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  • Review Article
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The neural and neurocomputational bases of recovery from post-stroke aphasia

Nature Reviews Neurology volume 16, pages 43–55 (2020)Cite this article

Abstract

Language impairment, or aphasia, is a disabling symptom that affects at least one third of individuals after stroke. Some affected individuals will spontaneously recover partial language function. However, despite a growing number of investigations, our understanding of how and why this recovery occurs is very limited. This Review proposes that existing hypotheses about language recovery after stroke can be conceptualized as specific examples of two fundamental principles. The first principle, degeneracy, dictates that different neural networks are able to adapt to perform similar cognitive functions, which would enable the brain to compensate for damage to any individual network. The second principle, variable neuro-displacement, dictates that there is spare capacity within or between neural networks, which, to save energy, is not used under standard levels of performance demand, but can be engaged under certain situations. These two principles are not mutually exclusive and might involve neural networks in both hemispheres. Most existing hypotheses are descriptive and lack a clear mechanistic account or concrete experimental evidence. Therefore, a better neurocomputational, mechanistic understanding of language recovery is required to inform research into new therapeutic interventions.

Key points

  • The mechanisms underlying recovery from post-stroke aphasia can be conceptualized as the engagement of degenerate networks or the use of spare capacity within or between networks via variable neuro-displacement.

  • Degenerate networks are not involved in the language task in the premorbid state, but can be engaged for that task after damage, either immediately or following experience-dependent plasticity.

  • Degenerate networks might include quiescent regions in the right hemisphere, the undamaged ventral or dorsal language pathway, or regions that supported a non-language activity before stroke.

  • The use of spare capacity within or between neural networks could be downregulated to save energy under standard levels of performance demand but upregulated when performance demand increases, for example when healthy individuals are performing a difficult task or in individuals after brain damage.

  • Spare capacity that might contribute to recovery from post-stroke aphasia includes the unaffected regions of damaged neural networks, or undamaged networks that perform other language-specific or domain-general executive functions.

  • Most theories of recovery from post-stroke aphasia are descriptive and lack concrete experimental evidence; a better understanding of the mechanisms underlying recovery, preferably in the form of computationally implemented models, is needed and the resultant mechanistic accounts will aid the design of therapeutic interventions.

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Fig. 1: The computationally implemented dual language pathways model.
Fig. 2: Potential mechanisms of recovery from post-stroke aphasia.

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References

  1. Broca, P. Sur le siège de la faculté du language articulé. Bull Soc. Anthropol. 6, 337–393 (1865).

    Google Scholar 

  2. Wernicke, C. Der aphasische Symptomencomplex, eine psychologische Studie auf anatomischer Basis (Cohn and Weigert, 1874).

    Google Scholar 

  3. Butler, R. A., Lambon Ralph, M. A. & Woollams, A. M. Capturing multidimensionality in stroke aphasia: mapping principal behavioural components to neural structures. Brain 137, 3248–3266 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. Lacey, E. H., Skipper-Kallal, L. M., Xing, S., Fama, M. E. & Turkeltaub, P. E. Mapping common aphasia assessments to underlying cognitive processes and their neural substrates. Neurorehabil. Neural Repair 31, 442–450 (2017).

    PubMed  PubMed Central  Google Scholar 

  5. Mirman, D. et al. Neural organization of spoken language revealed by lesion-symptom mapping. Nat. Commun. 6, 6762 (2015).

    CAS  PubMed  Google Scholar 

  6. Fridriksson, J. et al. Revealing the dual streams of speech processing. Proc. Natl Acad. Sci. USA 113, 15108–15113 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Benjamin, E. J. et al. Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation 135, e146–e603 (2017).

    PubMed  PubMed Central  Google Scholar 

  8. Engelter, S. T. et al. Epidemiology of aphasia attributable to first ischaemic stroke: incidence, severity, fluency, etiology, and thrombolysis. Stroke 37, 1379–1384 (2006).

    PubMed  Google Scholar 

  9. Boehme, A. K., Martin-Schild, S., Marshall, R. S. & Lazar, R. M. Effect of aphasia on acute stroke outcomes. Neurology 87, 2348–2354 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. Ellis, C., Simpson, A. N., Bonilha, H., Mauldin, P. D. & Simpson, K. N. The one-year attributable cost of poststroke aphasia. Stroke 43, 1429–1431 (2012).

    PubMed  PubMed Central  Google Scholar 

  11. Tsouli, S., Kyritsis, A. P., Tsagalis, G., Virvidaki, E. & Vemmos, K. N. Significance of aphasia after first-ever acute stroke: impact on early and late outcomes. Neuroepidemiology 33, 96–102 (2009).

    CAS  PubMed  Google Scholar 

  12. Lomas, J. & Kertesz, A. Patterns of spontaneous recovery in aphasic groups: a study of adult stroke patients. Brain Lang. 5, 388–401 (1978).

    CAS  PubMed  Google Scholar 

  13. Yagata, S. A. et al. Rapid recovery from aphasia after infarction of Wernicke’s area. Aphasiology 31, 951–980 (2017).

