Audiology and Speech Pathology - Research Publications
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ItemSpeech biometrics can predict cerebellar dysfunction in multiple sclerosis(International Parkinson and Movement Disorder Society, 2020-09-01)Objective: To objectively describe cerebellar mediated speech function using a multiparameter index that reflects pathology and quality of life in MS. Background: Cerebellar function plays a role in cognitive processing and motor control and is important for speech production. Multiple sclerosis (MS) can result in impaired cerebellar function, leading to speech deficits. These deficits can be detected by instrumental measurement. Speech as a marker of cerebellar impairment but is not well described. Method: The speech and clinical features of 85 people with MS (plus 21 matched controls) were assessed using objective acoustic analysis, validated questionnaires of quality of life and disease severity using the Scale for the Assessment and rating of Ataxia. Magnetic resonance imaging was used to measure cerebellar pathology. A regression model with eight speech variables were used to predict cerebellar function. A composite speech score was developed from the model and tested for prediction of fine motor function using the 9-hole peg test (9HPT), for correlations with imaging outcomes and self-assessed quality of life. Results: Speech timing metrics (eg. slow rate of syllable repetition, increased proportion of silence) were the strongest predictors of cerebellar impairment, alongside phonatory instability. The acoustic composite score accounted for 54% of variation in cerebellar dysfunction, was associated with cerebellar white matter volume (r=0.3, p=0.017), quality of life (r=0.5, p<0.001) and predicted an abnormal 9HPT with 85% accuracy. Conclusion: Motor cerebellar impairment in MS was reflected in an objective multi-feature speech metric.
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