Kinematic Features of Jaw and Lips Distinguish Symptomatic From Presymptomatic Stages of Bulbar Decline in Amyotrophic Lateral Sclerosis

Machine learning and brain-computer interface approaches in prognosis and individualized care strategies for individuals with amyotrophic lateral sclerosis: A systematic review

Amyotrophic lateral sclerosis (ALS) characterized by progressive degeneration of motor neurons is a debilitating disease, posing substantial challenges in both prognosis and daily life assistance. However, with the advancement of machine learning (ML) which is renowned for tackling many real-world settings, it can offer unprecedented opportunities in prognostic studies and facilitate individuals with ALS in motor-imagery tasks. ML models, such as random forests (RF), have emerged as the most common and effective algorithms for predicting disease progression and survival time in ALS. The findings revealed that RF models had an excellent predictive performance for ALS, with a testing R2 of 0.524 and minimal treatment effects of 0.0717 for patient survival time. Despite significant limitations in sample size, with a maximum of 18 participants, which may not adequately reflect the population diversity being studied, ML approaches have been effectively applied to ALS datasets, and numerous prognostic models have been tested using neuroimaging data, longitudinal datasets, and core clinical variables. In many literatures, the constraints of ML models are seldom explicitly enunciated. Therefore, the main objective of this research is to provide a review of the most significant studies on the usage of ML models for analyzing ALS. This review covers a variation of ML algorithms involved in applications in ALS prognosis besides, leveraging ML to improve the efficacy of brain-computer interfaces (BCIs) for ALS individuals in later stages with restricted voluntary muscular control. The key future advances in individualized care and ALS prognosis may include the advancement of more personalized care aids that enable real-time input and ongoing validation of ML in diverse healthcare contexts.

Multimodal speech biomarkers for remote monitoring of ALS disease progression

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons' speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n = 36) and non-bulbar onset (n = 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care.

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons' speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n = 36) and non-bulbar onset (n = 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care.

A multimodal approach to automated hierarchical assessment of bulbar involvement in amyotrophic lateral sclerosis

Introduction

As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement leads to progressive declines of speech and swallowing functions, significantly impacting social, emotional, and physical health, and quality of life. Standard clinical tools for bulbar assessment focus primarily on clinical symptoms and functional outcomes. However, ALS is known to have a long, clinically silent prodromal stage characterized by complex subclinical changes at various levels of the bulbar motor system. These changes accumulate over time and eventually culminate in clinical symptoms and functional declines. Detection of these subclinical changes is critical, both for mechanistic understanding of bulbar neuromuscular pathology and for optimal clinical management of bulbar dysfunction in ALS. To this end, we developed a novel multimodal measurement tool based on two clinically readily available, noninvasive instruments-facial surface electromyography (sEMG) and acoustic techniques-to hierarchically assess seven constructs of bulbar/speech motor control at the neuromuscular and acoustic levels. These constructs, including prosody, pause, functional connectivity, amplitude, rhythm, complexity, and regularity, are both mechanically and clinically relevant to bulbar involvement.

Methods

Using a custom-developed, fully automated data analytic algorithm, a variety of features were extracted from the sEMG and acoustic recordings of a speech task performed by 13 individuals with ALS and 10 neurologically healthy controls. These features were then factorized into 10 composite outcome measures using confirmatory factor analysis. Statistical and machine learning techniques were applied to these composite outcome measures to evaluate their reliability (internal consistency), validity (concurrent and construct), and efficacy for early detection and progress monitoring of bulbar involvement in ALS.

Results

The composite outcome measures were demonstrated to (1) be internally consistent and structurally valid in measuring the targeted constructs; (2) hold concurrent validity with the existing clinical and functional criteria for bulbar assessment; and (3) outperform the outcome measures obtained from each constituent modality in differentiating individuals with ALS from healthy controls. Moreover, the composite outcome measures combined demonstrated high efficacy for detecting subclinical changes in the targeted constructs, both during the prodromal stage and during the transition from prodromal to symptomatic stages.

Discussion

The findings provided compelling initial evidence for the utility of the multimodal measurement tool for improving early detection and progress monitoring of bulbar involvement in ALS, which have important implications in facilitating timely access to and delivery of optimal clinical care of bulbar dysfunction.

