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Multivariate Approaches to Understanding Aphasia and its Neural Substrates

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Abstract

Purpose of Review

Aphasia is often characterized in terms of subtype and severity, yet these constructs have limited explanatory power, because aphasia is inherently multifactorial both in its neural substrates and in its symptomatology. The purpose of this review is to survey current and emerging multivariate approaches to understanding aphasia.

Recent Findings

Techniques such as factor analysis and principal component analysis have been used to define latent underlying factors that can account for performance on batteries of speech and language tests, and for characteristics of spontaneous speech production. Multivariate lesion-symptom mapping has been shown to outperform univariate approaches to lesion-symptom mapping for identifying brain regions where damage is associated with specific speech and language deficits. It is increasingly clear that structural damage results in functional changes in wider neural networks, which mediate speech and language outcomes.

Summary

Multivariate statistical approaches are essential for understanding the complex relationships between the neural substrates of aphasia, and resultant profiles of speech and language function.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Wernicke C. Der Aphasische Symptomencomplex. Breslau: Cohn and Weigert; 1874.

    Google Scholar 

  2. Lichtheim L. On aphasia. Brain. 1885;7:433–84.

    Google Scholar 

  3. Geschwind N. Disconnexion syndromes in animals and man. I Brain. 1965;88:237–94.

    PubMed  CAS  Google Scholar 

  4. Geschwind N. Disconnexion syndromes in animals and man. II Brain. 1965;88:585–644.

    PubMed  CAS  Google Scholar 

  5. Goodglass H, Kaplan E. The assessment of aphasia and related disorders. 1st ed. Philadelphia: Lea and Febiger; 1972.

    Google Scholar 

  6. Marie P. La troisième circonvolution frontal gauche ne joue aucun rôle spécial dans la fonction du langage. Sem Médicale. 1906;26:241–7.

    Google Scholar 

  7. Goldstein K. Language and language disturbances. New York: Grune and Stratton; 1948.

    Google Scholar 

  8. Schuell H, Jenkins JJ, Jimenez-Pabon E. Aphasia in adults. New York: Harper & Row; 1964.

    Google Scholar 

  9. Corbetta M, Siegel JS, Shulman GL. On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury. Cortex. 2018;107:229–37.

    PubMed  PubMed Central  Google Scholar 

  10. Hickok G, Poeppel D. The cortical organization of speech processing. Nat Rev Neurosci. 2007;8:393–402.

    PubMed  CAS  Google Scholar 

  11. 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;62:816–47.

    PubMed  PubMed Central  Google Scholar 

  12. Wilson SM, Bautista A, McCarron A. Convergence of spoken and written language processing in the superior temporal sulcus. NeuroImage. 2018;171:62–74.

    PubMed  Google Scholar 

  13. Fedorenko E, Thompson-Schill SL. Reworking the language network. Trends Cogn Sci. 2014;18:120–6.

    PubMed  PubMed Central  Google Scholar 

  14. Geranmayeh F, Brownsett SLE, Wise RJS. Task-induced brain activity in aphasic stroke patients: what is driving recovery? Brain. 2014;137:2632–48.

    PubMed  PubMed Central  Google Scholar 

  15. Yen M, DeMarco AT, Wilson SM. Adaptive paradigms for mapping phonological regions in individual participants. NeuroImage. 2019;189:368–79.

    PubMed  Google Scholar 

  16. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62:42–52.

    PubMed  PubMed Central  CAS  Google Scholar 

  17. Turkeltaub PE, Messing S, Norise C, Hamilton RH. Are networks for residual language function and recovery consistent across aphasic patients? Neurology. 2011;76:1726–34.

    PubMed  PubMed Central  Google Scholar 

  18. Wilson SM, Brambati SM, Henry RG, Handwerker DA, Agosta F, Miller BL, et al. The neural basis of surface dyslexia in semantic dementia. Brain. 2009;132:71–86.

    PubMed  Google Scholar 

  19. Jones LV, Wepman JM. Dimensions of language performance in aphasia. J Speech Hear Res. 1961;4:220–32.

    PubMed  CAS  Google Scholar 

  20. Schuell H, Jenkins JJ, Carroll JB. A factor analysis of the Minnesota Test for Differential Diagnosis of Aphasia. J Speech Hear Res. 1962;5:349–69.

    PubMed  CAS  Google Scholar 

  21. Kertesz A, Phipps J. Numerical taxonomy of aphasia. Brain Lang. 1977;4:1–10.

    PubMed  CAS  Google Scholar 

  22. Clark C, Crockett DJ, Klonoff H. Factor analysis of the Porch Index of Communication Ability. Brain Lang. 1979;7:1–7.

