Comparing structure–function relationships in brain networks using EEG and fNIRS

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure–functional relationship has yet...

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Published inScientific reports Vol. 14; no. 1; pp. 28976 - 18
Main Authors Blanco, Rosmary, Preti, Maria Giulia, Koba, Cemal, Ville, Dimitri Van De, Crimi, Alessandro
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 22.11.2024
Nature Publishing Group
Nature Portfolio
Subjects
631/114/116/1925
631/1647/1453/1450
631/1647/527/1989
631/378
Adult
Brain
Brain - diagnostic imaging
Brain - physiology
Brain architecture
Brain Mapping - methods
Brain structure–function relationship
Cortex (motor)
EEG
Electroencephalography
Electroencephalography - methods
Female
fNIRS
Functional anatomy
Functional morphology
Hemodynamics
Hemodynamics - physiology
Humanities and Social Sciences
Humans
Infrared spectroscopy
Male
Mental task performance
multidisciplinary
Multimodal imaging
Nerve Net - diagnostic imaging
Nerve Net - physiology
Neural activity and Hemodynamic response
Neuroimaging
Oscillations
Science
Science (multidisciplinary)
Signal processing
Somatosensory cortex
Spectroscopy, Near-Infrared - methods
Spectrum analysis
Structure-Activity Relationship
Structure-function relationships
Young Adult
Multimodal imaging
Brain structure–function relationship
fNIRS
Neural activity and Hemodynamic response
EEG
Online AccessGet full text

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Abstract Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure–functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure–function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure–function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure–function relationship and brain organization when using affordable functional brain imaging.
AbstractList Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.
Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.
Abstract Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure–functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure–function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure–function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure–function relationship and brain organization when using affordable functional brain imaging.
ArticleNumber 28976
Author Ville, Dimitri Van De
Preti, Maria Giulia
Blanco, Rosmary
Koba, Cemal
Crimi, Alessandro
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Issue 1
Keywords Multimodal imaging
Brain structure–function relationship
fNIRS
Neural activity and Hemodynamic response
EEG
Language English
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SSID ssj0000529419
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Snippet Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The...
Abstract Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The...
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SubjectTerms 631/114/116/1925
631/1647/1453/1450
631/1647/527/1989
631/378
Adult
Brain
Brain - diagnostic imaging
Brain - physiology
Brain architecture
Brain Mapping - methods
Brain structure–function relationship
Cortex (motor)
EEG
Electroencephalography
Electroencephalography - methods
Female
fNIRS
Functional anatomy
Functional morphology
Hemodynamics
Hemodynamics - physiology
Humanities and Social Sciences
Humans
Infrared spectroscopy
Male
Mental task performance
multidisciplinary
Multimodal imaging
Nerve Net - diagnostic imaging
Nerve Net - physiology
Neural activity and Hemodynamic response
Neuroimaging
Oscillations
Science
Science (multidisciplinary)
Signal processing
Somatosensory cortex
Spectroscopy, Near-Infrared - methods
Spectrum analysis
Structure-Activity Relationship
Structure-function relationships
Young Adult
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Title Comparing structure–function relationships in brain networks using EEG and fNIRS
URI https://link.springer.com/article/10.1038/s41598-024-79817-x
https://www.ncbi.nlm.nih.gov/pubmed/39578593
https://www.proquest.com/docview/3132021477
https://www.proquest.com/docview/3132143309
https://pubmed.ncbi.nlm.nih.gov/PMC11584861
https://doaj.org/article/164dd802ed464c2899d9eb12e94daeec
Volume 14
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