Speech timing and monosyllabic diadochokinesis measures in the assessment of amyotrophic lateral sclerosis in Canadian French

PURPOSE

The primary objective of this study was to determine if speech and pause measures obtained using a passage reading task and timing measures from a monosyllabic diadochokinesis (DDK) task differ across speakers of Canadian French diagnosed with amyotrophic lateral sclerosis (ALS) presenting with and without bulbar symptoms, and healthy controls. The secondary objective was to determine if these measures can reflect the severity of bulbar symptoms.

METHOD

A total of 29 Canadian French speakers with ALS (classified as bulbar symptomatic [n = 14] or pre-symptomatic [n = 15]) and 17 age-matched healthy controls completed a passage reading task and a monosyllabic DDK task (/pa/ and /ta/), for up to three follow-up visits. Measures of speaking rate, total duration, speech duration, and pause events were extracted from the passage reading recordings using a semi-automated speech and pause analysis procedure. Manual analysis of DDK recordings provided measures of DDK rate and variability.

RESULT

Group comparisons revealed significant differences (p = < .05) between the symptomatic group and the pre-symptomatic and control groups for all passage measures and DDK rates. Only the DDK rate in /ta/ differentiated the pre-symptomatic and control groups. Repeated measures correlations revealed moderate correlations (rrm = > 0.40; p = < 0.05) between passage measures of total duration, speaking rate, speech duration, and number of pauses, and ALSFRS-R total and bulbar scores, as well as between DDK rate and ALSFRS-R total score.

CONCLUSION

Speech and pause measures in passage and timing measures in monosyllabic DDK tasks might be suitable for monitoring bulbar functional symptoms in French speakers with ALS, but more work is required to identify which measures are sensitive to the earliest stages of the disease.

Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation

Acoustic features extracted from speech can help with the diagnosis of neurological diseases and monitoring of symptoms over time. Temporal segmentation of audio signals into individual words is an important pre-processing step needed prior to extracting acoustic features. Machine learning techniques could be used to automate speech segmentation via automatic speech recognition (ASR) and sequence to sequence alignment. While state-of-the-art ASR models achieve good performance on healthy speech, their performance significantly drops when evaluated on dysarthric speech. Fine-tuning ASR models on impaired speech can improve performance in dysarthric individuals, but it requires representative clinical data, which is difficult to collect and may raise privacy concerns. This study explores the feasibility of using two augmentation methods to increase ASR performance on dysarthric speech: 1) healthy individuals varying their speaking rate and loudness (as is often used in assessments of pathological speech); 2) synthetic speech with variations in speaking rate and accent (to ensure more diverse vocal representations and fairness). Experimental evaluations showed that fine-tuning a pre-trained ASR model with data from these two sources outperformed a model fine-tuned only on real clinical data and matched the performance of a model fine-tuned on the combination of real clinical data and synthetic speech. When evaluated on held-out acoustic data from 24 individuals with various neurological diseases, the best performing model achieved an average word error rate of 5.7% and a mean correct count accuracy of 94.4%. In segmenting the data into individual words, a mean intersection-over-union of 89.2% was obtained against manual parsing (ground truth). It can be concluded that emulated and synthetic augmentations can significantly reduce the need for real clinical data of dysarthric speech when fine-tuning ASR models and, in turn, for speech segmentation.

At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with amyotrophic lateral sclerosis. We use an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. We show that this approach produces scores that progress faster than the gold standard Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (-0.86 ± 0.70 SD/year versus -0.73 ± 0.74 SD/year), resulting in smaller clinical trial sample size estimates (N = 76 versus N = 121). This method offers an ecologically valid and scalable measure for potential use in amyotrophic lateral sclerosis trials and clinical care.

3D Video Tracking Technology in the Assessment of Orofacial Impairments in Neurological Disease: Clinical Validation

PURPOSE

This study sought to determine whether clinically interpretable kinematic features extracted automatically from three-dimensional (3D) videos were correlated with corresponding perceptual clinical orofacial ratings in individuals with orofacial impairments due to neurological disorders.

