Impaired perceptual discrimination of complex objects in older adults at risk for dementia.

Tau pathology accumulates in the perirhinal cortex (PRC) of the medial temporal lobe (MTL) during the earliest stages of the Alzheimer's disease (AD), appearing decades before clinical diagnosis. Here, we leveraged perceptual discrimination tasks that target PRC function to detect subtle cognitive impairment even in nominally healthy older adults. Older adults who did not have a clinical diagnosis or subjective memory complaints were categorized into "at-risk" (score <26; n = 15) and "healthy" (score ≥26; n = 23) groups based on their performance on the Montreal Cognitive Assessment. The task included two conditions known to recruit the PRC: faces and complex objects (greebles). A scene condition, known to recruit the hippocampus, and a size control condition that does not rely on the MTL were also included. Individuals in the at-risk group were less accurate than those in the healthy group for discriminating greebles. Performance on either the face or size control condition did not predict group status above and beyond that of the greeble condition. Visual discrimination tasks that are sensitive to PRC function may detect early cognitive decline associated with AD.

Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis.

Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of automated acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using an automated assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale-Revised bulbar score (FRS-bulb) (median [interquartile range] of FRS-bulbar scores: 11[3]). The data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver-operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for automated acoustic analyses to detect and stratify ALS.

Development of a Speech-based Composite Score for Remotely Quantifying Language Changes in Frontotemporal Dementia.

BACKGROUND: Changes to speech and language are common symptoms across different subtypes of frontotemporal dementia (FTD). These changes affect the ability to communicate, impacting everyday functions. Accurately assessing these changes may help clinicians to track disease progression and detect response to treatment. OBJECTIVE: To determine which aspects of speech show significant change over time and to develop a novel composite score for tracking speech and language decline in individuals with FTD. METHOD: We recruited individuals with FTD to complete remote digital speech assessments based on a picture description task. Speech samples were analyzed to derive acoustic and linguistic measures of speech and language, which were tested for longitudinal change over the course of the study and were used to compute a novel composite score. RESULTS: Thirty-six (16 F, 20 M; M age = 61.3 years) individuals were enrolled in the study, with 27 completing a follow-up assessment 12 months later. We identified eight variables reflecting different aspects of language that showed longitudinal decline in the FTD clinical syndrome subtypes and developed a novel composite score based on these variables. The resulting composite score demonstrated a significant effect of change over time, high test-retest reliability, and a correlation with standard scores on various other speech tasks. CONCLUSION: Remote digital speech assessments have the potential to characterize speech and language abilities in individuals with FTD, reducing the burden of clinical assessments while providing a novel measure of speech and language abilities that is sensitive to disease and relevant to everyday function.

Hepatic Encephalopathy is Associated With Slow Speech on Objective Assessment.

INTRODUCTION: There are no available low-burden, point-of-care tests to diagnose, grade, and predict hepatic encephalopathy (HE). METHODS: We evaluated speech as a biomarker of HE in 76 English-speaking adults with cirrhosis. RESULTS: Three speech features significantly correlated with the following neuropsychiatric scores: speech rate, word duration, and use of particles. Patients with low neuropsychiatric scores had slower speech (22 words/min, P = 0.01), longer word duration (0.09 seconds/word, P = 0.01), and used fewer particles (0.85% fewer, P = 0.01). Patients with a history of overt HE had slower speech (23 words/min, P = 0.005) and longer word duration (0.09 seconds/word, P = 0.005). DISCUSSION: HE is associated with slower speech.

The contributions of spatial context and imagery to the recollection of single words.

A number of theories of hippocampal function have placed spatial context at the center of richly recollected memories, but the subjective and objective ways that spatial context underlies the recollection of single words has been largely overlooked and underexplained. In this study, we conducted three experiments to investigate the involvement of spatial context in the recollection of single words. In all three experiments, participants encoded single words with varying features such as location and color. The subjective experience of recollection was measured using remember/know judgments and participant self-report of the types of information they recollected about the words. Objectively, recollection was measured using source memory judgments for both spatial and non-spatial features associated with the words. Our results provide evidence that spatial context frequently accompanies the recollection of single, isolated words, reviving discussions on the role of the hippocampus in spatial and detailed recollection.

Hippocampal and Retrosplenial Goal Distance Coding After Long-term Consolidation of a Real-World Environment.

Recent research indicates the hippocampus may code the distance to the goal during navigation of newly learned environments. It is unclear however, whether this also pertains to highly familiar environments where extensive systems-level consolidation is thought to have transformed mnemonic representations. Here we recorded fMRI while University College London and Imperial College London students navigated virtual simulations of their own familiar campus (>2 years of exposure) and the other campus learned days before scanning. Posterior hippocampal activity tracked the distance to the goal in the newly learned campus, as well as in familiar environments when the future route contained many turns. By contrast retrosplenial cortex only tracked the distance to the goal in the familiar campus. All of these responses were abolished when participants were guided to their goal by external cues. These results open new avenues of research on navigation and consolidation of spatial information and underscore the notion that the hippocampus continues to play a role in navigation when detailed processing of the environment is needed for navigation.

Scenes facilitate associative memory and integration.

How do we form mental links between related items? Forming associations between representations is a key feature of episodic memory and provides the foundation for learning and guiding behavior. Theories suggest that spatial context plays a supportive role in episodic memory, providing a scaffold on which to form associations, but this has mostly been tested in the context of autobiographical memory. We examined the memory boosting effect of spatial stimuli in memory using an associative inference paradigm combined with eye-tracking. Across two experiments, we found that memory was better for associations that included scenes, even indirectly, compared to objects and faces. Eye-tracking measures indicated that these effects may be partly mediated by greater fixations to scenes compared to objects, but did not explain the differences between scenes and faces. These results suggest that scenes facilitate associative memory and integration across memories, demonstrating evidence in support of theories of scenes as a spatial scaffold for episodic memory. A shared spatial context may promote learning and could potentially be leveraged to improve learning and memory in educational settings or for memory-impaired populations.

The Primacy of Spatial Context in the Neural Representation of Events.

Some theories of episodic memory hypothesize that spatial context plays a fundamental role in episodic memory, acting as a scaffold on which episodes are constructed. A prediction based on this hypothesis is that spatial context should play a primary role in the neural representation of an event. To test this hypothesis in humans, male and female participants imagined events, composed of familiar locations, people, and objects, during an fMRI scan. We used multivoxel pattern analysis to determine the neural areas in which events could be discriminated based on each feature. We found that events could be discriminated according to their location in areas throughout the autobiographical memory network, including the parahippocampal cortex and posterior hippocampus, retrosplenial cortex, posterior cingulate cortex, precuneus, and medial prefrontal cortex. Events were also discriminable based on person and object features, but in fewer regions. Comparing classifier performance in regions involved in memory for scenes and events demonstrated that the location of an event was more accurately classified than the person or object involved. These results support theories that suggest that spatial context is a prominent defining feature of episodic memory.SIGNIFICANCE STATEMENT Remembered and imagined events are complex, consisting of many elements, including people, objects, and locations. In this study, we sought to determine how these types of elements differentially contribute to how the brain represents an event. Participants imagined events consisting of familiar locations, people, and objects (e.g., kitchen, mom, umbrella) while their brain activity was recorded with fMRI. We found that the neural patterns of activity in brain regions associated with spatial and episodic memory could distinguish events based on their location, and to some extent, based on the people and objects involved. These results suggest that the spatial context of an event plays an important role in how an event is represented in the brain.

Category specificity in the medial temporal lobe: A systematic review.

Theoretical accounts of medial temporal lobe (MTL) function ascribe different functions to subregions of the MTL including perirhinal, entorhinal, parahippocampal cortices, and the hippocampus. Some have suggested that the functional roles of these subregions vary in terms of their category specificity, showing preferential coding for certain stimulus types, but the evidence for this functional organization is mixed. In this systematic review, we evaluate existing evidence for regional specialization in the MTL for three categories of visual stimuli: faces, objects, and scenes. We review and synthesize across univariate and multivariate neuroimaging studies, as well as neuropsychological studies of cases with lesions to the MTL. Neuroimaging evidence suggests that faces activate the perirhinal cortex, entorhinal cortex, and the anterior hippocampus, while scenes engage the parahippocampal cortex and both the anterior and posterior hippocampus, depending on the contrast condition. There is some evidence for object-related activity in anterior MTL regions when compared to scenes, and in posterior MTL regions when compared to faces, suggesting that aspects of object representations may share similarities with face and scene representations. While neuroimaging evidence suggests some hippocampal specialization for faces and scenes, neuropsychological evidence shows that hippocampal damage leads to impairments in scene memory and perception, but does not entail equivalent impairments for faces in cases where the perirhinal cortex remains intact. Regional specialization based on stimulus categories has implications for understanding the mechanisms of MTL subregions, and highlights the need for the development of theoretical models of MTL function that can accommodate the differential patterns of specificity observed in the MTL.

