Multiscale effects of excitatory-inhibitory homeostasis in lesioned cortical networks: A computational study

摘要

Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics. Author summaryExcitatory-inhibitory (E-I) balance is an essential feature of cortical network function and is known to be maintained locally by homeostatic plasticity. In this work, we explore how the effects of such balancing mechanisms extend beyond the mesoscale and contribute to the maintenance of relevant macroscale properties of functional connectivity. More specifically, we suggest local E-I homeostasis is tied to the reorganization of large-scale functional networks following a focal lesion, providing an explanation for the recovery of relevant functional properties at a global level. To that end, we built a network model of interacting neural masses, constrained by the human connectome and accounting for local homeostasis of E-I balance. We show that this mechanism drives the recovery of properties such as modularity and small-worldness after simulated lesions and that the resultant patterns of change in excitability can be related to known late-onset symptoms of stroke such as seizures, depression, and chronic pain, in a lesion-dependent manner. Therefore, we propose E-I homeostasis as a relevant driver of recovery in lesioned networks and a contributing factor to the etiology of specific side effects of stroke, emerging as a byproduct of lesion-dependent changes in local excitability.