Dynamical consequences of lesions in cortical networks

Abstract

To understand the effects of a cortical lesion it is necessary to consider not only the loss of local neural function, but also the lesion-induced changes in the larger network of endogenous oscillatory interactions in the brain. To investigate how network embedding influences a region's functional role, and the consequences of its being damaged, we implement two models of oscillatory cortical interactions, both of which inherit their coupling architecture from the available anatomical connection data for macaque cerebral cortex. In the first model, node dynamics are governed by Kuramoto phase oscillator equations, and we investigate the sequence in which areas entrain one another in the transition to global synchrony. In the second model, node dynamics are governed by a more realistic neural mass model, and we assess long-run inter-regional interactions using a measure of directed information flow. Highly connected parietal and frontal areas are found to synchronize most rapidly, more so than equally highly connected visual and somatosensory areas, and this difference can be explained in terms of the network's clustered architecture. For both models, lesion effects extend beyond the immediate neighbors of the lesioned site, and the amplitude and dispersal of nonlocal effects are again influenced by cluster patterns in the network. Although the consequences of in vivo lesions will always depend on circuitry local to the damaged site, we conclude that lesions of parietal regions (especially areas 5 and 7a) and frontal regions (especially areas 46 and FEF) have the greatest potential to disrupt the integrative aspects of neocortical function.

Cerebral Energy Metabolism and the Brain’s Functional Network Architecture: An Integrative Review

“…Mathematical models of such network dynamics, as the Kuramoto phase oscillator, are now routinely used to describe the coupling of neuronal ensembles. 41 An emerging view that has been extensively adopted in experimental work at the micro-, meso-, and macro-scales expands on biophysical models of synchronous brain function to further state that neural activity is of fractal nature, and that the brain is a self-organized critical system (SOC). [42][43][44] Systems in a critical state are poised on the cusp of a transition between ordered and random behavior, and show complex patterning of fluctuations at all scales of space and time.…”

MR connectomics: Principles and challenges

“…Regions of high centrality participate in many of these short paths and are often essential for linking different communities to each other. Their loss is particularly disruptive to the network (Honey and Sporns, 2008). In the human brain, several regions in the frontal and parietal cortex have high centrality, particularly the posterior cingulate and precuneus , a key region of the brain's default mode network, see Figs.…”

Interpretation of Neuroimaging Data Based on Network Concepts

“…Hubs also have a unique capacity for facilitating information integration. Recent work (Honey and Sporns 2008) shows that damage to hub nodes in large-scale network simulations has the most dramatic effect on the integrative capacity of the remaining network, particularly when prefrontal (area 46 and FEF) or parietal (areas 5 and 7) cortices are lesioned. Merging such modeling work, with estimation of network operations in empirical data, will enable us to begin to understand why damage to certain areas causes a deficit.…”