    PubMed  Google Scholar 

  14. Maas, M. B. et al. The prognosis for aphasia in stroke. J. Stroke Cerebrovasc. Dis. 21, 350–357 (2012).

    PubMed  Google Scholar 

  15. Pedersen, P. M., Jørgensen, H. S., Nakayama, H., Raaschou, H. O. & Olsen, T. S. Aphasia in acute stroke: incidence, determinants, and recovery. Ann. Neurol. 38, 659–666 (1995).

    CAS  PubMed  Google Scholar 

  16. Hope, T. M. H. et al. Right hemisphere structural adaptation and changing language skills years after left hemisphere stroke. Brain 140, 1718–1728 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. Elkana, O., Frost, R., Kramer, U., Ben-Bashat, D. & Schweiger, A. Cerebral language reorganization in the chronic stage of recovery: a longitudinal fMRI study. Cortex 49, 71–81 (2013).

    PubMed  Google Scholar 

  18. Laska, A. C., Hellblom, A., Murray, V., Kahan, T. & Von Arbin, M. Aphasia in acute stroke and relation to outcome. J. Intern. Med. 249, 413–422 (2001).

    CAS  PubMed  Google Scholar 

  19. El Hachioui, H. et al. Screening tests for aphasia in patients with stroke: a systematic review. J. Neurol. 264, 211–220 (2017).

    CAS  PubMed  Google Scholar 

  20. Wade, D. T., Hewer, R. L., David, R. M. & Enderby, P. M. Aphasia after stroke: natural history and associated deficits. J. Neurol. Neurosurg. Psychiatry 49, 11–16 (1986).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Gilmore, N., Meier, E. L., Johnson, J. P. & Kiran, S. Non-linguistic cognitive factors predict treatment-induced recovery in chronic post-stroke aphasia. Arch. Phys. Med. Rehabil. 100, 1251–1258 (2019).

    PubMed  PubMed Central  Google Scholar 

  22. Seghier, M. L. et al. The PLORAS database: a data repository for predicting language outcome and recovery after stroke. Neuroimage 124, 1208–1212 (2016).

    PubMed  Google Scholar 

  23. Ramsey, L. E. et al. Behavioural clusters and predictors of performance during recovery from stroke. Nat. Hum. Behav. 1, 38 (2017).

    Google Scholar 

  24. Siegel, J. S. et al. Re-emergence of modular brain networks in stroke recovery. Cortex 101, 44–59 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. Hillis, A. E. et al. Predicting recovery in acute poststroke aphasia. Ann. Neurol. 83, 612–622 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Goodglass, H. & Kaplan, E. in The Assessment of Aphasia and Related Disorders (Lea & Febiger, 1983).

    Google Scholar 

  27. Patterson, K. & Ralph, M. A. Selective disorders of reading? Curr. Opin. Neurobiol. 9, 235–239 (1999).

    CAS  PubMed  Google Scholar 

  28. Saur, D. et al. Ventral and dorsal pathways for language. Proc. Natl Acad. Sci. USA 105, 18035–18040 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Heilman, K. M. Aphasia and the diagram makers revisited: an update of information processing models. J. Clin. Neurol. 2, 149–162 (2006).

    PubMed  PubMed Central  Google Scholar 

  30. Weiller, C., Bormann, T., Saur, D., Musso, M. & Rijntjes, M. How the ventral pathway got lost: and what its recovery might mean. Brain Lang. 118, 29–39 (2011).

    PubMed  Google Scholar 

  31. Friederici, A. D. & Gierhan, S. M. The language network. Curr. Opin. Neurobiol. 23, 250–254 (2013).

    CAS  PubMed  Google Scholar 

  32. Hickok, G. & Poeppel, D. Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language. Cognition 92, 67–99 (2004).

    PubMed  Google Scholar 

  33. Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8, 393–402 (2007).

    CAS  PubMed  Google Scholar 

  34. Saur, D. et al. Combining functional and anatomical connectivity reveals brain networks for auditory language comprehension. Neuroimage 49, 3187–3197 (2010).

    PubMed  Google Scholar 

  35. Ueno, T., Saito, S., Rogers, T. & Lambon Ralph, M. Lichtheim 2: synthesizing aphasia and the neural basis of language in a neurocomputational model of the dual dorsal-ventral language pathways. Neuron 72, 385–396 (2011).

    CAS  PubMed  Google Scholar 

  36. Kummerer, D. et al. Damage to ventral and dorsal language pathways in acute aphasia. Brain 136, 619–629 (2013).

    PubMed  PubMed Central  Google Scholar 

  37. Gajardo-Vidal, A. et al. How right hemisphere damage after stroke can impair speech comprehension. Brain 141, 3389–3404 (2018).

    PubMed  PubMed Central  Google Scholar 

  38. Hickok, G. et al. Bilateral capacity for speech sound processing in auditory comprehension: evidence from Wada procedures. Brain Lang. 107, 179–184 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Crinion, J. T., Lambon-Ralph, M. A., Warburton, E. A., Howard, D. & Wise, R. J. Temporal lobe regions engaged during normal speech comprehension. Brain 126, 1193–1201 (2003).

    PubMed  Google Scholar 

  40. Rice, G. E., Lambon Ralph, M. A. & Hoffman, P. The roles of left versus right anterior temporal lobes in conceptual knowledge: an ALE meta-analysis of 97 functional neuroimaging studies. Cereb. Cortex 25, 4374–4391 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. Halai, A. D., Parkes, L. M. & Welbourne, S. R. Dual-echo fMRI can detect activations in inferior temporal lobe during intelligible speech comprehension. Neuroimage 122, 214–221 (2015).

