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Automated Detection of Speech Timing Alterations in Autopsy-Confirmed Nonfluent/Agrammatic Variant Primary
Adolfo M García1, Ariane E Welch1, Maria Luisa Mandelli1
1From the Global Brain Health Institute (A.M.G.), University of California, San Francisco; Cognitive Neuroscience Center (A.M.G.), Universidad de San Andrés, Buenos Aires; National Scientific and Technical Research Council (CONICET) (A.M.G.), Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades (A.M.G.), Universidad de Santiago de Chile; Memory and Aging Center (A.E.W., M.L.M., S.L., J.D., B.M.R., D.L.L.P., B.L.M., W.S., M.L.G.-T.), Department of Neurology, University of California, San Francisco; Department of Communication Sciences and Disorders (M.L.H.), University of Texas at Austin; Department of Communication Sciences and Disorders (S.L.), Adelphi University, Garden City, NY; Cognitive Neurology and Aphasia Unit (M.J.T.P.), Centro de Investigaciones Médico-Sanitarias (M.J.T.P.), University of Malaga; Instituto de Investigación Biomédica de Málaga - IBIMA (M.J.T.P.), Malaga; Area of Psychobiology (M.J.T.P.), Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain; Sección Neurología (D.L.L.P.), Departamento de Especialidades, Facultad de Medicina, Universidad de Concepción, Chile; Centre for Neuroscience of Speech (A.P.V.), Department of Audiology & Speech Pathology, The University of Melbourne; and Redenlab (A.P.V.), Melbourne, Australia.
Background And Objectives:
Motor speech function, including speech timing, is a key domain for diagnosing nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard assessments use subjective, specialist-dependent evaluations, undermining reliability and scalability. Moreover, few studies have examined relevant anatomo-clinical alterations in patients with pathologically confirmed diagnoses. This study overcomes such caveats using automated speech timing analyses in a unique cohort of autopsy-proven cases.
Methods:
In a cross-sectional study, we administered an overt reading task and quantified articulation rate, mean syllable and pause duration, and syllable and pause duration variability. Neuroanatomical disruptions were assessed using cortical thickness and white matter (WM) atrophy analysis.
Results:
We evaluated 22 persons with nfvPPA (mean age: 67.3 years; 13 female patients) and confirmed underlying 4-repeat tauopathy, 15 persons with semantic variant primary progressive aphasia (svPPA; mean age: 66.5 years; 8 female patients), and 10 healthy controls (HCs; 70 years; 5 female patients). All 5 speech timing measures revealed alterations in persons with nfvPPA relative to both the HC and svPPA groups, controlling for dementia severity. The articulation rate robustly discriminated individuals with nfvPPA from HCs (area under the ROC curve [AUC] = 0.95), outperforming specialist-dependent perceptual measures of dysarthria and apraxia of speech severity. Patients with nfvPPA exhibited structural abnormalities in left precentral and middle frontal as well as bilateral superior frontal regions, including their underlying WM. The articulation rate correlated with atrophy of the left pars opercularis and supplementary/presupplementary motor areas. Secondary analyses showed that, controlling for dementia severity, all measures yielded greater deficits in patients with nfvPPA and corticobasal degeneration (nfvPPA-CBD, n = 12) than in those with progressive supranuclear palsy pathology (nfvPPA-PSP, n = 10). The articulation rate robustly discriminated between individuals in each subgroup (AUC = 0.82). More widespread cortical thinning was observed for the nfvPPA-CBD than the nfvPPA-PSP group across frontal regions.
Discussion:
Automated speech timing analyses can capture specific markers of nfvPPA while potentially discriminating between patients with different tauopathies. Thanks to its objectivity and scalability; this approach could support standard speech assessments.
Classification Of Evidence:
This study provides Class III evidence that automated speech analysis can accurately differentiate patients with nonfluent PPA from normal controls and patients with semantic variant PPA.
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