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

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. Some individuals with post-stroke language impairment, or aphasia, will spontaneously recover partial language function. In this Review, the authors propose that existing hypotheses about this recovery from aphasia can be considered as examples of two principles: degeneracy and variable neuro-displacement. 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.