P020 A Multi-Compartment Computational Approach to Cerebellar Circuit Dysfunction in Autism
Danilo Benozzo*1, Martina F. Rizza1, Danila Di Domenico1, Giorgia Pellavio1, Filippo Marchetti1, Egidio D’Angelo1,2, Claudia Casellato1
¹ Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
² Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
*Email: danilo.benozzo@unipv.it
Introduction
Modeling brain dynamics requires addressing processes that span different temporal and spatial scales [1]. This is crucial, when studying phenomena at the network level that are consequences of pharmacological or pathological alterations occurring at the single-cell level, such as changes in ionic or synaptic currents. Our aim is to study how single-cell dynamics affect circuit dynamics in a mouse model of autism (IB2 knock-out, KO), within the context of the cerebellar cortical microcircuit. Cerebellar implications in autism spectrum disorders (ASD) have been well-documented, showing a dependent association between cerebellar damages and an increased risk for ASD [2].
Methods
We re-parameterized a wild-type (WT) granule cell (GrC) multi-compartment model [3] to match the empirical properties of IB2-KO GrCs [4]. At the network level, we employed a bottom-up approach, by placing all the cell types that characterize the cerebellar cortex, preserving their physiological morphology, density and connection affinity. On the simulation side, the activity of each cell type was reproduced through a multi-compartment model interfaced with the NEURON simulator [5]. The entire process was managed by the Brain Scaffold Builder framework [6,7].
Results
In the WT GrC model we increased Na and K maximum conductances (gmax) to match the higher in/outward currents in IB2 GrCs. Tonic glutamate levels [glu] in mossy-fiber-GrC synapses and NMDA gmax were adjusted to replicate experimental I-f and NMDA currents, predicting [glu] at 11.2 µM and a 4x NMDA gmax increase. The IB2 GrC model was integrated into the canonical cerebellar circuit, assuming no other cell changes (empirical IB2 Purkinje cell (PC) spks/s = 51.8, std 11.7, no sign. vs. WT). Network comparisons revealed greater stimulus spread through the Gr-layer, Fig.1B. Fig.1C shows peri-stimulus histograms for both circuits under different input, predicting an overall firing increase (rates from 9.5x in GrCs to 1.6x in PCs).
Discussion
This bottom-up modelling framework enabled us to construct a representative microcircuit of the mouse cerebellar cortex, featuring a granular layer that replicates the alterations empirically observed in the IB2-KO model. This multiscale approach allows us to predict how the circuit dynamics respond to single-cell model modifications. In the granular layer, our results reflect the spatially expanded, higher E/I balance around IB2-KO GrCs observed in [4]. To further validate whole-circuit activity, we are currently comparing our predictions with in vitro MEA recordings from both WT and IB2-KO mice, in spontaneous regime and under mossy-fiber impulse stimulations.
Figure 1. A: Effect of NMDA gmax (kgmax NMDA) and ambient glutamate ([glu]) on NMDA currents in IB2-KO GrCs. B: How a 20 Hz stim propagates through the granular layer, applied to mossy fibers (mfs) within the circular target, r=40 µm. C: Firing rates of each cell type under three conditions: no stimulus, an 8 Hz Poisson basal input to all mfs, and basal input plus high-frequency stim targeted to 15 mfs.
Acknowledgements
Work supported by NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022)
References
[1]https://doi.org/10.1162/netn_a_00343
[2]https://doi.org/10.1016/j.ijdevneu.2004.09.006
[3]https://doi.org/10.3389/fncel.2017.00071
[4]https://doi.org/10.1523/jneurosci.1985-18.2019
[5]https://doi.org/10.1017/cbo9780511541612
[6]https://doi.org/10.1038/s42003-022-04213-y[7]https://ebrains.eu/service/brain-scaffold-builder/