METHOD

45 participants (19 diagnosed with motor neuron diseases [MNDs] and 26 poststroke) performed two nonspeech tasks (mouth opening and lip spreading) and one speech task (repetition of a sentence "Buy Bobby a Puppy") while being video-recorded in a standardized lab setting. The color video recordings of participants were assessed by an expert clinician-a speech language pathologist-on the severity of three orofacial measures: symmetry, range of motion (ROM), and speed. Clinically interpretable 3D kinematic features, linked to symmetry, ROM, and speed, were automatically extracted from video recordings, using a deep facial landmark detection and tracking algorithm for each of the three tasks. Spearman correlations were used to identify features that were significantly correlated (p value < .05) with their corresponding clinical scores. Clinically significant kinematic features were then used in the subsequent multivariate regression models to predict the overall orofacial impairment severity score.

RESULTS

Several kinematic features extracted from 3D video recordings were associated with their corresponding perceptual clinical scores, indicating clinical validity of these automatically derived measures. Different patterns of significant features were observed between MND and poststroke groups; these differences were aligned with clinical expectations in both cases.

CONCLUSIONS

The results show that kinematic features extracted automatically from simple clinical tasks can capture characteristics used by clinicians during assessments. These findings support the clinical validity of video-based automatic extraction of kinematic features.

Measuring Articulatory Patterns in Amyotrophic Lateral Sclerosis Using a Data-Driven Articulatory Consonant Distinctiveness Space Approach

PURPOSE

The aim of this study was to leverage data-driven approaches, including a novel articulatory consonant distinctiveness space (ACDS) approach, to better understand speech motor control in amyotrophic lateral sclerosis (ALS).

METHOD

Electromagnetic articulography was used to record tongue and lip movement data during the production of 10 consonants from healthy controls (n = 15) and individuals with ALS (n = 47). To assess phoneme distinctness, speech data were analyzed using two classification algorithms, Procrustes matching (PM) and support vector machine (SVM), and the area/volume of the ACDS. Pearson's correlation coefficient was used to examine the relationship between bulbar impairment and the ACDS. Analysis of variance was used to examine the effects of bulbar impairment on consonant distinctiveness and consonant classification accuracies in clinical subgroups.

RESULTS

There was a significant relationship between the ACDS and intelligible speaking rate (area, p = .003; volume, p = .010), and the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar subscore (area, p = .009; volume, p = .027). Consonant classification performance followed a consistent pattern with bulbar severity, where consonants produced by speakers with more severe ALS were classified less accurately (SVM = 75.27%; PM = 74.54%) than the healthy, asymptomatic, and mild-moderate groups. In severe ALS, area of the ACDS was significantly condensed compared to both asymptomatic (p = .004) and mild-moderate (p = .013) groups. There was no statistically significant difference in area between the severe ALS group and healthy speakers (p = .292).

CONCLUSIONS

Our comprehensive approach is sensitive to early oromotor changes in response due to disease progression. The preserved articulatory consonant space may capture the use of compensatory adaptations to counteract influences of neurodegeneration.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.22044320.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.

OBJECTIVE

This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.

METHODS

We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.

RESULTS

Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.

CONCLUSION

This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

Video-Based Facial Movement Analysis in the Assessment of Bulbar Amyotrophic Lateral Sclerosis: Clinical Validation

PURPOSE

Facial movement analysis during facial gestures and speech provides clinically useful information for assessing bulbar amyotrophic lateral sclerosis (ALS). However, current kinematic methods have limited clinical application due to the equipment costs. Recent advancements in consumer-grade hardware and machine/deep learning made it possible to estimate facial movements from videos. This study aimed to establish the clinical validity of a video-based facial analysis for disease staging classification and estimation of clinical scores.

METHOD

Fifteen individuals with ALS and 11 controls participated in this study. Participants with ALS were stratified into early and late bulbar ALS groups based on their speaking rate. Participants were recorded with a three-dimensional (3D) camera (color + depth) while repeating a simple sentence 10 times. The lips and jaw movements were estimated, and features related to sentence duration and facial movements were used to train a machine learning model for multiclass classification and to predict the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar subscore and speaking rate.

RESULTS

The classification model successfully separated healthy controls, the early ALS group, and the late ALS group with an overall accuracy of 96.1%. Video-based features demonstrated a high ability to estimate the speaking rate (adjusted R ² = .82) and a moderate ability to predict the ALSFRS-R bulbar subscore (adjusted R ² = .55).

