Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task
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 i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bönstrup, Marlene [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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11 |
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Enthalten in: Field study of a soft X-ray aerosol neutralizer combined with electrostatic classifiers for nanoparticle size distribution measurements - Nicosia, Alessia ELSEVIER, 2017, a journal of brain function, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:124 ; year:2016 ; day:1 ; month:01 ; pages:498-508 ; extent:11 |
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| DOI / URN: |
10.1016/j.neuroimage.2015.08.052 |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
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10.1016/j.neuroimage.2015.08.052 doi GBVA2016018000028.pica (DE-627)ELV019678991 (ELSEVIER)S1053-8119(15)00771-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 Bönstrup, Marlene verfasserin aut Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Schulz, Robert oth Feldheim, Jan oth Hummel, Friedhelm C. oth Gerloff, Christian oth Enthalten in Academic Press Nicosia, Alessia ELSEVIER Field study of a soft X-ray aerosol neutralizer combined with electrostatic classifiers for nanoparticle size distribution measurements 2017 a journal of brain function Orlando, Fla (DE-627)ELV001942808 volume:124 year:2016 day:1 month:01 pages:498-508 extent:11 https://doi.org/10.1016/j.neuroimage.2015.08.052 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 124 2016 1 0101 498-508 11 045F 610 |
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10.1016/j.neuroimage.2015.08.052 doi GBVA2016018000028.pica (DE-627)ELV019678991 (ELSEVIER)S1053-8119(15)00771-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 Bönstrup, Marlene verfasserin aut Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Schulz, Robert oth Feldheim, Jan oth Hummel, Friedhelm C. oth Gerloff, Christian oth Enthalten in Academic Press Nicosia, Alessia ELSEVIER Field study of a soft X-ray aerosol neutralizer combined with electrostatic classifiers for nanoparticle size distribution measurements 2017 a journal of brain function Orlando, Fla (DE-627)ELV001942808 volume:124 year:2016 day:1 month:01 pages:498-508 extent:11 https://doi.org/10.1016/j.neuroimage.2015.08.052 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 124 2016 1 0101 498-508 11 045F 610 |
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Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task |
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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. |
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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. |
| abstract_unstemmed |
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. |
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| title_short |
Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task |
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https://doi.org/10.1016/j.neuroimage.2015.08.052 |
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| author2 |
Schulz, Robert Feldheim, Jan Hummel, Friedhelm C. Gerloff, Christian |
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Schulz, Robert Feldheim, Jan Hummel, Friedhelm C. Gerloff, Christian |
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| doi_str |
10.1016/j.neuroimage.2015.08.052 |
| up_date |
2024-07-06T22:04:24.437Z |
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