The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review.

A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.

Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease.

Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson's disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.

Explaining predictors of discharge destination assessed along the patients' acute stroke journey.

INTRODUCTION: Accurate prediction of outcome destination at an early stage would help manage patients presenting with stroke. This study assessed the predictive ability of three machine learning (ML) algorithms to predict outcomes at four different stages as well as compared the predictive power of stroke scores. METHODS: Patients presenting with acute stroke to the Canberra Hospital between 2015 and 2019 were selected retrospectively. 16 potential predictors and one target variable (discharge destination) were obtained from the notes. k-Nearest Neighbour (kNN) and two ensemble-based classification algorithms (Adaptive Boosting and Bootstrap Aggregation) were employed to predict outcomes. Predictive accuracy was assessed at each of the four stages using both overall and per-class accuracy. The contribution of each variable to the prediction outcome was evaluated by the ensemble-based algorithm and using the Relief feature selection algorithm. Various combinations of stroke scores were tested using the aforementioned models. RESULTS: Of the three ML models, Adaptive Boosting demonstrated the highest accuracy (90%) at Stage 4 in predicting death while the highest overall accuracy (81.7%) was achieved by kNN (k=2/City-block distance). Feature importance analysis has shown that the most important features are the 24-hour Scandinavian Stroke Scale (SSS) and 24-hour National Institutes of Health Stroke Scale (NIHSS) scores, dyslipidaemia, hypertension and premorbid mRS score. For the initial and 24-hour scores, there was a higher correlation (0.93) between SSS scores than for NIHSS scores (0.81). Reducing the overall four scores to InitSSS/24hrNIHSS increased accuracy to 95% in predicting death (Adaptive Boosting) and overall accuracy to 85.4% (kNN). Accuracies at Stage 2 (pre-treatment, 11 predictors) were not far behind those at Stage 4. CONCLUSION: Our findings suggest that even in the early stages of management, a clinically useful prediction regarding discharge destination can be made. Adaptive Boosting might be the best ML model, especially when it comes to predicting death. The predictors' importance analysis also showed that dyslipidemia and hypertension contributed to the discharge outcome even more than expected. Further, surprisingly using mixed score systems might also lead to higher prediction accuracies.

Early diabetic eye damage: Comparing detection methods using diagnostic power.

It is now clear that retinal neuropathy precedes classical microvascular retinopathy in diabetes. Therefore, tests that underpin useful new endpoints must provide high diagnostic power well before the onset of moderate diabetic retinopathy. Hence, we compare detection methods of early diabetic eye damage. We reviewed data from a range of functional and structural studies of early diabetic eye disease and computed standardized effect size as a measure of diagnostic power, allowing the studies to be compared quantitatively. We then derived minimum performance criteria for tests to provide useful clinical endpoints. This included the criteria that tests should be rapid and easy so that children with type 1 diabetes can be followed into adulthood with the same tests. We also defined attributes that lend test data to further improve performance using Machine/Deep Learning. Data from a new form of objective perimetry suggested that the criteria are achievable.

Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson's disease.

Collecting resting-state electroencephalography (RSEEG) data is time-consuming and data sets are therefore often small. Because many machine learning (ML) algorithms work better with ample data, researchers looking to use RSEEG and ML to develop diagnostic models have used oversampling methods that may seem to contradict averaging methods used in conventional electroencephalography (EEG) research to improve the signal-to-noise ratio. Using eyes open (EO) and eyes closed (EC) recordings from 3 different research groups, we investigated the effect of different averaging and oversampling methods on classification metrics when classifying people with Parkinson's disease (PD) and controls. Both EC and EO recordings were used due to differences found between these methods. Our results indicated that grouping 58 electrodes into regions-of-interest (ROI) based on anatomical location is preferable to using single electrodes. Furthermore, although recording EO data led to slightly better classification, the number of data points for each participant was reduced and recordings for three participants entirely lost during pre-processing due to a higher level of artefacts than in the EC data.Clinical relevance- RSEEG is a potential biomarker for the diagnosis and prognostication of PD, but for RSEEG to have clinical relevance, it is necessary to establish which averaging and oversampling of data most reliably segregates the classes for people with PD and controls. We found that using of ROIs and EC data performed the best, as EO data was often contaminated with artefacts.

The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease.

Current tests of disease status in Parkinson's disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson's disease.

Postural sway correlates with cognition and quality of life in Parkinson's disease.

BACKGROUND: The severity of Parkinson's disease (PD) is difficult to assess objectively owing to the lack of a robust biological marker of underlying disease status, with consequent implications for diagnosis, treatment and prognosis. The current standard tool is the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), but this is hampered by variability between observers and within subjects. Postural sway has been shown to correlate with complex brain functioning in other conditions. This study aimed to investigate the relationship between postural sway, MDS-UPDRS and other non-motor measures of disease severity in patients with PD. METHOD: 25 patients with PD and 18 age-matched controls participated in the study. All participants underwent assessment of postural sway using a force plate, with eyes open and closed. In addition, participants underwent tests of cognition and quality of life: Montreal Cognitive Assessment (MoCA), Neuropsychiatry Unit Cognitive Assessment (NUCOG) and, for the patients, the Parkinson's Disease Questionnaire (PDQ-39-1), and assessment of clinical status using the motor component of the MDS-UPDRS. RESULTS: Patients swayed significantly more than controls. This was most obvious in the eyes-closed condition. Sway path length showed strong correlations with PDQ-39-1, MoCA and the verbal fluency component of the NUCOG, and, to a lesser degree, with the UPDRS-III in patients with PD. CONCLUSION: These results suggest that motor and non-motor symptoms of PD are associated in patients, and, in particular, that postural sway shows potential as a possible measure of underlying disease status in PD, either alone or in combination with other measures.