Language recovery after brain injury: a structural network control theory study

Abstract

Aphasia recovery after stroke depends on the condition of the remaining, extra-lesional brain network. Network control theory provides a unique, quantitative approach to assess the interaction between brain networks. In this longitudinal, large-scale, whole-brain connectome study, we evaluated whether controllability measures of language-related regions are associated with treated aphasia recovery.

Using probabilistic tractography and controlling for the effects of structural lesions, we reconstructed whole-brain diffusion tensor imaging connectomes from 68 individuals (20 female, 48 male) with chronic post-stroke aphasia who completed a 3-week language therapy. Applying principles of network control theory, we computed regional 1) average and 2) modal controllability, which decode the ability of a region to 1) spread control input through the brain network and 2) to facilitate brain state transitions. We tested the relationship between pre-treatment controllability measures of 20 language-related left hemisphere regions and improvements in naming six months after language therapy using multiple linear regressions and a parsimonious elastic net regression model with cross-validation.

Regional controllability of the inferior frontal gyrus pars opercularis, pars orbitalis, and the anterior insula were associated with treatment outcomes independently of baseline aphasia severity, lesion volume, age, education, and network size. Modal controllability of the inferior frontal gyrus pars opercularis was the strongest predictor of treated aphasia recovery with cross-validation and outperformed traditional graph theory, lesion load, and demographic measures.

Regional network control theory measures can reflect the status of the residual language network and its interaction with the remaining brain network, being able to predict language recovery after aphasia treatment.

Significance Statement

Predicting and understanding language recovery after brain injury remains a challenging, albeit a fundamental aspect of human neurology and neuroscience. In this study, we applied network control theory to fully harness the concept of brain networks as dynamic systems and to evaluate their interaction. We studied 68 stroke survivors with aphasia who underwent imaging and longitudinal behavioral assessments coupled with language therapy. We found that the controllability of the inferior frontal regional network significantly predicted recovery in language production six months after treatment. Importantly, controllability outperformed traditional demographic, lesion, and graph-theoretical measures. Our findings shed light on the neurobiological basis of human language and can be translated into personalized rehabilitation approaches.