Multiple sclerosis (Houndmills, Basingstoke, England) · 2026
Automated speech analysis reveals progressive linguistic changes associated with subsequent gray matter loss in multiple sclerosis.
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Šubert Martin, Tykalová Tereza, Novotný Michal, Srpová Barbora, Motýl Jiří, Krásenský Jan, Kudrna Matěj, Vaněčková Manuela, Horáková Dana, Uher Tomáš, Rusz Jan
Аннотация
BACKGROUND: The utility of high-level linguistic analysis for longitudinal monitoring of multiple sclerosis (MS) and its relationship with progressive brain volume loss remains largely unexplored.
OBJECTIVE: To characterize longitudinal changes in lexical and syntactic features in MS and determine whether linguistic alterations are associated with subsequent brain volume loss.
METHODS: Ninety-seven patients with MS and 80 age- and sex-matched healthy controls completed story narration tasks at baseline and 2-year follow-up. Recordings were automatically transcribed and analyzed to extract five linguistic features. Magnetic resonance imaging (MRI) assessments were performed between the 2- and 7-year follow-ups. Linear mixed-effects models assessed linguistic decline over 2 years and whether these early changes were associated with a subsequent 5-year brain volume loss.
RESULTS: Over 2 years, patients with MS showed changes in vocabulary range (β = 0.53, p = 0.008, q = 0.02) and phrase repetition (β = 0.56, p = 0.007, q = 0.02). Early changes in these lexical features were associated with subsequent 5-year decline in total gray matter volume. Linguistic changes were also related to cortical gray matter and cerebellar volume loss but not to hippocampal or white matter volume loss.
CONCLUSION: Automated speech analysis captures progressive linguistic decline in MS. Early linguistic changes were associated with subsequent brain volume loss, supporting speech-derived biomarkers of neurodegeneration.
OBJECTIVE: To characterize longitudinal changes in lexical and syntactic features in MS and determine whether linguistic alterations are associated with subsequent brain volume loss.
METHODS: Ninety-seven patients with MS and 80 age- and sex-matched healthy controls completed story narration tasks at baseline and 2-year follow-up. Recordings were automatically transcribed and analyzed to extract five linguistic features. Magnetic resonance imaging (MRI) assessments were performed between the 2- and 7-year follow-ups. Linear mixed-effects models assessed linguistic decline over 2 years and whether these early changes were associated with a subsequent 5-year brain volume loss.
RESULTS: Over 2 years, patients with MS showed changes in vocabulary range (β = 0.53, p = 0.008, q = 0.02) and phrase repetition (β = 0.56, p = 0.007, q = 0.02). Early changes in these lexical features were associated with subsequent 5-year decline in total gray matter volume. Linguistic changes were also related to cortical gray matter and cerebellar volume loss but not to hippocampal or white matter volume loss.
CONCLUSION: Automated speech analysis captures progressive linguistic decline in MS. Early linguistic changes were associated with subsequent brain volume loss, supporting speech-derived biomarkers of neurodegeneration.