FLEXBRAIN · The Flexible Brain: (Re-)shaping Adaptation in Semantic Cognition

The human brain is flexible. Neural networks adapt to cognitive demands by flexibly recruiting different regions and connections. Flexible network adaptation enables cognitive functions such as semantic cognition: the ability to use, manipulate, and generalize knowledge. When key nodes suffer damage, networks can adapt to recover function. Yet, brain lesions often severely impair semantic cognition. How the semantic network adapts to lesions is poorly understood. My hypothesis is that disruption of the semantic network can be compensated for by recruitment of domain-general networks. This notion is based on findings that disruption of semantic nodes inhibits semantic activity but increases activity in domain-general nodes. Yet, the behavioral relevance of domain-general recruitment is unclear. Compensation means that behavior can be preserved as other nodes work harder. Can domain-general networks effectively compensate for disruption of specialized nodes? Is this a common principle of flexible adaptation in the healthy young, aging, and lesioned brain? Unprecedented inhibitory and facilitatory neurostimulation will be used to unbalance and rebalance network adaptation in semantic cognition. Importantly, a novel network stimulation approach will target multiple nodes simultaneously. I ask three questions. (i) Can domain-general networks compensate for semantic network disruption? (ii) Is domain-general recruitment in the aging brain adaptive? (iii) Do domain-general networks drive flexible adaptation to lesions? Perturbing young brains will elucidate the relevance of network adaptation. Perturbing aging brains will probe compensatory reorganization. Facilitating lesioned brains will reshape flexible adaptation. Benefitting from my strong neurostimulation experience, we will elucidate the way the brain compensates for disruption. The potential impact of the project on current conceptions of brain plasticity, and for rehabilitative medicine in particular, is immense.