Stimulation mapping and whole-brain modeling reveal gradients of excitability in cortical networks
Poster No:
40
Submission Type:
Abstract Submission
Authors:
Davide Momi1, Zheng Wang2, Sara Parmigiani1, Ezequiel Mikulan3, Sorenza Bastiaens2, Mohammad Oveisi2, Kevin Kadak2, Gianluca Gaglioti3, Allison Waters4, Sean Hill2, Andrea Pigorini3, Corey Keller1, John Griffiths2
Institutions:
1Stanford University, Stanford, CA, 2Centre for Addiction and Mental Health, Toronto, Ontario, 3Università degli Studi di Milano, Milano, Italy, 4Icahn School of Medicine at Mount Sinai, New York, NY
First Author:
Co-Author(s):
Introduction:
The human brain comprises distinct resting-state networks (RSNs) characterized by spontaneous activity patterns (1). Prior research has revealed a hierarchical organization of these networks, ranging from high-order multimodal networks to low-order networks (2, 3). A critical inquiry for both fundamental and clinical studies on brain dynamics is whether this topographical organization influences how different brain regions engage in information processing based on their network affiliation. Furthermore, if such differences exist, do they manifest in distinct propagation patterns, governed by local versus global processes?
Methods:
To address these questions, we employed a dataset where simultaneous sEEG and hd-EEG was recorded following intracortical single pulse electrical stimulation on 36 patients (Figure 1A). We identified the Schaefer's parcel (4) that overlapped with the intracranial electrode responsible for delivering the stimulus, ultimately enabling us to determine the stimulated network (Figure 1B). After exploring empirical results, we used a computation connectome-based whole-brain model we developed recently (5) to present new insights into the role of recurrent feedback activity in stimulation-evoked brain responses (Figure 1C). We employed a virtual lesion approach (6) to isolate and prevent the stimulated network from receiving feedback input from the rest of the other non-stimulated RSNs (Figure D). This procedure allows us to evaluate the extent to which model-generated stimulation-evoked patterns relied on recurrent incoming connections from downstream brain areas that belong to the not stimulated networks. We hypothesized that for the condition where the stimulation was delivered to low-order networks the late responses will be relatively unchanged indicating that the activity observed in the empirical data highly depends on the intrinsic network dynamics. Conversely, for high-order networks we expect a progressive suppression of the late responses meaning that their propagation dynamics in the empirical data reflects an overall integrated pattern where feedback connections are necessary.
·Figure 1
Results:
Both the sEEG and the hd-EEG Global Mean Field Power (GMFP) shows a significantly stronger propagation pattern when the stimulus targeted high-order multimodal networks particularly for the late evoked responses (Figure 2A). This findings mirror the identical hierarchy previously reported in the literature through the analysis of functional and anatomical MRI data (2,3,7).
Our connectome-based neurophysiological model of stimulus-evoked responses achieves robust and accurate recovery (Figure 2B bottom). The analysis using a virtual lesion approach reveals that the late responses are highly dependent on whole- brain integrity for high-order networks and mainly restricted to intrinsic network properties when the stimulus is delivered to low-order networks. The GMFP for model-generated hd-EEG data run with the lesioned structural connectome reveals a change in the propagation dynamics compared to both empirical and standard simulated data, where this time larger evoked responses were recorded for the unimodal (Figure 2C). A significant response reduction was found for late responses of Frontoparietal and Default Mode networks compared to Somatomotor network (Figure 2D). This indicates that in the case of the unimodal network, the lesion does not significantly impact the evoked potentials (Figure 2E top/right) while for the high-order network a substantial reduction or disappearance of evoked components was observed (Figure 2E bottom/right).
Our connectome-based neurophysiological model of stimulus-evoked responses achieves robust and accurate recovery (Figure 2B bottom). The analysis using a virtual lesion approach reveals that the late responses are highly dependent on whole- brain integrity for high-order networks and mainly restricted to intrinsic network properties when the stimulus is delivered to low-order networks. The GMFP for model-generated hd-EEG data run with the lesioned structural connectome reveals a change in the propagation dynamics compared to both empirical and standard simulated data, where this time larger evoked responses were recorded for the unimodal (Figure 2C). A significant response reduction was found for late responses of Frontoparietal and Default Mode networks compared to Somatomotor network (Figure 2D). This indicates that in the case of the unimodal network, the lesion does not significantly impact the evoked potentials (Figure 2E top/right) while for the high-order network a substantial reduction or disappearance of evoked components was observed (Figure 2E bottom/right).
·Figure 2
Conclusions:
Overall, these novel discoveries pinpoint the presence of an RSN hierarchy based on information processing within the human brain, shedding light on the distinct propagation patterns shaped by either local or global dynamics. These findings have implications for understanding brain function in health and pathology, with potential applications in personalized interventions and therapeutic strategies.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
EEG/MEG Modeling and Analysis 2
Novel Imaging Acquisition Methods:
EEG
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
Computational Neuroscience
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
Machine Learning
Modeling
Systems
Tractography
1|2Indicates the priority used for review
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2. D. S. Margulies, S. S. Ghosh, A. Goulas, M. Falkiewicz, J. M. Huntenburg, G. Langs, G. Bezgin, S. B. Eickhoff, F.X. Castellanos, M. Petrides, E. Jefferies, J. Smallwood, Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. 113, 12574–12579 (2016).
3. C. Seguin, A. Razi, A. Zalesky, Inferring neural signalling directionality from undirected structural connectomes. Nat. Commun. 10, 4289 (2019).
4. A. Schaefer, R. Kong, E. M. Gordon, T. O. Laumann, X.-N. Zuo, A. J. Holmes, S. B. Eickhoff, B. T. T. Yeo, Local- Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex N.Y. N 1991 28, 3095–3114 (2018).
5. D. Momi, Z. Wang, J. D. Griffiths, TMS-evoked responses are driven by recurrent large-scale network dynamics. eLife 12, e83232 (2023).
6. H. Aerts, W. Fias, K. Caeyenberghs, D. Marinazzo, Brain networks under attack: robustness properties and the impact of lesions. Brain 139, 3063–3083 (2016).
7. M. J. Veit, A. Kucyi, W. Hu, C. Zhang, B. Zhao, Z. Guo, B. Yang, C. Sava-Segal, C. Perry, J. Zhang, K. Zhang, J. Parvizi, Temporal order of signal propagation within and across intrinsic brain networks. Proc. Natl. Acad. Sci.118, e2105031118 (2021).