    PubMed  Google Scholar 

  42. Fridriksson, J. et al. Modulation of frontal lobe speech areas associated with the production and perception of speech movements. J. Speech Lang. Hear. Res. 52, 812–819 (2009).

    PubMed  Google Scholar 

  43. Fedorenko, E., Behr, M. K. & Kanwisher, N. Functional specificity for high-level linguistic processing in the human brain. Proc. Natl Acad. Sci. USA 108, 16428–16433 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Warburton, E., Price, C. J., Swinburn, K. & Wise, R. J. Mechanisms of recovery from aphasia: evidence from positron emission tomography studies. J. Neurol. Neurosurg. Psychiatry 66, 155–161 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Walenski, M., Europa, E., Caplan, D. & Thompson, C. K. Neural networks for sentence comprehension and production: an ALE-based meta-analysis of neuroimaging studies. Hum. Brain Mapp. 40, 2275–2304 (2019).

    PubMed  PubMed Central  Google Scholar 

  46. Mazoyer, B. et al. Gaussian mixture modelling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness. PLOS ONE 9, e101165 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. Gordon, P. C., Hendrick, R. & Levine, W. H. Memory-load interference in syntactic processing. Psychol. Sci. 13, 425–430 (2002).

    PubMed  Google Scholar 

  48. Carretti, B., Borella, E., Cornoldi, C. & De Beni, R. Role of working memory in explaining the performance of individuals with specific reading comprehension difficulties: a meta-analysis. Learn. Individ. Differ. 19, 246–251 (2009).

    Google Scholar 

  49. Brownsett, S. L. et al. Cognitive control and its impact on recovery from aphasic stroke. Brain 137, 242–254 (2014).

    PubMed  Google Scholar 

  50. Mitchell, R. L., Vidaki, K. & Lavidor, M. The role of left and right dorsolateral prefrontal cortex in semantic processing: a transcranial direct current stimulation study. Neuropsychologia 91, 480–489 (2016).

    PubMed  Google Scholar 

  51. Szalay, G. et al. Microglia protect against brain injury and their selective elimination dysregulates neuronal network activity after stroke. Nat. Commun. 7, 11499 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Hillis, A. E. & Heidler, J. Mechanisms of early aphasia recovery. Aphasiology 16, 885–895 (2002).

    Google Scholar 

  53. Fu, Y., Liu, Q., Anrather, J. & Shi, F. D. Immune interventions in stroke. Nat. Rev. Neurol. 11, 524–535 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Dell, G. S., Schwartz, M. F., Martin, N., Saffran, E. M. & Gagnon, D. A. Lexical access in aphasic and nonaphasic speakers. Psychol. Rev. 104, 801–838 (1997).

    CAS  PubMed  Google Scholar 

  55. Teufel, C. & Fletcher, P. C. The promises and pitfalls of applying computational models to neurological and psychiatric disorders. Brain 139, 2600–2608 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. Tononi, G., Sporns, O. & Edelman, G. M. Measures of degeneracy and redundancy in biological networks. Proc. Natl Acad. Sci. USA 96, 3257–3262 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Edelman, G. M. & Gally, J. A. Degeneracy and complexity in biological systems. Proc. Natl Acad. Sci. USA 98, 13763–13768 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Price, C. J. & Friston, K. J. Degeneracy and cognitive anatomy. Trends Cogn. Sci. 6, 416–421 (2002).

    PubMed  Google Scholar 

  59. Finger, S., Buckner, R. L. & Buckingham, H. Does the right hemisphere take over after damage to Broca’s area? the Barlow case of 1877 and its history. Brain Lang. 85, 385–395 (2003).

    PubMed  Google Scholar 

  60. Grafman, J. Conceptualizing functional neuroplasticity. J. Commun. Disord. 33, 345–356 (2000).

    CAS  PubMed  Google Scholar 

  61. Qiu, W. H. et al. Evidence of cortical reorganization of language networks after stroke with subacute Broca’s aphasia: a blood oxygenation level dependent-functional magnetic resonance imaging study. Neural. Regen. Res. 12, 109–117 (2017).

    PubMed  PubMed Central  Google Scholar 

  62. Robson, H. et al. The anterior temporal lobes support residual comprehension in Wernicke’s aphasia. Brain 137, 931–943 (2014).

    PubMed  PubMed Central  Google Scholar 

  63. Turkeltaub, P. E., Messing, S., Norise, C. & Hamilton, R. H. Are networks for residual language function and recovery consistent across aphasic patients? Neurology 76, 1726–1734 (2011).

    PubMed  PubMed Central  Google Scholar 

  64. Crinion, J. & Price, C. J. Right anterior superior temporal activation predicts auditory sentence comprehension following aphasic stroke. Brain 128, 2858–2871 (2005).

    PubMed  Google Scholar 

  65. Griffis, J. C. et al. The canonical semantic network supports residual language function in chronic post-stroke aphasia. Hum. Brain Mapp. 38, 1636–1658 (2017).