    Google Scholar 

  23. Clark C, Crockett DJ, Klonoff H. Empirically derived groups in the assessment of recovery from aphasia. Brain Lang. 1979;7:240–51.

    PubMed  CAS  Google Scholar 

  24. Hanson WR, Riege WH, Metter EJ, Inman VW. Factor-derived categories of chronic aphasia. Brain Lang. 1982;15:369–80.

    PubMed  CAS  Google Scholar 

  25. Swinburn K, Porter G, Howard D. Comprehensive Aphasia Test. Hove: Psychology Press; 2004.

  26. Goodglass H, Kaplan E. The assessment of aphasia and related disorders. 2nd ed. Philadelphia: Lea and Febiger; 1983.

    Google Scholar 

  27. Hula WD, Doyle PJ, Stone CA, Austermann Hula SN, Kellough S, Wambaugh JL, et al. The Aphasia Communication Outcome Measure (ACOM): dimensionality, item bank calibration, and initial validation. J Speech Lang Hear Res. 2015;58:906–19.

    PubMed  Google Scholar 

  28. Butler RA, Ralph MAL, Woollams AM. Capturing multidimensionality in stroke aphasia: mapping principal behavioural components to neural structures. Brain. 2014;137:3248–66.

    PubMed  PubMed Central  Google Scholar 

  29. Mirman D, Chen Q, Zhang Y, Wang Z, Faseyitan OK, Coslett HB, et al. Neural organization of spoken language revealed by lesion-symptom mapping. Nat Commun. 2015;6:6762 Applied PCA to a battery of language measures to identify four underlying components, and then used lesion-symptom mapping to show that each component was associated with damage to specific brain regions.

    PubMed  PubMed Central  CAS  Google Scholar 

  30. Mirman D, Zhang Y, Wang Z, Coslett HB, Schwartz MF. The ins and outs of meaning: behavioral and neuroanatomical dissociation of semantically-driven word retrieval and multimodal semantic recognition in aphasia. Neuropsychologia. 2015;76:208–19.

    PubMed  PubMed Central  Google Scholar 

  31. Fridriksson J, Yourganov G, Bonilha L, Basilakos A, Den Ouden D-B, Rorden C. Revealing the dual streams of speech processing. Proc Natl Acad Sci U S A. 2016;113:15108–13.

    PubMed  PubMed Central  CAS  Google Scholar 

  32. Fridriksson J, den Ouden D-B, Hillis AE, Hickok G, Rorden C, Basilakos A, et al. Anatomy of aphasia revisited. Brain. 2018;141:848–62.

    PubMed  PubMed Central  Google Scholar 

  33. Halai AD, Woollams AM, Ralph MAL. Using principal component analysis to capture individual differences within a unified neuropsychological model of chronic post-stroke aphasia: revealing the unique neural correlates of speech fluency, phonology and semantics. Cortex. 2017;86:275–89.

    PubMed  PubMed Central  Google Scholar 

  34. Schwartz MF, Dell GS, Martin N, Gahl S, Sobel P. A case-series test of the interactive two-step model of lexical access: evidence from picture naming. J Mem Lang. 2006;54:228–64.

    Google Scholar 

  35. Wagenaar E, Snow C, Prins R. Spontaneous speech of aphasic patients: a psycholinguistic analysis. Brain Lang. 1975;2:281–303.

    PubMed  CAS  Google Scholar 

  36. Vermeulen J, Bastiaanse R, Van Wageningen B. Spontaneous speech in aphasia: a correlational study. Brain Lang. 1989;36:252–74.

    PubMed  CAS  Google Scholar 

  37. Casilio M, Rising K, Beeson PM, Bunton K, Wilson SM. Auditory-perceptual rating of connected speech in aphasia. Am J Speech Lang Pathol. 2019;28:550–68.

    PubMed  Google Scholar 

  38. DeDe G, Caplan D. Factor analysis of aphasic syntactic comprehension disorders. Aphasiology. 2006;20:123–35.

    Google Scholar 

  39. Caplan D, Waters G, DeDe G, Michaud J, Reddy A. A study of syntactic processing in aphasia I: behavioral (psycholinguistic) aspects. Brain Lang. 2007;101:103–50.

    PubMed  Google Scholar 

  40. Caplan D, Michaud J, Hufford R. Dissociations and associations of performance in syntactic comprehension in aphasia and their implications for the nature of aphasic deficits. Brain Lang. 2013;127:21–33.

