scholarly journals Predicting response to motor therapy in chronic stroke patients using Machine Learning

2018 ◽  
Author(s):  
Ceren Tozlu ◽  
Dylan Edwards ◽  
Aaron Boes ◽  
Douglas Labar ◽  
K. Zoe Tsagaris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evoked Potential ◽  
Motor Evoked Potential ◽  
Chronic Stroke ◽  
Motor Threshold ◽  
Stroke Patients ◽  
Post Intervention ◽  
Machine Learning Methods ◽  
Neurophysiological Data ◽  
The Difference

AbstractAccurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individual’s motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients’ impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61±11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy. Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment. Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated R-squared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment (). Pre-intervention Fugl-Meyer Assessment and difference in motor threshold between affected and unaffected hemispheres were commonly found as the strongest two predictors in the clinical model. The difference in motor threshold had greater importance than the absence or presence of motor evoked potential in the affected hemisphere. The various imaging metrics, including lesion overlap with the spinal cord, largely did not improve the model performance. The approach implemented here may enable clinicians to more accurately predict a chronic stroke patient’s individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.

scholarly journals Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke

2020 ◽  
Vol 34 (5) ◽  
pp. 428-439 ◽  
Author(s):  
Ceren Tozlu ◽  
Dylan Edwards ◽  
Aaron Boes ◽  
Douglas Labar ◽  
K. Zoe Tsagaris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Upper Limb ◽  
Motor Impairment ◽  
Chronic Stroke ◽  
Response To Therapy ◽  
Stroke Patients ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Difference

Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median [Formula: see text] P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.

Low-Threshold Monopolar Motor Mapping for Resection of Primary Motor Cortex Tumors

2012 ◽  
Vol 71 (suppl_1) ◽  
pp. ons104-ons115 ◽  
Author(s):  
Kathleen Seidel ◽  
Jürgen Beck ◽  
Lennart Stieglitz ◽  
Philippe Schucht ◽  
Andreas Raabe
Keyword(s):  
Motor Neurons ◽  
Evoked Potential ◽  
Primary Motor Cortex ◽  
Motor Evoked Potential ◽  
National Institutes Of Health ◽  
Motor Deficit ◽  
Motor Threshold ◽  
Motor Mapping ◽  
Low Threshold ◽  
A Minor

Abstract BACKGROUND: Microsurgery within eloquent cortex is a controversial approach because of the high risk of permanent neurological deficit. Few data exist showing the relationship between the mapping stimulation intensity required for eliciting a muscle motor evoked potential and the distance to the motor neurons; furthermore, the motor threshold at which no deficit occurs remains to be defined. OBJECTIVE: To evaluate the safety of low threshold motor evoked potential mapping for tumor resection close to the primary motor cortex. METHODS: Fourteen patients undergoing tumor surgery were included. Motor threshold was defined as the stimulation intensity that elicited motor evoked potentials from target muscles (amplitude &gt; 30 μV). Monopolar high-frequency motor mapping with train-of-5 stimuli (HF-TOF; pulse duration = 500 microseconds; interstimulus interval = 4.0 milliseconds; frequency = 250 Hz) was used to determine motor response--negative sites where incision and dissection could be performed. At sites negative to 3-mA HF-TOF stimulation, the tumor was resected. RESULTS: HF-TOF mapping localized the motor neurons within the precentral gyrus by using variable, low-stimulation intensities. The lowest motor thresholds after final resection ranged from 3 to 6 mA, indicating close proximity of motor neurons. Postoperatively, 12 patients had no new motor deficit, 1 patient had a minor new temporary deficit (M4+, National Institutes of Health Stroke Scale 1), and another patient had a minor new permanent deficit (M4+, National Institutes of Health Stroke Scale 2). Thirteen patients had complete or gross total resection. CONCLUSION: These preliminary data demonstrate that a monopolar HF-TOF threshold &gt; 3 mA was not associated with a significant new motor deficit.

Comparison of navigated and non-navigated TMS: motor threshold and motor evoked potential

2008 ◽  
Vol 1 (3) ◽  
pp. 245-246
Author(s):  
P. Julkunen ◽  
L. Säisänen ◽  
N. Danner ◽  
E. Niskanen ◽  
T. Hukkanen ◽  
...  
Keyword(s):  
Evoked Potential ◽  
Motor Evoked Potential ◽  
Motor Threshold

scholarly journals Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Hiren Kumar Thakkar ◽  
Wan-wen Liao ◽  
Ching-yi Wu ◽  
Yu-Wei Hsieh ◽  
Tsong-Hai Lee
Keyword(s):  
Machine Learning ◽  
Motor Function ◽  
Chronic Stroke ◽  
Learning Approaches ◽  
Stroke Patients ◽  
Training Set ◽  
Data Set ◽  
Clinically Significant ◽  
Task Oriented ◽  
Significant Motor

Abstract Background Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models. Methods This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models. Results Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77. Conclusions Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

Motor threshold, motor evoked potential, central motor conduction time

2021 ◽  
Author(s):  
Sein H. Schmidt ◽  
Stephan A. Brandt
Keyword(s):  
Magnetic Stimulation ◽  
Evoked Potential ◽  
Motor Evoked Potentials ◽  
Motor Evoked Potential ◽  
Conduction Time ◽  
Optical Navigation ◽  
Motor Threshold ◽  
Pathological Conditions ◽  
Latency Variability ◽  
First Time

In this chapter, we survey parameters influencing the assessment of the size and latency of motor evoked potentials (MEP), in normal and pathological conditions, and methods to allow for a meaningful quantification of MEP characteristics. In line with the first edition of this textbook, we extensively discuss three established mechanisms of intrinsic physiological variance and collision techniques that aim to minimize their influence. For the first time, in line with the ever wider use of optical navigation and targeting systems in brain stimulation, we discuss novel methods to capture and minimize the influence of extrinsic biophysical variance. Together, following the rules laid out in this chapter, transcranial magnetic stimulation (TMS) can account for spinal and extrinsic biophysical variance to advance investigations of the central origins of MEP size and latency variability.

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