Abstract
Background: Overt sentence reading in mild cognitive impairment (MCI) and mild-tomoderate
Alzheimer’s disease (AD) has been associated with slowness of speech, characterized by a
higher number of pauses, shorter speech units and slower speech rate and attributed to reduced working
memory/ attention and language capacity.
Objective: This preliminary case-control study investigates whether the temporal organization of
speech is associated with the volume of brain regions involved in overt sentence reading and explores
the discriminative ability of temporal speech parameters and standard volumetric MRI measures for the
classification of MCI and AD.
Methods: Individuals with MCI, mild-to-moderate AD, and healthy controls (HC) had a structural MRI
scan and read aloud sentences varying in cognitive-linguistic demand (length). The association between
speech features and regional brain volumes was examined by linear mixed-effect modeling. Genetic
programming was used to explore the discriminative ability of temporal and MRI features.
Results: Longer sentences, slower speech rate, and a higher number of pauses and shorter interpausal
units were associated with reduced volumes of the reading network. Speech-based classifiers performed
similarly to the MRI-based classifiers for MCI-HC (67% vs. 68%) and slightly better for AD-HC (80%
vs. 64%) and AD-MCI (82% vs. 59%). Adding the speech features to the MRI features slightly improved
the performance of MRI-based classification for AD-HC and MCI-HC but not HC-MCI.
Conclusion: The temporal organization of speech in overt sentence reading reflects underlying volume
reductions. It may represent a sensitive marker for early assessment of structural changes and cognitive-
linguistic deficits associated with healthy aging, MCI, and AD.
Keywords:
Alzheimer disease, mild cognitive impairment, cognitive aging, functional neuroimaging, sentence reading, temporal speech features, genetic programming, machine learning.
[1]
World Health Organization. World Report on Aging and Health. Luxembourg 2015.
[9]
López IK, Solé CJ, Eguiraun H, et al. Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach. Comput Speech Lang 2015; 30(1): 43-60.
[16]
Burke DM, Shafto MA. Language and aging. In: Craik FIM, Salthouse TA, Eds. Handbook of Aging and Cognition. (3rd ed.). Psychology Press 2008; pp. 373-443.
[19]
Emch M, Bastian CC, Koch K. Neural correlates of verbal working memory: An fMRI meta-analysis. Front Hum Neurosci 2019; 13(180)
[27]
Petrone C, Fuchs S, Krivokapić J. Consequences of working memory differences and phrasal length on pause duration and fundamental frequency. Proceedings of the 9th International Seminar on Speech Production (ISSP). 393-400.
[45]
Krivokapić J. Prosodic planning in speech production. In: Fuchs S, Weihrich M, Pape D, Perrier P, Eds. Speech Planning and Dynamics: Peter Lang. 2012; pp. 157-90.
[47]
Boersma P. Praat: Doing phonetics by computer [Computer program] (Version 60 23). Amsterdam, The Netherlands 2016.
[55]
Team RC R: A language and environment for statistical computing 2018.
[56]
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed effects models using lme4. arXiv preprint arXiv: 1406-5823.2014;
[57]
Littell RC, Milliken GA, Stroup WW, Wolfinger RD. SAS system for mixed models. North Carolina: SAS Institute 1996.
[61]
Miller JF. Cartesian genetic programming: Its status and future. Genet Program Evolvable Mach 2019; 1-40.
[63]
Muhamed SA, Newby R, Smith SL, Alty JE, Jamieson S, Kempster P. Objective evaluation of bradykinesia in Parkinson’s disease using evolutionary algorithms. In: BIOSIGNALS 2018; pp. 63-9.
[66]
Casanova R, Wagner B, Whitlow CT, et al. High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization. Front Neuroinform 2011; 5: 22.
[68]
Salvatore C, Cerasa A, Battista P, Gilardi MC, Quattrone A, Salvatore I. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: A machine learning approach. Front Neurosci 2015; 9: 307.