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Influence of Cognitive Function on Speech and Articulation Rate in Multiple Sclerosis

Published online by Cambridge University Press:  12 October 2012

Jonathan D. Rodgers ,
Kris Tjaden ,
Lynda Feenaughty ,
Bianca Weinstock-Guttman  and
Ralph H. B. Benedict
Jonathan D. Rodgers
Affiliation:
Jacobs Neurological Institute, Buffalo, New York
Kris Tjaden
Affiliation:
Department of Communicative Disorders and Sciences, State University of New York at Buffalo, Buffalo, New York
Lynda Feenaughty
Affiliation:
Department of Communicative Disorders and Sciences, State University of New York at Buffalo, Buffalo, New York
Bianca Weinstock-Guttman
Affiliation:
Jacobs Neurological Institute, Buffalo, New York Department of Neurology, State University of New York at Buffalo, Buffalo, New York
Ralph H. B. Benedict*
Affiliation:
Jacobs Neurological Institute, Buffalo, New York Department of Neurology, State University of New York at Buffalo, Buffalo, New York
*
Correspondence and reprint requests to: Ralph H. B. Benedict, Neurology, D-2, Buffalo General Hospital, 100 High Street, Buffalo, New York, 14203. E-mail: benedict@buffalo.edu
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Abstract

We examined cognitive predictors of speech and articulation rate in 50 individuals with multiple sclerosis (MS) and 23 healthy controls. We measured speech and articulation rate from audio-recordings of participants reading aloud and talking extemporaneously on a topic of their choice (i.e., self-generated speech). Articulation rate was calculated for each speech sample by removing lexically irrelevant vocalizations and pauses of >200 ms. Speech rate was similarly calculated including pauses. Concurrently, the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) battery, as well as standardized tests of sentence intelligibility and syllable repetition were administered. Analysis of variance showed that MS patients were slower on three of the four rate measures. Greater variance in rate measures was accounted for by cognitive variables for the MS group than controls. An information processing speed composite, as measured by the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT), was the strongest predictor among cognitive tests. A composite of memory tests related to self-generated speech, above and beyond information processing speed, but not to oral reading. Self-generated speech, in this study, was not found to relate more strongly to cognitive tests than simple reading. Implications for further research are discussed. (JINS, 2012, 18, 1–8)

Keywords

Multiple sclerosisArticulation disordersSpeech production measurementNeuropsychologyCognitive scienceNeurobehavioral manifestations
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 19 , Issue 2 , February 2013 , pp. 173 - 180
Copyright
Copyright © The International Neuropsychological Society 2012