Cognitive mapping style relates to posterior-anterior hippocampal volume ratio.

As London taxi drivers acquire "the knowledge" and develop a detailed cognitive map of London, their posterior hippocampi (pHPC) gradually increase in volume, reflecting an increasing pHPC/aHPC volume ratio. In the mnemonic domain, greater pHPC/aHPC volume ratios in young adults have been found to relate to better recollection ability, indicating that the balance between pHPC and aHPC volumes might be reflective of cross-domain individual differences. Here, we examined participants' self-reported use of cognitive map-based navigational strategies in relation to their pHPC/aHPC hippocampal volume ratio. We find that greater reported cognitive map use was related to significantly greater posterior, relative to anterior, hippocampal volume in two separate samples of young adults. Further, greater reported cognitive map usage correlated with better performance on a self-initiated navigation task. Together, these data help to advance our understanding of differences between aHPC and pHPC and the greater role of pHPC in spatial mapping.

Functional connectivity of hippocampal and prefrontal networks during episodic and spatial memory based on real-world environments.

Several recent studies have compared episodic and spatial memory in neuroimaging paradigms in order to understand better the contribution of the hippocampus to each of these tasks. In the present study, we build on previous findings showing common neural activation in default network areas during episodic and spatial memory tasks based on familiar, real-world environments (Hirshhorn et al. (2012) Neuropsychologia 50:3094-3106). Following previous demonstrations of the presence of functionally connected sub-networks within the default network, we performed seed-based functional connectivity analyses to determine how, depending on the task, the hippocampus and prefrontal cortex differentially couple with one another and with distinct whole-brain networks. We found evidence for a medial prefrontal-parietal network and a medial temporal lobe network, which were functionally connected to the prefrontal and hippocampal seeds, respectively, regardless of the nature of the memory task. However, these two networks were functionally connected with one another during the episodic memory task, but not during spatial memory tasks. Replicating previous reports of fractionation of the default network into stable sub-networks, this study also shows how these sub-networks may flexibly couple and uncouple with one another based on task demands. These findings support the hypothesis that episodic memory and spatial memory share a common medial temporal lobe-based neural substrate, with episodic memory recruiting additional prefrontal sub-networks.

Cognitive components of aging-related increase in word-finding difficulty.

Word-finding difficulty (WFD) is a common cognitive complaint in aging, manifesting both in natural speech and in controlled laboratory tests. Various theories of cognitive aging have addressed WFD, and understanding its underlying mechanisms can help to clarify whether it has diagnostic value for neurodegenerative disease. Two influential "information-universal" theories attribute it to rather broad changes in cognition. The processing speed theory posits a general slowdown of all cognitive processes, while the inhibitory deficit hypothesis (IDH) predicts a specific problem in suppressing irrelevant information. One "information specific" theory of language production, the transmission deficit hypothesis (TDH), posits a breakdown in retrieval of phonological word forms from a corresponding lemma. To adjudicate between these accounts, we administered an online gamified covert naming task featuring picture-word interference (PWI), previously validated to elicit similar semantic interference and phonological facilitation effects as overt naming tasks. 125 healthy adults aged 18 to 85 completed the task, along with a battery of executive function tasks and a naturalistic speech sample to quantify WFD in connected speech. PWI effects provided strong support for the TDH but limited support for IDH, in that semantic interference increased and phonological facilitation decreased across the lifespan. However, neither of these effects on single-word retrieval associated with WFD measured in connected speech. Rather, overall reaction time for word retrieval (controlling for psychomotor slowing) was the best predictor of spontaneous WFD and executive function decline, suggesting processing speed as the key factor, and that verbal reaction time may be an important clinical measure.

Linguistic changes in neurodegenerative diseases relate to clinical symptoms.