    PubMed  Google Scholar 

  66. Skipper-Kallal, L. M., Lacey, E. H., Xing, S. & Turkeltaub, P. E. Right hemisphere remapping of naming functions depends on lesion size and location in poststroke aphasia. Neural. Plast. 2017, 8740353 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. Cardebat, D. et al. Behavioural and neurofunctional changes over time in healthy and aphasic subjects: a PET language activation study. Stroke 34, 2900–2906 (2003).

    PubMed  Google Scholar 

  68. Blank, S. C., Bird, H., Turkheimer, F. & Wise, R. J. Speech production after stroke: the role of the right pars opercularis. Ann. Neurol. 54, 310–320 (2003).

    PubMed  Google Scholar 

  69. Thiel, A. et al. Plasticity of language networks in patients with brain tumours: a positron emission tomography activation study. Ann. Neurol. 50, 620–629 (2001).

    CAS  PubMed  Google Scholar 

  70. Crisp, J. & Lambon Ralph, M. A. Unlocking the nature of the phonological-deep dyslexia continuum: the keys to reading aloud are in phonology and semantics. J. Cogn. Neurosci. 18, 348–362 (2006).

    PubMed  Google Scholar 

  71. Hartwigsen, G. et al. Rapid short-term reorganization in the language network. eLife 6, e25964 (2017).

    PubMed  PubMed Central  Google Scholar 

  72. Xu, J. S. et al. Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial IA. Neuroimage 79, 62–71 (2013).

    PubMed  Google Scholar 

  73. Geranmayeh, F., Leech, R. & Wise, R. J. Network dysfunction predicts speech production after left hemisphere stroke. Neurology 86, 1296–1305 (2016).

    PubMed  PubMed Central  Google Scholar 

  74. Southwell, D. G., Hervey-Jumper, S. L., Perry, D. W. & Berger, M. S. Intraoperative mapping during repeat awake craniotomy reveals the functional plasticity of adult cortex. J. Neurosurg. 124, 1460–1469 (2016).

    PubMed  Google Scholar 

  75. Collignon, O. et al. Impact of blindness onset on the functional organization and the connectivity of the occipital cortex. Brain 136, 2769–2783 (2013).

    PubMed  Google Scholar 

  76. Kujala, T. et al. Electrophysiological evidence for cross-modal plasticity in humans with early- and late-onset blindness. Psychophysiology 34, 213–216 (1997).

    CAS  PubMed  Google Scholar 

  77. Burton, H. & McLaren, D. G. Visual cortex activation in late-onset, braille naive blind individuals: an fMRI study during semantic and phonological tasks with heard words. Neurosci. Lett. 392, 38–42 (2006).

    CAS  PubMed  Google Scholar 

  78. Anderson, C. A., Wiggins, I. M., Kitterick, P. T. & Hartley, D. E. H. Adaptive benefit of cross-modal plasticity following cochlear implantation in deaf adults. Proc. Natl Acad. Sci. USA 114, 10256–10261 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Saur, D. et al. Dynamics of language reorganization after stroke. Brain 129, 1371–1384 (2006).

    PubMed  Google Scholar 

  80. Keidel, J. L., Welbourne, S. R. & Lambon Ralph, M. A. Solving the paradox of the equipotential and modular brain: a neurocomputational model of stroke vs. slow-growing glioma. Neuropsychologia 48, 1716–1724 (2010).

    PubMed  Google Scholar 

  81. Raichle, M. E. & Gusnard, D. A. Appraising the brain’s energy budget. Proc. Natl Acad. Sci. USA 99, 10237–10239 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Niven, J. E. Neuronal energy consumption: biophysics, efficiency and evolution. Curr. Opin. Neurobiol. 41, 129–135 (2016).

    CAS  PubMed  Google Scholar 

  83. Manring, N. D. & Johnson, R. E. Modelling and designing a variable-displacement open-loop pump. J. Dyn. Syst. Meas. Control. 118, 267–271 (1996).

    Google Scholar 

  84. Fearnley, J. M. & Lees, A. J. Ageing and Parkinson disease: substantia nigra regional selectivity. Brain 114, 2283–2301 (1991).

    PubMed  Google Scholar 

  85. Farah, M. J. & McClelland, J. L. A computational model of semantic memory impairment - modality specificity and emergent category specificity. J. Exp. Psychol. Gen. 120, 339–357 (1991).

    CAS  PubMed  Google Scholar 

  86. Szaflarski, J. P., Allendorfer, J. B., Banks, C., Vannest, J. & Holland, S. K. Recovered vs. not-recovered from post-stroke aphasia: the contributions from the dominant and non-dominant hemispheres. Restor. Neurol. Neurosci. 31, 347–360 (2013).

    PubMed  PubMed Central  Google Scholar 

  87. Heiss, W. D., Kessler, J., Thiel, A., Ghaemi, M. & Karbe, H. Differential capacity of left and right hemispheric areas for compensation of poststroke aphasia. Ann. Neurol. 45, 430–438 (1999).

    CAS  PubMed  Google Scholar 

  88. Postman-Caucheteux, W. A. et al. Single-trial fMRI shows contralesional activity linked to overt naming errors in chronic aphasic patients. J. Cogn. Neurosci. 22, 1299–1318 (2010).