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References

Allard, E.R., Williams, D.F. (2008). Listeners’ perceptions of speech and language disorders. Journal of Communication Disorders, 41, 108–123. doi:10.1016/j.jcomdis.2007.05.002CrossRefGoogle ScholarPubMed
Arnett, P.A., Smith, M.M., Barwick, F.H., Benedict, R.H.B., Ahlstrom, B.P. (2008). Oralmotor slowing in multiple sclerosis: Relationship to neuropsychological tasks requiring an oral response. Journal of the International Neuropsychological Society, 14, 454–462. doi:10.1017/s1355617708080508CrossRefGoogle ScholarPubMed
Arnett, P.A., Strober, L.B. (2011). Cognitive and neurobehavioral features in multiple sclerosis. Expert Review of Neurotherapeutics, 11, 411–424. doi:10.1586/ern.11.12CrossRefGoogle ScholarPubMed
Bayles, K.A., Tomoeda, C.K. (1993). Arizona battery for communication disorders of dementia. Tucson, AZ: Canyonlands.Google Scholar
Baylor, C., Yorkston, K., Bamer, A., Britton, D., Amtmann, D. (2010). Variables associated with communicative participation in people with multiple sclerosis: A regression analysis. American Journal of Speech-Language Pathology, 19, 143–153. doi:10.1044/1058-0360(2009/08-0087)CrossRefGoogle ScholarPubMed
Beck, A.T., Steer, R.A., Brown, G.K. (2000). BDI-fast screen for medical patients: Manual. San Antonia, TX: Psychological CorporationGoogle Scholar
Benedict, R.H.B. (1997). Brief visuospatial memory test - revised: professional manual. Odessa, FL: Psychological Assessment ResourcesGoogle Scholar
Benedict, R.H.B., Bobholz, J.H. (2007). Multiple sclerosis. Seminars in Neurology, 27, 78–85. doi:10.1055/s-2006-956758CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Cookfair, D., Gavett, R., Gunther, M., Munschauer, F., Garg, N., Weinstock-Guttman, B. (2006). Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). Journal of the International Neuropsychological Society, 12, 549–558. doi:10.1017/s1355617706060723CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Fischer, J.S., Archibald, C.J., Arnett, P.A., Beatty, W.W., Bobholz, J., Munschauer, F. (2002). Minimal neuropsychological assessment of MS patients: A consensus approach. Clinical Neuropsychologist, 16, 381–397. doi:10.1076/clin.16.3.381.13859CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Fishman, I., McClellan, M.M., Bakshi, R., Weinstock-Guttman, B. (2003). Validity of the Beck Depression Inventory-Fast Screen in multiple sclerosis. Multiple Sclerosis Journal, 9, 393–396. doi:10.1191/1352458503ms902oaCrossRefGoogle ScholarPubMed
Benedict, R.H.B., Holtzer, R., Motl, R.W., Foley, F.W., Kaur, S., Hojnacki, D., Weinstock-Guttman, B. (2011). Upper and Lower Extremity Motor Function and Cognitive Impairment in Multiple Sclerosis. Journal of the International Neuropsychological Society, 17, 643–653. doi:10.1017/s1355617711000403CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Ramasamy, D., Munschauer, F.E., Weinstock-Guttman, B., Zivadinov, R. (2009). Memory impairment in multiple sclerosis: Correlation with deep gray matter and mesial temporal atrophy. Journal of Neurology, Neurosurgery, & Psychiatry, 80, 201–206.CrossRefGoogle Scholar
Benedict, R.H.B., Weinstock-Guttman, B., Fishman, I., Sharma, J., Tjoa, C.W., Bakshi, R. (2004). Prediction of neuropsychological impairment in multiple sclerosis: Comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Archives of Neurology, 61, 226–230. doi:10.1001/archneur.61.2.226CrossRefGoogle ScholarPubMed
Benton, A.L., Sivan, A.B., Hamsher, K., Varney, N.R., Spreen, O. (1994). Contributions to neuropsychological assessment: A clinical manual (2nd ed.). New York: Oxford University Press.Google Scholar
Clark, H.H., Fox Tree, J. (2002). Using Uh and Um in spontaneous speaking. Cognition, 84, 73–111.CrossRefGoogle Scholar
Compston, A., Coles, A. (2008). Multiple sclerosis. Lancet, 372, 1502–1517. doi:10.1016/s0140-6736(08)61620-7CrossRefGoogle ScholarPubMed
Delis, D.C., Kaplan, E., Kramer, J.H. (2001). Delis-Kaplan Executive Function System. San Antonio, TX: Psychological CorporationGoogle Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., Ober, B.A. (2000). California Verbal Learning Test: Adult version. San Antonio, TX: Psychological Corporation.Google Scholar
Drake, A.S., Weinstock-Guttman, B., Morrow, S.A., Hojnacki, D., Munschauer, F.E., Benedict, R.H.B. (2010). Psychometrics and normative data for the Multiple Sclerosis Functional Composite: Replacing the PASAT with the Symbol Digit Modalities Test. Multiple Sclerosis, 16, 228–237. doi:10.1177/1352458509354552CrossRefGoogle ScholarPubMed
Duffy, J.R. (2005). Motor speech disorders: Substrates, differential diagnosis, and management (2nd ed.). St. Louis: Elsevier Science.Google Scholar
Gadesmann, M., Miller, N. (2008). Reliability of speech diadochokinetic test measurement. International Journal of Language & Communication Disorders, 43, 41–54. doi:10.1080/13682820701234444CrossRefGoogle ScholarPubMed
Goldman-Eisler, F. (1968). Psycholinguistics: Experiments in spontaneous speech. New York: Academic Press.Google Scholar
Gronwall, D. (1977). Paced auditory serial addition task: A measure of recovery from concussion. Perceptual and Motor Skills, 44, 367–373.CrossRefGoogle ScholarPubMed