Background: The detection and characterization of speech changes may help in the identification and monitoring of neurodegenerative diseases. However, there is limited research validating the relationship between speech changes and clinical symptoms across a wide range of neurodegenerative diseases. Method: We analyzed speech recordings from 109 patients who were diagnosed with various neurodegenerative diseases, including Alzheimer's disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 speech features, including the acoustics of speech and linguistic properties of spoken language. We investigated the relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Mattis Dementia Rating Scale (DRS), Western Aphasia Battery (WAB), and Boston Naming Task (BNT) in a heterogeneous patient population. Result: Linguistic features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE and DRS scores were associated with the use of shorter words and fewer prepositional phrases. Increased impairment on WAB and BNT was correlated with the use of fewer nouns but more pronouns. Patients also differed from healthy adults as their speech duration was significantly shorter with more pauses. Conclusion: Linguistic changes such as the use of simpler vocabularies and syntax were detectable in patients with different neurodegenerative diseases and correlated with cognitive decline. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.

The Generalizability of Longitudinal Changes in Speech Before Alzheimer's Disease Diagnosis.

BACKGROUND: Language impairment in Alzheimer's disease (AD) has been widely studied but due to limited data availability, relatively few studies have focused on the longitudinal change in language in the individuals who later develop AD. Significant differences in speech have previously been found by comparing the press conference transcripts of President Bush and President Reagan, who was later diagnosed with AD. OBJECTIVE: In the current study, we explored whether the patterns previously established in the single AD-healthy control (HC) participant pair apply to a larger group of individuals who later receive AD diagnosis. METHODS: We replicated previous methods on two larger corpora of longitudinal spontaneous speech samples of public figures, consisting of 10 and 9 AD-HC participant pairs. As we failed to find generalizable patterns of language change using previous methodology, we proposed alternative methods for data analysis, investigating the benefits of using different language features and their change with age, and compiling the single features into aggregate scores. RESULTS: The single features that showed the strongest results were moving average type:token ratio (MATTR) and pronoun-related features. The aggregate scores performed better than the single features, with lexical diversity capturing a similar change in two-thirds of the participants. CONCLUSION: Capturing universal patterns of language change prior to AD can be challenging, but the decline in lexical diversity and changes in MATTR and pronoun-related features act as promising measures that reflect the cognitive changes in many participants.

How Much Speech Data Is Needed for Tracking Language Change in Alzheimer's Disease? A Comparison of Random Length, 5-Min, and 1-Min Spontaneous Speech Samples.

Introduction: Changes in speech can act as biomarkers of cognitive decline in Alzheimer's disease (AD). While shorter speech samples would promote data collection and analysis, the minimum length of informative speech samples remains debated. This study aims to provide insight into the effect of sample length in analyzing longitudinal recordings of spontaneous speech in AD by comparing the original random length, 5- and 1-minute-long samples. We hope to understand whether capping the audio improves the accuracy of the analysis, and whether an extra 4 min conveys necessary information. Methods: 110 spontaneous speech samples were collected from decades of Youtube videos of 17 public figures, 9 of whom eventually developed AD. 456 language features were extracted and their text-length-sensitivity, comparability, and ability to capture change over time were analyzed across three different sample lengths. Results: Capped audio files had advantages over the random length ones. While most extracted features were statistically comparable or highly correlated across the datasets, potential effects of sample length should be acknowledged for some features. The 5-min dataset presented the highest reliability in tracking the evolution of the disease, suggesting that the 4 extra minutes do convey informative data. Conclusion: Sample length seems to play an important role in extracting the language feature values from speech and tracking disease progress over time. We highlight the importance of further research into optimal sample length and standardization of methods when studying speech in AD.

Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis.

Home-based speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. Furthermore, these challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of remote-capable acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using a remote-capable assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale - Revised bulbar score (FRS-bulb). Data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for remote acoustic analyses to detect and stratify ALS.

Evaluating the utility of daily speech assessments for monitoring depression symptoms.

Objective: Depression is a common mental health disorder and a major public health concern, significantly interfering with the lives of those affected. The complex clinical presentation of depression complicates symptom assessments. Day-to-day fluctuations of depression symptoms within an individual bring an additional barrier, since infrequent testing may not reveal symptom fluctuation. Digital measures such as speech can facilitate daily objective symptom evaluation. Here, we evaluated the effectiveness of daily speech assessment in characterizing speech fluctuations in the context of depression symptoms, which can be completed remotely, at a low cost and with relatively low administrative resources. Methods: Community volunteers (N = 16) completed a daily speech assessment, using the Winterlight Speech App, and Patient Health Questionnaire-9 (PHQ-9) for 30 consecutive business days. We calculated 230 acoustic and 290 linguistic features from individual's speech and investigated their relationship to depression symptoms at the intra-individual level through repeated measures analyses. Results: We observed that depression symptoms were linked to linguistic features, such as less frequent use of dominant and positive words. Greater depression symptomatology was also significantly correlated with acoustic features: reduced variability in speech intensity and increased jitter. Conclusions: Our findings support the feasibility of using acoustic and linguistic features as a measure of depression symptoms and propose daily speech assessment as a tool for better characterization of symptom fluctuations.

