by Hilgo Bruining1,2†, Richard Hardstone3,4†, Erika L. Juarez-Martinez2,3†, Jan Sprengers2†, Arthur-Ervin Avramiea3, Sonja Simpraga3,5, Simon J. Houtman3, Simon-Shlomo Poil5, Eva Dallares3, Satu Palva6, Bob Oranje2, J. Matias Palva6,7, Huibert D. Mansvelder3, Klaus Linkenkaer-Hansen3*.
1Dept Child and Adolescent Psychiatry, Amsterdam UMC, 2Dept Psychiatry, UMC Utrecht, 3Dept Integrative Neurophys, VU Amsterdam, 4Neuroscience Institute, New York University School of Medicine, USA, 5NBT Analytics BV, 6Neuroscience Center, University of Helsinki, 7BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital
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.
Balance between excitation & inhibition is a key principle for neuronal network organization and information processing, but has been difficult to quantify in humans. We developed a method to estimate E/I ratio non-invasively from spontaneous oscillations. Using a previously published computational model, we show that while activity goes up with increased excitation, fluctuations in oscillation amplitude are strongest when there is a balance between excitation and inhibition (green signals). This suggests that the relationship between the amplitude of oscillations and the fluctuations in amplitude relate to the E/I balance, where amplitude and fluctuations correlate positively for inhibition-dominated networks, and negatively for excitation-dominated networks. See article
Looking at network activity produced over time in the model, we observe these predicted relationships. Therefore, we can estimate the functional excitation/inhibition ratio (fE/I) from ongoing oscillations, with fE/I = 1 when excitation and inhibition balance.
Power-law scaling—a hallmark of criticality—has been observed on different levels, e.g., in the distribution of neuronal avalanches in vitro and in vivo (Beggs & Plenz, J Neurosci 2003), but also in the decay of temporal correlations in behavioral performance and ongoing oscillations in humans (Linkenkaer-Hansen et al., J Neurosci 2001). Interestingly, fE/I correlates even better with a measure of neuronal avalanches (K), suggesting that the fE/I algorithm allows a measurement of criticality from single-channel data.
Applying the algorithm to a large human EEG dataset, we see that on average fE/I is close to 1 (A). To validate the algorithm, we applied it to data recorded from subjects who received a drug that enforces inhibition (B), and found the expected decrease in fE/I (C).
To test clinical applicability, we recorded EEG in unmedicated children with autism spectrum disorder (ASD). Compared to controls, we found no difference in the mean fE/I, but a higher variance in ASD, suggesting that autism is characterized by physiological heterogeneity.
To test the long-standing hypothesis that E/I changes are driven by epilepsy comorbidity, we stratified the ASD group into those with and without EEG abnormalities. Children without abnormalities had higher fE/I, and also higher fE/I than typically developing children. This suggests that both increased and decreased excitation/inhibition ratio could be associated with autism spectrum disorder. Therefore, the fE/I algorithm shows promise as a method to stratify patients, which could guide personalized treatment options.
The fE/I algorithm was designed by Richard Hardstone and is described in the Patent claim (NL2020601) “Method of determining brain activity”; with priority date 16 March 2018. Data and code can be found here , and the fE/I algorithm can be found at here