Dataset Information

Automated detection of progressive speech changes in early Alzheimer's disease.

ABSTRACT: Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech-based composite scores could be used to monitor change and detect response to treatment in future research.

Highlights

Longitudinal speech samples were analyzed to characterize speech changes in early AD.Acoustic and linguistic measures showed significant change over 18 months.A novel speech composite score was computed to characterize longitudinal change.The speech composite correlated with primary and secondary trial endpoints.Automated speech analysis could facilitate remote, high frequency monitoring in AD.

SUBMITTER: Robin J

PROVIDER: S-EPMC10286224 | biostudies-literature | 2023 Apr-Jun

REPOSITORIES: biostudies-literature

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Automated detection of progressive speech changes in early Alzheimer's disease.

Robin Jessica J Xu Mengdan M Balagopalan Aparna A Novikova Jekaterina J Kahn Laura L Oday Abdi A Hejrati Mohsen M Hashemifar Somaye S Negahdar Mohammadreza M Simpson William W Teng Edmond E

Alzheimer's & dementia (Amsterdam, Netherlands) 20230401 2

Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significan ...[more]

PMID: 37361261

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Project description:Successful communication in daily life depends on accurate decoding of speech signals that are acoustically degraded by challenging listening conditions. This process presents the brain with a demanding computational task that is vulnerable to neurodegenerative pathologies. However, despite recent intense interest in the link between hearing impairment and dementia, comprehension of acoustically degraded speech in these diseases has been little studied. Here we addressed this issue in a cohort of 19 patients with typical Alzheimer's disease and 30 patients representing the three canonical syndromes of primary progressive aphasia (non-fluent/agrammatic variant primary progressive aphasia; semantic variant primary progressive aphasia; logopenic variant primary progressive aphasia), compared to 25 healthy age-matched controls. As a paradigm for the acoustically degraded speech signals of daily life, we used noise-vocoding: synthetic division of the speech signal into frequency channels constituted from amplitude-modulated white noise, such that fewer channels convey less spectrotemporal detail thereby reducing intelligibility. We investigated the impact of noise-vocoding on recognition of spoken three-digit numbers and used psychometric modelling to ascertain the threshold number of noise-vocoding channels required for 50% intelligibility by each participant. Associations of noise-vocoded speech intelligibility threshold with general demographic, clinical and neuropsychological characteristics and regional grey matter volume (defined by voxel-based morphometry of patients' brain images) were also assessed. Mean noise-vocoded speech intelligibility threshold was significantly higher in all patient groups than healthy controls, and significantly higher in Alzheimer's disease and logopenic variant primary progressive aphasia than semantic variant primary progressive aphasia (all P < 0.05). In a receiver operating characteristic analysis, vocoded intelligibility threshold discriminated Alzheimer's disease, non-fluent variant and logopenic variant primary progressive aphasia patients very well from healthy controls. Further, this central hearing measure correlated with overall disease severity but not with peripheral hearing or clear speech perception. Neuroanatomically, after correcting for multiple voxel-wise comparisons in predefined regions of interest, impaired noise-vocoded speech comprehension across syndromes was significantly associated (P < 0.05) with atrophy of left planum temporale, angular gyrus and anterior cingulate gyrus: a cortical network that has previously been widely implicated in processing degraded speech signals. Our findings suggest that the comprehension of acoustically altered speech captures an auditory brain process relevant to daily hearing and communication in major dementia syndromes, with novel diagnostic and therapeutic implications.

Project description:BackgroundLanguage impairment is an important marker of neurodegenerative disorders. Despite this, there is no universal system of terminology used to describe these impairments and large inter-rater variability can exist between clinicians assessing language. The use of natural language processing (NLP) and automated speech analysis (ASA) is emerging as a novel and potentially more objective method to assess language in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). No studies have analyzed how variables extracted through NLP and ASA might also be correlated to language impairments identified by a clinician.MethodsAudio recordings (n=30) from participants with AD, MCI, and controls were rated by clinicians for word-finding difficulty, incoherence, perseveration, and errors in speech. Speech recordings were also transcribed, and linguistic and acoustic variables were extracted through NLP and ASA. Correlations between clinician-rated speech characteristics and the variables were compared using Spearman's correlation. Exploratory factor analysis was applied to find common factors between variables for each speech characteristic.ResultsClinician agreement was high in three of the four speech characteristics: word-finding difficulty (ICC = 0.92, p<0.001), incoherence (ICC = 0.91, p<0.001), and perseveration (ICC = 0.88, p<0.001). Word-finding difficulty and incoherence were useful constructs at distinguishing MCI and AD from controls, while perseveration and speech errors were less relevant. Word-finding difficulty as a construct was explained by three factors, including number and duration of pauses, word duration, and syntactic complexity. Incoherence was explained by two factors, including increased average word duration, use of past tense, and changes in age of acquisition, and more negative valence.ConclusionsVariables extracted through automated acoustic and linguistic analysis of MCI and AD speech were significantly correlated with clinician ratings of speech and language characteristics. Our results suggest that correlating NLP and ASA with clinician observations is an objective and novel approach to measuring speech and language changes in neurodegenerative disorders.

Dataset Information

Automated detection of progressive speech changes in early Alzheimer's disease.

Highlights

Publications

Automated detection of progressive speech changes in early Alzheimer's disease.

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