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.

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study.

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal, and fast-progressive neurodegenerative disease characterized by the degeneration of motor neurons. ALS patients often experience an initial misdiagnosis or a diagnostic delay due to the current unavailability of an efficient biomarker. Since impaired speech is typical in ALS, we hypothesized that functional differences between healthy and ALS participants during speech tasks can be explained by cortical pattern changes, thereby leading to the identification of a neural biomarker for ALS. In this pilot study, we collected magnetoencephalography (MEG) recordings from three early-diagnosed patients with ALS and three healthy controls during imagined (covert) and overt speech tasks. First, we computed sensor correlations, which showed greater correlations for speakers with ALS than healthy controls. Second, we compared the power of the MEG signals in canonical bands between the two groups, which showed greater dissimilarity in the beta band for ALS participants. Third, we assessed differences in functional connectivity, which showed greater beta band connectivity for ALS than healthy controls. Finally, we performed single-trial classification, which resulted in highest performance with beta band features (∼ 98%). These findings were consistent across trials, phrases, and participants for both imagined and overt speech tasks. Our preliminary results indicate that speech-evoked beta oscillations could be a potential neural biomarker for diagnosing ALS. To our knowledge, this is the first demonstration of the detection of ALS from single-trial neural signals.

The Impact of Stimulus Length in Tongue and Lip Movement Pattern Stability in Amyotrophic Lateral Sclerosis.

PURPOSE: This study aimed to investigate the effect of stimulus signal length on tongue and lip motion pattern stability in speakers diagnosed with amyotrophic lateral sclerosis (ALS) compared to healthy controls. METHOD: Electromagnetic articulography was used to derive articulatory motion patterns from individuals with mild (n = 27) and severe (n = 16) ALS and healthy controls (n = 25). The spatiotemporal index (STI) was used as a measure of articulatory stability. Two experiments were conducted to evaluate signal length effects on the STI: (a) the effect of the number of syllables on STI values and (b) increasing lengths of subcomponents of a single phrase. Two-way mixed analyses of variance were conducted to assess the effects of syllable length and group on the STI for the tongue tip (TT), tongue back (TB), and lower lip (LL). RESULTS: Experiment 1 showed a significant main effect of syllable length (TT, p < .001; TB, p < .001; and LL, p < .001) and group (TT, p = .037; TB, p = .007; and LL, p = .017). TB and LL stability was generally higher with speech stimuli that included a greater number of syllables. Articulatory variability was significantly higher in speakers diagnosed with ALS compared to healthy controls. Experiment 2 showed a significant main effect of length (TT, p < .001; TB, p = .015; and LL, p < .001), providing additional support that STI values tend to be greater when calculated on longer speech signals. CONCLUSIONS: Articulatory stability is influenced by the length of speech signals and manifests similarly in both healthy speakers and persons with ALS. TT stability may be significantly impacted by phonemic content due to greater movement flexibility. Compared to healthy controls, there was an increase in articulatory variability in those with ALS, which likely reflects deviations in speech motor control. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24463924.

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.

Pharyngeal Pressure Variability During Volitional Swallowing Maneuvers.

PURPOSE: Within-individual pharyngeal swallowing pressure variability differs among pharyngeal regions in healthy individuals and increases with age. It remains unknown if pharyngeal pressure variability is impacted by volitional swallowing tasks. We hypothesized that pressure variability would increase during volitional swallowing maneuvers and differ among pharyngeal regions depending on the type of swallowing task being performed. METHOD: Pharyngeal high-resolution manometry was used to record swallowing pressure data from 156 healthy participants during liquid (5 cc) or saliva swallows, and during volitional swallowing tasks including effortful swallow, Mendelsohn maneuver, Masako maneuver, or during postural adjustments. The coefficient of variation was used to determine pressure variability of velopharynx, tongue base, hypopharynx, and upper esophageal sphincter regions. Repeated-measures analysis of variance was used on log-transformed data to examine effects of pharyngeal region and swallowing tasks on swallow-to-swallow variability. RESULTS: There was a significant main effect of task with greater pressure variability for the effortful swallow (p = .002), Mendelsohn maneuver (p < .001), Masako maneuver (p = .002), and the head turn (p = .006) compared with normal effort swallowing. There was also a significant main effect of region (p < .01). In general, swallowing pressure variability was lower for the tongue base and upper esophageal sphincter regions than the hypopharynx. There was no significant interaction of task and region (effortful, p = .182; Mendelsohn, p = .365; Masako, p = .885; chin tuck, p = .840; head turn, p = .059; and inverted, p = .773). CONCLUSIONS: Pharyngeal swallowing pressure variability increases in healthy individuals during volitional swallowing tasks. Less stable swallow patterns may result when tasks are less automatic and greater in complexity. These findings may have relevance to swallowing motor control integrity in healthy aging and individuals with neurogenic dysphagia.

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.

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.