    PubMed  PubMed Central  Google Scholar 

  41. Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess. 1995;7:286–99.

    Google Scholar 

  42. Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods. 1999;4:272–99.

    Google Scholar 

  43. Tabachnick BG, Fidell LS. Using multivariate statistics. 6th ed. Boston: Pearson; 2007.

    Google Scholar 

  44. Fergadiotis G, Wright HH, West TM. Measuring lexical diversity in narrative discourse of people with aphasia. Am J Speech Lang Pathol. 2013;22:S397–408.

    PubMed  Google Scholar 

  45. . Walker GM, Hickok G, Fridriksson J. A cognitive psychometric model for assessment of picture naming abilities in aphasia. Psychol Assess. 2018;30:809–26 Developed an innovative multidimensional cognitive psychometric model of picture-naming with theoretical and clinical utility, and provided preliminary validity evidence.

    PubMed  PubMed Central  Google Scholar 

  46. Batchelder WH. Multinomial processing tree models and psychological assessment. Psychol Assess. 1998;10:331–44.

    Google Scholar 

  47. Erdfelder E, Auer T-S, Hilbig BE, Aßfalg A, Moshagen M, Nadarevic L. Multinomial processing tree models: a review of the literature. J Psychol. 2009;217:108–24.

    Google Scholar 

  48. Lord FM, Novick MR, Birnbaum A. Statistical theories of mental test scores. Oxford: Addison-Wesley; 1968.

    Google Scholar 

  49. Edelen MO, Reeve BB. Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Qual Life Res. 2007;16:5–18.

    PubMed  Google Scholar 

  50. del Toro CM, Bislick LP, Comer M, Velozo C, Romero S, Rothi LJG, et al. Development of a short form of the Boston Naming Test for individuals with aphasia. J Speech Lang Hear Res. 2011;54:1089–100.

    PubMed  Google Scholar 

  51. Fergadiotis G, Kellough S, Hula WD. Item response theory modeling of the Philadelphia Naming Test. J Speech Lang Hear Res. 2015;58:865–77.

    PubMed  Google Scholar 

  52. Mohr JP. Broca’s area and Broca’s aphasia. In: Whitaker H, Whitaker H, editors. Studies in neurolinguistics, vol. 1. New York: Academic Press; 1976.

    Google Scholar 

  53. Damasio H, Grabowski TJ, Tranel D, Hichwa RD, Damasio AR. A neural basis for lexical retrieval. Nature. 1996;380:499–505.

    PubMed  CAS  Google Scholar 

  54. Bates E, Wilson SM, Saygin AP, Dick F, Sereno MI, Knight RT, et al. Voxel-based lesion-symptom mapping. Nat Neurosci. 2003;6:448–50.

    PubMed  CAS  Google Scholar 

  55. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. NeuroImage. 2000;11:805–21.

    PubMed  CAS  Google Scholar 

  56. Schwartz MF, Kimberg DY, Walker GM, Faseyitan O, Brecher A, Dell GS, et al. Anterior temporal involvement in semantic word retrieval: voxel-based lesion-symptom mapping evidence from aphasia. Brain. 2009;132:3411–27.

    PubMed  PubMed Central  Google Scholar 

  57. Wilson SM, Henry ML, Besbris M, Ogar JM, Dronkers NF, Jarrold W, et al. Connected speech production in three variants of primary progressive aphasia. Brain. 2010;133:2069–88.

    PubMed  PubMed Central  Google Scholar 

  58. Rorden C, Karnath HO. Using human brain lesions to infer function: a relic from a past era in the fMRI age? Nat Rev Neurosci. 2004;5:812–9.

    CAS  Google Scholar 

  59. Husain M, Nachev P. Space and the parietal cortex. Trends Cogn Sci. 2007;11:30–6.

    PubMed  PubMed Central  Google Scholar 

  60. Mah Y-H, Husain M, Rees G, Nachev P. Human brain lesion-deficit inference remapped. Brain. 2014;37:2522–31.

    Google Scholar 

  61. Herbet G, Lafargue G, Duffau H. Rethinking voxel-wise lesion-deficit analysis: a new challenge for computational neuropsychology. Cortex. 2015;64:413–6.

    PubMed  Google Scholar 

  62. Xu T, Jha A, Nachev P. The dimensionalities of lesion-deficit mapping. Neuropsychologia. 2018;115:134–41 Explained the negative consequences of univariate approaches to lesion-symptom mapping ignoring patterns of lesion distribution and distal effects of lesions, and advocated multivariate alternatives and systematic evaluation with synthetic data.