    PubMed  PubMed Central  Google Scholar 

  89. Szaflarski, J. P. et al. Poststroke aphasia recovery assessed with functional magnetic resonance imaging and a picture identification task. J. Stroke Cerebrovasc. Dis. 20, 336–345 (2011).

    PubMed  Google Scholar 

  90. van Oers, C. A. et al. Contribution of the left and right inferior frontal gyrus in recovery from aphasia. A functional MRI study in stroke patients with preserved hemodynamic responsiveness. Neuroimage 49, 885–893 (2010).

    PubMed  Google Scholar 

  91. Fridriksson, J. Preservation and modulation of specific left hemisphere regions is vital for treated recovery from anomia in stroke. J. Neurosci. 30, 11558–11564 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Rice, G. E., Caswell, H., Moore, P., Lambon Ralph, M. A. & Hoffman, P. Revealing the dynamic modulations that underpin a resilient neural network for semantic cognition: an FMRI investigation in patients with anterior temporal lobe resection. Cereb. Cortex 28, 3004–3016 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. Binney, R. J. & Ralph, M. A. L. Using a combination of fMRI and anterior temporal lobe rTMS to measure intrinsic and induced activation changes across the semantic cognition network. Neuropsychologia 76, 170–181 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. Jung, J. & Ralph, M. A. L. Mapping the dynamic network interactions underpinning cognition: a cTBS-fMRI study of the flexible adaptive neural system for semantics. Cereb. Cortex 26, 3580–3590 (2016).

    PubMed  PubMed Central  Google Scholar 

  95. Welbourne, S. R., Woollams, A. M., Crisp, J. & Ralph, M. A. L. The role of plasticity-related functional reorganization in the explanation of central dyslexias. Cogn. Neuropsychol. 28, 65–108 (2011).

    PubMed  Google Scholar 

  96. Hagoort, P., Wassenaar, M. & Brown, C. Real-time semantic compensation in patients with agrammatic comprehension: electrophysiological evidence for multiple-route plasticity. Proc. Natl Acad. Sci. USA 100, 4340–4345 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Crittenden, B. M., Mitchell, D. J. & Duncan, J. Task encoding across the multiple demand cortex is consistent with a frontoparietal and cingulo-opercular dual networks distinction. J. Neurosci. 36, 6147–6155 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Fedorenko, E., Duncan, J. & Kanwisher, N. Broad domain generality in focal regions of frontal and parietal cortex. Proc. Natl Acad. Sci. USA 110, 16616–16621 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Woolgar, A., Bor, D. & Duncan, J. Global increase in task-related fronto-parietal activity after focal frontal lobe lesion. J. Cogn. Neurosci. 25, 1542–1552 (2013).

    PubMed  Google Scholar 

  100. Murray, L. L. The effects of varying attentional demands on the word retrieval skills of adults with aphasia, right hemisphere brain damage, or no brain damage. Brain Lang. 72, 40–72 (2000).

    CAS  PubMed  Google Scholar 

  101. Murray, L. L. Attention and other cognitive deficits in aphasia: presence and relation to language and communication measures. Am. J. Speech Lang. Pathol. 21, s51–s64 (2012).

    PubMed  Google Scholar 

  102. Su, C.-Y., Wuang, Y.-P., Lin, Y.-H. & Su, J.-H. The role of processing speed in post-stroke cognitive dysfunction. Arch. Clin. Neuropsychol. 30, 148–160 (2015).

    PubMed  Google Scholar 

  103. Rajtar-Zembaty, A. et al. Application of the trail making test in the assessment of cognitive flexibility in patients with speech disorders after ischaemic cerebral stroke. Aktual. Neurol. 15, 11–17 (2015).

    Google Scholar 

  104. Sharp, D. J., Turkheimer, F. E., Bose, S. K., Scott, S. K. & Wise, R. J. Increased frontoparietal integration after stroke and cognitive recovery. Ann. Neurol. 68, 753–756 (2010).

    PubMed  Google Scholar 

  105. Allendorfer, J. B., Kissela, B. M., Holland, S. K. & Szaflarski, J. P. Different patterns of language activation in post-stroke aphasia are detected by overt and covert versions of the verb generation fMRI task. Med. Sci. Monit. 18, CR135–CR137 (2012).

    PubMed  PubMed Central  Google Scholar 

  106. Barlow, T. On a case of double cerebral hemiplegia, with cerebral symmetrical lesions. Br. Med. J. 2, 103–104 (1877).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Gowers, W. R. in A Manual of Diseases of the Nervous System 2nd edn Vol. 2 (ed. Blakiston, P.) 110–125 (P. Blakiston, Son & Co, 1893).

  108. Dunst, B. et al. Neural efficiency as a function of task demands. Intelligence 42, 22–30 (2014).

    PubMed  PubMed Central  Google Scholar 

  109. Morcom, A. M. & Henson, R. N. A. Increased prefrontal activity with ageing reflects nonspecific neural responses rather than compensation. J. Neurosci. 38, 7303–7313 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Winhuisen, L. et al. Role of the contralateral inferior frontal gyrus in recovery of language function in poststroke aphasia: a combined repetitive transcranial magnetic stimulation and positron emission tomography study. Stroke 36, 1759–1763 (2005).

