Pyramidal tract activation due to subthalamic deep brain stimulation in Parkinson's disease

Mahlknecht, Philipp; Akram, Harith; Georgiev, Dejan; Tripoliti, Elina; Candelario, Joseph; Zacharia, André; Zrinzo, Ludvic; Hyam, Jonathan A.; Hariz, Marwan; Foltynie, Thomas; Rothwell, John C.; Limousin, Patricia

doi:10.1002/mds.27042

Cited by 57 publications

(61 citation statements)

References 30 publications

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“…Figure 2 shows that an anterior, lower and lateral coordinate is associated with a higher risk to trigger PTSE. These findings are in line with other descriptions [10,26,39]. The therapeutic effect is predicted as maximal in the posterosuperior part of the STN and its vicinity.…”

Section: Discussionsupporting

confidence: 80%

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery

Baumgarten

Haegelen

Zhao

et al. 2018

Stereotact Funct Neurosurg

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Background: Moving from awake surgery under local anesthesia to asleep surgery under general anesthesia will require to precisely predict the outcome of deep brain stimulation. Objective: To propose a data-driven prediction of both the therapeutic effect and side effects of the surgery. Methods: The retrospective intraoperative data from 30 patients operated on in the subthalamic nucleus were used to train an artificial neural network to predict the deep brain stimulation outcome. A leave-one-out validation was undertaken to give a predictive performance that would reflect the performance of the predictive model in clinical practice. Three-dimensional coordinates and the amount of current of the electrodes were used to train the model. Results: 130 electrode positions were reviewed. The areas under the curve were 0.902 and 0.89 for therapeutic and side effects, respectively. The mean sensitivity and specificity were 93.07% (SD 0.95) and 69.24% (SD 5.27) for the therapeutic effect, 73.47% (SD 10.55) and 91.82% (SD 0.12) for the side effect. Conclusion: Data-driven prediction could be an additional modality to predict deep brain stimulation outcome. Further validation is needed to precisely use this method for performing surgery under general anesthesia.

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Section: Discussionsupporting

confidence: 80%

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery

Baumgarten

Haegelen

Zhao

et al. 2018

Stereotact Funct Neurosurg

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“…As such, recent clinical interest in using EMG to better characterize activation of the IC during subthalamic DBS [e.g. Mahlknecht et al, 2017; Bally et al, 2017], or cortically evoked potentials from HDP antidromic activation [e.g. Walker et al, 2012], represent excellent opportunities for the DBS modeling and clinical communities to converge on analyses that would be mutually beneficial.…”

Section: Discussionmentioning

confidence: 99%

“…Stimulation of the IC is commonly associated with motor contraction side effects of subthalamic DBS [Tommasi et al, 2008; Mahlknecht et al, 2017]. Stimulation of the HDP is thought to be a major contributor to the therapeutic effects of subthalamic DBS [Walker et al, 2012; Sanders and Jaeger, 2016].…”

Section: Methodsmentioning

confidence: 99%

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

2018

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Medical imaging has played a major role in defining the general anatomical targets for deep brain stimulation (DBS) therapies. However, specifics on the underlying brain circuitry that is directly modulated by DBS electric fields remain relatively undefined. Detailed biophysical modeling of DBS provides an approach to quantify the theoretical responses to stimulation at the cellular level, and has established a key role for axonal activation in the therapeutic mechanisms of DBS. Estimates of DBS-induced axonal activation can then be coupled with advances in defining the structural connectome of the human brain to provide insight into the modulated brain circuitry and possible correlations with clinical outcomes. These pathway-activation models (PAMs) represent powerful tools for DBS research, but the theoretical predictions are highly dependent upon the underlying assumptions of the particular modeling strategy used to create the PAM. In general, three types of PAMs are used to estimate activation: 1) field-cable (FC) models, 2) driving force (DF) models, and 3) volume of tissue activated (VTA) models. FC models represent the “gold standard” for analysis but at the cost of extreme technical demands and computational resources. Consequently, DF and VTA PAMs, derived from simplified FC models, are typically used in clinical research studies, but the relative accuracy of these implementations is unknown. Therefore, we performed a head-to-head comparison of the different PAMs, specifically evaluating DBS of three different axonal pathways in the subthalamic region. The DF PAM was markedly more accurate than the VTA PAMs, but none of these simplified models were able to match the results of the patient-specific FC PAM across all pathways and combinations of stimulus parameters. These results highlight the limitations of using simplified predictors to estimate axonal stimulation and emphasize the need for novel algorithms that are both biophysically realistic and computationally simple.

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“…DBS activates STN neurons, resulting in the suppression of rigidity by preventing the clinical expression of pathological oscillations in the motor network . Side effects may be provoked by simultaneously activating corticospinal and/or corticobulbar fibers adjacent to the STN . Patients then complain about muscle contraction, speech impairment, or limb stiffness.…”

mentioning

confidence: 99%

“…3 Side effects may be provoked by simultaneously activating corticospinal and/or corticobulbar fibers adjacent to the STN. [4][5][6] Patients then complain about muscle contraction, speech impairment, or limb stiffness. Tetanic muscle contraction leading to fixed postures can be observed clinically.…”

mentioning

confidence: 99%

Short pulse width in subthalamic stimulation in Parkinson's disease: a randomized, double‐blind study

Bouthour¹,

Wegrzyk

Momjian

et al. 2017

Movement Disorders

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width range, current steering, and other programmable features of the device. It is not possible to test all settings in a single programming visit, and more studies are needed to define the optimal parameter space for target signs.Another limitation was the lack of data on efficacy of chronic stimulation at a short pulse width. The participants were only exposed to each stimulation setting for a short time during a single programming visit; it is possible that long-term stimulation would have revealed differences between short and conventional pulse widths that were not apparent during the acute visit. However, the blinded assessment of motor signs (UPDRS III) during an acute stimulation challenge has previously been used as the primary efficacy endpoint in DBS studies [8][9][10] and reflected the chronic benefit of DBS.Despite these limitations, few controlled studies are aimed at achieving optimization of DBS programming, and this is the first double-blind assessment of the effect of a shorter pulse width and 1 of only a handful of DBS programming studies that have ever been conducted in a double-blind condition.In conclusion, stimulation using a shorter than currently recommended pulse width may be more efficient at achieving therapeutic efficacy and less likely to reach a side effect threshold. This may translate into a fundamentally new basic parameter setting for patients with DBS in PD. Supporting DataAdditional Supporting Information may be found in the online version of this article at the publisher's website. Short Pulse Width in AbstractBackground: We investigated the acute effect of short pulse widths on the therapeutic window in subthalamic nucleus deep brain stimulation in Parkinson's disease. Methods: We assessed 10 PD patients with STN-DBS at a 60-ms pulse width. We randomly and doubleblindedly applied 10-to 50-ms pulse widths. The principal outcome was the therapeutic window (difference --

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Pyramidal tract activation due to subthalamic deep brain stimulation in Parkinson's disease

Cited by 57 publications

References 30 publications

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

Short pulse width in subthalamic stimulation in Parkinson's disease: a randomized, double‐blind study

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