    PubMed  PubMed Central  Google Scholar 

  63. Inoue K, Madhyastha T, Rudrauf D, Mehta S, Grabowski T. What affects detectability of lesion-deficit relationships in lesion studies? NeuroImage Clin. 2014;6:388–97.

    PubMed  PubMed Central  Google Scholar 

  64. Sperber C, Karnath H-O. Impact of correction factors in human brain lesion-behavior inference. Hum Brain Mapp. 2017;38:1692–701.

    PubMed  Google Scholar 

  65. Wilson SM, Lam D, Babiak MC, Perry DW, Shih T, Hess CP, et al. Transient aphasias after left hemisphere resective surgery. J Neurosurg. 2015;123:581–93.

    PubMed  PubMed Central  Google Scholar 

  66. Bonilha L, Fridriksson J. Subcortical damage and white matter disconnection associated with non-fluent speech. Brain. 2009;132:e108.

    PubMed  Google Scholar 

  67. Corbetta M, Ramsey L, Callejas A, Baldassarre A, Hacker CD, Siegel JS, et al. Common behavioral clusters and subcortical anatomy in stroke. Neuron. 2015;85:927–41.

    PubMed  PubMed Central  CAS  Google Scholar 

  68. Siegel JS, Ramsey LE, Snyder AZ, Metcalf NV, Chacko RV, Weinberger K, et al. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci U S A. 2016;113:E4367–76 Many post-stroke deficits, including aphasia, can be predicted based on measure of functional connectivity, especially reduced interhemispheric communication.

    PubMed  PubMed Central  CAS  Google Scholar 

  69. Caplan D, Waters G, Kennedy D, Alpert N, Makris N, Dede G, et al. A study of syntactic processing in aphasia II: neurological aspects. Brain Lang. 2007;101:151–77.

    PubMed  Google Scholar 

  70. Wilson SM, Ogar JM, Laluz V, Growdon M, Jang J, Glenn S, et al. Automated MRI-based classification of primary progressive aphasia variants. NeuroImage. 2009;47:1558–67.

    PubMed  PubMed Central  Google Scholar 

  71. Zhang Y, Kimberg DY, Coslett HB, Schwartz MF, Wang Z. Multivariate lesion-symptom mapping using support vector regression. Hum Brain Mapp. 2014;35:5861–76.

    PubMed  PubMed Central  Google Scholar 

  72. Pustina D, Coslett HB, Ungar L, Faseyitan OK, Medaglia JD, Avants B, et al. Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions. Hum Brain Mapp. 2017;38:5603–15.

    PubMed  PubMed Central  Google Scholar 

  73. Hope TMH, Seghier ML, Leff AP, Price CJ. Predicting outcome and recovery after stroke with lesions extracted from MRI images. NeuroImage Clin. 2013;2:424–33.

    PubMed  PubMed Central  Google Scholar 

  74. Chen R, Hillis AE, Pawlak M, Herskovits EH. Voxelwise Bayesian lesion-deficit analysis. NeuroImage. 2008;40:1633–42.

    PubMed  PubMed Central  Google Scholar 

  75. Vapnik V. Statistical learning theory. New York: Wiley; 1998.

    Google Scholar 

  76. Yourganov G, Smith KG, Fridriksson J, Rorden C. Predicting aphasia type from brain damage measured with structural MRI. Cortex. 2015;73:203–15.

    PubMed  PubMed Central  Google Scholar 

  77. Yourganov G, Fridriksson J, Rorden C, Gleichgerrcht E, Bonilha L. Multivariate connectome-based symptom mapping in post-stroke patients: networks supporting language and speech. J Neurosci. 2016;36:6668–79.

    PubMed  PubMed Central  CAS  Google Scholar 

  78. Saur D, Ronneberger O, Kümmerer D, Mader I, Weiller C, Klöppel S. Early functional magnetic resonance imaging activations predict language outcome after stroke. Brain. 2010;133:1252–64.

    PubMed  Google Scholar 

  79. Xing S, Lacey EH, Skipper-Kallal LM, Jiang X, Harris-Love ML, Zeng J, et al. Right hemisphere grey matter structure and language outcomes in chronic left hemisphere stroke. Brain. 2015;139:227–41.

    PubMed  PubMed Central  Google Scholar 

  80. Lacey EH, Skipper-Kallal LM, Xing S, Fama ME, Turkeltaub PE. Mapping common aphasia assessments to underlying cognitive processes and their neural substrates. Neurorehabil Neural Repair. 2017;31:442–50.

