Evaluating three stuttering assessments through network analysis, random forests and cluster analysis

Ward, David; Miller, Ronan; Nikolaev, Alexandre

doi:10.1016/j.jfludis.2020.105823

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…We did find a relationship in the expected direction; however, the correlation was weak for each of the four OASES domains and for the OASES overall impact score. This finding is in line with Ward et al (2021), who identified a gap between behavioral and social measures of stuttering in commonly used stuttering assessment tools. This also suggests that the subjective impact of stuttering is influenced by other factors beyond stuttering severity that were not assessed in this study and could be further explored in future research.…”

Section: Discussionsupporting

confidence: 90%

Self-Reported Stuttering Severity Is Accurate: Informing Methods for Large-Scale Data Collection in Stuttering

Horton,

Jackson,

Boyce

et al. 2024

J Speech Lang Hear Res

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Purpose: To our knowledge, there are no data examining the agreement between self-reported and clinician-rated stuttering severity. In the era of big data, self-reported ratings have great potential utility for large-scale data collection, where cost and time preclude in-depth assessment by a clinician. Equally, there is increasing emphasis on the need to recognize an individual's experience of their own condition. Here, we examined the agreement between self-reported stuttering severity compared to clinician ratings during a speech assessment. As a secondary objective, we determined whether self-reported stuttering severity correlated with an individual's subjective impact of stuttering. Method: Speech-language pathologists conducted face-to-face speech assessments with 195 participants (137 males) aged 5–84 years, recruited from a cohort of people with self-reported stuttering. Stuttering severity was rated on a 10-point scale by the participant and by two speech-language pathologists. Participants also completed the Overall Assessment of the Subjective Experience of Stuttering (OASES). Clinician and participant ratings were compared. The association between stuttering severity and the OASES scores was examined. Results: There was a strong positive correlation between speech-language pathologist and participant-reported ratings of stuttering severity. Participant-reported stuttering severity correlated weakly with the four OASES domains and with the OASES overall impact score. Conclusions: Participants were able to accurately rate their stuttering severity during a speech assessment using a simple one-item question. This finding indicates that self-report stuttering severity is a suitable method for large-scale data collection. Findings also support the collection of self-report subjective experience data using questionnaires, such as the OASES, which add vital information about the participants' experience of stuttering that is not captured by overt speech severity ratings alone.

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Section: Discussionsupporting

confidence: 90%

Self-Reported Stuttering Severity Is Accurate: Informing Methods for Large-Scale Data Collection in Stuttering

Horton,

Jackson,

Boyce

et al. 2024

J Speech Lang Hear Res

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“…44,45 The small number of individuals per delivery mode (24 for vaginal delivery and 19 for cesarean) might limit the power needed to detect effects. However, other studies have demonstrated RFs to be useful with small sample sizes, including 26 46 , 30 47 and 35 48 samples. Because RFs can perform internal validation using subsets of data and out-of-bag (OOB) scores, there is less concern associated with small datasets.…”

Section: Discussionmentioning

confidence: 99%

EXPLANA: A user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies

Fouquier,

Stanislawski,

O’Connor

et al. 2024

Preprint

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The potential for disease treatment through gut microbiome modification has contributed to an increase in longitudinal microbiome studies (LMS). Gut microbiome modification can occur through factors such as diet, probiotics, or fecal transplants. Scientific data often motivates researchers to perform exploratory analyses to identify features that relate to a response. However, LMS are challenging to analyze, often leading to lost information and research barriers. LMS analytic challenges include data integration, compositionality, dimensionality reduction, and the need for mixed-effects models for non-independent data. Additionally, LMS can be observational or interventional, and relevant comparisons of interest might differ for these two study types. For example, in an observational study, measurements are made over time and show natural fluctuations in symptoms/measurements, so the baseline measurement might not be a reference point of primary interest; whereas, in an interventional study, the baseline value often coincides with the start of treatment and is a key reference point. Thus, the optimal way to calculate feature changes for each subject over time is dependent on different reference values. To address these challenges, we developed EXPLANA, a data-driven feature-selection workflow that supports numerical and categorical data. We implemented machine-learning models for repeated measures, feature-selection methods, and visualizers explaining how selected features relate to the response. With one script, analysts can build models to select and evaluate important features and obtain an analytic report that textually and graphically summarizes results. EXPLANA had good performance using twenty simulated data models yielding an average area under the curve (AUC) of 0.91 (range: 0.79-1.0; SD = 0.05) and better performance compared to an existing tool (AUC: 0.95 and 0.56; precision: 0.82, and 0.14, respectively). EXPLANA is a flexible, data-driven tool that simplifies LMS analyses and can identify unique features that are predictive of outcomes of interest through a straightforward workflow.

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“…The Overall Assessment of the Speaker’s Experience of Stuttering (OASES) was used to assess the impact of stuttering from the speaker’s perspective. 36 These two measures—the objective speech measure calculated by the clinician and the subjective impact measure reported by the participant—were not highly correlated (Spearman’s rank correlation test r s = −0.02, P = 0.93), reflecting the discrepancy between clinician scores of observed speech behaviours and self-perceived impact, which is commonly reported in persistent developmental stuttering 37 ( Supplementary Table 2 ).…”

Section: Methodsmentioning

confidence: 99%

Localization of stuttering based on causal brain lesions

Theys,

Jaakkola,

Melzer

et al. 2024

Brain

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Stuttering affects approximately 1 in 100 adults and can result in significant communication problems and social anxiety. It most often occurs as a developmental disorder but can also be caused by focal brain damage. These latter cases may lend unique insight into the brain regions causing stuttering. Here, we investigated the neuroanatomical substrate of stuttering using three independent datasets: (i) case reports from the published literature of acquired neurogenic stuttering following stroke (n = 20, 14 males/six females, 16–77 years); (ii) a clinical single study cohort with acquired neurogenic stuttering following stroke (n = 20, 13 males/seven females, 45–87 years); and (iii) adults with persistent developmental stuttering (n = 20, 14 males/six females, 18–43 years). We used the first two datasets and lesion network mapping to test whether lesions causing acquired stuttering map to a common brain network. We then used the third dataset to test whether this lesion-based network was relevant to developmental stuttering. In our literature dataset, we found that lesions causing stuttering occurred in multiple heterogeneous brain regions, but these lesion locations were all functionally connected to a common network centred around the left putamen, including the claustrum, amygdalostriatal transition area and other adjacent areas. This finding was shown to be specific for stuttering (PFWE < 0.05) and reproducible in our independent clinical cohort of patients with stroke-induced stuttering (PFWE < 0.05), resulting in a common acquired stuttering network across both stroke datasets. Within the common acquired stuttering network, we found a significant association between grey matter volume and stuttering impact for adults with persistent developmental stuttering in the left posteroventral putamen, extending into the adjacent claustrum and amygdalostriatal transition area (PFWE < 0.05). We conclude that lesions causing acquired neurogenic stuttering map to a common brain network, centred to the left putamen, claustrum and amygdalostriatal transition area. The association of this lesion-based network with symptom severity in developmental stuttering suggests a shared neuroanatomy across aetiologies.

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Evaluating three stuttering assessments through network analysis, random forests and cluster analysis

Cited by 11 publications

References 39 publications

Self-Reported Stuttering Severity Is Accurate: Informing Methods for Large-Scale Data Collection in Stuttering

Self-Reported Stuttering Severity Is Accurate: Informing Methods for Large-Scale Data Collection in Stuttering

EXPLANA: A user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies

Localization of stuttering based on causal brain lesions

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