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Current Alzheimer Research

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer’s Disease

Author(s): Céline De Looze*, Amir Dehsarvi, Narin Suleyman, Lisa Crosby, Belinda Hernández, Robert F. Coen, Brian A. Lawlor and Richard B. Reilly

Volume 19, Issue 8, 2022

Published on: 30 September, 2022

Page: [606 - 617] Pages: 12

DOI: 10.2174/1567205019666220805110248

Price: $65

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

« Previous Next »
[1]
World Health Organization. World Report on Aging and Health. Luxembourg 2015.
[2]
Verfaillie SCJ, Slot RER, Dicks E, et al. A more randomly organized grey matter network is associated with deteriorating language and global cognition in individuals with subjective cognitive decline. Hum Brain Mapp 2018; 39(8): 3143-51.
[http://dx.doi.org/10.1002/hbm.24065] [PMID: 29602212]
[3]
Dicks E, Tijms BM, Ten Kate M, et al. Gray matter network measures are associated with cognitive decline in mild cognitive impairment. Neurobiol Aging 2018; 61: 198-206.
[http://dx.doi.org/10.1016/j.neurobiolaging.2017.09.029] [PMID: 29111486]
[4]
Wang WY, Yu JT, Liu Y, et al. Voxel based meta analysis of grey matter changes in Alzheimer’s disease. Transl Neurodegener 2015; 4(1): 6.
[http://dx.doi.org/10.1186/s40035-015-0027-z]
[5]
Tondelli M, Wilcock GK, Nichelli P, Jager CA, Jenkinson M, Zamboni G. Structural MRI changes detectable up to ten years before clinical Alzheimer’s disease. Neurobiol Aging 2012; 33(4): 825.e25-36.
[http://dx.doi.org/10.1016/j.neurobiolaging.2011.05.018] [PMID: 21782287]
[6]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 263-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[7]
Meilán JJG, Martínez SF, Carro J, López DE, Millian ML, Arana JM. Speech in Alzheimer’s disease: Can temporal and acoustic parameters discriminate dementia? Dement Geriatr Cogn Disord 2014; 37(5-6): 327-34.
[http://dx.doi.org/10.1159/000356726] [PMID: 24481220]
[8]
König A, Satt A, Sorin A, et al. Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimers Dement (Amst) 2015; 1(1): 112-24.
[http://dx.doi.org/10.1016/j.dadm.2014.11.012] [PMID: 27239498]
[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.
[10]
Roark B, Mitchell M, Hosom JP, Hollingshead K, Kaye J. Spoken language derived measures for detecting mild cognitive impairment. IEEE Trans Audio Speech Lang Process 2011; 19(7): 2081-90.
[http://dx.doi.org/10.1109/TASL.2011.2112351] [PMID: 22199464]
[11]
Dodge HH, Mattek N, Gregor M, et al. Social markers of mild cognitive impairment: Proportion of word counts in free conversational speech. Curr Alzheimer Res 2015; 12(6): 513-9.
[http://dx.doi.org/10.2174/1567205012666150530201917] [PMID: 26027814]
[12]
Asgari M, Kaye J, Dodge H. Predicting mild cognitive impairment from spontaneous spoken utterances. Alzheimers Dement (N Y) 2017; 3(2): 219-28.
[http://dx.doi.org/10.1016/j.trci.2017.01.006] [PMID: 29067328]
[13]
Ahmed O, Benois PJ, Allard M, Catheline G, Amar CB. Recognition of Alzheimer’s disease and Mild Cognitive Impairment with multimodal image derived biomarkers and multiple kernel learning. Neurocomputing 2017; 220: 98-110.
[http://dx.doi.org/10.1016/j.neucom.2016.08.041]
[14]
Levelt WJM, Roelofs A, Meyer AS. A theory of lexical access in speech production. Behav Brain Sci 1999; 22(1): 1-38.
[http://dx.doi.org/10.1017/S0140525X99001776] [PMID: 11301520]
[15]
Shafto MA, Tyler LK. Language in the aging brain: The network dynamics of cognitive decline and preservation. Science 2014; 346(6209): 583-7.
[http://dx.doi.org/10.1126/science.1254404] [PMID: 25359966]
[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.
[17]
Indefrey P. The spatial and temporal signatures of word production components: A critical update. Front Psychol 2011; 2(255): 255.
[http://dx.doi.org/10.3389/fpsyg.2011.00255] [PMID: 22016740]
[18]