PROSA-a multicenter prospective observational study to develop low-burden digital speech biomarkers in ALS and FTD.

Objective: There is a need for novel biomarkers that can indicate disease state, project disease progression, or assess response to treatment for amyotrophic lateral sclerosis (ALS) and associated neurodegenerative diseases such as frontotemporal dementia (FTD). Digital biomarkers are especially promising as they can be collected non-invasively and at low burden for patients. Speech biomarkers have the potential to objectively measure cognitive, motor as well as respiratory symptoms at low-cost and in a remote fashion using widely available technology such as telephone calls. Methods: The PROSA study aims to develop and evaluate low-burden frequent prognostic digital speech biomarkers. The main goal is to create a single, easy-to-perform battery that serves as a valid and reliable proxy for cognitive, respiratory, and motor domains in ALS and FTD. The study will be a multicenter 12-months observational study aiming to include 75 ALS and 75 FTD patients as well as 50 healthy controls and build on three established longitudinal cohorts: DANCER, DESCRIBE-ALS and DESCRIBE-FTD. In addition to the extensive clinical phenotyping in DESCRIBE, PROSA collects a comprehensive speech protocol in fully remote and automated fashion over the telephone at four time points. This longitudinal speech data, together with gold standard measures, will allow advanced speech analysis using artificial intelligence for the development of speech-based phenotypes of ALS and FTD patients measuring cognitive, motor and respiratory symptoms. Conclusion: Speech-based phenotypes can be used to develop diagnostic and prognostic models predicting clinical change. Results are expected to have implications for future clinical trial stratification as well as supporting innovative trial designs in ALS and FTD.

Automated detection of progressive speech changes in early Alzheimer's disease.

Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech-based composite scores could be used to monitor change and detect response to treatment in future research. HIGHLIGHTS: Longitudinal speech samples were analyzed to characterize speech changes in early AD.Acoustic and linguistic measures showed significant change over 18 months.A novel speech composite score was computed to characterize longitudinal change.The speech composite correlated with primary and secondary trial endpoints.Automated speech analysis could facilitate remote, high frequency monitoring in AD.

Validation of automated pipeline for the assessment of a motor speech disorder in amyotrophic lateral sclerosis (ALS).

Background and objective: Amyotrophic lateral sclerosis (ALS) frequently causes speech impairments, which can be valuable early indicators of decline. Automated acoustic assessment of speech in ALS is attractive, and there is a pressing need to validate such tools in line with best practices, including analytical and clinical validation. We hypothesized that data analysis using a novel speech assessment pipeline would correspond strongly to analyses performed using lab-standard practices and that acoustic features from the novel pipeline would correspond to clinical outcomes of interest in ALS. Methods: We analyzed data from three standard speech assessment tasks (i.e., vowel phonation, passage reading, and diadochokinesis) in 122 ALS patients. Data were analyzed automatically using a pipeline developed by Winterlight Labs, which yielded 53 acoustic features. First, for analytical validation, data were analyzed using a lab-standard analysis pipeline for comparison. This was followed by univariate analysis (Spearman correlations between individual features in Winterlight and in-lab datasets) and multivariate analysis (sparse canonical correlation analysis (SCCA)). Subsequently, clinical validation was performed. This included univariate analysis (Spearman correlation between automated acoustic features and clinical measures) and multivariate analysis (interpretable autoencoder-based dimensionality reduction). Results: Analytical validity was demonstrated by substantial univariate correlations (Spearman's ρ > 0.70) between corresponding pairs of features from automated and lab-based datasets, as well as interpretable SCCA feature groups. Clinical validity was supported by strong univariate correlations between automated features and clinical measures (Spearman's ρ > 0.70), as well as associations between multivariate outputs and clinical measures. Conclusion: This novel, automated speech assessment feature set demonstrates substantial promise as a valid tool for analyzing impaired speech in ALS patients and for the further development of these technologies.