CONCLUSIONS

The proposed approach based on a 3D camera and machine learning algorithms represents an easy-to-use and inexpensive system that can be included as part of a clinical assessment of bulbar ALS to integrate facial movement analysis with other clinical data seamlessly.

Voice Onset Time in Early- and Late-Stage Amyotrophic Lateral Sclerosis

PURPOSE

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects bulbar functions including speech and voice. Voice onset time (VOT) was examined in speakers with ALS in early and late stages to explore the coordination of the articulatory and phonatory systems during speech production.

METHOD

VOT was measured in nonword /bap/ produced by speakers with early-stage ALS (n = 11), late-stage ALS (n = 6), and healthy controls (n = 13), and compared with speech performance decline (a marker of disease progression) in ALS.

RESULTS

Overall comparison of the VOT values among the three groups showed a significant difference, F(2,27) = 11.71, p < .01. Speakers in late-stage ALS displayed longer voicing lead (negative VOT) than both healthy speakers and speakers in early-stage ALS. VOT was also significantly negatively correlated with speech performance (i.e., Intelligible Speaking Rate), r(15) = .74, p < .01.

CONCLUSIONS

Speakers with more severe ALS showed greater occurrence of voicing lead and longer voicing lead. Findings show voicing precedes articulatory onset with disease progression in the production of bilabial stops, which suggests that the relative timing of coordination between the supralaryngeal structures and the phonatory system is affected in the late stage of ALS.

Tongue and Lip Acceleration as a Measure of Speech Decline in Amyotrophic Lateral Sclerosis

PURPOSE

The goal of this study was to examine the efficacy of acceleration-based articulatory measures in characterizing the decline in speech motor control due to amyotrophic lateral sclerosis (ALS).

METHOD

Electromagnetic articulography was used to record tongue and lip movements during the production of 20 phrases. Data were collected from 50 individuals diagnosed with ALS. Articulatory kinematic variability was measured using the spatiotemporal index of both instantaneous acceleration and speed signals. Linear regression models were used to analyze the relationship between variability measures and intelligible speaking rate (a clinical measure of disease progression). A machine learning algorithm (support vector regression, SVR) was used to assess whether acceleration or speed features (e.g., mean, median, maximum) showed better performance at predicting speech severity in patients with ALS.

RESULTS

As intelligible speaking rate declined, the variability of acceleration of tongue and lip movement patterns significantly increased (p < 0.001). The variability of speed and vertical displacement did not significantly predict speech performance measures. Additionally, based on R2 and root mean square error (RMSE) values, the SVR model was able to predict speech severity more accurately from acceleration features (R2 = 0.601, RMSE = 38.453) and displacement features (R2 = 0.218, RMSE = 52.700) than from speed features (R2 = 0.554, RMSE = 40.772).

CONCLUSION

Results from these models highlight differences in speech motor control in participants with ALS. The variability in acceleration of tongue and lip movements increases as speech performance declines, potentially reflecting physiological deviations due to the progression of ALS. Our findings suggest that acceleration is a more sensitive indicator of speech deterioration due to ALS than displacement and speed and may contribute to improved algorithm designs for monitoring disease progression from speech signals.

Protocol for psychometric evaluation of the Amyotrophic Lateral Sclerosis - Bulbar Dysfunction Index (ALS-BDI): a prospective longitudinal study

INTRODUCTION

Early detection and tracking of bulbar dysfunction in amyotrophic lateral sclerosis (ALS) are critical for directing management of the disease. Current clinical bulbar assessment tools are lacking, while existing physiological instrumental assessments are often inaccessible and cost-prohibitive for clinical application. The goal of our research is to develop and validate a brief and reliable, clinician-administered assessment tool-the ALS-Bulbar Dysfunction Index (ALS-BDI). This publication describes the study protocol that has been established to ascertain the tools' psychometric properties.

METHODS AND ANALYSIS

The ALD-BDI's development closely follows guidelines outlined by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). Through the proposed study protocol, we expect to establish psychometric properties of both individual test items of the ALS-BDI as well as the final version of the entire tool, including test-retest and inter-rater reliability, construct validity using gold-standard assessment methods and responsiveness.

