Utianski, Rene L.

Purpose: While acquired apraxia of speech (AOS) has been the focus of intervention research for decades, little information is reported about the demographic representation of study participants. This article examines the extent to which published AOS treatment studies report participants' age, sex/gender, race/ethnicity, language background, and socioeconomic status; identifies critical gaps in the literature; and considers implications for representativeness. This information supports the important call to capture these patient factors systematically and thereby advance equity in research and clinical care. Method: This study is part of a larger systematic review examining treatment outcomes for acquired AOS. A comprehensive search of databases from 1951 to April 2024 was completed to determine the proportion of treatment articles that reported demographic information related to diversity. Studies were reviewed by trained raters using a standardized data extraction protocol with a consensus protocol. Results: While participant age and sex/gender were reported in nearly all studies (99% and 100%, respectively), only 24% of studies reported on race/ethnicity, 15% provided information on participants' language background, and none reported socioeconomic status. Among studies that included race/ethnicity, 89% of the participants were White, with minimal representation of other racial/ethnic groups. Conclusions: The underreporting of race/ethnicity, language background, and socioeconomic factors limits the generalizability and ecological validity of AOS treatment research. The field must prioritize inclusive participant recruitment and consistent demographic reporting. We propose a practical template for use in both research and clinical contexts to support more thorough data collection and to inform future work in diagnosis, assessment, and intervention for AOS. Supplemental Material: https://doi.org/10.23641/asha.31172353

Background Motor speech disorders are early, common, and functionally limiting features of atypical parkinsonian disorders (APDs) such as progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), and multiple system atrophy (MSA). These impairments are underrecognized and undertreated in neurology clinics. Objectives This review aims to characterize speech impairment in APDs, offer practical guidance for clinical evaluation, highlight the role of Speech‐Language pathologists (SLPs) in diagnosis and management, and outline current and emerging management strategies. Methods A narrative review was conducted by the Diagnosis and Treatment Working Group of CurePSP's Centers of Care, integrating literature and clinical experience to summarize evaluation, diagnosis, and treatment of motor speech disorders in APDs. Results Speech changes in APDs are often mixed dysarthrias with hypokinetic, spastic, and/or ataxic components, and may include apraxia of speech; these are frequently more severe and progress quicker than in Parkinson's disease. These features can assist in differential diagnosis and should prompt early referral to SLPs. Despite the high prevalence of speech and voice changes, comprehensive assessment of motor speech disorders is uncommon in neurology clinics. Current evidence regarding the efficacy of interventions is mixed. Digital acoustic analysis and neuromodulation offer promising directions for diagnosis and treatment. Conclusions Early, collaborative management of motor speech impairment elevates care in APDs. Neurologists and SLPs must work together to improve recognition, diagnosis, and care. Future research should focus on objective biomarkers and personalized therapies to support communication, autonomy, and quality of life for individuals living with APDs.

Purpose: This viewpoint addresses the complexity of the nomenclature of progressive speech-language disorders, which often exists at the intersection of descriptive symptoms, clinical syndromes, and underlying neuropathology. Using primary progressive apraxia of speech (PPAOS) as an example, we explore how overlapping diagnostic frameworks employed by speech-language pathologists, neurologists, and other clinicians and researchers can lead to different diagnostic labels that may evolve throughout disease progression. These discrepancies and changes can complicate patient understanding, clinical care, and affect eligibility for clinical trials. We highlight the importance of diagnostic specificity, flexibility, and multidisciplinary collaboration in improving patient counseling, prognostication, treatment planning, and clinical research alignment, particularly in the context of the growing number of clinical trials and specialized, multidisciplinary clinics. Conclusions: This viewpoint explores how symptom-based, syndrome-based, and pathology-based diagnoses can work together to support patient care and research. Accurate and specific diagnosis of speech-language disorders has important implications for patient understanding of their condition, identity, prognostic accuracy, access to care, clinical trial enrollment, and treatment planning and efficacy. Misdiagnosis or vague labeling can result in inappropriate interventions, delayed support, and skewed research findings. We argue that multidisciplinary collaboration and flexibility in recognizing evolving symptom-based and syndrome-based labels over the disease course—and as the fields learn more—is essential to empower patients with accurate, actionable information. Embracing both the specificity and the complexity of progressive speech-language disorders can both enhance communication and facilitate research.

