Abstract
Experimental and modeling work of neural activity has described recurrent and attractor dynamic patterns in cerebral microcircuits. However, it is still poorly understood whether similar dynamic principles exist or can be generalizable to the large-scale level. Here, we applied dynamic graph theory-based analyses to evaluate the dynamic streams of whole-brain functional connectivity over time across cognitive states. Dynamic connectivity in local networks is located in attentional areas during tasks and primary sensory areas during rest states, and dynamic connectivity in distributed networks converges in the default mode network (DMN) in both task and rest states. Importantly, we find that distinctive dynamic connectivity patterns are spatially associated with Allen Human Brain Atlas genetic transcription levels of synaptic long-term potentiation and long-term depression-related genes. Our findings support the neurobiological basis of large-scale attractor-like dynamics in the heteromodal cortex within the DMN, irrespective of cognitive state.
Highlights
Experimental and modeling work of neural activity has described recurrent and attractor dynamic patterns in cerebral microcircuits
During the time course of each task, local dynamic connectivity accumulated a disproportionate number of local streams that reached attentional and task-related areas, while changes in distributed connections showed that global streams of connectivity consistently reached prominent areas of the default mode network (DMN) in all time windows for all types of tasks
The distributed dynamic connectivity map can only be achieved after the exclusion of modular connections, while the topology of the local dynamic connectivity map is equivalent to the map obtained when all connectivity is included in the analysis
Summary
Experimental and modeling work of neural activity has described recurrent and attractor dynamic patterns in cerebral microcircuits. It is still poorly understood whether similar dynamic principles exist or can be generalizable to the large-scale level. The characterization of the temporal changes of functional connectivity networks remain elusive, and there are no commonly accepted notions about how self-organizing collective interactions of neurons emerge at the large-scale brain level[2,3]. We characterized large-scale connectivity changes in time by localizing nodes that display a high degree of functional streams (or dynamic paths on graphs) converging to specific points of the cortical space and across multiple brain states as a proxy of attractor-like behavior. We investigated whether dynamic connectivity of largescale brain networks are founded on specific cellular and molecular mechanisms through neuroimaging–genetics expression interactions in the human cerebral cortex[12,13,14,15]
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