Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

Abstract

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

Complexity and 1/f slope jointly reflect brain states

“…From this perspective, our results suggest that 1-70 Hz and 1-40 Hz frequency ranges share the characteristic of representing the global state of cortical activity. Further work could include the modeling of tight and loose coupling regimes between excitation and inhibition, which has been suggested as a more plausible mechanism of cortical E/I balance regulation (Dehghani et al, 2016;Denève & Machens, 2016;Trakoshis et al, 2020;Denève & Machens, 2016). These limitations are probably why we also observe a reduced range of both LZc and 1/f slope, despite modeling a broad E/I balance range.…”

“…Despite this potential limitation of our simulations, which lacked oscillations, we observe the same general behavior in EEG and ECoG data, which does present oscillatory activity. It should be noted that the exponent of the power-law has been characterized in different frequency ranges across the literature (He et al, 2010;Becker et al, 2018;Lombardi et al, 2017;Miskovic et al, 2019;Trakoshis et al, 2020;Schaworonkow & Voytek, n.d.). In this line, the frequency ranges that we employed here were based on generating interpretations that could be extrapolated for both local and global measures of field potentials.…”

“…Specifically, models have been shown that the background 1/f slope of the power spectral density (PSD) emerges from the sum of stochastic excitatory and inhibitory currents (Destexhe et al, 2001;Sheehan et al, 2018;Gao et al, 2017). Moreover, empirical validation of these models has shown that the E/I balance can be properly inferred from background activity by parameterizing the 1/f shape of the PSD (Gao et al, 2017;Trakoshis et al, 2020).…”

Computation of the electroencephalogram (EEG) from network models of point neurons

“…First, when studying computational models of brain function, our work allows quantitative rather than qualitative comparison of how different models match EEG data, thereby leading to better and more objective validations of different hypotheses about neural computations.Second, our work represents a crucial step in enabling a reliable inference, from real EEG data, of how different neural circuit parameters contribute to brain functions and brain pathologies.Since the EEG conflates many circuit-level aggregate neural phenomena organized over a wide range of frequencies, it is difficult to infer from its measure the value of key neural parameters, such as for example the ratio between excitation and inhibition[1,53]. Developing tractable neural networks that include an explicit relationship between the EEG response and neural network parameters is a way to address this issue.…”

“…By fitting such models to real EEG data, estimates of neural network parameters (such as the ratio between excitation and inhibition or properties of network connectivity) can be obtained from EEG spectra or evoked potentials. This approach could be used, for example, to test the influential theories of the excitationinhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders[54,55], to empirically measure how this balance changes between patients with autistic disorder syndrome and control subjects[53], or to individuate the neural correlates of diseases that show alterations of EEG activity[56][57][58][59][60]. Thus, our EEG proxies have clear relevance for connecting EEG in human experiments to cellular and network data in health and disease.Although more work is needed to be able to interpret empirical EEGs in terms of network models, there are several facts that indicate that our proxies can potentially help in this respect.Recent attempts to infer neural parameters from EEGs or other non-invasive signals, based on network models that use less accurate proxies than the ones developed here, are nevertheless beginning to provide credible estimates of key parameters of underlying neural circuit such as excitation-inhibition ratios[53,61], as well as accurate descriptions of cortical dynamics.…”