Wingfield A, Grossman M. Language and the aging brain: Patterns of neural compensation revealed by functional brain imaging. J Neurophysiol 2006; 96(6): 2830-9.
[http://dx.doi.org/10.1152/jn.00628.2006] [PMID: 17110737]
[19]
Emch M, Bastian CC, Koch K. Neural correlates of verbal working memory: An fMRI meta-analysis. Front Hum Neurosci 2019; 13(180)
[20]
Nasios G, Dardiotis E, Messinis L. From broca and wernicke to the neuromodulation era: Insights of brain language networks for neurorehabilitation. Behav Neurol 2019; 2019: 9894571.
[http://dx.doi.org/10.1155/2019/9894571] [PMID: 31428210]
[21]
Methqal I, Marsolais Y, Wilson MA, Monchi O, Joanette Y. More expertise for a better perspective: Task and strategy driven adaptive neurofunctional reorganization for word production in high performing older adults. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2019; 26(2): 190-221.
[http://dx.doi.org/10.1080/13825585.2017.1423021] [PMID: 29334837]
[22]
Hoyau E, Boudiaf N, Cousin E, et al. Aging modulates the hemispheric specialization during word production. Front Aging Neurosci 2017; 9(125): 125.
[http://dx.doi.org/10.3389/fnagi.2017.00125] [PMID: 28536520]
[23]
Peele JE. Language and aging. In: Zubicaray GId, Schiller NO, Eds. The Oxford Handbook of Neurolinguistics. Oxford University Press 2019; pp. 295-316.
[http://dx.doi.org/10.31234/osf.io/xp29u]
[24]
Pleger B, Timmann D. The role of the human cerebellum in linguistic prediction, word generation and verbal working memory: Evidence from brain imaging, non invasive cerebellar stimulation and lesion studies. Neuropsychologia 2018; 115: 204-10.
[http://dx.doi.org/10.1016/j.neuropsychologia.2018.03.012] [PMID: 29530801]
[25]
De Looze C, Kelly F, Crosby L, et al. Changes in speech chunking in reading aloud is a marker of mild cognitive impairment and mild to moderate Alzheimer’s disease. Curr Alzheimer Res 2018; 15(9): 828-47.
[http://dx.doi.org/10.2174/1567205015666180404165017] [PMID: 29623841]
[26]
Swets B, Desmet T, Hambrick DZ, Ferreira F. The role of working memory in syntactic ambiguity resolution: A psychometric approach. J Exp Psychol Gen 2007; 136(1): 64-81.
[http://dx.doi.org/10.1037/0096-3445.136.1.64] [PMID: 17324085]
[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.
[28]
Swets B, Jacovina ME, Gerrig Richard J. Effects of conversational pressures on speech planning. Discourse Process 2013; 50(1): 23-51.
[http://dx.doi.org/10.1080/0163853X.2012.727719]
[29]
Ferreira F. Effects of length and syntactic complexity on initiation times for prepared utterances. J Mem Lang 1995; 30(2): 210-33.
[http://dx.doi.org/10.1016/0749-596X(91)90004-4]
[30]
Krivokapi J. Prosodic planning: Effects of phrasal length and complexity on pause duration. J Phonetics 2007; 35(2): 162-79.
[http://dx.doi.org/10.1016/j.wocn.2006.04.001] [PMID: 18379639]
[31]
Kemper S, Sumner A. The structure of verbal abilities in young and older adults. Psychol Aging 2001; 16(2): 312-22.
[http://dx.doi.org/10.1037/0882-7974.16.2.312] [PMID: 11405318]
[32]
Sandak R, Einar Mencl W, Frost SJ, Pugh KR. The neurobiological basis of skilled and impaired reading: Recent findings and new directions. Sci Stud Read 2004; 8(3): 273-92.
[http://dx.doi.org/10.1207/s1532799xssr0803_6]
[33]
Hirshorn EA, Dye MW, Hauser PC, Supalla TR, Bavelier D. Neural networks mediating sentence reading in the deaf. Front Hum Neurosci 2014; 8: 394.
[http://dx.doi.org/10.3389/fnhum.2014.00394] [PMID: 24959127]
[34]
Christodoulou JA, Del Tufo SN, Lymberis J, et al. Brain bases of reading fluency in typical reading and impaired fluency in dyslexia. PLoS One 2014; 9(7): e100552.
[http://dx.doi.org/10.1371/journal.pone.0100552] [PMID: 25058010]
[35]
Hampson M, Tokoglu F, Sun Z, et al. Connectivity-behavior analysis reveals that functional connectivity between left BA39 and Broca’s area varies with reading ability. Neuroimage 2006; 31(2): 513-9.
[http://dx.doi.org/10.1016/j.neuroimage.2005.12.040] [PMID: 16497520]
[36]
