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Cited by 362Neurobiology of Language and BilingualismDementia and Cognitive Impairment ResearchVoice and Speech Disorders

Connected Speech in Neurodegenerative Language Disorders: A Review

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Boschi, Veronica, Catricalà, Eleonora, Consonni, Monica, Chesi, Cristiano, Moro, Andrea, Cappa, Stefano F.·2017·Frontiers in Psychology
DOI 10.3389/fpsyg.2017.00269PMC PMC5337522
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Abstract

Language assessment has a crucial role in the clinical diagnosis of several neurodegenerative diseases. The analysis of extended speech production is a precious source of information encompassing the phonetic, phonological, lexico-semantic, morpho-syntactic, and pragmatic levels of language organization. The knowledge about the distinctive linguistic variables identifying language deficits associated to different neurodegenerative diseases has progressively improved in the last years. However, the heterogeneity of such variables and of the way they are measured and classified limits any generalization and makes the comparison among studies difficult. Here we present an exhaustive review of the studies focusing on the linguistic variables derived from the analysis of connected speech samples, with the aim of characterizing the language disorders of the most prevalent neurodegenerative diseases, including primary progressive aphasia, Alzheimer's disease, movement disorders, and amyotrophic lateral sclerosis. A total of 61 studies have been included, considering only those reporting group analysis and comparisons with a group of healthy persons. This review first analyzes the differences in the tasks used to elicit connected speech, namely picture description, story narration, and interview, considering the possible different contributions to the assessment of different linguistic domains. This is followed by an analysis of the terminologies and of the methods of measurements of the variables, indicating the need for harmonization and standardization. The final section reviews the linguistic domains affected by each different neurodegenerative disease, indicating the variables most consistently impaired at each level and suggesting the key variables helping in the differential diagnosis among diseases. While a large amount of valuable information is already available, the review highlights the need of further work, including the development of automated methods, to take advantage of the richness of connected speech analysis for both research and clinical purposes.

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  • ASLA offers objective, examiner-independent, multidimensional results via brief oral production tasks, through measures of the acoustic speech signal and/or its transcription.

    “ASLA offers ob jective, examiner-independent, multidimensional results via brief oral production tasks, through measures of the acoustic speech sig nal (e.g. speech timing, pitch variability) and/or its transcription (e.g. syntactic complexity, semantic specificity). 47,48”

    From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [48]

