Introduction
Temporal patterning of neural synchronization was observed to be related to symptoms of multiple neurological and neuropsychiatric disorders. In particular, alterations in the patterns of intermittent synchrony in multiple spectral bands (including gamma band) were found in autism spectrum disorder (ASD), Alzheimer’s disease (AD), and frontotemporal dementia (FTD) as demonstrated in recent electrophysiological studies with resting state EEG recordings [1,2,3]. Computational models may provide mechanistic insights into how synaptic parameters and network properties shape properties of intermittent synchronization dynamics and might underlie the neural dynamics observed experimentally in pathological conditions.
Methods
We used a conductance-based models of pyramidal neurons and interneuron to simulate systems of synaptically connected randomly organized networks of excitatory and inhibitory neurons, that exhibits gamma-band activity, and studied how changes in the local vs long-range connectivity impact the temporal patterning of intermittent gamma synchrony, following the recent model developments presented in [4,5]. The intermittently synchronized dynamics was characterized using measures (such as measures of the distributions of the desynchronization intervals duration) similar to those used in the experimental investigations of human EEG recordings as mentioned above.
Results
Numerical simulations showed that synaptic strength affected not only the average synchronization strength but also the distribution of desynchronization intervals durations (including situations where the former is not substantially different, but the latter is markedly altered). This happened in a way that stronger local connectivity tended to prolong desynchronization intervals, whereas stronger long-range connectivity tended to shorten them across a fairly broad parameter range. The changes in the temporal patterning of synchronized dynamics in these networks may lead to the changes in how these networks respond to common input signals affecting mutual synchronizability of connected networks.
Discussion
Our results suggest that synaptic parameters not only exert control over the strength of gamma synchrony but also its fine temporal structure over relatively short time scales. The differences in synchronizability properties between the networks may be responsible for the differences in the information processing in these networks and their effective communication efficiency. It is plausible to hypothesize that the experimentally observed differences in the patterning of the synchronous dynamics (as mentioned in the Introduction) may be related to the synaptically-induced changes in the temporal patterning of the synchronized dynamics and changes in synchronizability of the networks as those found in numerical simulations.
References
1. Malaia, E. A., Ahn, S., & Rubchinsky, L. L. (2020). Autism Research, 13, 24-31. https://doi.org/10.1002/aur.2219
2. Ahn, S., Malaia, E. A., & Rubchinsky, L. L. (2025). Clinical Neurophysiology, 177, 2110931. https://doi.org/10.1016/j.clinph.2025.2110931
3. Ahn, S., Rubchinsky, L. L., & Malaia, E. A. (2025). Biological Psychology, 199, 109077. https://doi.org/10.1016/j.biopsycho.2025.109077
4. Nguyen, Q. A., & Rubchinsky, L. L. (2021). Chaos, 31, 043134. https://doi.org/10.1063/5.0042451
5. Nguyen, Q. A., & Rubchinsky, L. L. (2024). Cognitive Neurodynamics, 18, 3821–3837. https://doi.org/10.1007/s11571-024-10150-9
Acknowledgement
