P197 Resilience of local microcircuitry firing dynamics to selective connectivity degeneration

Simachew Mengiste*1, Ad Aertsen2, Demian Battaglia1, Arvind Kumar3

1Functional System Dynamics / LNCA UMR 7364, University of Strasbourg, France
2BCCN / University of Freiburg, Germany
3KTH Royal Institute of Technology, Stockholm, Sweden

*Email: mengiste@unistra.fr


Introduction
Local microcircuit connectivity within local cortical microcircuits shapes spiking dynamics, influencing firing rate, synchrony, and regularity (thus information bandwidth). Often modeled as random and sparse (Erdös-Rényi, ER) or with small-world or scale-free properties, connectivity derived from detailed connectomic reconstructions (Egger et al., 2014) display dense cell clusters, diverging from mere randomness.
Neurodegenerative diseases (e.g., Alzheimer’s) induce neuronal and synaptic loss, disrupting dynamics. We systematically examine how pruning affects microcircuits with different topologies, revealing that resilience strongly depends on connectivity, with the "real connectome" being particularly robust.


Methods
We studied three random network topologies—Erdös-Rényi (ER), small-world (SW), and scale-free (SF)—plus a fourth based on real connectome (RC) reconstructions. Neurons were modeled as leaky integrate-and-fire units, with excitatory and inhibitory inputs shaping membrane potential dynamics.

Network degeneration was simulated via progressive pruning of synapses and neurons, using random or targeted sequences based on node degree or centrality. We analyzed firing rate, correlations, and spiking variability (coefficient of variation), alongside net synaptic currents received on average. Structural changes were assessed via graph metrics. We then systematically probed how firing dynamics evolved in the four ensembles along neurodegeneration.

Results
Using different network topologies and neurodegenerative strategies, we found that activity states were largely independent of topology across the different ensembles. Degeneration induced similar firing rate and synchrony variations across neurodegenerative schemes. We hypothesized that E-I balance changes, rather than topology, drove these dynamics. The effective synaptic weight (ESW) best predicted network activity, explaining firing rate, variability, and synchrony—except pairwise correlation, which depended on shared presynaptic neighbors and connection density. The real connectome (RC) followed similar ESW dependencies but exhibited broader stability ranges for all different firing parameters.

Discussion
While most neurodegeneration models focus on long-range connectivity changes, local microcircuits are also affected, altering synchrony and information processing. We find that local circuit dynamics are indeed disrupted, but less dependent on precise connectivity than expected. Instead, the effective synaptic weight (ESW) emerges as a stronger predictor of network behavior, making it a key measure for assessing function in both healthy and diseased states. The anomalous stability of firing parameters in networks with realistic connectivity suggests that microcircuit properties may have evolved to enhance functional resilience.




Acknowledgements
ANR PEPR Santé Numérique "BHT - Brain Health Trajectories"
ReferencesEgger, R., Dercksen, V. J., Udvary, D., Hege, H.-C., & Oberlaender, M. (2014). Generation of dense statistical connectomes from sparse morphological data.Frontiers in Neuroanatomy,8, 129. https://doi.org/10.3389/fnana.2014.00129