Person:

Madsen, Joseph

Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition—slowing, increased correlation, and flickering—in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.

Brain activity is characterized by oscillatory activity that spans at least two orders of magnitude. Previous investigations of the spatiotemporal dynamics of this wide range of oscillatory behavior has led to the concept that long-range intercortical interactions are expressed in low-frequency patterns while higher frequencies reflect more local intracortical connectivity. This inverse relationship between frequency and spatial coherence is seen as one of a small number of nearly universal rules governing brain activity. While there is substantial evidence for this proposition, there are surprisingly few direct, quantitative investigations of this phenomenon, especially in human cortex. To more completely characterize the spatial characteristics of ongoing brain activity, we investigated the coherence in di erent brain states- awake and sleep, at di erent frequencies, and with respect to a wide range of distances using standard pial surface macroelectrode arrays (1 cm spacing in an 8x8 cm grid), mesogrids (5 mm spacing), microgrids (1 mm spacing), and microelectrode arrays (400 micron spacing in 4x4mm arrays). As expected, we found that correlations and coherence decreases as a function of increasing interelectrode distance and as a function of frequency. We observed a robust linear relationship up until 1 cm; for distances 1 cm and greater, the relationship was largely non-linear. This relationship was not strongly a ected by speci ccortical lobe, nor was the overall coherence signi cantly di erent between awake and asleep states. These data are congruent with the overall notion that frequency and spatial relationships are inversely related with faster frequencies being more focal and provides an important quantitative assessment of that relationship with implications for the spatial scale of neural processing and recordings.

The neocortex is composed of six anatomically and physiologically specialized layers. It has been proposed that integration of activity across cortical areas is mediated anatomically by associative connections terminating in superficial layers, and physiologically by slow cortical rhythms. However, the means through which neocortical anatomy and physiology interact to coordinate neural activity remains obscure. Using laminar microelectrode arrays in 19 human participants, we found that most EEG activity is below 10-Hz (delta/theta) and generated by superficial cortical layers during both wakefulness and sleep. Cortical surface grid, grid-laminar, and dual-laminar recordings demonstrate that these slow rhythms are synchronous within upper layers across broad cortical areas. The phase of this superficial slow activity is reset by infrequent stimuli and coupled to the amplitude of faster oscillations and neuronal firing across all layers. These findings support a primary role of superficial slow rhythms in generating the EEG and integrating cortical activity.