    PubMed  Google Scholar 

  111. Leff, A. et al. A physiological change in the homotopic cortex following left posterior temporal lobe infarction. Ann. Neurol. 51, 553–558 (2002).

    PubMed  Google Scholar 

  112. Schofield, T. M. et al. Changes in auditory feedback connections determine the severity of speech processing deficits after stroke. J. Neurosci. 32, 4260–4270 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Lee, Y. S., Zreik, J. T. & Hamilton, R. H. Patterns of neural activity predict picture-naming performance of a patient with chronic aphasia. Neuropsychologia 94, 52–60 (2017).

    PubMed  Google Scholar 

  114. Fischer-Baum, S., Jang, A. & Kajander, D. The cognitive neuroplasticity of reading recovery following chronic stroke: a representational similarity analysis approach. Neural Plast. 2017, 2761913 (2017).

    PubMed  PubMed Central  Google Scholar 

  115. Saur, D. et al. Early functional magnetic resonance imaging activations predict language outcome after stroke. Brain 133, 1252–1264 (2010).

    PubMed  Google Scholar 

  116. Tyler, L. K., Wright, P., Randall, B., Marslen-Wilson, W. D. & Stamatakis, E. A. Reorganization of syntactic processing following left-hemisphere brain damage: does right-hemisphere activity preserve function? Brain 133, 3396–3408 (2010).

    PubMed  PubMed Central  Google Scholar 

  117. Heiss, W. D. & Thiel, A. A proposed regional hierarchy in recovery of post-stroke aphasia. Brain Lang. 98, 118–123 (2006).

    PubMed  Google Scholar 

  118. Ferbert, A. et al. Interhemispheric inhibition of the human motor cortex. J. Physiol. 453, 525–546 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Corbetta, M., Kincade, M. J., Lewis, C., Snyder, A. Z. & Sapir, A. Neural basis and recovery of spatial attention deficits in spatial neglect. Nat. Neurosci. 8, 1603–1610 (2005).

    CAS  PubMed  Google Scholar 

  120. Thiel, A. et al. Effects of noninvasive brain stimulation on language networks and recovery in early poststroke aphasia. Stroke 44, 2240–2246 (2013).

    PubMed  Google Scholar 

  121. Schapiro, A. C., McClelland, J. L., Welbourne, S. R., Rogers, T. T. & Lambon Ralph, M. A. Why bilateral damage is worse than unilateral damage to the brain. J. Cogn. Neurosci. 25, 2107–2123 (2013).

    PubMed  Google Scholar 

  122. Berthier, M. L., Pulvermuller, F., Davila, G., Casares, N. G. & Gutierrez, A. Drug therapy of post-stroke aphasia: a review of current evidence. Neuropsychol. Rev. 21, 302–317 (2011).

    PubMed  Google Scholar 

  123. Castren, E. & Hen, R. Neuronal plasticity and antidepressant actions. Trends Neurosci. 36, 259–267 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Ramanathan, D., Tuszynski, M. H. & Conner, J. M. The basal forebrain cholinergic system is required specifically for behaviourally mediated cortical map plasticity. J. Neurosci. 29, 5992–6000 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Woodhead, Z. V. et al. Auditory training changes temporal lobe connectivity in ‘Wernicke’s aphasia’: a randomized trial. J. Neurol. Neurosurg. Psychiatry 88, 586–594 (2017).

    PubMed  Google Scholar 

  126. Naidech, A. M. et al. Phenytoin exposure is associated with functional and cognitive disability after subarachnoid hemorrhage. Stroke 36, 583–587 (2005).

    CAS  PubMed  Google Scholar 

  127. Conroy, P., Sotiropoulou Drosopoulou, C., Humphreys, G. F., Halai, A. D. & Lambon Ralph, M. A. Time for a quick word? The striking benefits of training speed and accuracy of word retrieval in post-stroke aphasia. Brain 141, 1815–1827 (2018).

    PubMed  Google Scholar 

  128. Woodhead, Z. V. J. et al. Randomized trial of iReadMore word reading training and brain stimulation in central alexia. Brain 141, 2127–2141 (2018).

    PubMed  PubMed Central  Google Scholar 

  129. Zumbansen, A., Peretz, I. & Hebert, S. Melodic intonation therapy: back to basics for future research. Front. Neurol. 5, 11 (2014).

    Google Scholar 

  130. Berthier, M. L. & Pulvermuller, F. Neuroscience insights improve neurorehabilitation of poststroke aphasia. Nat. Rev. Neurol. 7, 86–97 (2011).

    PubMed  Google Scholar 

  131. Dignam, J. et al. Intensive versus distributed aphasia therapy: a nonrandomized, parallel-group, dosage-controlled study. Stroke 46, 2206–2211 (2015).

    PubMed  Google Scholar 

  132. Dignam, J. K., Rodriguez, A. D. & Copland, D. A. Evidence for intensive aphasia therapy: consideration of theories from neuroscience and cognitive psychology. PM R 8, 254–267 (2016).

    PubMed  Google Scholar 

  133. Plaut, D. C., McClelland, J. L., Seidenberg, M. S. & Patterson, K. Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychol. Rev. 103, 56–115 (1996).

