Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task
Open accessAbstract
Dynamic causal modelling (DCM) has extended the understanding of brain network dynamics in a variety of functional systems. In the motor system, DCM studies based on functional magnetic resonance imaging (fMRI) or on magneto-/electroencephalography (M/EEG) have demonstrated movement-related causal information flow from secondary to primary motor areas and have provided evidence for nonlinear cross-frequency interactions among motor areas. The present study sought to investigate to what extent fMRI- and EEG-based DCM might provide complementary and synergistic insights into neuronal network dynamics. Both modalities share principal similarities in the formulation of the DCM. Thus, we hypothesized that DCM based on induced EEG responses (DCM-IR) and on fMRI would reveal congruent task-dependent network dynamics. Brain electrical (63-channel surface EEG) and Blood Oxygenation Level Dependent (BOLD) signals were recorded in separate sessions from 14 healthy participants performing simple isometric right and left hand grips. DCM-IR and DCM-fMRI were used to estimate coupling parameters modulated by right and left hand grips within a core motor network of six regions comprising bilateral primary motor cortex (M1), ventral premotor cortex (PMv) and supplementary motor area (SMA). We found that DCM-fMRI and DCM-IR similarly revealed significant grip-related increases in facilitatory coupling between SMA and M1 contralateral to the active hand. A grip-dependent interhemispheric reciprocal inhibition between M1 bilaterally was only revealed by DCM-fMRI but not by DCM-IR. Frequency-resolved coupling analysis showed that the information flow from contralateral SMA to M1 was predominantly a linear alpha-to-alpha (9-13Hz) interaction. We also detected some cross-frequency coupling from SMA to contralateral M1, i.e., between lower beta (14-21Hz) at the SMA and higher beta (22-30Hz) at M1 during right hand grip and between alpha (9-13Hz) at SMA and lower beta (14-21Hz) at M1 during left hand grip. In conclusion, the strategy of informing EEG source-space configurations with fMRI-derived coordinates, cross-validating basic connectivity maps and analysing frequency coding allows for deeper insight into the motor network architecture of the human brain. The present results provide evidence for the robustness of non-invasively measured causal information flow from secondary motor areas such as SMA towards M1 and further contribute to the validation of the methodological approach of multimodal DCM to explore human network dynamics.
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Interhemispheric connections may be difficult to identify robustly in source-level EEG.
“interhemispheric connections may be difficult to identify robustly in source-level EEG36”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [36]
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Methods for analysis of brain connectivity: An IFCN-sponsored review
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Ageing changes effective connectivity of motor networks during bimanual finger coordination
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- Authors
- Bönstrup, M., Schulz, R., Feldheim, J., Hummel, F. C., & Gerloff, C.
- Affiliations
- University Medical Center Hamburg-Eppendorf (DE)
- Corresponding author
- Gerloff, Christian
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 124
- Issue
- Pt A
- Pages
- 498-508
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1205325697
- Cited by
- 56
- References
- 107
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 3 · 2020: 9 · 2019: 6 · 2018: 5 · 2017: 5 · 2016: 6
- FWCI
- 3.20
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- German Research Foundation (SFB936, SFB 936-C1, SFB 936-C4)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- dynamic causal modeling, motor cortex, supplementary motor area, primary motor cortex, ventral premotor cortex, interhemispheric inhibition, cross-frequency coupling, alpha oscillations, beta oscillations, EEG-fMRI integration, brain network dynamics
- MeSH terms
- Adult, Alpha Rhythm, Bayes Theorem, Beta Rhythm, Brain Mapping, Electroencephalography, Female, Humans, Isometric Contraction, Magnetic Resonance Imaging, Male, Models, Neurological, Motor Activity, Motor Cortex, Neural Pathways, Hand Strength, Young Adult
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