Hartelius, L., Buder, E.H., Strand, E.A. (1997). Long-term phonatory instability in individuals with multiple sclerosis. Journal of Speech Language and Hearing Research, 40, 1056–1072.CrossRefGoogle ScholarPubMed
Hartelius, L., Runmarker, B., Andersen, O. (2000). Prevalence and characteristics of dysarthria in a multiple-sclerosis incidence cohort: Relation to neurological data. Folia Phoniatrica Et Logopaedica, 52, 160–177.CrossRefGoogle Scholar
Huber, J.E., Darling, M. (2011). Effect of Parkinson's disease on the production of structured and unstructured speaking tasks: Respiratory physiologic and linguistic considerations. Journal of Speech Language and Hearing Research, 54, 33–46. doi:10.1044/1092-4388(2010/09-0184)CrossRefGoogle ScholarPubMed
Kent, R.D., Duffy, J., Kent, J.F., Vorperian, H.K., Thomas, J.E. (1999). Quantification of motor speech abilities in stroke: Time-energy analyses of syllable and word repetition. Journal of Medical Speech-Language Pathology, 7, 83–90.Google Scholar
Kent, R.D., Kent, J.F., Rosenbek, J.C. (1987). Maximum performance tests of speech production. Journal of Speech and Hearing Disorders, 52, 367–387.CrossRefGoogle ScholarPubMed
Klugman, T.M., Ross, E. (2002). Perceptions of the impact of speech, language, swallowing, and hearing difficulties on quality of life of a group of South African persons with multiple sclerosis. Folia Phoniatrica Et Logopaedica, 54, 201–221. doi:10.1159/000063194CrossRefGoogle ScholarPubMed
Kurtzke, J.F. (1983). Rating neurologic impairment in multiple-sclerosis: An Expanded Disability Status Scale (EDSS). Neurology, 33, 1444–1452.CrossRefGoogle ScholarPubMed
Lowit, A., Brendel, B., Dobinson, C., Howell, P. (2006). An investigation into the influences of age, pathology, and cognition on speech production. Journal of Medical Speech-Language Pathology, 14, 253–262.Google ScholarPubMed
Mackenzie, C., Green, J. (2009). Cognitive-linguistic deficit and speech intelligibility in chronic progressive multiple sclerosis. International Journal of Language & Communication Disorders, 44, 401–420. doi:10.1080/13682820802697879CrossRefGoogle ScholarPubMed
Milenkovic, P. (2002). TF32: University of Wisconsin-Madison.Google Scholar
Parmenter, B.A., Testa, S.M., Schretlen, D.J., Weinstock-Guttman, B., Benedict, R.H.B. (2010). The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). Journal of the International Neuropsychological Society, 16, 6–16. doi:10.1017/s1355617709990750CrossRefGoogle ScholarPubMed
Polman, C.H., Reingold, S.C., Edan, G., Filippi, M., Hartung, H.P., Kappos, L., Wolinsky, J.S. (2005). Diagnostic criteria for multiple sclerosis: 2005 Revisions to the “McDonald Criteria”. Annals of Neurology, 58, 840–846. doi:10.1002/ana.20703CrossRefGoogle Scholar
Rosano, C., Brach, J., Studenski, S., Longstreth, W.T., Newman, A.B. (2007). Gait variability is associated with subclinical brain vascular abnormalities in high-functioning older adults. Neuroepidemiology, 29, 193–200. doi:10.1159/000111582CrossRefGoogle ScholarPubMed
Rosano, C., Studenski, S.A., Aizenstein, H.J., Boudreau, R.M., Longstreth, W.T., Newman, A.B. (2012). Slower gait, slower information processing and smaller prefrontal area in older adults. Age and Ageing, 41, 58–64. doi:10.1093/ageing/afr113CrossRefGoogle ScholarPubMed
Smith, A. (1982). Symbol digit modalities test: Manual. Los Angeles: Western Psychological Services.Google Scholar
Tasko, S.M., McClean, M.D. (2004). Variations in articulatory movement with changes in speech task. Journal of Speech Language and Hearing Research, 47, 85–100. doi:10.1044/1092-4388(2004/008)CrossRefGoogle ScholarPubMed
Tjaden, K., Watling, E. (2003). Characteristics of diadochokinesis in multiple sclerosis and Parkinson's disease. Folia Phoniatrica Et Logopaedica, 55, 241–259. doi:10.1159/000072155CrossRefGoogle ScholarPubMed
Tjaden, K., Wilding, G.E. (2004). Rate and loudness manipulations in dysarthria: Acoustic and perceptual findings. Journal of Speech Language and Hearing Research, 47, 766–783. doi:10.1044/1092-4388(2004/058)CrossRefGoogle ScholarPubMed
Tjaden, K., Wilding, G.E. (2011). Effects of speaking task on intelligibility in Parkinson's disease. Clinical Linguistics & Phonetics, 25, 155–168. doi:10.3109/02699206.2010.520185CrossRefGoogle ScholarPubMed
Weismer, G. (2006). Philosophy of research in motor speech disorders. Clinical Linguistics & Phonetics, 20, 315–349. doi:10.1080/02699200400024806CrossRefGoogle ScholarPubMed
Westbury, J., Dembowski, J.S. (1993). Articulatory kinematics of normal diadochokinetic performance. Annual Bulletin of the Research Institute of Logopedics and Phoniatrics, 27, 13–36.Google Scholar
Yorkston, K., Beukelman, D., Tice, R. (1996). Sentence intelligibility test. Lincoln, NE: Institute for Rehabilitation Science and Engineering at Madonna Rehabilitation Hospital.Google Scholar
Yorkston, K.M., Beukelman, D.R. (1984). Assessment of intelligibility of dysarthric speech. Austin, TX: PRO-ED.Google Scholar
Yorkston, K.M., Klasner, E.R., Bowen, J., Ehde, D.M., Gibbons, L.E., Johnson, K., Kraft, G. (2003). Characteristics of multiple sclerosis as a function of the severity of speech disorders. Journal of Medical Speech-Language Pathology, 11, 73–84.Google Scholar