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Sign-specific stimulation 'hot' and 'cold' spots in Parkinson's disease validated with machine learning.

ABSTRACT: Deep brain stimulation of the subthalamic nucleus has become a standard therapy for Parkinson's disease. Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific Parkinsonian signs when informed by precise locations of stimulation. We studied 275 Parkinson's disease patients who underwent subthalamic nucleus deep brain stimulation between 2003 and 2018. We selected pre-deep brain stimulation and best available post-deep brain stimulation scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to deep brain stimulation. Volumes of tissue activated were computed and weighted by (i) tremor, (ii) rigidity, (iii) bradykinesia and (iv) axial signs changes. Then, sign-specific sites of optimal ('hot spots') and suboptimal efficacy ('cold spots') were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n = 51 subthalamic nucleus deep brain stimulation patients from another institution). Tremor and rigidity hot spots were largely located outside and dorsolateral to the subthalamic nucleus whereas hot spots for bradykinesia and axial signs had larger overlap with the subthalamic nucleus. Using volume of tissue activated overlap with sign-specific hot and cold spots, support vector machine classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering volume of tissue activated overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using volume of tissue activated overlap with the sign-specific hot and cold spots. In two independent datasets, distinct brain areas could predict sign-specific clinical changes in Parkinson's disease patients with subthalamic nucleus deep brain stimulation. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement.

SUBMITTER: Boutet A

PROVIDER: S-EPMC8042250 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesFirstly, to identify subthalamic region stimulation clusters that predict maximum improvement in rigidity, bradykinesia and tremor, or emergence of side-effects; and secondly, to map-out the cortical fingerprint, mediated by the hyperdirect pathways which predict maximum efficacy.MethodsHigh angular resolution diffusion imaging in twenty patients with advanced Parkinson's disease was acquired prior to bilateral subthalamic nucleus deep brain stimulation. All contacts were screened one-year from surgery for efficacy and side-effects at different amplitudes. Voxel-based statistical analysis of volumes of tissue activated models was used to identify significant treatment clusters. Probabilistic tractography was employed to identify cortical connectivity patterns associated with treatment efficacy.ResultsAll patients responded well to treatment (46% mean improvement off medication UPDRS-III [p < 0.0001]) without significant adverse events. Cluster corresponding to maximum improvement in tremor was in the posterior, superior and lateral portion of the nucleus. Clusters corresponding to improvement in bradykinesia and rigidity were nearer the superior border in a further medial and posterior location. The rigidity cluster extended beyond the superior border to the area of the zona incerta and Forel-H2 field. When the clusters where averaged, the coordinates of the area with maximum overall efficacy was X = -10(-9.5), Y = -13(-1) and Z = -7(-3) in MNI(AC-PC) space. Cortical connectivity to primary motor area was predictive of higher improvement in tremor; whilst that to supplementary motor area was predictive of improvement in bradykinesia and rigidity; and connectivity to prefrontal cortex was predictive of improvement in rigidity.InterpretationThese findings support the presence of overlapping stimulation sites within the subthalamic nucleus and its superior border, with different cortical connectivity patterns, associated with maximum improvement in tremor, rigidity and bradykinesia.

Project description:Recent studies have identified that peripheral stimulation in Parkinson's disease (PD) is effective in tremor reduction, indicating that a peripheral feedback loop plays an important role in the tremor reset mechanism. This was an open-label, quasi-experimental, pre- and post-test design, single-blind, single-group study involving 20 tremor-dominant PD patients. The objective of this study is to explore the effect of electrical muscle stimulation (EMS) as an adjunctive treatment for resting tremor during "on" period and to identify the best machine learning model to predict the suitable stimulation level that will yield the longest period of tremor reduction or tremor reset time. In this study, we used a Parkinson's glove to evaluate, stimulate, and quantify the tremors of PD patients. This adjustable glove incorporates a 3-axis gyroscope to measure tremor signals and an EMS to provide an on-demand muscle stimulation to suppress tremors. Machine learning models were applied to identify the suitable pulse amplitude (stimulation level) in five classes that led to the longest tremor reset time. The study was registered at the www.clinicaltrials.gov under the name "The Study of Rest Tremor Suppression by Using Electrical Muscle Stimulation" (NCT02370108). Twenty tremor-dominant PD patients were recruited. After applying an average pulse amplitude of 6.25 (SD 2.84) mA and stimulation period of 440.7 (SD 560.82) seconds, the total time of tremor reduction, or tremor reset time, was 329.90 (SD 340.91) seconds. A significant reduction in tremor parameters during stimulation was demonstrated by a reduction of Unified Parkinson's Disease Rating Scale (UPDRS) scores, and objectively, with a reduction of gyroscopic data (p < 0.05, each). None of the subjects reported any serious adverse events. We also compared gyroscopic data with five machine learning techniques: Logistic Regression, Random Forest, Support Vector Machine (SVM), Neural Network (NN), and Long-Short-Term-Memory (LSTM). The machine learning model that gave the highest accuracy was LSTM, which obtained: accuracy = 0.865 and macro-F1 = 0.736. This study confirms the efficacy of EMS in the reduction of resting tremors in PD. LSTM was identified as the most effective model for predicting pulse amplitude that would elicit the longest tremor reset time. Our study provides further insight on the tremor reset mechanism in PD.

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Sign-specific stimulation 'hot' and 'cold' spots in Parkinson's disease validated with machine learning.

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