Large scale network modelling of the effect of sensory and electric brain stimulation.
Poster No:
1648
Submission Type:
Abstract Submission
Authors:
Mónica Otero Ferreiro1,2, Felipe Torres3, Caroline Lea-Carnall4, Alejandro Weinstein3, Joana Cabral5, WAEL EL-DEREDY6
Institutions:
1Universidad San Sebastián, Santiago de Chile, RM. Metropolitana, 2Centro Basal Ciencia & Vida, Santiago de Chile, Chile, 3Universidad de Valparaíso, Valparaíso, Valparaíso, 4University of Manchester, Manchester, Manchester, 5University of Minho, Braga, Portugal, 6UNIVERSIDAD DE VALPARAISO, Valparaíso, Valparaíso
First Author:
Mónica Otero Ferreiro
Universidad San Sebastián|Centro Basal Ciencia & Vida
Santiago de Chile, RM. Metropolitana|Santiago de Chile, Chile
Universidad San Sebastián|Centro Basal Ciencia & Vida
Santiago de Chile, RM. Metropolitana|Santiago de Chile, Chile
Co-Author(s):
Introduction:
The phenomenon referred to as neural entrainment, characterized by the synchronization of neural oscillations with external stimulus frequencies, is considered a potential mechanism explaining the impact of external sensory and electric stimulation on the brain. This entrainment occurs as ongoing oscillations align their phases with the driving stimulus. Yet, the interaction between stimulation parameters and ongoing brain network dynamics is poorly understood.
Methods:
In this research, we use the concepts of resonance and loosely coupled oscillators to model the emergence of oscillatory networks in the brain which exhibit a preference for specific frequencies of incoming stimulation. A large-scale model of coupled oscillators was implemented using a partially forced Kuramoto Model (FKM) [1] with realistic connectivity structure and connection delays [2], to generate a realistic frequency spectrum comparable to real EEG [3,4]. Structural connectivity information was computed from the human connectome, based on the AAL atlas [5]. Intrinsic frequencies of nodes (brain regions) were set to 40Hz as in [3,4]. As it has been previously demonstrated in [4], the addition of delays to the Kuramoto model promotes the emergence of multi-state metastability in the system, which has been associated with the capacity of the brain to transition between network configurations and has been related to cognitive flexibility. We identify the parameters of FKM in which we obtained maximal spectral entropy of the model as a proxy for multi-state metastability. Using this set of parameters, we investigated the effect of the structural information of the node stimulated (brain region) and the frequency of stimulation in the propagation of this external stimulation. The activation of nodes due to stimulation was computed using the power spectrum and phase locking value of the signals during stimulation compared to the ongoing simulated signals.
Results:
Simulated signals with similar spectral features to those from EEG recordings were obtained using the FKM model. Moreover, spectral profiles of the simulated signals were found to be different depending of the parameters of coupling and mean delay of the FKM. Different patterns of propagation emerge when different nodes are stimulated with varied frequencies. In figure1, we can observe that nodes activation changed depending on the driver node (node stimulated) (see figure 1a), but also depending on the frequency of stimulation (see figure 1b). Furthermore, we found that spectral profiles of nodes changed as a result of the stimulation depending on the frequency of stimulation. In figure 2a) is shown the spectral profile of several nodes in resting state (ongoing activity); in figure 2b and 2C is shown the effect of the stimulation at one location (brain region) and two different frequencies 43Hz and 13Hz respectively. Our findings suggest that structure information of the stimulated node (degree and strength of connections) determined the number of nodes that can be activated through the repetitive stimulation. Furthermore, we found a selective preference of different neural networks for specific stimulation frequencies, and this preference is dependent on the stimulated node (brain area), but also the state of metastability of the system. Additionally, we observed that new functional subnetworks, different to those existing in simulated ongoing signals, emerge as the result of the stimulation at specific locations and using specific frequencies.
Conclusions:
Our findings suggest that FKM is suitable for the simulation of the effect of external stimulation in the brain. This study corroborates the importance of using computational models to guide the selection of the parameters of stimulation to selectively activate specific neuronal networks.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 2
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Keywords:
Computational Neuroscience
Electroencephaolography (EEG)
Modeling
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2. Yeung, M. S. et al. (1999). Time delay in the Kuramoto model of coupled oscillators. Physical review letters, 82(3), 648.
3. Cabral, J. et. al. (2014). Exploring mechanisms of spontaneous functional connectivity in MEG: how delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations. Neuroimage, 90, 423-435.
4. Cabral, J. et al. Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome. Commun Phys 5, 184 (2022). https://doi.org/10.1038/s42005-022-00950-y.
5. Tzourio-Mazoyer et. al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273-289.