    CAS  PubMed  Google Scholar 

  134. Bucur, M. & Papagno, C. Are transcranial brain stimulation effects long-lasting in post-stroke aphasia? A comparative systematic review and meta-analysis on naming performance. Neurosci. Biobehav. Rev. 102, 264–289 (2019).

    PubMed  Google Scholar 

  135. Ren, C. L. et al. Effect of low-frequency rTMS on aphasia in stroke patients: a meta-analysis of randomized controlled trials. PLOS ONE 9, e102557 (2014).

    PubMed  PubMed Central  Google Scholar 

  136. Wiethoff, S., Hamada, M. & Rothwell, J. C. Variability in response to transcranial direct current stimulation of the motor cortex. Brain Stimul. 7, 468–475 (2014).

    PubMed  Google Scholar 

  137. Lopez-Alonso, V., Cheeran, B., Rio-Rodriguez, D. & Fernandez-del-Olmo, M. Inter-individual variability in response to non-invasive brain stimulation paradigms. Brain Stimul. 7, 372–380 (2014).

    PubMed  Google Scholar 

  138. Sliwinska, M. W. et al. Stimulating multiple-demand cortex enhances vocabulary learning. J. Neurosci. 37, 7606–7618 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Elsner, B., Kugler, J., Pohl, M. & Mehrholz, J. Transcranial direct current stimulation (tDCS) for improving aphasia in patients with aphasia after stroke. Cochrane Database Syst. Rev. 5, CD009760 (2015).

    Google Scholar 

  140. Chalela, J. A. et al. Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet 369, 293–298 (2007).

    PubMed  PubMed Central  Google Scholar 

  141. O’Brien, P., Sellar, R. J. & Wardlaw, J. M. Fogging on T2-weighted MR after acute ischaemic stroke: how often might this occur and what are the implications? Neuroradiology 46, 635–641 (2004).

    PubMed  Google Scholar 

  142. Wieshmann, U. C. et al. Reduced anisotropy of water diffusion in structural cerebral abnormalities demonstrated with diffusion tensor imaging. Magn. Reson. Imaging 17, 1269–1274 (1999).

    CAS  PubMed  Google Scholar 

  143. Gong, G. L. et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex 19, 524–536 (2009).

    PubMed  Google Scholar 

  144. Marebwa, B. K. et al. Chronic post-stroke aphasia severity is determined by fragmentation of residual white matter networks. Sci. Rep. 7, 8188 (2017).

    PubMed  PubMed Central  Google Scholar 

  145. Ivanova, M. V. et al. Diffusion-tensor imaging of major white matter tracts and their role in language processing in aphasia. Cortex 85, 165–181 (2016).

    PubMed  Google Scholar 

  146. Xing, S., Lacey, E. H., Skipper-Kallal, L. M., Zeng, J. & Turkeltaub, P. E. White matter correlates of auditory comprehension outcomes in chronic post-stroke aphasia. Front. Neurol. 8, 54 (2017).

    PubMed  PubMed Central  Google Scholar 

  147. Demeurisse, G. & Capon, A. Brain activation during a linguistic task in conduction aphasia. Cortex 27, 285–294 (1991).

    CAS  PubMed  Google Scholar 

  148. Hillis, A. E. et al. Subcortical aphasia and neglect in acute stroke: the role of cortical hypoperfusion. Brain 125, 1094–1104 (2002).

    CAS  PubMed  Google Scholar 

  149. Geranmayeh, F., Chau, T. W., Wise, R. J. S., Leech, R. & Hampshire, A. Domain-general subregions of the medial prefrontal cortex contribute to recovery of language after stroke. Brain 140, 1947–1958 (2017).

    PubMed  PubMed Central  Google Scholar 

  150. Krainik, A., Hund-Georgiadis, M., Zysset, S. & von Cramon, D. Y. Regional impairment of cerebrovascular reactivity and BOLD signal in adults after stroke. Stroke 36, 1146–1152 (2005).

    PubMed  Google Scholar 

  151. Geranmayeh, F., Wise, R. J., Leech, R. & Murphy, K. Measuring vascular reactivity with breath-holds after stroke: a method to aid interpretation of group-level BOLD signal changes in longitudinal fMRI studies. Hum. Brain Mapp. 36, 1755–1771 (2015).

    PubMed  PubMed Central  Google Scholar 

  152. Geranmayeh, F., Brownsett, S. L. & Wise, R. J. Task-induced brain activity in aphasic stroke patients: what is driving recovery? Brain 137, 2632–2648 (2014).

    PubMed  PubMed Central  Google Scholar 

  153. Barulli, D. & Stern, Y. Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends Cogn. Sci. 17, 502–509 (2013).

    PubMed  Google Scholar 

  154. Nyberg, L. et al. Age-related and genetic modulation of frontal cortex efficiency. J. Cogn. Neurosci. 26, 746–754 (2014).

    PubMed  Google Scholar 

  155. Herbet, G., Maheu, M., Costi, E., Lafargue, G. & Duffau, H. Mapping neuroplastic potential in brain-damaged patients. Brain 139, 829–844 (2016).

    PubMed  Google Scholar 

  156. Thiel, A. et al. From the left to the right: how the brain compensates progressive loss of language function. Brain Lang. 98, 57–65 (2006).