ETHICS AND DISSEMINATION

This study has been reviewed and approved by research ethics boards at two data collection sites: Sunnybrook Health Science Centre, primary (Toronto, Canada; ID3080) and Mass General Brigham (#2013P001746, Boston, USA). Prior to participation in the study, the participants sign the informed consent in accordance with the Declaration of Helsinki. Once validated, the ALS-BDI will be disseminated to key stakeholders. Following validation, the ALS-BDI and any required training material will be implemented for clinical use in a context of a multidisciplinary ALS clinic and used as an outcome measure for clinical trials in ALS research.

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.

Assessment of dysarthria and dysphagia in patients with amyotrophic lateral sclerosis: Review of the current evidence

Bulbar dysfunction is a common presentation of amyotrophic lateral sclerosis (ALS) and significantly impacts quality of life of people with ALS (PALS). The current paper reviews measurements of dysarthria and dysphagia specific to ALS to identify efficient and valid assessment measures. Using such assessment measures will lead to improved management of bulbar dysfunction in ALS. Measures reviewed for dysarthria in PALS are organized into three categories: acoustic, kinematic, and strength. A set of criteria are used to evaluate the effectiveness of the measures' identification of speech impairments, measurement of functional verbal communication, and clinical applicability. Assessments reviewed for dysphagia in PALS are organized into six categories: patient reported outcomes, dietary intake, pulmonary function and airway defense capacity, bulbar function, dysphagia/aspiration screens, and instrumental evaluations. Measurements that have good potential for clinical use are highlighted in both topic areas. Additionally, areas of improvement for clinical practice and research are identified and discussed. In general, no single speech measure fulfilled all the criteria, although a few measures were identified as potential diagnostic tools. Similarly, few objective measures that were validated and replicated with large sample sizes were found for diagnosis of dysphagia in PALS. Importantly, clinical applicability was found to be limited; thus, a collaborative team focused on implementation science would be helpful to improve the clinical uptake of assessments. Overall, the review highlights the need for further development of clinically viable and efficient measurements that use a multidisciplinary approach.

Tracking the Costs of Clear and Loud Speech: Interactions Between Speech Motor Control and Concurrent Visuomotor Tracking

Purpose Prior work has demonstrated that competing tasks impact habitual speech production. The purpose of this investigation was to quantify the extent to which clear and loud speech are affected by concurrent performance of an attention-demanding task. Method Speech kinematics and acoustics were collected while participants spoke using habitual, loud, and clear speech styles. The styles were performed in isolation and while performing a secondary tracking task. Results Compared to the habitual style, speakers exhibited expected increases in lip aperture range of motion and speech intensity for the clear and loud styles. During concurrent visuomotor tracking, there was a decrease in lip aperture range of motion and speech intensity for the habitual style. Tracking performance during habitual speech did not differ from single-task tracking. For loud and clear speech, speakers retained the gains in speech intensity and range of motion, respectively, while concurrently tracking. A reduction in tracking performance was observed during concurrent loud and clear speech, compared to tracking alone. Conclusions These data suggest that loud and clear speech may help to mitigate motor interference associated with concurrent performance of an attention-demanding task. Additionally, reductions in tracking accuracy observed during concurrent loud and clear speech may suggest that these higher effort speaking styles require greater attentional resources than habitual speech.

The Effects of Symptom Onset Location on Automatic Amyotrophic Lateral Sclerosis Detection Using the Correlation Structure of Articulatory Movements

Purpose Kinematic measurements of speech have demonstrated some success in automatic detection of early symptoms of amyotrophic lateral sclerosis (ALS). In this study, we examined how the region of symptom onset (bulbar vs. spinal) affects the ability of data-driven models to detect ALS. Method We used a correlation structure of articulatory movements combined with a machine learning model (i.e., artificial neural network) to detect differences between people with ALS and healthy controls. The performance of this system was evaluated separately for participants with bulbar onset and spinal onset to examine how region of onset affects classification performance. We then performed a regression analysis to examine how different severity measures and region of onset affects model performance. Results The proposed model was significantly more accurate in classifying the bulbar-onset participants, achieving an area under the curve of 0.809 relative to the 0.674 achieved for spinal-onset participants. The regression analysis, however, found that differences in classifier performance across participants were better explained by their speech performance (intelligible speaking rate), and no significant differences were observed based on region of onset when intelligible speaking rate was accounted for. Conclusions Although we found a significant difference in the model's ability to detect ALS depending on the region of onset, this disparity can be primarily explained by observable differences in speech motor symptoms. Thus, when the severity of speech symptoms (e.g., intelligible speaking rate) was accounted for, symptom onset location did not affect the proposed computational model's ability to detect ALS.

