Infra-slow fluctuations in cortical potentials and respiration drive fast cortical EEG rhythms in sleeping and waking states.

OBJECTIVE: Infra-slow fluctuations (ISF, 0.008-0.1 Hz) characterize hemodynamic and electric potential signals of human brain. ISFs correlate with the amplitude dynamics of fast (>1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood-brain barrier (BBB). It is unclear if physiological rhythms like respiration drive or track fast cortical oscillations, and the role of sleep in this coupling is unknown. METHODS: We used high-density full-band electroencephalography (EEG) in healthy human volunteers (N = 21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling. RESULTS: The phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling being evident during sleep. Phases of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states, with different contributions. CONCLUSIONS: ISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations. SIGNIFICANCE: We propose that these slow physiological phases play a significant role in coordinating cortical excitability, which is a fundamental aspect of brain function.

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics.

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network's activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.