Abstract
The importance of the large number of thin-diameter and unmyelinated axons that connect different cortical areas is unknown. The pronounced propagation delays in these axons may prevent synchronization of cortical networks and therefore hinder efficient information integration and processing. Yet, such global information integration across cortical areas is vital for higher cognitive function. We hypothesized that delays in communication between cortical areas can disrupt synchronization and therefore enhance the set of activity trajectories and computations interconnected networks can perform. To evaluate this hypothesis, we studied the effect of long-range cortical projections with propagation delays in interconnected large-scale cortical networks that exhibited spontaneous rhythmic activity. Long-range connections with delays caused the emergence of metastable, spatio-temporally distinct activity states between which the networks spontaneously transitioned. Interestingly, the observed activity patterns correspond to macroscopic network dynamics such as globally synchronized activity, propagating wave fronts, and spiral waves that have been previously observed in neurophysiological recordings from humans and animal models. Transient perturbations with simulated transcranial alternating current stimulation (tACS) confirmed the multistability of the interconnected networks by switching the networks between these metastable states. Our model thus proposes that slower long-range connections enrich the landscape of activity states and represent a parsimonious mechanism for the emergence of multistability in cortical networks. These results further provide a mechanistic link between the known deficits in connectivity and cortical state dynamics in neuropsychiatric illnesses such as schizophrenia and autism, as well as suggest non-invasive brain stimulation as an effective treatment for these illnesses.
Highlights
Cognition emerges from the organized temporal structure of electric activity in large, interconnected cortical networks [1,2,3]
To understand the effect of long-range projections (LRPs) on the dynamics of two interconnected cortical networks, we built a largescale computational model of two networks connected by LRPs (Fig. 1A) where each network consisted of a two-dimensional sheet of excitatory pyramidal cells (4006400 PYs) and a matched sheet of inhibitory interneurons (2006200 INs)
When examining the spatio-temporal activity patterns, we found that networks demonstrated three behaviors during antiphase transcranial alternating current stimulation (tACS), which were grouped by k-means clustering of their crosscorrelograms. ‘‘Strong antiphase’’ behavior occurred when the two networks were individually entrained by their respective stimulation (Fig. 8C; Delay = 10 msec, P(local) = 0.99; G(LRP) = 0.06). ‘‘Interspersed weak firing’’ was a result of networks firing in response to both their stimulation as well as the excitation from the other network, resulting in a series of strong and weak UP states (Fig. 8D; Delay = 10 msec; P(local) = 0.99; G(LRP) = 0.09)
Summary
Cognition emerges from the organized temporal structure of electric activity in large, interconnected cortical networks [1,2,3]. The network topology is a key determinant of the types of macroscopic activity patterns a network can generate [4,5,6,7,8,9,10,11] Understanding this structure-function relationship provides important insight into normal brain function and into the mechanistic basis of psychiatric illnesses such as schizophrenia and autism that likely represent ‘‘connectivity disorders’’ [12,13,14,15]. These connectivity disorders are associated with both structural and functional impairments in connectivity [16,17,18,19]. Most studies of interconnected networks have focused on how networks synchronize via fast LRPs, with the exception of recent theoretical work that highlights the additional complexity and computational abilities of networks that include physiological delays [23,24,25]
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