Poster No:
63
Submission Type:
Abstract Submission
Authors:
Saeed Makkinayeri1, Roberto Guidotti1, Alessio Basti1, Mark Woolrich2, Chetan Gohil3, Mauro Pettorruso1, Maria Ermolova4, Risto Ilmoniemi5, Ulf Ziemann6, Gian Luca Romani1, Vittorio Pizzella1, Laura Marzetti1
Institutions:
1University of Chieti-Pescara, Chieti, Abruzzo, 2University of Oxford, Oxford, Oxon, 3OHBA, Department of Psychiatry, University of Oxford, Oxford, N/A, 4University of Tübingen, Tübingen, Baden-Württemberg, 5Aalto University, Espoo, Espoo, 6University of Tübingen, Tübingen, Baden-Wuettemberg
First Author:
Co-Author(s):
Chetan Gohil
OHBA, Department of Psychiatry, University of Oxford
Oxford, N/A
Ulf Ziemann
University of Tübingen
Tübingen, Baden-Wuettemberg
Introduction:
Baseline brain activity involves coordinated interplay among brain regions organized in networks, primarily identified in fMRI as Resting State Networks (RSNs). Rehabilitation and brain stimulation approaches targeted to RSNs have recently emerged as particularly effective [1] . Other evidence from EEG-TMS has shown that stimulation efficacy depends on temporal brain dynamics [2]. However, prior research using such dynamics has focused on local EEG properties, limiting insights into effects of RSNs dynamics. This study uses Hidden Markov Models (HMMs) to identify large-scale brain network states from pre-stimulus EEG-TMS data targeting the left motor cortex, to examine their association with canonical RSNs using the Yeo atlas and to evaluate the relation between their dynamics and corticospinal excitability as measured by the amplitude of Motor Evoked Potentials (MEPs).
Methods:
EEG-TMS data were collected from 20 right-handed participants (27 ± 4 years) with no neurological or psychiatric disorders. TMS targeted the left motor cortex, delivering about 1000 pulses at 110% of the Resting Motor Threshold. EMG from hand muscles and 128-channel EEG were concurrently recorded. Filtering (1-40 Hz), artifact removal, and MEP identification were among the preprocessing steps.
Source reconstruction employed LCMV beamforming on a 6-mm source grid in MNI space. Using the AAL atlas, source time courses were reduced to a single representative time course per parcel via PCA. Symmetric orthogonalization minimized zero-lag coupling, and a random search algorithm aligned parcel time course signs across subjects.
Pre-stimulus parcel time courses were concatenated, z-transformed, and expanded using Time-Delayed Embedding (TDE) to capture transient brain states. PCA reduced TDE dimensions to 119 components, retaining 65% variance. Brain states were identified using an HMM and the Viterbi algorithm, extracting metrics such as fractional occupancy (FO), lifetime, and interval time. Spectral analysis using multi-tapers calculated power spectral density (PSD) and coherence (COH) across 3-30 Hz.
The Network Correspondence Toolbox assessed spatial similarity between brain states and YEO atlas networks using the Dice Overlap Coefficient. Statistical analyses, including one-way rmANOVA with Bonferroni-corrected post-hoc tests, examined differences in state metrics (FO, lifetime, interval time). A two-way rmANOVA explored the relationship between brain states, their duration (short- vs. long-lasting), and MEP amplitude.
Results:
Our analysis demonstrates that the TDE-HMM successfully identifies nine distinct brain states in the pre-stimulus EEG-TMS data with unique spectral, temporal, and spatial features. Such brain states feature an average lifetime of 99.7 ms. The distribution of FO across subjects indicates that the identified states are not biased by between-subject differences, suggesting that the model captures dynamic rather than static (time-averaged) FC patterns.
The state to RSN correspondence analysis revealed that, in general, states show overlap with multiple resting-state networks. Nevertheless, it was possible to clearly identify two HMM states corresponding to the visual network, one with the sensori-motor network, one withe the Default Mode Network, and two with the Dorsal Attention Network.
Conclusions:
This study demonstrates that the TDE-HMM approach identifies large-scale brain states resembling fMRI resting-state networks during pre-stimulus EEG-TMS. These states have distinct spatial, temporal, and spectral features, and their engagement correlates with corticospinal excitability. Larger MEPs occur when the motor network is more active before stimulation, highlighting the importance of pre-stimulus neural states in modulating excitability. These findings support state-dependent neurostimulation, bridging gaps between network-based targeting and brain state timing.
Brain Stimulation:
TMS 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Data analysis
Electroencephaolography (EEG)
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
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.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
EEG/ERP
TMS
Computational modeling
Which processing packages did you use for your study?
Other, Please list
-
EEGLAB, FieldTrip, TESA
Provide references using APA citation style.
[1] Cash, R. F. H., Weigand, A., Zalesky, A., Siddiqi, S. H., Downar, J., Fitzgerald, P. B., & Fox, M. D. (2021). Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression. Biological Psychiatry, 90(10), 689–700. https://doi.org/10.1016/j.biopsych.2020.05.033
[2] Zrenner, C., Desideri, D., Belardinelli, P., & Ziemann, U. (2018). Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex. Brain Stimulation, 11(2), 374–389. https://doi.org/10.1016/j.brs.2017.11.016
[3] Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011, 1–9. https://doi.org/10.1155/2011/156869
[4] Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (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. https://doi.org/10.1006/nimg.2001.0978
[5] Kong, R. Q., Zhou, H., Ke, X., ... & Yeo, B. T. T. (2024). A network correspondence toolbox for quantitative evaluation of novel neuroimaging results. bioRxiv. https://doi.org/10.1101/2024.06.17.599426
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