Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis.

Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of automated acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using an automated assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale-Revised bulbar score (FRS-bulb) (median [interquartile range] of FRS-bulbar scores: 11[3]). The data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver-operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for automated acoustic analyses to detect and stratify ALS.

Facial Landmark Tracking in Videos of Individuals with Neurological Impairments: Is There a Trade-off Between Smoothness and Accuracyƒ.

Orofacial kinematics are valuable markers of function and progression in a variety of neurological disorders. Recent advances in facial landmark detection have been used to improve landmark tracking in video, for example by accounting for interframe optical flow. It has been demonstrated that finetuning (a type of transfer learning) can improve the performance of some facial landmark detection systems. Here, we asked whether a neural network model that is pretrained using video data (supervision by registration, SBR) can be finetuned to improve landmark detection and tracking, using data from the Toronto Neuroface Dataset (n=36), which comprises 3 different clinical populations. We finetuned the supervision by registration (SBR) model using data from 3 individuals from each of 3 clinical populations (n=9), with or without neurological impairments. The remaining individuals from our dataset (n=27) were used for evaluation. Finetuning SBR moderately improved the model's accuracy but substantially increased the smoothness of tracked landmarks. This suggests that finetuning on video-trained models, like SBR, could improve the estimation of orofacial kinematics in individuals with neurological impairments. This could be used to improve the detection and characterization of neurological diseases using video data.Clinical Relevance-This work demonstrated that transfer learning applied to video-trained facial landmark detectors could improve the measurement of orofacial kinematics in individuals with neurological impairments.

Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics.

Recent work has highlighted that people who have had TIA may have abnormal motor and cognitive function. We aimed to quantify deficits in a cohort of individuals who had TIA and measured changes in their abilities to perform behavioural tasks over 1 year of follow-up using the Kinarm Exoskeleton robot. We additionally considered performance and change over time in an active control cohort of migraineurs. Individuals who had TIA or migraine completed 8 behavioural tasks that assessed cognition as well as motor and sensory functionality in the arm. Participants in the TIA cohort were assessed at 2, 6, 12, and 52 weeks after symptom resolution. Migraineurs were assessed at 2 and 52 weeks after symptom resolution. We measured overall performance on each task using an aggregate metric called Task Score and quantified any significant change in performance including the potential influence of learning. We recruited 48 individuals to the TIA cohort and 28 individuals to the migraine cohort. Individuals in both groups displayed impairments on robotic tasks within 2 weeks of symptom cessation and also at approximately 1 year after symptom cessation, most commonly in tests of cognitive-motor integration. Up to 51.3% of people in the TIA cohort demonstrated an impairment on a given task within 2-weeks of symptom resolution, and up to 27.3% had an impairment after 1 year. In the migraine group, these numbers were 37.5% and 31.6%, respectively. We identified that up to 18% of participants in the TIA group, and up to 10% in the migraine group, displayed impairments that persisted for up to 1 year after symptom resolution. Finally, we determined that a subset of both cohorts (25-30%) experienced statistically significant deteriorations in performance after 1 year. People who have experienced transient neurological symptoms, such as those that arise from TIA or migraine, may continue to experience lasting neurological impairments. Most individuals had relatively stable task performance over time, with some impairments persisting for up to 1 year. However, some individuals demonstrated substantial changes in performance, which highlights the heterogeneity of these neurological disorders. These findings demonstrate the need to consider factors that contribute to lasting neurological impairment, approaches that could be developed to alleviate the lasting effects of TIA or migraine, and the need to consider individual neurological status, even following transient neurological symptoms.

Principal Components Analysis Using Data Collected From Healthy Individuals on Two Robotic Assessment Platforms Yields Similar Behavioral Patterns.

BACKGROUND: Kinarm Standard Tests (KSTs) is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior (>100 variables per individual). We have previously used principal component analysis (PCA) to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab (EP). Here, we performed PCA using data from the Kinarm Exoskeleton Lab (EXO), and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further dimensionality reduction was possible by using PCA across behavioral tasks. METHODS: Healthy participants were assessed using the Kinarm EXO (N = 469) and EP (N = 170-200). Four behavioral tasks (six assessments in total) were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching (APM)] and three motor tasks [Visually Guided Reaching (VGR), Object Hit (OH), and Object Hit and Avoid (OHA)]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type (orthogonal vs. oblique) was decided on a per-task basis. To assess agreement, we compared principal components (PCs) across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments. RESULTS: By applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58-75% while accounting for 76-87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values (0.75-0.99) between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance. CONCLUSION: PCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations.

Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing.