Xu J, Kemeny S, Park G, Frattali C, Braun A. Language in context: Emergent features of word, sentence, and narrative comprehension. Neuroimage 2005; 25(3): 1002-15.
[http://dx.doi.org/10.1016/j.neuroimage.2004.12.013] [PMID: 15809000]
[37]
Helder A, Broek P, Karlsson J, Van Leijenhorst L. Neural correlates of coherence-break detection during reading of narratives. Sci Stud Read 2017; 21(6): 463-79.
[http://dx.doi.org/10.1080/10888438.2017.1332065]
[38]
Bourguignon NJ. A rostro caudal axis for language in the frontal lobe: The role of executive control in speech production. Neurosci Biobehav Rev 2014; 47: 431-44.
[http://dx.doi.org/10.1016/j.neubiorev.2014.09.008] [PMID: 25305636]
[39]
Bonhage CE, Mueller JL, Friederici AD, Fiebach CJ. Combined eye tracking and fMRI reveals neural basis of linguistic predictions during sentence comprehension. Cortex 2015; 68: 33-47.
[http://dx.doi.org/10.1016/j.cortex.2015.04.011] [PMID: 26003489]
[40]
De Looze C, Dehsarvi A, Crosby L, et al. Cognitive and structural correlates of conversational speech timing in mild cognitive impairment and mild to moderate Alzheimer’s disease: Relevance for early detection approaches. Front Aging Neurosci 2021; 13: 637404.
[http://dx.doi.org/10.3389/fnagi.2021.637404] [PMID: 33986656]
[41]
Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 270-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.008] [PMID: 21514249]
[42]
Randolph C, Tierney MC, Mohr E, Chase TN. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. J Clin Exp Neuropsychol 1998; 20(3): 310-9.
[http://dx.doi.org/10.1076/jcen.20.3.310.823] [PMID: 9845158]
[43]
Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53(4): 695-9.
[http://dx.doi.org/10.1111/j.1532-5415.2005.53221.x] [PMID: 15817019]
[44]
Robertson IH, Manly T, Andrade J, Baddeley BT, Yiend J. Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia 1997; 35(6): 747-58.
[http://dx.doi.org/10.1016/S0028-3932(97)00015-8] [PMID: 9204482]
[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.
[46]
Fuchs S, Petrone C, Krivokapić J, Hoole P. Acoustic and respiratory evidence for utterance planning in German. J Phonetics 2013; 41(1): 29-47.
[http://dx.doi.org/10.1016/j.wocn.2012.08.007]
[47]
Boersma P. Praat: Doing phonetics by computer [Computer program] (Version 60 23). Amsterdam, The Netherlands 2016.
[48]
Dale AM, Fischl B, Sereno MI. Cortical surface based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999; 9(2): 179-94.
[http://dx.doi.org/10.1006/nimg.1998.0395] [PMID: 9931268]
[49]
Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 2010; 53(1): 1-15.
[http://dx.doi.org/10.1016/j.neuroimage.2010.06.010] [PMID: 20547229]
[50]
Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 2002; 33(3): 341-55.
[http://dx.doi.org/10.1016/S0896-6273(02)00569-X] [PMID: 11832223]
[51]
Han X, Jovicich J, Salat D, et al. Reliability of MRI derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. Neuroimage 2006; 32(1): 180-94.
[http://dx.doi.org/10.1016/j.neuroimage.2006.02.051] [PMID: 16651008]
[52]
Jovicich J, Czanner S, Greve D, et al. Reliability in multi-site structural MRI studies: Effects of gradient non linearity correction on phantom and human data. Neuroimage 2006; 30(2): 436-43.
[http://dx.doi.org/10.1016/j.neuroimage.2005.09.046] [PMID: 16300968]
[53]
Newman SD, Malaia E, Seo R, Cheng H. The effect of individual differences in working memory capacity on sentence comprehension: An FMRI study. Brain Topogr 2013; 26(3): 458-67.
[http://dx.doi.org/10.1007/s10548-012-0264-8] [PMID: 23124385]
[54]
Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 2017; 155: 530-48.
[http://dx.doi.org/10.1016/j.neuroimage.2017.03.057] [PMID: 28414186]
[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.
[58]
Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed effects models. Methods Ecol Evol 2013; 4(2): 133-42.
[http://dx.doi.org/10.1111/j.2041-210x.2012.00261.x]
[59]
Turner AJ, Miller JF. Introducing a cross platform open source cartesian genetic programming library. Genet Program Evolvable Mach 2015; 16(1): 83-91.
