Predicting mild cognitive impairment from spontaneous spoken utterances

Meysam Asgari, Jeffrey Kaye, Hiroko Dodge, Meysam Asgari, Jeffrey Kaye, Hiroko Dodge

Abstract

Introduction: Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial.

Methods: Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI (ClinicalTrials.gov: NCT01571427). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants.

Results: MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%.

Discussion: Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.

Keywords: Biomarkers; Conversational interactions; Early identification; Mild cognitive impairment (MCI); Social markers; Speech characteristics.

Figures

Fig. 1
Fig. 1
Block diagram of extracting and modeling linguistic features of participants' transcriptions to distinguish participants with MCI from those with intact cognition. Abbreviation: MCI, mild cognitive impairment.

References

    1. Taler V., Phillips N.A. Language performance in Alzheimer's disease and mild cognitive impairment: A comparative review. J Clin Exp Neuropsychol. 2008;30:501–556.
    1. Tombaugh T.N. Trail making test a and b: normative data stratified by age and education. Arch Clin Neuropsychol. 2004;19:203–214.
    1. Murphy K.J., Rich J.B., Troyer A.K. Verbal fluency patterns in amnestic mild cognitive impairment are characteristic of Alzheimer's type dementia. J Int Neuropsychol Soc. 2006;12:570–574.
    1. Wechsler D. Wechsler adult intelligence scale–fourth edition (wais–iv). London: Pearson; 2014.
    1. Fraser K.C., Meltzer J.A., Rudzicz F. Linguistic features identify Alzheimers disease in narrative speech. J Alzheimers Dis. 2015;49:407–422.
    1. Roark B., Mitchell M., Hollingshead K. Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing. Association for Computational Linguistics; 2007. Syntactic complexity measures for detecting mild cognitive impairment; pp. 1–8.
    1. Forbes-McKay K., Shanks M.F., Venneri A. Profiling spontaneous speech decline in Alzheimer's disease: A longitudinal study. Acta Neuropsychiatr. 2013;25:320–327.
    1. Romero B., Kurz A. Deterioration of spontaneous speech in ad patients during a 1-year follow-up: Homogeneity of profiles and factors associated with progression. Dement Geriatr Cogn Disord. 1996;7:35–40.
    1. Pennebaker JW, Francis ME, Booth RJ. Linguistic Inquiry and Word Count: Liwc 2001. Mahwah: Lawrence Erlbaum Associates; 2001; 71: p. 2001.
    1. Mehl M.R., Gosling S.D., Pennebaker J.W. Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life. J Pers Soc Psychol. 2006;90:862–877.
    1. Dodge H.H., Mattek N., Gregor M., Bowman M., Seelye A., Ybarra O. Social markers of mild cognitive impairment: Proportion of word counts in free conversational speech. Curr Alzheimer Res. 2014;12:513–519.
    1. Dodge H.H., Zhu J., Mattek N.C., Bowman M., Ybarra O., Wild K.V. Web-enabled conversational interactions as a method to improve cognitive functions: Results of a 6-week randomized controlled trial. Alzheimers Dement (N Y) 2015;1:1–12.
    1. Kemper S., LaBarge E., Ferraro F.R., Cheung H., Cheung H., Storandt M. On the preservation of syntax in Alzheimer's disease: Evidence from written sentences. Arch Neurol. 1993;50:81–86.
    1. Lyons K., Kemper S., LaBarge E., Ferraro F.R., Balota D., Storandt M. Oral language and Alzheimer's disease: A reduction in syntactic complexity. Aging Neuropsychol Cogn. 1994;1:271–281.
    1. Bucks R., Singh S., Cuerden J.M., Wilcock G.K. Analysis of spontaneous, conversational speech in dementia of Alzheimer type: Evaluation of an objective technique for analysing lexical performance. Aphasiology. 2000;14:71–91.
    1. Singh S., Bucks R.S., Cuerden J.M. Evaluation of an objective technique for analysing temporal variables in DAT spontaneous speech. Aphasiology. 2001;15:571–583.
    1. Oulhaj A., Wilcock G.K., Smith A.D., de Jager C.A. Predicting the time of conversion to mci in the elderly role of verbal expression and learning. Neurology. 2009;73:1436–1442.
    1. Fraser K.C., Rudzicz F., Rochon E. INTERSPEECH. Citeseer; 2013. Using text and acoustic features to diagnose progressive aphasia and its subtypes; pp. 2177–2181.
    1. Clark D., Kapur P., Geldmacher D., Brockington J., Harrell L., DeRamus T. Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease. Cortex. 2014;55:202–218.
    1. Berisha V., Wang S., LaCross A., Liss J. Tracking discourse complexity preceding Alzheimer's disease diagnosis: A case study comparing the press conferences of Presidents Ronald Reagan and George Herbert Walker Bush. J Alzheimers Dis. 2015;45:959–963.
    1. Ahmed S., Haigh A.M., de Jager C.A., Garrard P. Connected speech as a marker of disease progression in autopsy-proven Alzheimers disease. Brain. 2013;136:3727–3737.
    1. Kempler D. Language changes in dementia of the Alzheimer type. Dementia and Communication. 1995:98–114.
    1. Salmon D.P., Butters N., Chan A.S. The deterioration of semantic memory in Alzheimer's disease. Can J Exp Psychol. 1999;53:108–117.
    1. Jarrold W, Peintner B, Wilkins D, Vergryi D., Richey C, Gorno-Tempini ML, et al. Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. In: Proceedings of the ACL Workshop on Computational Linguistics and Clinical Psychology. 2014. p. 27–36.
    1. Lehr M., Prud'hommeaux E.T., Shafran I., Roark B. INTERSPEECH. 2012. Fully automated neuropsychological assessment for detecting mild cognitive impairment; pp. 1039–1042.
    1. Liang P, Taskar B, Klein D. Alignment by agreement. In: Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics: Association for Computational Linguistics. 2006. p. 104–111.
    1. Tóth L., Gosztolya G., Vincze V., Hoffmann I., Szatlóczki G. ISCA; Dresden: 2015. Automatic detection of mild cognitive impairment from spontaneous speech using asr.
    1. Morris J.C., Ernesto C., Schafer K., Coats M., Leon S., Sano M. Clinical dementia rating training and reliability in multicenter studies the Alzheimer's disease cooperative study experience. Neurology. 1997;48:1508–1510.
    1. Watson D., Clark L.A., Tellegen A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J Pers Soc Psychol. 1988;54:1063–1070.
    1. Pennebaker J.W., Booth R.J., Francis M.E. liwc. net; Austin, TX: 2007. Linguistic inquiry and word count: LIWC [computer software]
    1. Hutto CJ, Gilbert E. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media. 2014.
    1. Schwartz H.A., Eichstaedt J.C., Kern M.L., Dziurzynski L., Ramones S.M., Agrawal M. Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS One. 2013;8:e73791.
    1. Smola A.J., Schölkopf B. A tutorial on support vector regression. Stat Comput. 2004;14:199–222.
    1. Breiman L. Random forests. Machine Learn. 2001;45:5–32.
    1. Schölkopf B., Smola A.J. MIT press; Cambridge: 2002. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond.
    1. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O. Scikit-learn: Machine learning in python. The J Machine Learn Res. 2011;12:2825–2830.
    1. Hanley J.A., McNeil B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.
    1. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 1996;58:267–288.
    1. Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998;10:1895–1923.
    1. Mathuranath P., George A., Cherian P., Alexander A., Sarma S.G., Sarma P.S. Effects of age, education and gender on verbal fluency. J Clin Exp Neuropsychol. 2003;25:1057–1064.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir