Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

Quantitative Speech Assessment in Ataxia-Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Markers

Dysarthria is a common and debilitating symptom of many neurodegenerative diseases, including those resulting in ataxia. Changes to speech lead to significant reductions in quality of life, impacting the speaker in most daily activities. Recognition of its importance as an objective outcome measure in clinical trials for ataxia is growing. Its viability as an endpoint across the disease spectrum (i.e. pre-symptomatic onwards) means that trials can recruit ambulant individuals and later-stage individuals who are often excluded because of difficulty completing lower limb tasks. Here we discuss the key considerations for speech testing in clinical trials including hardware selection, suitability of tasks and their role in protocols for trials and propose a core set of tasks for speech testing in clinical trials. Test batteries could include forms suitable for remote short, sensitive and easy to use, with norms available in several languages. The use of artificial intelligence also could improve accuracy and automaticity of analytical pipelines in clinic and trials.

Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers.

Pausing and fluency in speech of patients with relapsing-remitting multiple sclerosis

Multiple Sclerosis (MS) causes a variety of symptoms in speech production, such as more frequent pauses and an increase in the duration of pauses in the speech. However, there is almost no data on whether the disease affects speech fluency in other ways, such as changes in the frequency of disfluencies in speech. The main question of this study is the following: if we examine speech fluency in speech tasks requiring different cognitive load, will there be a difference between patients and controls? Twenty people with relapsing-remitting MS (3 men and 17 women) and 20 age- and education-matched control speakers (4 men and 16 women) participated in the study. Speech samples were recorded with each participant in three speech tasks: 1) spontaneous narratives about their own lives, 2) narratives about their previous day, and 3) narrative recalls based on a heard text. In the speech samples, pauses and disfluencies were annotated and the duration of pauses was measured. Then, the frequency of pauses and disfluencies were calculated and the types of disfluencies were examined. The results show that there are differences in the frequency and duration of pauses between people with MS and controls. However, there were no significant differences in the frequency of disfluencies between the groups. The same types of disfluencies occurred in the same frequency in both groups. The results help to better understand the speech production processes in MS.

Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics

Neurodegenerative disease often affects speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to delineate healthy speech from speech of people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We subjected 74 spectral and temporal prosodic features from the speech recordings to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.

Speech deficits in multiple sclerosis: a narrative review of the existing literature

Multiple sclerosis (MS) is a chronic inflammatory and demyelinating autoimmune disease. MS patients deal with motor and sensory impairments, visual disabilities, cognitive disorders, and speech and language deficits. The study aimed to record, enhance, update, and delve into our present comprehension of speech deficits observed in patients with MS and the methodology (assessment tools) studies followed. The method used was a search of the literature through the databases for May 2015 until June 2022. The reviewed studies offer insight into speech impairments most exhibited by MS patients. Patients with MS face numerous communication changes concerning the phonation system (changes observed concerning speech rate, long pause duration) and lower volume. Moreover, the articulation system was affected by the lack of muscle synchronization and inaccurate pronunciations, mainly of vowels. Finally, there are changes regarding prosody (MS patients exhibited monotonous speech). Findings indicated that MS patients experience communication changes across various domains. Based on the reviewed studies, we concluded that the speech system of MS patients is impaired to some extent, and the patients face many changes that impact their conversational ability and the production of slower and inaccurate speech. These changes can affect MS patients' quality of life.

Articulatory speech measures can be related to the severity of multiple sclerosis

Background

Dysarthria is one of the most frequent communication disorders in patients with Multiple Sclerosis (MS), with an estimated prevalence of around 50%. However, it is unclear if there is a relationship between dysarthria and the severity or duration of the disease.

Objective

Describe the speech pattern in MS, correlate with clinical data, and compare with controls.

Methods

A group of MS patients (n = 73) matched to healthy controls (n = 37) by sex and age. Individuals with neurological and/or systemic conditions that could interfere with speech were excluded. MS group clinical data were obtained through the analysis of medical records. The speech assessment consisted of auditory-perceptual and speech acoustic analysis, from recording the following speech tasks: phonation and breathing (sustained vowel/a/); prosody (sentences with different intonation patterns) and articulation (diadochokinesis; spontaneous speech; diphthong/iu/repeatedly).

Results

In MS, 72.6% of the individuals presented mild dysarthria, with alterations in speech subsystems: phonation, breathing, resonance, and articulation. In the acoustic analysis, individuals with MS were significantly worse than the control group (CG) in the variables: standard deviation of the fundamental frequency (p = 0.001) and maximum phonation time (p = 0.041). In diadochokinesis, individuals with MS had a lower number of syllables, duration, and phonation time, but larger pauses per seconds, and in spontaneous speech, a high number of pauses were evidenced as compared to CG. Correlations were found between phonation time in spontaneous speech and the Expanded Disability Status Scale (EDSS) (r = - 0.238, p = 0.043) and phonation ratio in spontaneous speech and EDSS (r = -0.265, p = 0.023), which indicates a correlation between the number of pauses during spontaneous speech and the severity of the disease.