[http://dx.doi.org/10.1007/s10710-014-9233-1]
[60]
Qu BY, Zhu YS, Jiao YC, Wu MY, Suganthan PN, Liang JJ. A survey on multi objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm Evol Comput 2018; 38: 1-11.
[http://dx.doi.org/10.1016/j.swevo.2017.06.002]
[61]
Miller JF. Cartesian genetic programming: Its status and future. Genet Program Evolvable Mach 2019; 1-40.
[62]
Dehsarvi A, Liang SL. Classification of resting state fMRI for olfactory dysfunction in Parkinson’s disease using evolutionary algorithms.Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 264-265.
[http://dx.doi.org/10.1145/3205651.3205681]
[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.
[64]
Picardi C, Cosgrove J, Smith SL, Jamieson S, Alty JE. Objective assessment of cognitive impairment in Parkinson’s disease using evolutionary algorithm. European Conference on the Applications of Evolutionary Computation. Springer, Cham. 2017; 109-24.
[http://dx.doi.org/10.1007/978-3-319-55849-3_8]
[65]
Adaszewski S, Dukart J, Kherif F, Frackowiak R, Draganski B. How early can we predict Alzheimer’s disease using computational anatomy? Neurobiol Aging 2013; 34(12): 2815-26.
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.06.015] [PMID: 23890839]
[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.
[67]
Liu M, Zhang D, Shen D. View centralized multi atlas classification for Alzheimer’s disease diagnosis. Hum Brain Mapp 2015; 36(5): 1847-65.
[http://dx.doi.org/10.1002/hbm.22741] [PMID: 25624081]
[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.
[69]
Sørensen L, Igel C, Liv Hansen N, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp 2016; 37(3): 1148-61.
[http://dx.doi.org/10.1002/hbm.23091] [PMID: 26686837]
[70]
Costafreda SG, Dinov ID, Tu Z, et al. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage 2011; 56(1): 212-9.
[http://dx.doi.org/10.1016/j.neuroimage.2011.01.050] [PMID: 21272654]
[71]
Basaia S, Agosta F, Wagner L, et al. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019; 21: 101645.
[http://dx.doi.org/10.1016/j.nicl.2018.101645] [PMID: 30584016]
[72]
Hagoort P. On Broca, brain, and binding: A new framework. Trends Cogn Sci 2005; 9(9): 416-23.
[http://dx.doi.org/10.1016/j.tics.2005.07.004] [PMID: 16054419]
[73]
Ettinger VH, McAllister A, Lundberg P, Karlsson T, Engström M. Higher language ability is related to angular gyrus activation increase during semantic processing, independent of sentence incongruency. Front Hum Neurosci 2016; 10: 110.
[http://dx.doi.org/10.3389/fnhum.2016.00110] [PMID: 27014040]
[74]
Henderson JM, Choi W, Luke SG, Desai RH. Neural correlates of fixation duration in natural reading: Evidence from fixation related fMRI. Neuroimage 2015; 119: 390-7.
[http://dx.doi.org/10.1016/j.neuroimage.2015.06.072] [PMID: 26151101]
[75]
Pisella L. Visual perception is dependent on visuospatial working memory and thus on the posterior parietal cortex. Ann Phys Rehabil Med 2017; 60(3): 141-7.
[http://dx.doi.org/10.1016/j.rehab.2016.01.002] [PMID: 26926263]
[76]
Baldo JV, Dronkers NF. The role of inferior parietal and inferior frontal cortex in working memory. Neuropsychology 2006; 20(5): 529-38.
[http://dx.doi.org/10.1037/0894-4105.20.5.529] [PMID: 16938015]
[77]
Vallar G, Di Betta AM, Silveri MC. The phonological short term store rehearsal system: Patterns of impairment and neural correlates. Neuropsychologia 1997; 35(6): 795-812.
[http://dx.doi.org/10.1016/S0028-3932(96)00127-3] [PMID: 9204486]
[78]
Peters F, Collette F, Degueldre C, Sterpenich V, Majerus S, Salmon E. The neural correlates of verbal short term memory in Alzheimer’s disease: An fMRI study. Brain 2009; 132(Pt 7): 1833-46.
[http://dx.doi.org/10.1093/brain/awp075] [PMID: 19433442]
[79]
Caplan D. Functional neuroimaging studies of syntactic processing. J Psycholinguist Res 2001; 30(3): 297-320.
[http://dx.doi.org/10.1023/A:1010495018484] [PMID: 11523276]