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

Validation of Articulatory Rate and Imprecision Judgments in Speech of Individuals With Amyotrophic Lateral Sclerosis

Purpose Perceptual judgments of articulatory function are commonly used by speech-language pathologists to evaluate articulatory performance in individuals with amyotrophic lateral sclerosis (ALS). The goal of this study was to evaluate the psychometric properties (e.g., reliability, validity) of these perceptual measures to inform their application as part of a comprehensive bulbar assessment tool in ALS. Method Preexisting data from 51 individuals with ALS were obtained from a larger longitudinal study. Five independent raters provided perceptual judgments of articulatory rate and imprecision in a sentence task. Inter- and intrarater reliability of these judgments were assessed. Perceptual ratings were correlated with an acoustic measure of articulatory rate, in syllables per second, obtained from passage-reading recordings. Both perceptual and acoustic measures were correlated with gold-standard kinematic tongue and jaw movement measures, recorded from sentences using electromagnetic articulography. Results The results revealed good inter- and intrarater reliability of perceptual judgments of articulatory function. Strong correlations were observed between perceptual ratings of articulatory rate and imprecision and acoustic measures of articulatory rate and kinematic measures of tongue speed. Conclusions These findings support the clinical application of perceptual judgments of articulatory function as valid and reliable measures of underlying articulatory changes in bulbar ALS. Additional research is needed to understand the responsiveness of these measures to clinical changes in articulatory function in ALS.

Tongue- and Jaw-Specific Articulatory Changes and Their Acoustic Consequences in Talkers With Dysarthria due to Amyotrophic Lateral Sclerosis: Effects of Loud, Clear, and Slow Speech

Purpose This study aimed to determine how tongue and jaw displacement changes impact acoustic vowel contrast in talkers with amyotrophic lateral sclerosis (ALS) and controls. Method Ten talkers with ALS and 14 controls participated in this study. Loud, clear, and slow speech cues were used to elicit tongue and jaw kinematic as well as acoustic changes. Speech kinematics was recorded using three-dimensional articulography. Independent tongue and jaw displacements were extracted during the diphthong /ai/ in kite. Acoustic distance between diphthong onset and offset in Formant 1-Formant 2 vowel space indexed acoustic vowel contrast. Results In both groups, all three speech modifications elicited increases in jaw displacement (typical < slow < loud < clear). By contrast, only slow speech elicited significantly increased independent tongue displacement in the ALS group (typical = loud = clear < slow), whereas all three speech modifications elicited significantly increased independent tongue displacement in controls (typical < loud < clear = slow). Furthermore, acoustic vowel contrast significantly increased in response to clear and slow speech in the ALS group, whereas all three speech modifications elicited significant increases in acoustic vowel contrast in controls (typical < loud < slow < clear). Finally, only jaw displacements accounted for acoustic vowel contrast gains in the ALS group. In controls, however, independent tongue displacements accounted for increases in vowel acoustic contrast during loud and slow speech, whereas jaw and independent tongue displacements accounted equally for acoustic vowel contrast change during clear speech. Conclusion Kinematic findings suggest that slow speech may be better suited to target independent tongue displacements in talkers with ALS than clear and loud speech. However, given that gains in acoustic vowel contrast were comparable for slow and clear speech cues in these talkers, future research is needed to determine potential differential impacts of slow and clear speech on perceptual measures, such as intelligibility. Finally, findings suggest that acoustic vowel contrast gains are predominantly jaw driven in talkers with ALS. Therefore, the acoustic and perceptual consequences of direct instructions of enhanced jaw movements should be compared to cued speech modification, such as clear and slow speech in these talkers.

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.