Sensitive quantification of cerebellar speech abnormalities using deep learning models.

Objective: Objective, sensitive, and meaningful disease assessments are critical to support clinical trials and clinical care. Speech changes are one of the earliest and most evident manifestations of cerebellar ataxias. The purpose of this work is to develop models that can accurately identify and quantify these abnormalities. Methods: We use deep learning models such as ResNet 18 , that take the time and frequency partial derivatives of the log-mel spectrogram representations of speech as input, to learn representations that capture the motor speech phenotype of cerebellar ataxia. We train classification models to separate patients with ataxia from healthy controls as well as regression models to estimate disease severity. Results: Our model was able to accurately distinguish healthy controls from individuals with ataxia, including ataxia participants with no detectable clinical deficits in speech. Furthermore the regression models produced accurate estimates of disease severity, were able to measure subclinical signs of ataxia, and captured disease progression over time in individuals with ataxia. Conclusion: Deep learning models, trained on time and frequency partial derivatives of the speech signal, can detect sub-clinical speech changes in ataxias and sensitively measure disease change over time. Significance: Such models have the potential to assist with early detection of ataxia and to provide sensitive and low-burden assessment tools in support of clinical trials and neurological care.

Real-life Wrist Movement Patterns Capture Motor Impairment in Individuals with Ataxia-Telangiectasia.

Sensitive motor outcome measures are needed to efficiently evaluate novel therapies for neurodegenerative diseases. Devices that can passively collect movement data in the home setting can provide continuous and ecologically valid measures of motor function. We tested the hypothesis that movement patterns extracted from continuous wrist accelerometer data capture motor impairment and disease progression in ataxia-telangiectasia. One week of continuous wrist accelerometer data were collected from 31 individuals with ataxia-telangiectasia and 27 controls aged 2-20 years old. Longitudinal wrist sensor data were collected in 14 ataxia-telangiectasia participants and 13 controls. A novel algorithm was developed to extract wrist submovements from the velocity time series. Wrist sensor features were compared with caregiver-reported motor function on the Caregiver Priorities and Child Health Index of Life with Disabilities survey and ataxia severity on the neurologist-performed Brief Ataxia Rating Scale. Submovements became smaller, slower, and less variable in ataxia-telangiectasia compared to controls. High-frequency oscillations in submovements were increased, and more variable and low-frequency oscillations were decreased and less variable in ataxia-telangiectasia. Wrist movement features correlated strongly with ataxia severity and caregiver-reported function, demonstrated high reliability, and showed significant progression over a 1-year interval. These results show that passive wrist sensor data produces interpretable and reliable measures that are sensitive to disease change, supporting their potential as ecologically valid motor biomarkers. The ability to obtain these measures from a low-cost sensor that is ubiquitous in smartwatches could help facilitate neurological care and participation in research regardless of geography and socioeconomic status.

Patterns of Contagious Yawning and Itching Differ Amongst Adults With Autistic Traits vs. Psychopathic Traits.

Both individuals with diagnosed with Autism Spectrum Disorder (ASD) and individuals high in psychopathic traits show reduced susceptibility to contagious yawning; that is, yawning after seeing or hearing another person yawn. Yet it is unclear whether the same underlying processes (e.g., reduced eye gaze) are responsible for the relationship between reduced contagion and these very different types of clinical traits. College Students (n = 97) watched videos of individuals yawning or scratching (a form of contagion not reliant on eye gaze for transmission) while their eye movements were tracked. They completed the Interpersonal Reactivity Index (IRI), the Autism-Spectrum Quotient (AQ), the Psychopathy Personality Inventory-Revised (PPI-R), and the Adolescent and Adult Sensory Processing Disorder Checklist. Both psychopathic traits and autistic traits showed an inverse relationship to contagious yawning, consistent with previous research. However, the relationship between autistic (but not psychopathic) traits and contagious yawning was moderated by eye gaze. Furthermore, participants high in autistic traits showed typical levels of contagious itching whereas adults high in psychopathic traits showed diminished itch contagion. Finally, only psychopathic traits were associated with lower overall levels of empathy. The findings imply that the underlying processes contributing to the disruptions in contagious yawning amongst individuals high in autistic vs. psychopathic traits are distinct. In contrast to adults high in psychopathic traits, diminished contagion may appear amongst people with high levels of autistic traits secondary to diminished attention to the faces of others, and in the absence of a background deficit in emotional empathy.