[80]
Jobard G, Vigneau M, Mazoyer B, Tzourio MN. Impact of modality and linguistic complexity during reading and listening tasks. Neuroimage 2007; 34(2): 784-800.
[http://dx.doi.org/10.1016/j.neuroimage.2006.06.067] [PMID: 17110132]
[81]
Price CJ. A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage 2012; 62(2): 816-47.
[http://dx.doi.org/10.1016/j.neuroimage.2012.04.062] [PMID: 22584224]
[82]
Humphries C, Binder JR, Medler DA, Liebenthal E. Syntactic and semantic modulation of neural activity during auditory sentence comprehension. J Cogn Neurosci 2006; 18(4): 665-79.
[http://dx.doi.org/10.1162/jocn.2006.18.4.665] [PMID: 16768368]
[83]
Posthuma D, Baaré WF, Hulshoff Pol HE, Kahn RS, Boomsma DI, De Geus EJ. Genetic correlations between brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed. Twin Res 2003; 6(2): 131-9.
[http://dx.doi.org/10.1375/136905203321536254] [PMID: 12723999]
[84]
Mariën P, Ackermann H, Adamaszek M, et al. Consensus paper: Language and the cerebellum: An ongoing enigma. Cerebellum 2014; 13(3): 386-410.
[PMID: 24318484]
[85]
Prat CS, Keller TA, Just MA. Individual differences in sentence comprehension: A functional magnetic resonance imaging investigation of syntactic and lexical processing demands. J Cogn Neurosci 2007; 19(12): 1950-63.
[http://dx.doi.org/10.1162/jocn.2007.19.12.1950] [PMID: 17892384]
[86]
Prabhakaran V, Narayanan K, Zhao Z, Gabrieli JDE. Integration of diverse information in working memory within the frontal lobe. Nat Neurosci 2000; 3(1): 85-90.
[http://dx.doi.org/10.1038/71156] [PMID: 10607400]
[87]
Nissim NR, O’Shea AM, Bryant V, Porges EC, Cohen R, Woods AJ. Frontal structural neural correlates of working memory performance in older adults. Front Aging Neurosci 2017; 8: 328.
[http://dx.doi.org/10.3389/fnagi.2016.00328] [PMID: 28101053]
[88]
Singh CV, Husain M. The functional role of the inferior parietal lobe in the dorsal and ventral stream dichotomy. Neuropsychologia 2009; 47(6): 1434-48.
[http://dx.doi.org/10.1016/j.neuropsychologia.2008.11.033] [PMID: 19138694]
[89]
Krohn S, Froeling M, Leemans A, et al. Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple acquisition MRI. Hum Brain Mapp 2019; 40(11): 3299-320.
[http://dx.doi.org/10.1002/hbm.24599] [PMID: 31090254]
[90]
Ashraf GM, Chatzichronis S, Alexiou A, et al. BrainFD: Measuring the intracranial brain volume with fractal dimension. Front Aging Neurosci 2021; 13: 765185.
[http://dx.doi.org/10.3389/fnagi.2021.765185] [PMID: 34899274]
[91]
Narayanan G, Prabhu K, Chaudhuri A, et al. Neuro-protective effect of ayurveda formulation, saraswatharishtam, on scopolamine induced memory impairment in animal model. Pharmacogn J 2020; 12(3): 465-72.
[http://dx.doi.org/10.5530/pj.2020.12.72]
[92]
Patterson KE, Graham N, Hodges JR. Reading in dementia of the Alzheimer type: A preserved ability? Neuropsychology 1994; 8(3): 395-407.
[http://dx.doi.org/10.1037/0894-4105.8.3.395]
[93]
Lebowitz BK, Weinstein C, Beiser A, et al. Lifelong reading disorder and mild cognitive impairment: Implications for diagnosis. J Alzheimers Dis 2016; 50(1): 41-5.
[http://dx.doi.org/10.3233/JAD-150543] [PMID: 26639959]
[94]
Laws KR, Duncan A, Gale TM. ‘Normal’ semantic phonemic fluency discrepancy in Alzheimer’s disease? A meta-analytic study. Cortex 2010; 46(5): 595-601.
[http://dx.doi.org/10.1016/j.cortex.2009.04.009] [PMID: 19560132]
[95]
Weakley A, Schmitter EM, Anderson J. Analysis of verbal fluency ability in amnestic and non amnestic mild cognitive impairment. Arch Clin Neuropsychol 2013; 28(7): 721-31.
[http://dx.doi.org/10.1093/arclin/act058] [PMID: 23917346]
[96]
Vaughan RM, Coen RF, Kenny R, Lawlor BA. Preservation of the semantic verbal fluency advantage in a large population based sample: Normative data from the TILDA study. J Int Neuropsychol Soc 2016; 22(5): 570-6.
[http://dx.doi.org/10.1017/S1355617716000291] [PMID: 27055803]

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