Wav2DDK: Analytical and Clinical Validation of an Automated Diadochokinetic Rate Estimation Algorithm on Remotely Collected Speech.

PURPOSE: Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. Remote collection of speech tasks provides a convenient alternative to in-clinic visits, but scoring these assessments can be a laborious process for clinicians. This work describes Wav2DDK, an automated algorithm for estimating the diadochokinetic (DDK) rate on remotely collected audio from healthy participants and participants with amyotrophic lateral sclerosis (ALS). METHOD: Wav2DDK was developed using a corpus of 970 DDK assessments from healthy and ALS speakers where ground truth DDK rates were provided manually by trained annotators. The clinical utility of the algorithm was demonstrated on a corpus of 7,919 assessments collected longitudinally from 26 healthy controls and 82 ALS speakers. Corpora were collected via the participants' own mobile device, and instructions for speech elicitation were provided via a mobile app. DDK rate was estimated by parsing the character transcript from a deep neural network transformer acoustic model trained on healthy and ALS speech. RESULTS: Algorithm estimated DDK rates are highly accurate, achieving .98 correlation with manual annotation, and an average error of only 0.071 syllables per second. The rate exactly matched ground truth for 83% of files and was within 0.5 syllables per second for 95% of files. Estimated rates achieve a high test-retest reliability (r = .95) and show good correlation with the revised ALS functional rating scale speech subscore (r = .67). CONCLUSION: We demonstrate a system for automated DDK estimation that increases efficiency of calculation beyond manual annotation. Thorough analytical and clinical validation demonstrates that the algorithm is not only highly accurate, but also provides a convenient, clinically relevant metric for tracking longitudinal decline in ALS, serving to promote participation and diversity of participants in clinical research. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.23787033.

Estimation of forced vital capacity using speech acoustics in patients with ALS.

In this study, we present and provide validation data for a tool that predicts forced vital capacity (FVC) from speech acoustics collected remotely via a mobile app without the need for any additional equipment (e.g. a spirometer). We trained a machine learning model on a sample of healthy participants and participants with amyotrophic lateral sclerosis (ALS) to learn a mapping from speech acoustics to FVC and used this model to predict FVC values in a new sample from a different study of participants with ALS. We further evaluated the cross-sectional accuracy of the model and its sensitivity to within-subject change in FVC. We found that the predicted and observed FVC values in the test sample had a correlation coefficient of .80 and mean absolute error between .54 L and .58 L (18.5% to 19.5%). In addition, we found that the model was able to detect longitudinal decline in FVC in the test sample, although to a lesser extent than the observed FVC values measured using a spirometer, and was highly repeatable (ICC = 0.92-0.94), although to a lesser extent than the actual FVC (ICC = .97). These results suggest that sustained phonation may be a useful surrogate for VC in both research and clinical environments.

Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis.

Bulbar deterioration in amyotrophic lateral sclerosis (ALS) is a devastating characteristic that impairs patients' ability to communicate, and is linked to shorter survival. The existing clinical instruments for assessing bulbar function lack sensitivity to early changes. In this paper, using a cohort of N = 65 ALS patients who provided regular speech samples for 3-9 months, we demonstrated that it is possible to remotely detect early speech changes and track speech progression in ALS via automated algorithmic assessment of speech collected digitally.

Restoration of prosocial behavior in rats after heroin self-administration via chemogenetic activation of the anterior insular cortex.

The anterior insular cortex (AIC) mediates various social, emotional, and interoceptive components of addiction. We recently demonstrated a disruption of prosocial behavior following heroin self-administration in rats, as assessed by examining the animals' propensity to rescue its cagemate from a plastic restrainer while having simultaneous access to heroin. To examine the possibility that heroin-induced deficits in prosocial function are mediated by the AIC, the present study examined the effects of chemogenetic activation or inhibition of excitatory AIC pyramidal neurons on heroin-induced prosocial deficits. After establishment of baseline rescuing behavior, rats received bilateral infusions of viral vectors encoding either a control virus (AAV-CaMKIIα-GFP), stimulatory DREADD (AAV-CaMKIIα-hM3Dq-mCherry) (Experiment 1), or inhibitory DREADD (AAV-CaMKIIα-hM4Di-mCherry) (Experiment 2), into the AIC. Rats were then allowed to self-administer heroin (0.06 mg/kg/infusion) 6 hr/day for 2 weeks. Prior to re-assessment of prosocial behavior, animals were administered clozapine-N-oxide (1.5 mg/kg, i.p.) to assess the effects of chemogenetic activation or inhibition of the AIC. Relative to control animals, chemogenetic activation of the AIC reversed deficits in rescuing behavior induced by heroin, whereas chemogenetic inhibition of the AIC had no effect. We hypothesize that stimulatory neuromodulation of the AIC may be a novel approach for restoring prosociality in opiate abuse.

Repeatability of Commonly Used Speech and Language Features for Clinical Applications.

INTRODUCTION: Changes in speech have the potential to provide important information on the diagnosis and progression of various neurological diseases. Many researchers have relied on open-source speech features to develop algorithms for measuring speech changes in clinical populations as they are convenient and easy to use. However, the repeatability of open-source features in the context of neurological diseases has not been studied. METHODS: We used a longitudinal sample of healthy controls, individuals with amyotrophic lateral sclerosis, and individuals with suspected frontotemporal dementia, and we evaluated the repeatability of acoustic and language features separately on these 3 data sets. RESULTS: Repeatability was evaluated using intraclass correlation (ICC) and the within-subjects coefficient of variation (WSCV). In 3 sets of tasks, the median ICC were between 0.02 and 0.55, and the median WSCV were between 29 and 79%. CONCLUSION: Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.

Effects of heroin on rat prosocial behavior.

Opioid use disorders are characterized in part by impairments in social functioning. Previous research indicates that laboratory rats, which are frequently used as animal models of addiction-related behaviors, are capable of prosocial behavior. For example, under normal conditions, when a 'free' rat is placed in the vicinity of rat trapped in a plastic restrainer, the rat will release or 'rescue' the other rat from confinement. The present study was conducted to determine the effects of heroin on prosocial behavior in rats. For 2 weeks, rats were given the opportunity to rescue their cagemate from confinement, and the occurrence of and latency to free the confined rat was recorded. After baseline rescuing behavior was established, rats were randomly selected to self-administer heroin (0.06 mg/kg/infusion i.v.) or sucrose pellets (orally) for 14 days. Next, rats were retested for rescuing behavior once daily for 3 days, during which they were provided with a choice between freeing the trapped cagemate and continuing to self-administer their respective reinforcer. Our results indicate that rats self-administering sucrose continued to rescue their cagemate, whereas heroin rats chose to self-administer heroin and not rescue their cagemate. These findings suggest that rats with a history of heroin self-administration show deficits in prosocial behavior, consistent with specific diagnostic criteria for opioid use disorder. Behavioral paradigms providing a choice between engaging in prosocial behavior and continuing drug use may be useful in modeling and investigating the neural basis of social functioning deficits in opioid addiction.