P168 Evaluating Effective Connectivity and Control Theory to Understand rTMS-Induced Network Effects
Riccardo Leone*1,2,3, Michele Allegra4, Xenia Kobeleva1,2
1 Computational Neurology Group, Ruhr University Bochum, 44801, Bochum, Germany.
2 Faculty of Medicine, University of Bonn, 53127, Bonn, Germany.
3 German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany.
4 Padova Neuroscience Center, University of Padova,35129,Padova, Italy
* Email: riccardoleone1991@gmail.com
Introduction
Computational neuroscience might contribute to a better understanding of neurostimulation by modeling its effects on brain networks. Effective connectivity (EC) and EC-based network control theory could provide a theory-driven framework for elucidating neurostimulation-induced network effects [1]. We thus tested whether EC and control energy could explain changes in resting-state fMRI (rs-fMRI) metrics induced by repetitive transcranial magnetic stimulation (rTMS). We hypothesized that EC and control energy would outperform functional connectivity (FC) and structural connectivity (SC) in explaining rTMS effects.
Methods
Twenty-one subjects received inhibitory 1Hz rTMS (20 min) at frontal, occipital or temporo-parietal sites, with rs-fMRI acquired pre- and post-stimulation. Whole-brain EC was estimated using regression Dynamic Causal Modeling. Control energy from the stimulated node (i.e., driver node) to each downstream target node was computed from the EC model. We quantified rTMS effects at a node level as pre- vs post- changes in: i) FC with the driver region, ii) amplitude of low-frequency fluctuations (ALFF), and iii) nodal FC strength with the whole brain. We correlated these changes with a series of pre-stimulus predictors: SC, FC, EC between each target and the driver node, and energy needed to control each target from the driver node.
Results
rTMS generally reduced whole-brain FC with each stimulated driver node, as well as ALFF, and nodal FC strength, with frontal stimulation yielding more widespread effects. EC and control energy showed significant correlations with the change in FC with the driver node and nodal FC strength. Nonetheless, significant associations of similar or greater magnitude were also observed with simple FC, thus failing to demonstrate a clear advantage of EC and EC-based control energy to evaluate rTMS-induced effects. Changes in ALFF were not significantly correlated with any pre-TMS variable.
Discussion
Contrary to our main hypothesis, EC and EC-based control energy did not provide significantly better explanations of 1Hz rTMS-induced changes compared to model-agnostic FC. Our results question the current utility of EC and EC-based control theory models for understanding the effects of 1-Hz rTMS on brain networks. Given the complex interplay of neurobiological processes induced by rTMS that are not directly linked to the network spread of TMS pulses (e.g., synaptic plasticity), future work should implement EC and EC-based control energy to explain the effects of simpler protocols of neurostimulation.
Acknowledgements
References
1. Manjunatha KKH, Baron G, Benozzo D, Silvestri E, Corbetta M, Chiuso A, et al. (2024) Controlling target brain regions by optimal selection of input nodes.PLoS Comput Biol20(1): e1011274. https://doi.org/10.1371/journal.pcbi.1011274
