Deterministic and Stochastic Components of Cortical Down States: Dynamics and Modulation

Abstract

Slow oscillations are an emergent activity of the cerebral cortex network consisting of alternating periods of activity (Up states) and silence (Down states). Up states are periods of persistent cortical activity that share properties with that of underlying wakefulness. However, the occurrence of Down states is almost invariably associated with unconsciousness, both in animal models and clinical studies. Down states have been attributed relevant functions, such as being a resetting mechanism or breaking causal interactions between cortical areas. But what do Down states consist of? Here, we explored in detail the network dynamics (e.g., synchronization and phase) during these silent periodsin vivo(male mice),in vitro(ferrets, either sex), andin silico, investigating various experimental conditions that modulate them: anesthesia levels, excitability (electric fields), and excitation/inhibition balance. We identified metastability as two complementary phases composing such quiescence states: a highly synchronized “deterministic” period followed by a low-synchronization “stochastic” period. The balance between these two phases determines the dynamical properties of the resulting rhythm, as well as the responsiveness to incoming inputs or refractoriness. We propose detailed Up and Down state cycle dynamics that bridge cortical properties emerging at the mesoscale with their underlying mechanisms at the microscale, providing a key to understanding unconscious states.SIGNIFICANCE STATEMENTThe cerebral cortex expresses slow oscillations consisting of Up (active) and Down (silent) states. Such activity emerges not only in slow wave sleep, but also under anesthesia and in brain lesions. Down states functionally disconnect the network, and are associated with unconsciousness. Based on a large collection of data, novel data analysis approaches and computational modeling, we thoroughly investigate the nature of Down states. We identify two phases: a highly synchronized “deterministic” period, followed by a low-synchronization “stochastic” period. The balance between these two phases determines the dynamic properties of the resulting rhythm and responsiveness to incoming inputs. This finding reconciles different theories of slow rhythm generation and provides clues about how the brain switches from conscious to unconscious brain states.

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

“…From this common description, we derive characterization metrics that are agnostic about the data’s origins. Such a generalized approach may be applied to different types of propagating fluctuating activity associated with different brain states, including various expressions of slow waves 54 , 55 (as we do here), burst suppressions, 56 , 57 or waves in higher frequencies. 58 , 59 This way, we arrive at relatable wave characterizations and avoid comparing apples to oranges ( Figure 2 ).…”

H-current modulation of cortical Up and Down states

“…In other words, the Down state duration increased with the duration of Up states, albeit at a slower rate. This can be partially explained because Down states have an activity-dependent component which can be mediated largely by hyperpolarizing currents, probably potassium currents 6,9 . Indeed, previous experimental and computational studies have revealed a dynamic link among Up state and Down state durations 8,24,26,28 .…”

“…Slow oscillations, characterized by interspersed Up and Down states, are thus a hallmark of both intact and isolated cortical networks. Up states, periods of persistent firing, arise from recurrent excitation and network reverberation, while Down states (silent periods) are triggered by the build-up of inhibitory currents during Up states [6][7][8][9] .…”

M-current modulation of cortical slow oscillations: Network dynamics and computational modeling

“…Slow wave activity is a state of cortical bistability, characterized by the spontaneous and almost periodic emergence of Up (active; neuronal depolarization and firing) and Down (silent; neuronal hyperpolarization) states, in which network interactions are disrupted [ 11 ], complexity is low [ 12 , 13 ], synchrony is high [ 14 ], and information processing is therefore impaired. In order for cortical circuits to be in an asynchronous state that can process information [ 15 ] the system should leave the slow wave attractor [ 16 , 17 ] and recover spatiotemporal dynamics and properties that support wakefulness [ 18 ].…”