    PubMed  Google Scholar 

  157. Stern, Y. Cognitive reserve in ageing and Alzheimer disease. Lancet Neurol. 11, 1006–1012 (2012).

    PubMed  PubMed Central  Google Scholar 

  158. Puente, A. N., Lindbergh, C. A. & Miller, L. S. The relationship between cognitive reserve and functional ability is mediated by executive functioning in older adults. Clin. Neuropsychol. 29, 67–81 (2015).

    PubMed  Google Scholar 

  159. Uiterwijk, R. et al. Total cerebral small vessel disease MRI score is associated with cognitive decline in executive function in patients with hypertension. Front. Ageing Neurosci. 8, 301 (2016).

    Google Scholar 

  160. Molad, J. et al. Only white matter hyperintensities predicts post-stroke cognitive performances among cerebral small vessel disease markers: results from the TABASCO study. J. Alzheimers Dis. 56, 1293–1299 (2017).

    PubMed  Google Scholar 

  161. Woollams, A. M., Madrid, G. & Lambon Ralph, M. A. Using neurostimulation to understand the impact of pre-morbid individual differences on post-lesion outcomes. Proc. Natl Acad. Sci. USA 114, 12279–12284 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

J.D.S. is a Wellcome Clinical PhD Fellow funded by grant 203914/Z/16/Z, awarded to the universities of Manchester, Leeds, Newcastle and Sheffield, UK. The authors’ research is supported by a European Research Council Advanced Grant to M.A.L.R. (GAP: 670428) and a Rosetrees Trust grant to A.H. and M.A.L.R. (A1699).

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Authors and Affiliations

  1. Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

    James D. Stefaniak

  2. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK

    James D. Stefaniak, Ajay D. Halai & Matthew A. Lambon Ralph

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Glossary

Neurocomputational

In a neurocomputational model, the structure or function is constrained by neurobiological or neuroanatomical characteristics.

Conduction aphasia

A type of acquired language deficit in which individuals have relatively preserved comprehension but impaired repetition and phonologically disrupted fluent speech.

Anomic aphasia

A type of mild acquired language deficit in which individuals have word-finding difficulties yet relatively preserved comprehension, repetition and speech production.

Non-invasive brain stimulation

(NIBS). A range of techniques, including transcranial magnetic stimulation and transcranial direct current stimulation, that can modulate activity in specific brain networks or regions using electromagnetic fields or electrical current.

Degeneracy

A term used to refer to brain regions or networks that are sufficient to perform a cognitive task but do so only when other structurally dissimilar networks that normally perform that task are damaged.

Variable neuro-displacement

The process whereby a neural network utilizes its spare capacity and increases its activity and/or performance in situations of increased difficulty. Under standard performance demands, activity in these areas is downregulated to save energy. Variable neuro-displacement aims to titrate performance against energy cost.

Domain-general, multidemand executive networks

Brain regions or networks that are activated across a variety of cognitive tasks or domains when task difficulty is increased.

Transcallosal disinhibition

The proposal that homologous regions in the two hemispheres try to inhibit each other’s function and thus, following damage to one hemisphere, function in the contralateral region is released from this constraint.

Quiescent

Brain regions that are not activated during a language task in healthy individuals but can become activated and engaged by that language task after stroke are said to be quiescent.

Independent component analysis

A multivariate, data-driven analysis technique that can be used to decompose functional MRI data into statistically independent functional networks.

Pseudomodular

Modular cognitive systems comprise independent, fixed, discrete processing occurring in separate computational components. ‘Pseudomodular’ refers to a processing architecture that seems to be modular in form but can be reprogrammed to change functions within and between the computational components.

Spare capacity

The extent to which a neural network can increase its activity and/or performance in situations of increased task difficulty.

Distributed representations

Information coded across multiple processing units within computational models or across multiple areas of the brain.

Graceful degradation

A nonlinear pattern of decline in which performance is minimally reduced at low to moderate levels of damage.

Triangle computational model of reading aloud

An implemented computational model of reading aloud that includes three interconnected representational systems: orthography (written word forms), phonology (the sound structure of words) and semantics (word meaning).

Parametric correlation

An approach used in some functional neuroimaging studies which involves varying the parameter of interest (for example, speech rate) in a graded way and exploring which brain regions show activity changes that correlate with that parameter.

Multivoxel pattern analysis

A multivariate analysis technique that takes into account spatial patterns of activity across multiple brain voxels rather than assuming activity in each voxel is independent.

Representational similarity analysis

A multivariate analysis technique that calculates similarities between multivoxel functional MRI responses to different stimulus representations.

Dynamic causal modelling

A method of analysing functional neuroimaging data that infers causal interactions between brain regions (effective connectivity) rather than looking only for statistical correlations between their activity (functional connectivity).

Melodic intonation therapy

A type of speech and language therapy that uses music to encourage fluent speech production through improved intonation and rhythm.

Phonotactic statistics

The pattern and frequency of the sound sequences that are found in a language.

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Stefaniak, J.D., Halai, A.D. & Lambon Ralph, M.A. The neural and neurocomputational bases of recovery from post-stroke aphasia. Nat Rev Neurol 16, 43–55 (2020). https://doi.org/10.1038/s41582-019-0282-1

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