Authors :
Presenting Author: Thomas Richner, PhD – Mayo Clinic
Raunak Singh, MD – Mayo clinic, Rochester, Minnesota
Martynas Dervinis, PhD – Mayo Clinic
Brian Lundstrom, MD PhD – Mayo clinic, Rochester, Minnesota
Rationale:
The brain is a highly recurrent neural network that must maintain a stable homeostatic level of neural activity. Seizures are hypothesized to be transient events where stability is lost. Most treatments alter the level of excitation or inhibition in the brain. However, invasive electrical brain stimulation (EBS) can decrease, or at certain settings, increase, epileptiform activity. Therefore, EBS must act via a mixture of interacting mechanisms. We explored the interaction of stimulation with inhibition and two forms of adaptation: spike frequency adaptation (SFA), and short-term synaptic depression (STD) using a computational neuroscience framework. We quantified the stabilizing effects of stimulation and observed its ability to suppress large discharges. Methods:
We developed a network model in which the excitatory neurons exhibit SFA and excitatory synapses exhibit STD. We extended our previous analysis of feed-forward networks with dual adaptation (Lundstrom & Richner, 2023) to address continuous spike rate recurrent neural networks. We quantified the stability of unbalanced networks in which excitation exceeds inhibition using Lyapunov spectra and investigated spontaneous discharges reminiscent of interictal epileptiform discharges (IEDs) while varying the level of stimulation.
Results:
We found the following results: (1) SFA and STD improved the dynamical stability of a wide range of networks, even those with imbalanced excitation and inhibition, as measured by the Lyapunov spectrum; (2) Quiescent networks with little inhibition and a lack of external drive, produced aberrant excitatory discharges reminiscent of IEDs; (3) Stimulation engaged SFA and STD to increase network stability and suppress discharges. Critically, the presence of both SFA and STD in quiescent (e.g., disconnected subnetworks) networks was necessary for discharge generation. However, the combination of SFA, STD, and elevated stimulation was sufficient to suppress discharges. Interestingly, weak stimulation could increase the discharge rate in some networks, while strong stimulation fully suppressed discharges (see figure 1). Conclusions:
Stimulation increases network stability by engaging adaptation mechanisms, SFA and STD. Discharges reminiscent of IEDs can form in networks lacking sufficient inhibition, but these discharges can be eliminated by increasing stimulation. This modeling provides a framework for understanding one of the mechanisms of therapeutic electrical brain stimulation.
Figure 1. Spontaneous discharges (bottom) in a network model (top) are suppressed by stimulation (middle). The spike rate (A.U.) of over-connected excitatory neurons (multicolored) is reduced due to STD and SFA induced by stimulation, enabling non-adapting inhibitory neurons to match excitation. These three forms of negative feedback (STD, SFA, and inhibition) restore network stability from T = 35-50 s.
Lundstrom, B. N., & Richner, T. J. (2023). Neural adaptation and fractional dynamics as a window to underlying neural excitability. PLOS Computational Biology, 19(2), e1010527. https://doi.org/10.1371/journal.pcbi.1010527
Funding: NIH NINDS R01NS129622 and K23NS112339