Abstract Background Primary progressive apraxia of speech (PPAOS) is a frontotemporal lobar degeneration spectrum disorder characterized by progressive speech motor planning/programming decline. Patients often develop neurological symptoms, frequently evolving into progressive supranuclear palsy (PSP)/corticobasal syndrome (CBS) overlap syndromes. Requests for information about disease progression and causes of death are common amongst our clinical and research encounters; however, empiric evidence to guide these discussions is lacking. Understanding causes of death may enhance counseling and support targeted interventions to delay fatal outcomes. This study aimed to describe causes of death in PPAOS. Methods Data from 29 deceased PPAOS patients (16 males) seen in research studies (July 2010–May 2023) were reviewed. Causes of death were established from medical charts and family reports. Fifteen had prosodic and 12 had phonetic‐predominant PPAOS; two had no clear predominance. Median age at onset was 67.08 years, disease duration 9.78 years, and age at death 76.25 years. 28 patients were white and one was Asian Indian. Results Overall, fifteen (52%) patients died from overall deterioration (cachexia/dehydration), nine (31%) from aspiration pneumonia, and the remainder from falls (10%), asphyxiation (3%), or medical aid in dying (3%). There was no clear association with AOS subtype and cause of death. Conclusions Most deaths in PPAOS resulted from systemic failure or dysphagia‐related complications. Half of the patients died without a clear precipitating event, described by family members as “shutting down,” similar to other prior studies that have shown central nervous system deterioration associated with dementia as cause of death. Aspiration pneumonia caused 34% of deaths, highlighting the need for early swallowing management, including assessments, dietary adjustments, and caregiver education. Falls underscore the importance of addressing balance and motor impairments through proactive prevention strategies. Other factors, such as access to healthcare and medical and psychiatric comorbidities were not analyzed but may influence outcomes. Future research should validate these findings in larger, diverse cohorts and compare outcomes to motor‐onset presentations of PSP and CBS. A comprehensive, multidisciplinary approach addressing motor speech impairments, dysphagia, and fall risks is essential to improve care and potentially extend survival for patients with PPAOS.

Abstract Background To promote equitable access to neurologic diagnostics, our team developed MASTERMIND—the Mobile Assessment of Speech To Evaluate, Recognize, and Monitor Indicators of Neurologic health and Disease. One subtest, the MASTERMIND Assessment of Naming, includes ten pictures randomly selected from a list of 45 words varying in frequency. This study aimed to 1) evaluate the feasibility of a remotely self‐administered naming test for patients with cognitive, linguistic, and/or communication difficulties and 2) assess the sensitivity and specificity of performance measures (accuracy, error type, and response time) for detecting aphasia and cognitive‐communication difficulties. Method The task was remotely administered to 138 controls, 26 cognitively impaired individuals, 15 with motor speech disorders, 8 with cognitive concerns, and 6 with aphasia. Participants named pictures displayed on‐screen, advancing through the task by clicking “Next.” Responses were processed using natural language processing (NLP). Audio was transcribed with Whisper Large‐v3, and responses were matched to stimuli within a 0.4‐second threshold. Errors were categorized by type (semantic, phonemic, or unclear source) through consensus between two raters. Performance metrics (accuracy, error type, response time) were analyzed using Kruskal‐Wallis and Pearson's Chi‐Square tests, with AUROC analysis evaluating diagnostic performance. Results Differences in accuracy, response time, and error type were observed across groups (Table 1). Controls and cognitively impaired participants performed similarly, with longer response times observed in patients with aphasia and motor speech disorders. Accuracy alone was a poor predictor of all patients versus controls (AUROC = 0.468) but prediction was better in distinguishing aphasia patients from 1) controls (AUROC = 0.698), 2) cognitively impaired participants (AUROC = 0.679), and 3) those with MSDs (AUROC = 0.670). Conclusions The MASTERMIND Assessment of Naming is a feasible, scalable, self‐administered diagnostic tool enhanced by automated NLP. It provides critical insights through response time, accuracy, and error analysis. Limitations include potential bias in error classification and fixed processing thresholds. The study highlights MASTERMIND's potential for patient triaging and screening. Future research will evaluate the sensitivity of combined performance metrics to disease progression, expand sample sizes and diversity, and refine usability based on participant feedback.

Abstract Background Primary progressive apraxia of speech (PPAOS) is a neurodegenerative disorder affecting speech motor planning/programming. Current interventions for PPAOS are limited and often adapted from treatments for nondegenerative AOS, which rely on intensive practice to maintain speech accuracy for practiced items. Systematic interventions for PPAOS are scarce, and no studies have explored differential effects across AOS subtypes. PPAOS subtypes include those with predominant phonetic (articulatory distortions and substitutions) or prosodic (slow speech rate, segmentation) speech changes. These subtypes likely differ in their neural underpinnings and may respond variably to intervention. Delayed auditory feedback (DAF), which introduces a slight delay in hearing one's speech, has shown promise in altering speech rate and fluency for stuttering and hypokinetic dysarthria, though its effects on stroke‐related AOS have been mixed. Recent studies suggest DAF can increase speech rate in patients with AOS in the context of nonfluent/agrammatic primary progressive aphasia. We hypothesized DAF would improve speech clarity and fluency in PPAOS, with subtype‐specific effects: phonetic PPAOS may see slower, more accurate speech, while prosodic PPAOS may produce faster, less segmented speech. Method Four PPAOS patients (3 prosodic, 1 phonetic) completed a web‐based speech battery with and without DAF. Feedback delay was calibrated individually using the DAF Pro app. Speech metrics were analyzed acoustically, and blinded perceptual ratings of deviant speech features were conducted. Results Key data are summarized in Table 1. Three out of four patients found the DAF helpful and two re‐recorded with DAF to allow for quantitative analysis. Patient 1 produced words faster with DAF but had slower total sentence production times; he did not find it helpful. Patient 2 spoke slower with DAF. Both patients produced AMRs faster with DAF, while SMRs were faster for Patient 1 and slower for Patient 2. Blinded perceptual judgments of speech clarity and prosody revealed mixed effects of DAF across participants. Conclusions Preliminary results suggest DAF may differentially benefit phonetic and prosodic PPAOS, particularly in sentences compared to word production. Further research is needed to assess clinical significance, refine DAF calibration, and explore patient‐perceived improvements with guided practice in a larger, more diverse sample.

