Space-time structure of the connectome explains frequency-dependent activity in MEG
Poster No:
1250
Submission Type:
Abstract Submission
Authors:
Spase Petkoski1, Viktor Jirsa2
Institutions:
1Aix-Marseille University, Marseille, Marseille, 2Aix-Marseille University, Marseille, Bouches du Rhône
First Author:
Co-Author:
Introduction:
Network theory significantly advanced our understanding of brain activity (Bassett & Sporns, 2017), and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. In a recent work (Petkoski & Jirsa, 2022), however, we demonstrated that the traditional graph theory is deeply rooted in a particle view of information processing, and we extend it to a dual, particle-wave, perspective that is a necessity for the study of brain rhythms. Frequency and time delays become inseparable properties of the network and together with the connectome determine the synchronization and nodal activity (Petkoski & Jirsa, 2019).
When applied to the database of the Human Connectome Project, normalized connectome robustly explains the emergence of frequency-specific network cores in MEG recordings during rest, including the visual and default mode networks. We also showed that the predictive value of the metrics is further improved by incorporating time-delays derived from cortico-cortical evoked potentials (CCEP) (Lemarechal et al., 2022).
When applied to the database of the Human Connectome Project, normalized connectome robustly explains the emergence of frequency-specific network cores in MEG recordings during rest, including the visual and default mode networks. We also showed that the predictive value of the metrics is further improved by incorporating time-delays derived from cortico-cortical evoked potentials (CCEP) (Lemarechal et al., 2022).
Methods:
Connectomes were derived from the first release of diffusion tractography imaging of 100 healthy subjects part of the Human Connectome Project (Van Essen et al., 2013). Time delays were derived from 780 patients with epilepsy (387 females; age at evaluation from 2 to 61years old; mean age 24 ± 14) explored with SEEG included in the F-TRACT protocol (Trebaul et al., 2018). Only significant CCEPs with a peak latency comprised in the first 80 ms were selected, in order to limit the analysis to the early N1 component. (Lemarechal et al., 2022).
To compare the empirical with the predicted activation patterns at different frequencies of MEG, we compared the similarities between the vectors of normalized activity in both cases. The same procedure was repeated for the regions with the strongest activity only, where the threshold was set at different levels.
To compare the empirical with the predicted activation patterns at different frequencies of MEG, we compared the similarities between the vectors of normalized activity in both cases. The same procedure was repeated for the regions with the strongest activity only, where the threshold was set at different levels.
Results:
We integrated time delays due to finite transmission speeds, and derive a normalization of the connectome for communication through coherence (Fries, 2015). For weak couplings, dynamics of the oscillatory system are captured by phase models, of which the simplest and the most elaborate is the Kuramoto model (Kuramoto, 1984). We use the insight that the impact of the direct link in the phase difference between oscillators can be separated from the rest of the network leading to a normalization of network weights wij by the term cosΩτij, where Ω is the frequency and τij is the time-delay due to axonal propagation over the link between the nodes i and j (Petkoski & Jirsa, 2022).
We compare empirical and predicted relative activation patterns of all the brain regions for the frequencies of 1 - 80 Hz. For the time-delays of the normalized connectome in the first case we assume homogeneous propagation velocity, which is often used as a first approximation, resulting in time delays being defined by the lengths of the links (Sanz-Leon, Knock, Spiegler, & Jirsa, 2015). Then, we use the time-delays from the stimulation and we quantify the improved predictive value of spectral strength and capacity.
We compare empirical and predicted relative activation patterns of all the brain regions for the frequencies of 1 - 80 Hz. For the time-delays of the normalized connectome in the first case we assume homogeneous propagation velocity, which is often used as a first approximation, resulting in time delays being defined by the lengths of the links (Sanz-Leon, Knock, Spiegler, & Jirsa, 2015). Then, we use the time-delays from the stimulation and we quantify the improved predictive value of spectral strength and capacity.
·Cortical resonant patterns of activity of 100 subjects for different frequencies. The activation of resting-state networks is also projected in the frequencies.
Conclusions:
The incorporation of signal transmission delays in the connectome's metrics completes the characterization of the spatiotemporal skeleton, within which oscillatory brain activity can be amplified by the properties of the medium supporting it, i.e., it provides a corpus resonantiae. We propose that the activation of certain parts of the brain, that are related to different tasks, can be explained as being anatomically prewired. We have demonstrated that the connectome has such properties and allows for selective, frequency-dependent information processing that could support the differentiation of brain activity for various processes and frequency bands.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
EEG/MEG Modeling and Analysis 2
Keywords:
Computational Neuroscience
Modeling
Other - synchronization
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Fries, P. (2015). Rhythms for Cognition: Communication through Coherence. Neuron, 88(1), 220–235. https://doi.org/10.1016/j.neuron.2015.09.034
Kuramoto, Y. (1984). Chemical Oscillations, Waves, and Turbulence (Vol. 19). Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69689-3
Lemarechal, J. D. (2022). A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials. Brain, 145(5), 1653–1667. https://doi.org/10.1093/brain/awab362
Petkoski, S. (2019). Transmission time delays organize the brain network synchronization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 377(2153), 20180132. https://doi.org/10.1098/rsta.2018.0132
Petkoski, S. (2022). Normalizing the brain connectome for communication through synchronization. Network Neuroscience, 6(3), 1–23. https://doi.org/10.1162/netn_a_00231
Sanz-Leon, P. (2015). Mathematical framework for large-scale brain network modeling in The Virtual Brain. Neuroimage, 111, 385–430. https://doi.org/10.1016/j.neuroimage.2015.01.002
Trebaul, L.. (2018). Probabilistic functional tractography of the human cortex revisited. NeuroImage, 181(June), 414–429. https://doi.org/10.1016/J.NEUROIMAGE.2018.07.039
Van Essen, D. C. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041