Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function
Abstract
Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.
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The spatial information carried by EEG signals plays an important role in AD/HC classification.
“The spatial information carried by EEG signals plays an important role in AD/HC classification (Babiloni et al., 2005; Sakkalis, 2011; Tafreshi et al., 2019; Van Mierlo et al., 2014).”
From <scp>Spatial–temporal</scp> graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram (2022) · cites this paper as Babiloni et al., 2005
Electrophysiological source imaging allows mapping functional and effective connectivity in source space, a notion of the electrophysiological connectome (eConnectome).
“ESI allows to map functional/effective connectivity in source space – a notion of the electrophysiological connectome (eConnectome) (6, 97).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [97]
fMRI-biased regularization algorithms share the assumption that EEG/MEG sources are more likely to occur where fMRI shows activation.
“such algorithms all share and utilize a common assumption that EEG/MEG sources are more likely to occur at where fMRI views as activated.”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [97]
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- Authors
- Babiloni, F., Cincotti, F., Babiloni, C., Carducci, F., Mattia, D., Astolfi, L., Basilisco, A., Rossini, P. M., Ding, L., Ni, Y., Cheng, J., Christine, K., Sweeney, J., & He, B.
- Affiliations
- Fondazione Santa Lucia (IT)
- Centro San Giovanni di Dio Fatebenefratelli (IT)
- Sapienza University of Rome (IT)
- Università Campus Bio-Medico (IT)
- University of Minnesota (US)
- University of Illinois Chicago (US)
- Corresponding author
- Babiloni, F.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-01
- Volume
- 24
- Issue
- 1
- Pages
- 118-131
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1992259561
- Cited by
- 412
- References
- 60
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 8 · 2023: 12 · 2022: 15 · 2021: 14 · 2020: 13 · 2019: 18 · 2018: 26 · 2017: 23 · 2016: 21 · 2015: 22 · 2014: 27 · 2013: 18 · 2012: 27
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Open Research (University of Surrey) (submittedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- National Science Foundation
- University of Illinois at Chicago
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- cortical functional connectivity, directed transfer function, high-resolution EEG, functional MRI, hemodynamic response, premotor cortex, prefrontal cortex, parietal cortex
- MeSH terms
- Adult, Brain Mapping, Cerebral Cortex, Computer Simulation, Dominance, Cerebral, Electroencephalography, Female, Humans, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motor Activity, Motor Cortex, Nerve Net, Synaptic Transmission, Parietal Lobe, Psychomotor Performance, Statistics as Topic, Linear Models, Prefrontal Cortex, Imaging, Three-Dimensional
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