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  1. Altmann L. J. Kempler D. Andersen E. S. (2001). Speech errors in Alzheimer's diseasereevaluating morphosyntactic preservation. J. Speech Lang. Hear. Res. 44, 1069– 1082. 10.1044/1092-4388(2001/085) 11708528 · DOI
  2. Ash S. Grossman M. (2015). Why study connected speech production?, in Cognitive Neuroscience of Natural Language Use, ed Willems R. M. (Cambridge: Cambridge University Press), 29– 58.
  3. Ash S. Moore P. Antani S. McCawley G. Work M. Grossman M. (2006). Trying to tell a tale. Discourse impairments in progressive aphasia and frontotemporal dementia. Neurology 66, 1405– 1413. 10.1212/01.wnl.0000210435.72614.38 16682675 · DOI
  4. Cotelli M. Borroni B. Manenti R. Ginex V. Calabria M. Moro A.. (2007). Universal grammar in the frontotemporal dementia spectrum: evidence of a selective disorder in the corticobasal degeneration syndrome. Neuropsychologia 45, 3015– 3023. 10.1016/j.neuropsychologia.2007.05.012 17640688 · DOI
  5. Fraser K. C. Ben-David N. Hirst G. Graham N. Rochon E. (2015b). Sentence segmentation of aphasic speech, in HLT-NAACL, (Denver), 862– 871.
  6. Fraser K. C. Hirst G. Meltzer J. A. Mack J. E. Thompson C. K. (2014b). Using statistical parsing to detect agrammatic aphasia, in Proceedings of the 2014 Workshop on Biomedical Natural Language Processing ACL (Baltimore, MD), 134.
  7. Fraser K. Rudzicz F. Graham N. Rochon E. (2013). Automatic speech recognition in the diagnosis of primary progressive aphasia, in Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies (Grenoble), 47– 54.
  8. Goodglass H. Barresi B. Kaplan E. (1983). The Boston Diagnostic Aphasia Examination. Philadelphia: Lippincott Williams and Willkins. A Wolters Kluwer Company.
  9. Guinn C. I. Habash A. (2012). Language analysis of speakers with dementia of the Alzheimer's type, in AAAI Fall Symposium: Artificial Intelligence for Gerontechnology, (Menlo Park, CA), 8– 13.
  10. Jarrold W. Peintner B. Yeh E. Krasnow R. Javitz H. Swan G. (2010). Language analytics for assessing brain health: cognitive impairment, depression and pre-symptomatic Alzheimer's disease, in Brain Informatics eds Yao Y. Sun R. Poggio T. Liu J. Zhong N. Huang J. (Berlin; Heidelberg: Springer), 299– 307.
  11. Kemper S. LaBarge E. Ferraro F. R. Cheung H. Cheung H. Storandt M. (1993). On the preservation of syntax in Alzheimer's disease: evidence from written sentences. Arch. Neurol. 50, 81– 86. 10.1001/archneur.1993.00540010075021 8418805 · DOI
  12. Mayer M. (1969). Frog, Where Are You? New York, NY: Dial Press.
  13. McKeith I. G. Dickson D. W. Lowe J. Emre M. O'brien J. T. Feldman H.. (2005). Diagnosis and management of dementia with Lewy bodies third report of the DLB consortium. Neurology 65, 1863– 1872. 10.1212/01.wnl.0000187889.17253.b1 16237129 · DOI · Paper page
  14. Moro A. (2015). The Boundaries of Babel. The Brain and the Enigma of Impossible Langauges, 2nd Edn. Cambridge, MA: MIT Press.
  15. Orimaye S. O. Wong J. S. M. Golden K. J. (2014). Learning predictive linguistic features for Alzheimer's disease and related dementias using verbal utterances, in Proceedings of the 1st Workshop on Computational Linguistics and Clinical Psychology (CLPsych) (Baltimore: Association for Computational Linguistics), 78– 87.
  16. Prins R. S. Bastiaanse R. (2004). Analysing the spontaneous speech of aphasic speakers. Aphasiology 18, 1075– 1091. 10.1080/02687030444000534 · DOI
  17. Schulz C. M. (1976). Peanuts Jubilee: My life and Art with Charlie Brown and Others. Ringwood, VIC: Penguin Books.
  18. Ska B. Duong A. (2005). Communication, discours et démence. Psychol. Neuropsychiatr. Vieil 3, 125– 133. 15929926
  19. Swinburn K. Porter G. Howard D. (2004). Comprehensive Aphasia Test. Hove, UK: Psychology Press.
  20. Thomas C. Kešelj V. Cercone N. Rockwood K. Asp E. (2005). Automatic detection and rating of dementia of Alzheimer type through lexical analysis of spontaneous speech, in 2005 IEEE International Conference on Mechatronics and Automation, Vol. 3 (Niagara Falls), 1569– 1574.
  21. Yancheva M. Fraser K. Rudzicz F. (2015). Using linguistic features longitudinally to predict clinical scores for Alzheimer's disease and related dementias, in 6th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT) (Dresden), 134.

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Authors
Boschi, V., Catricalà, E., Consonni, M., Chesi, C., Moro, A., & Cappa, S. F.
Affiliations
  • Fondazione IRCCS Istituto Neurologico Carlo Besta (IT)
  • Centro San Giovanni di Dio Fatebenefratelli (IT)
Corresponding author
Cappa, Stefano F.
ORCID
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2017-03-06
Volume
8
Pages
269-269
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2017.00269
PMC
PMC5337522
OpenAlex
W2594894191
Cited by
362
References
119
Citations by year
2026: 38 · 2025: 50 · 2024: 62 · 2023: 47 · 2022: 43 · 2021: 43 · 2020: 45 · 2019: 19 · 2018: 11 · 2017: 4
FWCI
2.42
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Voice and Speech Disorders
Keywords
neurodegenerative language disorders, primary progressive aphasia, Alzheimer's disease, movement disorders, amyotrophic lateral sclerosis, picture description, storytelling, language domains, differential diagnosis, automated speech analysis

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