Modeling the Impact of Lesions in the Human Brain

Abstract

Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous (“resting-state”) neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions. Every year, millions of people suffer the consequences of brain damage, as a result of stroke, traumatic brain injury, cancer or degenerative brain disease. The cognitive and behavioral symptoms of focal lesions of the brain are highly variable and in many cases depend on the location of the lesion site. Can we predict the functional impact of such lesions on the basis of a computational model of the brain's structure and dynamics? Numerous other systems that form complex networks have been analyzed for their vulnerability to structural damage. In many cases, the degree to which such systems are perturbed depends on network attributes of the deleted nodes and connections. We apply this network approach to investigate the structural and functional impact of localized lesions of a model of the cerebral cortex. When we delete nodes that occupy, in the intact brain, a highly central position, we find that the dynamic interactions between nodes in the remaining brain are greatly disturbed. In contrast, deletion of less central nodes has relatively little effect. In the model, some of the most disruptive lesion sites correspond to locations in the brain where lesions produce complex cognitive disturbances. Our modeling approach aims towards linking disturbances of structural brain networks to specific clinical outcomes.