P275 A Computational Framework for Investigating the Impact of Neurostimulation on Different Configurations of Neuronal Assemblies
Spandan Sengupta*1, 2, Milad Lankarany1, 2, 3, 4, 5
1Krembil Brain Institute, University Health Network, Toronto, ON, Canada
2Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
3Department of Physiology, University of Toronto, Toronto, ON, Canada
4KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada5Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
*Email: spandan.sengupta@mail.utoronto.ca
Introduction
Pathological oscillations in brain circuits are a known biomarker of neurological disorders, exemplified by increased power in the beta (12-30 Hz) frequency band in Parkinson’s disease [1]. Neurostimulation techniques like Deep Brain Stimulation (DBS) can disrupt pathological oscillations and improve symptoms [2]. However, how and why different stimulation patterns have different impacts on circuits is not fully understood. Recent studies show stimulation-induced biomarkers such as Evoked Resonant Neural Activity (ERNA) [3] associated with stimulation patterns (e.g. frequency) and circuit motifs (e.g. strength of excitatory-inhibitory connectivity) [4]. To study how stimulation patterns impact different circuit motifs, we developed a computational framework to model electrical stimulation on pre and post synaptic activity of neurons embedded in neuronal networks.
Methods
We wanted to study the effect of electrical stimulation, and in particular, the effect of different frequencies during and after stimulation on different circuit motifs. We employed spiking neural networks composed of leaky integrate-and-fire (LIF) neurons combined in a variety of excitatory-inhibitory configurations. To model DBS, we implemented perturbations analogous to electrical stimulation. To further explore how electrical stimulation affects pathological oscillations, we used Brunel’s network [5] tuned to show oscillatory activity at specific frequencies. We aim to study how different patterns of stimulation can suppress these oscillations.
Results
Aligned with experimental findings, our simulations demonstrated that continuous high-frequency electrical stimulation induced more suppression of neuronal activity compared to low-frequency stimulation [6]. In some circuit motifs, we also observed sustained low-frequency oscillatory activity after the high-frequency stimulation had ended (Fig 1B). We aim to characterise the impact of different frequencies on Brunel’s network and their ability to suppress pathological oscillations. We expect to find stimulation patterns that can disrupt these oscillations and qualitatively the circuit activity to a more healthy physiological state. We expect these frequencies to be dependent on the excitatory-inhibitory characteristics of the network.
Discussion
Our study utilizes a simplified model of LIF neurons configured in different motifs, offering a foundational understanding of oscillation modulation with different patterns of electrical stimulation. Future research can expand this model to incorporate more biophysically realistic circuits, such as those found in the hippocampus, critical for memory processing[7], or the basal ganglia, implicated in movement disorders[8]. Investigating these complex circuits will further bridge the gap between computational models and the intricate dynamics of brain networks in health and disease, potentially leading to refined therapeutic strategies.
Figure 1. A: Schematic of a circuit comprised of populations A (exc) and B (inh) that project to O, along with recurrent connections. Electrical stimulation is applied to neurons in A B: Population firing rate during and after 100 Hz electrical stimulation. Dashed red lines indicate that start and end of stimulation. C: Schematic for DBS implementation
Acknowledgements
I would like to thank Dr Frances Skinner (University of Toronto) for her supervision and her help in conceptualising this research idea. I would also to thank Dr Shervin Safavi (Max Planck Institute for Biological Cybernetics) and Dr Thomas Knoesche (Max Planck Institute for Human Cognitive and Brain Sciences) for their help with the modelling and theoretical aspects of this work.
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