    PubMed  PubMed Central  Google Scholar 

  81. DeMarco AT, Turkeltaub PE. A multivariate lesion symptom mapping toolbox and examination of lesion-volume biases and correction methods in lesion-symptom mapping. Hum Brain Mapp. 2018;39:4169–82.

    PubMed  Google Scholar 

  82. Pustina D, Avants B, Faseyitan OK, Medaglia JD, Coslett HB. Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations. Neuropsychologia. 2018;115:154–66 Described a new approach to multivariate lesion-symptom mapping, and demonstrated that it outperformed a univariate approach on simulated and real aphasia data.

    PubMed  Google Scholar 

  83. Thye M, Mirman D. Relative contributions of lesion location and lesion size to predictions of varied language deficits in post-stroke aphasia. NeuroImage Clin. 2018;20:1129–38.

    PubMed  PubMed Central  Google Scholar 

  84. Wilson SM, DeMarco AT, Henry ML, Gesierich B, Babiak M, Miller BL, et al. Variable disruption of a syntactic processing network in primary progressive aphasia. Brain. 2016;139:2994–3006.

    PubMed  PubMed Central  Google Scholar 

  85. Griffis JC, Nenert R, Allendorfer JB, Szaflarski JP. Linking left hemispheric tissue preservation to fMRI language task activation in chronic stroke patients. Cortex. 2017;96:1–18.

    PubMed  PubMed Central  Google Scholar 

  86. Griffis JC, Nenert R, Allendorfer JB, Vannest J, Holland S, Dietz A, et al. The canonical semantic network supports residual language function in chronic post-stroke aphasia. Hum Brain Mapp. 2017;38:1636–58.

    PubMed  Google Scholar 

  87. Skipper-Kallal LM, Lacey EH, Xing S, Turkeltaub PE. Functional activation independently contributes to naming ability and relates to lesion site in post-stroke aphasia: lesion site, activity, and naming in stroke. Hum Brain Mapp. 2017;38:2051–66.

    PubMed  Google Scholar 

  88. Skipper-Kallal LM, Lacey EH, Xing S, Turkeltaub PE. Right hemisphere remapping of naming functions depends on lesion size and location in poststroke aphasia. Neural Plast. 2017;2017:8740353.

    PubMed  PubMed Central  Google Scholar 

  89. Ramsey LE, Siegel JS, Lang CE, Strube M, Shulman GL, Corbetta M. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav. 2017;1:0038.

    PubMed  PubMed Central  Google Scholar 

  90. Siegel JS, Seitzman BA, Ramsey LE, Ortega M, Gordon EM, Dosenbach NUF, et al. Re-emergence of modular brain networks in stroke recovery. Cortex. 2018;101:44–59.

    PubMed  PubMed Central  Google Scholar 

  91. Bonilha L, Rorden C, Fridriksson J. Assessing the clinical effect of residual cortical disconnection after ischemic strokes. Stroke. 2014;45:988–93.

    PubMed  PubMed Central  Google Scholar 

  92. Marebwa BK, Fridriksson J, Yourganov G, Feenaughty L, Rorden C, Bonilha L. Chronic post-stroke aphasia severity is determined by fragmentation of residual white matter networks. Sci Rep. 2017;7:8188.

    PubMed  PubMed Central  Google Scholar 

  93. Seghier ML, Patel E, Prejawa S, Ramsden S, Selmer A, Lim L, et al. The PLORAS database: a data repository for predicting language outcome and recovery after stroke. NeuroImage. 2016;124:1208–12.

    PubMed  PubMed Central  Google Scholar 

  94. MacWhinney B, Fromm D. AphasiaBank as BigData. Semin Speech Lang. 2016;37:10–22.

    PubMed  PubMed Central  Google Scholar 

  95. Lukic S, Barbieri E, Wang X, Caplan D, Kiran S, Rapp B, et al. Right hemisphere grey matter volume and language functions in stroke aphasia. Neural Plast. 2017;2017:5601509.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors thank Andrew T. DeMarco for helpful comments on an earlier version of this manuscript.

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Authors and Affiliations

  1. Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Stephen M. Wilson

  2. Audiology and Speech Pathology Program, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA

    William D. Hula

  3. Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, USA

    William D. Hula

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Correspondence to Stephen M. Wilson.

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Stephen M. Wilson declares no potential conflicts of interest. William D. Hula reports grants from NIDCD (R01 DC013270, R21 DC016080), during the conduct of the study.

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Wilson, S.M., Hula, W.D. Multivariate Approaches to Understanding Aphasia and its Neural Substrates. Curr Neurol Neurosci Rep 19, 53 (2019). https://doi.org/10.1007/s11910-019-0971-6

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