Slowed articulation rate is a sensitive diagnostic marker for identifying non-fluent primary progressive aphasia

ABSTRACT

Background: Primary progressive aphasia (PPA) is a neurodegenerative aphasic syndrome with three distinct clinical variants: non-fluent (nfvPPA), logopenic (lvPPA), and semantic (svPPA). Speech (non-) fluency is a key diagnostic marker used to aid identification of the clinical variants, and researchers have been actively developing diagnostic tools to assess speech fluency. Current approaches reveal coarse differences in fluency between subgroups, but often fail to clearly differentiate nfvPPA from the variably fluent lvPPA. More robust subtype differentiation may be possible with finer-grained measures of fluency.

Aims: We sought to identify the quantitative measures of speech rate – including articulation rate and pausing measures – that best differentiated PPA subtypes, specifically the non-fluent group (nfvPPA) from the more fluent groups (lvPPA, svPPA). The diagnostic accuracy of the quantitative speech rate variables was compared to that of a speech fluency impairment rating made by clinicians.

Methods & Procedures: Automatic estimates of pause and speech segment durations and rate measures were derived from connected speech samples of participants with PPA (N = 38; 11 nfvPPA, 14 lvPPA, 13 svPPA) and healthy age-matched controls (N = 8). Clinician ratings of fluency impairment were made using a previously validated clinician rating scale developed specifically for use in PPA. Receiver operating characteristic (ROC) analyses enabled a quantification of diagnostic accuracy.

Outcomes & Results: Among the quantitative measures, articulation rate was the most effective for differentiating between nfvPPA and the more fluent lvPPA and svPPA groups. The diagnostic accuracy of both speech and articulation rate measures was markedly better than that of the clinician rating scale, and articulation rate was the best classifier overall. Area under the curve (AUC) values for articulation rate were good to excellent for identifying nfvPPA from both svPPA (AUC = .96) and lvPPA (AUC = .86). Cross-validation of accuracy results for articulation rate showed good generalisability outside the training dataset.

Conclusions: Results provide empirical support for (1) the efficacy of quantitative assessments of speech fluency and (2) a distinct non-fluent PPA subtype characterised, at least in part, by an underlying disturbance in speech motor control. The trend towards improved classifier performance for quantitative rate measures demonstrates the potential for a more accurate and reliable approach to subtyping in the fluency domain, and suggests that articulation rate may be a useful input variable as part of a multidimensional clinical subtyping approach.

KEYWORDS:

• PPA
• subtyping
• fluency
• speech rate
• articulation rate

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Summary AUC estimates quantify classifier performance across the entire FPR/(1-Specificity) range, the upper limit of which is operationally undesirable for a clinically useful test. Partial AUC analyses allow for the estimation of AUC across a restricted, clinically relevant FPR range (i.e., low FPR).

Additional information

Funding

This research was supported by NIH-NINDS [grant R21-NS077051 (to BCD)], NIH-NIDCD [grants 1R01DC009890 and R01 DC0135470 (to JRG & YY)], and by NIH-NIDCD training [grant 5T32DC000038-23] (supporting CC) and also from Harvard Catalyst |The Harvard Clinical and Translational Science Center (National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health Award UL1 TR001102) and financial contributions from Harvard University and its affiliated academic healthcare centers.