Towards discovery and implementation of neurophysiologic biomarkers of Alzheimer's disease using entropy methods.

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disease that leads to substantial loss of quality of life. Therapies currently available for AD do not modify the disease course and have limited efficacy in symptom control. As such, novel and precise therapies tailored to individual patients' neurophysiologic profiles are needed.

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.

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.

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.

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.

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.

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.

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.

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.