Conclusion

The speech profile in MS patients was mild dysarthria, with a decline in the phonatory, respiratory, resonant, and articulatory subsystems of speech, respectively, in order of prevalence. The increased number of pauses during speech and lower rates of phonation ratio can reflect the severity of MS.

Word finding, prosody and social cognition in multiple sclerosis

BACKGROUND

Impairments in speech and social cognition have been reported in people with multiple sclerosis (pwMS), although their relationships with neuropsychological outcomes and their clinical utility in MS are unclear.

OBJECTIVES

To evaluate word finding, prosody and social cognition in pwMS relative to healthy controls (HC).

METHODS

We recruited people with relapsing MS (RMS, n = 21), progressive MS (PMS, n = 24) and HC (n = 25) from an outpatient MS clinic. Participants completed a battery of word-finding, social cognitive, neuropsychological and clinical assessments and performed a speech task for prosodic analysis.

RESULTS

Of 45 pwMS, mean (SD) age was 49.4 (9.4) years, and median (range) Expanded Disability Severity Scale score was 3.5 (1.0-6.5). Compared with HC, pwMS were older and had slower information processing speed (measured with the Symbol Digit Modalities Test, SDMT) and higher depression scores. Most speech and social cognitive measures were associated with information processing speed but not with depression. Unlike speech, social cognition consistently correlated with intelligence and memory. Visual naming test mean response time (VNT-MRT) demonstrated worse outcomes in MS versus HC (p = .034, Nagelkerke's R²  = 65.0%), and in PMS versus RMS (p = .009, Nagelkerke's R²  = 50.2%). Rapid automatised object naming demonstrated worse outcomes in MS versus HC (p = .014, Nagelkerke's R²  = 49.1%). These word-finding measures showed larger effect sizes than that of the SDMT (MS vs. HC, p = .010, Nagelkerke's R²  = 40.6%; PMS vs. RMS, p = .023, Nagelkerke's R²  = 43.5%). Prosody and social cognition did not differ between MS and HC.

CONCLUSIONS

Word finding, prosody and social cognition in MS are associated with information processing speed and largely independent of mood. Impairment in visual object meaning perception is potentially a unique MS disease-related deficit that could be further explored and cautiously considered as an adjunct disability metric for MS.

Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study

BACKGROUND

An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria.

METHOD

The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve.

RESULTS

The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant.

CONCLUSION

Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

Multiple sclerosis (MS) is a progressive disease that often affects the cerebellum. It is characterised by demyelination, inflammation, and neurodegeneration within the central nervous system. Damage to the cerebellum in MS is associated with increased disability and decreased quality of life. Symptoms include gait and balance problems, motor speech disorder, upper limb dysfunction, and oculomotor difficulties. Monitoring symptoms is crucial for effective management of MS. A combination of clinical, neuroimaging, and task-based measures is generally used to diagnose and monitor MS. This paper reviews the present and new tools used by clinicians and researchers to assess cerebellar impairment in people with MS (pwMS). It also describes recent advances in digital and home-based monitoring for people with MS.

Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

PURPOSE

The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis.

METHOD

Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate.

RESULTS

Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions.

DISCUSSION

A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.

Adults with a history of recreational cannabis use have altered speech production

Stereotypical depictions of speech in cannabis users often suggest slow, laboured output, yet objective evidence supporting this assumption is extremely limited. We know that depressants or hallucinogenic drugs such as cannabis can cause acute changes in communication and speech rate, but the long-lasting effects of cannabis use on speech are not well described. The aim of this study was to investigate speech in individuals with a history of recreational cannabis use compared to non-drug-using healthy controls. Speech samples were collected from a carefully described cohort of 31 adults with a history of cannabis use (but not use of illicit stimulant drugs) and 40 non-drug-using controls. Subjects completed simple and complex speech tasks including a monologue, a sustained vowel, saying the days of the week, and reading a phonetically balanced passage. Audio samples were analysed objectively using acoustic analysis for measures of timing, vocal control, and quality. Subtle differences in speech timing, vocal effort, and voice quality may exist between cannabis and control groups, however data remain equivocal. After controlling for lifetime alcohol and tobacco use and applying a false discovery rate, only spectral tilt (vocal effort and intensity) differed between groups and appeared to change in line with duration of abstinence from cannabis use. Differences between groups may reflect longer term changes to the underlying neural control of speech. Our digital analysis of speech shows there may be a signal differentiating individuals with a history of recreational cannabis use from healthy controls, in line with similar findings from gait and hand function studies.