg balance scale. The clinical outcome was related to the DBS electrode coordinates in x, y, z plane as revealed by image-based reconstruction (SureTune™). Binomial generalized linear mixed models with fixed-effect variables electrode asymmetry, parkinsonian subtype, medication, age class and clinical DBS induced changes were analyzed.ResultsSubthalamic nucleus-deep brain stimulation improved all motor, balance and FoG scores in MED OFF condition, however there were heterogeneous results in MED ON condition. DBS electrode reconstructed coordinates impacted the responsiveness of axial symptoms. FoG and balance responders showed slightly more medially located STN electrode coordinates and less medio-lateral asymmetry of the electrode reconstructed coordinates across hemispheres compared to non-responders.ConclusionDeep brain stimulation electrode reconstructed coordinates, particularly electrode asymmetry on the medio-lateral axis affected the post-operative responsiveness of balance and FoG symptoms in PD patients.
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Project description:The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s.
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Project description:Although the clinical use of deep brain stimulation (DBS) is increasing, its basic mechanisms of action are still poorly understood. Platinum/iridium electrodes were inserted into the subthalamic nucleus of rats with unilateral 6-OHDA-induced lesions of the medial forebrain bundle. Six behavioral parameters were compared with respect to their potential to detect DBS effects. Locomotor function was quantified by (i) apomorphine-induced rotation, (ii) initiation time, (iii) the number of adjusting steps in the stepping test, and (iv) the total migration distance in the open field test. Sensorimotor neglect and anxiety were quantified by (v) the retrieval bias in the corridor test and (vi) the ratio of migration distance in the center versus in the periphery in the open field test, respectively. In our setup, unipolar stimulation was found to be more efficient than bipolar stimulation for achieving beneficial long-term DBS effects. Performance in the apomorphine-induced rotation test showed no improvement after 6 weeks. DBS reduced the initiation time of the contralateral paw in the stepping test after 3 weeks of DBS followed by 3 weeks without DBS. Similarly, sensorimotor neglect was improved. The latter two parameters were found to be most appropriate for judging therapeutic DBS effects.
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