Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that sleep oscillations can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. Increase of synaptic strength led to more synchronized global SO. These results were compared to human data to validate probable models of biophysically realistic slow waves. The model producing mixed states provided the best match to the spatial coherence profiles obtained in the human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Adolescent sleep defects and dopaminergic hyperactivity in mice with a schizophrenia-linked Shank3 mutation

Shank3 is a shared risk gene for autism spectrum disorders and schizophrenia. Sleep defects have been characterized for autism models with Shank3 mutations, however, evidence has been lacking for the potential sleep defects caused by Shank3 mutation associated with schizophrenia and how early in development these defects may occur. Here we characterized the sleep architecture of adolescent mice carrying a schizophrenia-linked, R1117X mutation in Shank3. We further employed GRABDA dopamine sensor and fiber photometry to record dopamine release in the nucleus accumbens during sleep/wake states. Our results show that homozygous mutant R1117X mice have significantly reduced sleep in the dark phase during adolescence, altered electroencephalogram power especially during the rapid-eye-movement sleep, and dopamine hyperactivity during sleep but not during wakefulness. Further analyses suggest that these adolescent defects in sleep architecture and dopaminergic neuromodulation tightly correlate with the social novelty preference later in adulthood and predict adult social performance during same-sex social interactions. Our results provide novel insights to the sleep phenotypes in mouse models of schizophrenia and the potential use of developmental sleep as a predictive metric for adult social symptoms. Together with recent studies in other Shank3 models, our work underscores the idea that Shank3-involved circuit disruptions may be one of the shared pathologies in certain types of schizophrenia and autism. Future research is needed to establish the causal relationship among adolescent sleep defects, dopaminergic dysregulation, and adult behavioral changes in Shank3 mutation animals and other models.

Can sleep protect memories from catastrophic forgetting?

Continual learning remains an unsolved problem in artificial neural networks. The brain has evolved mechanisms to prevent catastrophic forgetting of old knowledge during new training. Building upon data suggesting the importance of sleep in learning and memory, we tested a hypothesis that sleep protects old memories from being forgotten after new learning. In the thalamocortical model, training a new memory interfered with previously learned old memories leading to degradation and forgetting of the old memory traces. Simulating sleep after new learning reversed the damage and enhanced old and new memories. We found that when a new memory competed for previously allocated neuronal/synaptic resources, sleep replay changed the synaptic footprint of the old memory to allow overlapping neuronal populations to store multiple memories. Our study predicts that memory storage is dynamic, and sleep enables continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize interference.

Ionic and synaptic mechanisms of seizure generation and epileptogenesis

The biophysical mechanisms underlying epileptogenesis and the generation of seizures remain to be better understood. Among many factors triggering epileptogenesis are traumatic brain injury breaking normal synaptic homeostasis and genetic mutations disrupting ionic concentration homeostasis. Impairments in these mechanisms, as seen in various brain diseases, may push the brain network to a pathological state characterized by increased susceptibility to unprovoked seizures. Here, we review recent computational studies exploring the roles of ionic concentration dynamics in the generation, maintenance, and termination of seizures. We further discuss how ionic and synaptic homeostatic mechanisms may give rise to conditions which prime brain networks to exhibit recurrent spontaneous seizures and epilepsy.

Role of KCC2-dependent potassium efflux in 4-Aminopyridine-induced Epileptiform synchronization

A balance between excitation and inhibition is necessary to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be limited, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the KCC2 co-transporter in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices, of either sex, revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). We developed a biophysically realistic network model implementing dynamics of ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of the inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Our model predicts that interneuron stimulation triggered an increase of interneuron firing, which was accompanied by an increase in the intracellular chloride concentration and a subsequent KCC2-dependent gradual accumulation of the extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.