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Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study
Conclusion: Our results advance the portrayal of the human cerebrovascular thrombi proteome. The exploratory SVM analysis outlined sets of proteins for a proof-of-principle characterization of our cohort cardioembolic and atherothrombotic samples. The integrated analysis proposed herein could be further developed and retested on a larger patients population to better understand stroke origin and the associated cerebrovascular pathophysiology.
Source: Frontiers in Neurology - November 5, 2020 Category: Neurology Source Type: research

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke
Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice.
Source: Frontiers in Neurology - October 15, 2020 Category: Neurology Source Type: research

Role of Artificial Intelligence in TeleStroke: An Overview
Teleneurology has provided access to neurological expertise and state-of-the-art stroke care where previously they have been inaccessible. The use of Artificial Intelligence with machine learning to assist telestroke care can be revolutionary. This includes more rapid and more reliable diagnosis through imaging analysis as well as prediction of hospital course and 3-month prognosis. Intelligent Electronic Medical Records can search free text and provide decision assistance by analyzing patient charts. Speech recognition has advanced enough to be reliable and highly convenient. Smart contextually aware communication and ale...
Source: Frontiers in Neurology - October 6, 2020 Category: Neurology Source Type: research

Using Transcranial Direct Current Stimulation to Augment the Effect of Motor Imagery-Assisted Brain-Computer Interface Training in Chronic Stroke Patients —Cortical Reorganization Considerations
Conclusion: MI-BCI improved the motor function of the stroke-affected arm in chronic stroke patients with moderate to severe impairment. tDCS did not confer overall additional benefit although there was a trend toward greater benefit. Cortical activity changes in the contralesional M1 associated with functional improvement suggests a possible role for the contralesional M1 in stroke recovery in more severely affected patients. This has important implications in designing neuromodulatory interventions for future studies and tailoring treatment.Clinical Trial Registration: The study was registered at https://clinicaltrials.gov (NCT01897025).
Source: Frontiers in Neurology - August 26, 2020 Category: Neurology Source Type: research

Editorial: Machine Learning and Decision Support in Stroke
Source: Frontiers in Neurology - May 28, 2020 Category: Neurology Source Type: research

Middle School Students Effectively Improve Stroke Knowledge and Pass Them to Family Members in China Using Stroke 1-2-0
Conclusion: Middle school and high school students can effectively use Stroke 1-2-0 to improve their stroke knowledge and pass this knowledge to their family members. Sustained educational efforts and repeated educational events are needed though.
Source: Frontiers in Neurology - April 7, 2020 Category: Neurology Source Type: research

Impact of 25-Hydroxyvitamin D on the Prognosis of Acute Ischemic Stroke: Machine Learning Approach
Conclusions: 25-hydroxyvitamin D deficiency was highly prevalent in Korea and low 25-hydroxyvitamin D level was associated with poor outcome in patients with AIS. The machine learning approach of extreme gradient boosting was also useful to assess stroke prognosis along with logistic regression analysis.
Source: Frontiers in Neurology - January 30, 2020 Category: Neurology Source Type: research

Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning
Acute stroke is often superimposed on chronic damage from previous cerebrovascular events. This background will inevitably modulate the impact of acute injury on clinical outcomes to an extent that will depend on the precise anatomical pattern of damage. Previous attempts to quantify such modulation have employed only reductive models that ignore anatomical detail. The combination of automated image processing, large-scale data, and machine learning now enables us to quantify the impact of this with high-dimensional multivariate models sensitive to individual variations in the detailed anatomical pattern. We introduce and ...
Source: Frontiers in Neurology - January 23, 2020 Category: Neurology Source Type: research

Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke
Conclusions: Machine learning algorithms can be employed to develop prognostic predictive biomarkers for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.
Source: Frontiers in Neurology - January 14, 2020 Category: Neurology Source Type: research

Predicting Chronic Subdural Hematoma Recurrence and Stroke Outcomes While Withholding Antiplatelet and Anticoagulant Agents
Conclusion: ML modeling is feasible. However, large well-designed prospective multicenter studies are needed for accurate ML so that clinicians can balance the risks of recurrence with the risk of TEEs, especially for high-risk anticoagulated patients.
Source: Frontiers in Neurology - January 14, 2020 Category: Neurology Source Type: research

Alternative Motor Task-Based Pattern Training With a Digital Mirror Therapy System Enhances Sensorimotor Signal Rhythms Post-stroke
Mirror therapy (MT) facilitates motor learning and induces cortical reorganization and motor recovery from stroke. We applied the new digital mirror therapy (DMT) system to compare the cortical activation under the three visual feedback conditions: (1) no mirror visual feedback (NoMVF), (2) bilateral synchronized task-based mirror visual feedback training (BMVF), and (3) reciprocal task-based mirror visual feedback training (RMVF). During DMT, EEG recordings, including time-dependent event-related desynchronization (ERD) signal amplitude in both mu and beta bands, were obtained from the standard C3 (ispilesional hemisphere...
Source: Frontiers in Neurology - November 21, 2019 Category: Neurology Source Type: research

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event
Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.
Source: Frontiers in Neurology - November 11, 2019 Category: Neurology Source Type: research

A Paradigm Shift: Rehabilitation Robotics, Cognitive Skills Training, and Function After Stroke
Conclusion: The ALPS protocol was the first to extend cognitive strategy training to robot-assisted therapy. The intervention in this development of concept pilot trial was feasible and well-tolerated, with good potential to optimize paretic UE performance following robot-assisted therapy.
Source: Frontiers in Neurology - October 14, 2019 Category: Neurology Source Type: research

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer
Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We ad...
Source: Frontiers in Neurology - September 17, 2019 Category: Neurology Source Type: research

Intra-arterial Administration of Human Umbilical Cord Blood Derived Cells Inversed Learning Asymmetry Resulting From Focal Brain Injury in Rat
Conclusions: Intraarterial infusion of HUCB-derived cells inversed lateralized performance of learning task resulting from focal brain damage. The inversion was not visible in any other of the used motor as well as cognitive tests. The observed behavioral effect of cell infusion was also not related to the range of the brain damage. Our findings contribute to describing the effects of systemic treatment with the HUCB-derived cells on functional recovery following focal brain injury.
Source: Frontiers in Neurology - August 12, 2019 Category: Neurology Source Type: research

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