Abstract
Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although whole-brain computational models have reproduced this neuromodulatory effect, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, we introduce a local inhibitory feedback as a plausible biophysical mechanism that enables the integration of whole-brain activity, and that interacts with the other neuromodulatory influences to facilitate the transition between different functional segregation/integration regimes in the brain.
Highlights
Integration and segregation of brain activity are nowadays two well-established brain organization principles [1,2,3,4]
Each node corresponds to a brain area and is represented by a neural mass consisting of three populations [35, 36]: pyramidal neurons, excitatory interneurons, and inhibitory interneurons (Fig 1A)
Our model shows an increase in functional integration at intermediate values of the parameters that resemble the cholinergic and noradrenergic systems, following an inverted-U relation with the neuromodulation
Summary
Integration and segregation of brain activity are nowadays two well-established brain organization principles [1,2,3,4]. Functional segregation refers to the existence of specialized brain regions, allowing the local processing of information Integration coordinates these local activities in order to produce a coherent response to complex tasks or environmental contexts [1, 2]. From a structural point of view, the complex functional organization of the brain is possible thanks to an anatomical connectivity that combines both integrated and segregated network characteristics, having small-world and modular properties [7]. The integration and segregation of brain activity are not static over time [3, 12] In this context, an interesting question emerges: How does the brain manage to produce dynamical transitions between different functional states from a rigid anatomical structure?
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