BACKGROUND: Traditional clinical assessments are used extensively in neurology; however, they can be coarse, which can also make them insensitive to change. Kinarm is a robotic assessment system that has been used for precise assessment of individuals with neurological impairments. However, this precision also leads to the challenge of identifying whether a given change in performance reflects a significant change in an individual's ability or is simply natural variation. Our objective here is to derive confidence intervals and thresholds of significant change for Kinarm Standard Tests™ (KST). METHODS: We assessed participants twice within 15 days on all tasks presently available in KST. We determined the 5-95% confidence intervals for each task parameter, and derived thresholds for significant change. We tested for learning effects and corrected for the false discovery rate (FDR) to identify task parameters with significant learning effects. Finally, we calculated intraclass correlation of type ICC [1, 2] (ICC-C) to quantify consistency across assessments. RESULTS: We recruited an average of 56 participants per task. Confidence intervals for Z-Task Scores ranged between 0.61 and 1.55, and the threshold for significant change ranged between 0.87 and 2.19. We determined that 4/11 tasks displayed learning effects that were significant after FDR correction; these 4 tasks primarily tested cognition or cognitive-motor integration. ICC-C values for Z-Task Scores ranged from 0.26 to 0.76. CONCLUSIONS: The present results provide statistical bounds on individual performance for KST as well as significant changes across repeated testing. Most measures of performance had good inter-rater reliability. Tasks with a higher cognitive burden seemed to be more susceptible to learning effects, which should be taken into account when interpreting longitudinal assessments of these tasks.

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics.

BACKGROUND: Epilepsy is a common neurological disorder characterized by recurrent seizures, along with comorbid cognitive and psychosocial impairment. Current gold standards of assessment can quantify cognitive and motor performance, but may not capture all subtleties of behavior. Here, we study the feasibility of assessing various upper limb sensorimotor and cognition functions in people with epilepsy using the Kinarm robotic assessment system. We quantify performance across multiple behavioral domains and additionally consider the possible effects of epilepsy subtype and medication. METHODS: We recruited individuals with a variety of epilepsy subtypes. Participants performed 8 behavioral tasks that tested motor, cognitive, and sensory domains. We collected data on the same tasks from a group of control participants that had no known neurological impairments. We quantified performance using Task Scores, which provide a composite measure of overall performance on a given task and are adjusted for age, sex, and handedness. RESULTS: We collected data from 46 individuals with epilepsy and 92 control participants. The assessment was well-tolerated, with no adverse events recorded. Cognitive tasks testing spatial working memory, executive function, and motor response inhibition were the most frequently impaired in the epilepsy cohort, with 33/46 (72%) being outside the normal range on at least one of these tasks. Additionally, 29/46 (63%) were impaired on at least one task testing primarily motor skill, and 14/46 (30%) were impaired on a proprioceptive sensory task. People with either focal epilepsy or generalized epilepsy performed significantly worse on both motor and cognitive tasks than control participants after correcting for multiple comparisons. There were no statistical differences between generalized and focal epilepsy groups on Task Scores. Finally, individuals taking topiramate trended toward having worse performance on a spatial working memory task than other individuals with epilepsy who were not taking topiramate. CONCLUSIONS: Kinarm robotic assessment is feasible in individuals with epilepsy and is well-tolerated. Our robotic paradigm can detect impairments in various sensorimotor and cognitive functions across the population with epilepsy. Future studies will explore the role of epilepsy subtype and medications.

The Impact of Transient Ischemic Attack (TIA) on Brain and Behavior.

Transient ischemic attack (TIA) was originally defined as self-resolving focal cerebral ischemia with symptoms lasting <24 h. The newer definition also added the limitation that there should be no evidence of acute brain tissue infarction, to recognize that acute injury to the brain can result from ischemia of <24-h duration. However, several recent findings suggest that having a TIA correlates with deficits that can persist far beyond the resolution of clinical symptoms, even in the absence of imaging evidence of ischemic tissue injury. These deficits may be the result of subtle perturbations to brain structure and/or function that are not easily appreciated using the standard clinical and imaging tools that are currently employed in practice. Here, we will discuss evidence that suggests that TIA may lead to lasting changes to the structure and function of the brain.

Using principal component analysis to reduce complex datasets produced by robotic technology in healthy participants.

BACKGROUND: The KINARM robot produces a granular dataset of participant performance metrics associated with proprioceptive, motor, visuospatial, and executive function. This comprehensive battery includes several behavioral tasks that each generate 9 to 20 metrics of performance. Therefore, the entire battery of tasks generates well over 100 metrics per participant, which can make clinical interpretation challenging. Therefore, we sought to reduce these multivariate data by applying principal component analysis (PCA) to increase interpretability while minimizing information loss. METHODS: Healthy right-hand dominant participants were assessed using a bilateral KINARM end-point robot. Subjects (Ns = 101-208) were assessed using 6 behavioral tasks and automated software generated 9 to 20 metrics related to the spatial and temporal aspects of subject performance. Data from these metrics were converted to Z-scores prior to PCA. The number of components was determined from scree plots and parallel analysis, with interpretability considered as a qualitative criterion. Rotation type (orthogonal vs oblique) was decided on a per task basis. RESULTS: The KINARM performance data, per task, was substantially reduced (range 67-79%), while still accounting for a large amount of variance (range 70-82%). The number of KINARM parameters reduced to 3 components for 5 out of 6 tasks and to 5 components for the sixth task. Many components were comprised of KINARM parameters with high loadings and only some cross loadings were observed, which demonstrates a strong separation of components. CONCLUSIONS: Complex participant data produced by the KINARM robot can be reduced into a small number of interpretable components by using PCA. Future applications of PCA may offer potential insight into specific patterns of sensorimotor impairment among patient populations.