Poster No:
1356
Submission Type:
Abstract Submission
Authors:
Jeehye An1, Simon Adriano Munoz Lagunas1, Leon Stefanovski2, Petra Ritter2
Institutions:
1Charité Universitätsmedizin Berlin, Berlin, Berlin, 2Charité University Hospital Berlin, Berlin, Berlin
First Author:
Jeehye An
Charité Universitätsmedizin Berlin
Berlin, Berlin
Co-Author(s):
Petra Ritter
Charité University Hospital Berlin
Berlin, Berlin
Introduction:
Ion channels represent a crucial link between genetic information and electrical activity of the brain. While their activity can be studied at the cellular and molecular levels, connecting these observations to whole-brain dynamics remains a challenge. Large-scale brain models allow for the simulation of brain network activity at the macroscopic level. By incorporating ion channel surrogates into these models, it becomes possible to simulate the impact of changes in ion channel parameters on emergent dynamics at the level of neural populations.
Methods:
In this study, we used the Larter-Breakspear model implemented in The Virtual Brain (TVB) software, a Python-based open-source brain simulation platform, to model whole brain dynamics. The Larter-Breakspear model is a conductance-based neural mass model with chaotic oscillator dynamics that includes ion gradient dynamics and allows for the manipulation of ion channel properties in simulations. We present analysis using both single-node and large-scale networks, the latter using tracer-based mouse structural connectome from the Allen Institute. In addition, we performed fitting using a stochastic grid optimization with mouse local field potential (LFP) data from the Allen Institute and analysed best-fitting simulation dynamics. Analysis of different chaotic dynamics was performed using Poincaré maps, which also served to determine chaoticity of the dynamical regimes. In addition, Lyapunov spectrums were computed from simulated time series data as indicator of chaotic dynamics of the system. We explored the role of conduction speed, global coupling, and ion channel parameters on emergent patterns of synchronization and metastability, as well as reactivity to external stimulation.
Results:
We successfully reproduced the oscillatory, chaotic, and fixed-point regimes using our bio-physically inspired mouse brain network model. Notably, altering calcium ion channel dynamics led to different dynamical regimes, which in turn modulated the simulated system's sensitivity to external stimulation, as shown using dynamical functional connectivity analysis. In addition, we gained a deeper understanding in the different chaotic dynamical regimes with our detailed analysis of Poincaré maps and Lyapunov spectrum. We present an extensive analysis of chaotic dynamics, elucidating methods to measure and distinguish different chaotic regimes within the neural mass model. Fitting simulated functional connectivity (FC) with empirical FC derived from electrophysiological data showed a high fit, with correlations as high as 0.89.
Conclusions:
Previous studies using the Larter-Breakspear model mostly used binary structural connectivity and either zero-lag or constant conduction delays. Meanwhile, the current study employs weighted structural connectivity and distance-dependent conduction delays, emphasizing increased biophysical realism. Neural mass models are a useful tool to bridge between microscopic activity of individual neurons and macroscopic brain dynamics, such as firing rates, synchronization, and oscillatory patterns. The combination of the conductance-based neural mass model and the analysis methods implemented here allows us to simulate altered ion channel gradients and to observe their effects on whole-brain dynamics. This provides the potential to link molecular, cellular, and network-level mechanisms that underlie neural function and dysfunction. Beyond theoretical insights, our results suggest the potential to elucidate mechanisms underlying brain disorders that involve genes implicated in ion channel activity, such as epilepsy or several neuropsychiatric disorders.
Brain Stimulation:
Deep Brain Stimulation
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Exploratory Modeling and Artifact Removal 1
Methods Development 2
Keywords:
ANIMAL STUDIES
Cellular
Computational Neuroscience
Data analysis
Modeling
Other - large-scale modeling
1|2Indicates the priority used for review
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