Purpose: Model interpretability is a critical requirement for deploying artificial intelligence (AI) applications in clinical speech settings, yet existing evaluation methods often overlook this aspect. The complex nature of speech features and the opaque decision-making processes of AI models underscore the need for a tailored framework to assess interpretability in speech-based clinical AI systems. This article proposes an initial framework composed of eight key factors to consider when evaluating the interpretability of clinical speech AI models. These factors are categorized into two groups: functional factors and clinician-centered factors. The functional factors, which can be assessed independently without user involvement, include faithfulness and computational efficiency. The clinician-centered factors include four established ones from existing literature (cognitive load, human–AI task performance, mental model, and user trust) and two new factors tailored to the unique demands of clinical speech applications: clinical understandability and decision relevance. We further suggest evaluation methods for each of the identified factors and propose modifying existing instruments such as the System Usability Scale and the Healthcare Systems Usability Scale to evaluate the two newly introduced factors. Conclusions: Our identified factors form an initial framework for evaluating the interpretability of speech-based clinical AI systems, supporting the effective integration of AI into clinical workflows. Future works include conducting an evaluation experiment using the proposed framework and refining it further based on the findings from the experiment.

Dysarthric speech poses significant challenges for clinical assessment and diagnosis due to wide variability in the nature and severity of speech impairments associated with it. To address this, this study investigates the phonetic variations in different types of dysarthric speech using the deep learning model Phonet. Recordings of the word “Catastrophe” from 54 patients diagnosed with dysarthria were analyzed to extract posterior probabilities of consonants and vowels. The analysis revealed distinct posterior probability patterns for consonants and vowels across three types of dysarthria, indicating disorder-specific articulatory-based speech characteristics. Moreover, within each dysarthric group, variations in posterior probabilities aligned with the severity of rated articulation difficulty, reflecting nuanced intra-disease phonetic differences. These findings demonstrate Phonet’s ability to capture subtle speech variations and link them to both dysarthria type and severity. By quantifying these phonetic alterations, this study offers a novel and robust framework for objectively evaluating articulation difficulties in dysarthric speech. The results emphasize the clinical potential of Phonet as an advanced tool for improving the diagnosis and monitoring of dysarthria, paving the way for innovative applications in speech-based clinical assessments.

Background: Progressive apraxia of speech (PAOS) is a neurodegenerative disorder most commonly arising from a 4-repeat tauopathy that affects the programming or planning of speech, although many patients develop a Parkinson-plus syndrome. The role of neuroinflammation as a disease mechanism is unknown. We investigated the spatial pattern of neuroinflammation in PAOS using PET and evaluated whether it is associated with disease severity, presence of Parkinson-plus features, and tau PET uptake. Patients and Methods: Twenty-five PAOS patients (13 with Parkinson-plus features) and 30 controls underwent inflammatory 11C-ER176 and tau 18F-flortaucipir PET scans as well as detailed clinical assessments. Multiple linear regression analyses compared the inflammatory standardized uptake value ratio (SUVR) between PAOS and controls and assessed the relationship with clinical test scores. Pearson correlation analysis examined the relationship between inflammatory and tau SUVRs. Results: PAOS had greater neuroinflammatory SUVR than controls in the precentral, supplementary motor area, frontal gyri, putamen, pallidum, subthalamic nucleus, and red nucleus (adjusted P < 0.05). Greater neuroinflammation was associated with worse parkinsonism, and patients who had developed Parkinson-plus features showed a broader pattern of neuroinflammation, extending to prefrontal, temporal and parietal cortices. Neuroinflammation correlated with tau uptake in most (96%) PAOS patients (r > 0.3, P < 0.05). The higher correlation was associated with worse parkinsonism (r = 0.61, P < 0.05) and disease severity (r = 0.54, P < 0.05). Conclusions: 11C-ER176 PET analysis demonstrates neuroinflammation in frontal and subcortical regions in PAOS which is particularly severe and widespread with the development of Parkinson-plus clinical features. The colocalization of neuroinflammation and tau may support a relationship between these disease mechanisms.