Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models
Background: Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targ...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Brain Stimulation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1935861X25000774 |
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| author | Saeed Makkinayeri Roberto Guidotti Alessio Basti Mark W. Woolrich Chetan Gohil Mauro Pettorruso Maria Ermolova Risto J. Ilmoniemi Ulf Ziemann Gian Luca Romani Vittorio Pizzella Laura Marzetti |
| author_facet | Saeed Makkinayeri Roberto Guidotti Alessio Basti Mark W. Woolrich Chetan Gohil Mauro Pettorruso Maria Ermolova Risto J. Ilmoniemi Ulf Ziemann Gian Luca Romani Vittorio Pizzella Laura Marzetti |
| author_sort | Saeed Makkinayeri |
| collection | DOAJ |
| description | Background: Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization. Objective: We aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability. Methods: This study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined. Results: We extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network. Conclusions: These findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments. |
| format | Article |
| id | doaj-art-e7bb26701aa0490cbad667cf73384cc5 |
| institution | OA Journals |
| issn | 1935-861X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain Stimulation |
| spelling | doaj-art-e7bb26701aa0490cbad667cf73384cc52025-08-20T02:33:20ZengElsevierBrain Stimulation1935-861X2025-05-0118380080910.1016/j.brs.2025.03.020Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov modelsSaeed Makkinayeri0Roberto Guidotti1Alessio Basti2Mark W. Woolrich3Chetan Gohil4Mauro Pettorruso5Maria Ermolova6Risto J. Ilmoniemi7Ulf Ziemann8Gian Luca Romani9Vittorio Pizzella10Laura Marzetti11Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, ItalyDepartment of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, ItalyDepartment of Engineering and Geology, G. d'Annunzio University of Chieti-Pescara, Pescara, ItalyOxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, Warneford Hospital, Oxford, Oxford, United KingdomOxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, Warneford Hospital, Oxford, Oxford, United KingdomDepartment of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, ItalyDepartment of Neurology & Stroke, University of Tübingen, Tübingen, GermanyDepartment of Neuroscience and Biomedical Engineering, Aalto University, Espoo, FinlandDepartment of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, GermanyInstitute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, ItalyDepartment of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, ItalyInstitute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Department of Engineering and Geology, G. d'Annunzio University of Chieti-Pescara, Pescara, Italy; Corresponding author. Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.Background: Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization. Objective: We aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability. Methods: This study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined. Results: We extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network. Conclusions: These findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments.http://www.sciencedirect.com/science/article/pii/S1935861X25000774Resting state networksElectroencephalographyTranscranial magnetic stimulation (TMS)Motor evoked potential (MEP)Corticospinal excitabilityNetwork-based stimulation |
| spellingShingle | Saeed Makkinayeri Roberto Guidotti Alessio Basti Mark W. Woolrich Chetan Gohil Mauro Pettorruso Maria Ermolova Risto J. Ilmoniemi Ulf Ziemann Gian Luca Romani Vittorio Pizzella Laura Marzetti Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models Brain Stimulation Resting state networks Electroencephalography Transcranial magnetic stimulation (TMS) Motor evoked potential (MEP) Corticospinal excitability Network-based stimulation |
| title | Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models |
| title_full | Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models |
| title_fullStr | Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models |
| title_full_unstemmed | Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models |
| title_short | Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models |
| title_sort | investigating brain network dynamics in state dependent stimulation a concurrent electroencephalography and transcranial magnetic stimulation study using hidden markov models |
| topic | Resting state networks Electroencephalography Transcranial magnetic stimulation (TMS) Motor evoked potential (MEP) Corticospinal excitability Network-based stimulation |
| url | http://www.sciencedirect.com/science/article/pii/S1935861X25000774 |
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