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  • Review Article
  • Published:

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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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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