Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks.
- Authors
- Mantini, D; Marzetti, L; Corbetta, M; Romani, G L; Del Gratta, C
- Year
- 2010
- Journal
- Brain topography
- PMID
- 20052528
- DOI
- 10.1007/s10548-009-0132-3
- PMCID
- PMC5682027
Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes.
Schematic representation of the analysis used for linking ERP activity and fMRI networks. The azure and red arrows illustrate the steps for EEG and fMRI data analysis, respectively. The ERP data are processed with temporal ICA, to detect independent neuronal activations. The fMRI data were analyzed with spatial ICA, to identify the spatial distribution of large-scale networks. Finally, for each ERP IC scalp map, source localization is performed jointly using the spatial maps of task-related networks as fMRI priors.
Spatio-temporal analysis of the five networks consistently found across subjects, which spatially overlap with the dorsal attention, the ventral attention, the core, the visual and the sensory-motor networks. For each network, the cortical representation is shown (left panel), along with the time-course in response to rare events (right panel). The average time-courses are normalized because they refer to the average activity of the brain patterns. The thick red line in each graph represents the hemodynamic response function (HRF) best-fit to all data points. (CUN=cuneus; LIN=lingual gyrus; FUS=fusiform gyrus; INS=insula; ACC=anterior cingulate; MFG=middle frontal gyrus; IFG=inferior frontal gyrus; SMA=supplementary motor area; TPJ=temporoparietal junction; IPL=intraparietal lobule; IPS=intraparietal sulcus; FEF=frontal eye field; PRE=precuneus; PCC=posterior cingulate; MI=primary motor area; SI=primary somatosensory area).
ERP analysis of the grand average signals. (a) Scalp maps showing the temporal evolution of the ERP response to rare events; (b) Scalp topography of ERPs associated with rare and frequent events, colored in red and blue respectively; (c) Source distribution for rare-event ERP by WMNLS localization.
Spatio-temporal analysis of the detected ERP ICs. For each IC, the time-domain response to rare and frequent events, the scalp map, and the normalized source power in the main areas of the visual, the core and the ventral attention networks (in blue, red and green colors respectively) are shown. The statistical significance of the ERP-ICs across the brain areas is also provided. (CUN=cuneus; LIN=lingual gyrus; FUS=fusiform gyrus; INS=insula; ACC=anterior cingulate; MFG=middle frontal gyrus; IFG=inferior frontal gyrus; SMA=supplementary motor area; TPJ=temporoparietal junction; IPL=intraparietal lobule).
No entities extracted from this document yet.
No uploaded files.
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Advances in Electrophysiological Research. | 2015 | 26259089 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| AI-powered remote monitoring of brain responses to clear and incomprehensible speech via speckle pattern analysis. | Segal N et al. | — | 2025 | → |
| Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review. | Park D et al. | — | 2025 | → |
| Infants' resting-state functional connectivity and event-related potentials: A multimodal approach to investigating the neural basis of infant novelty detection. | Kanel D et al. | — | 2025 | → |
| Neuroimaging insights into the psychosocial impact of the COVID-19 pandemic: a systematic review. | Guo Y et al. | — | 2025 | → |
| Precision neurodiversity: personalized brain network architecture as a window into cognitive variability. | Mohammad SI et al. | — | 2025 | → |
| Sensory Representation of Neural Networks Using Sound and Color for Medical Imaging Segmentation. | Silva ILD et al. | — | 2025 | → |
| Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis. | Jang H et al. | — | 2024 | → |
| Functional MR elastography measures visual cortex stiffening proportional to visual contrast intensity in regions of activation. | Palnitkar HR et al. | — | 2024 | → |
| Remote Detection of Human Brain Reaction to Speech by AI Speckle Pattern Analysis | Segal N et al. | — | 2024 | — |
| Towards real-time identification of large-scale brain states for improved brain state-dependent stimulation. | Marzetti L et al. | — | 2024 | → |
| Exploring Network Properties Across Preclinical Stages of Alzheimer's Disease Using a Visual Short-Term Memory and Attention Task with High-Density Electroencephalography: A Brain-Connectome Neurophysiological Study. | Lazarou I et al. | — | 2022 | → |
| Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? | Labounek R et al. | — | 2021 | → |
| Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI. | Philiastides MG et al. | — | 2021 | → |
| Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting- to task-state: Evidence from a simultaneous event-related EEG-fMRI study. | Li F et al. | — | 2020 | → |
| Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. | Marino M et al. | — | 2018 | → |
| Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI. | Marino M et al. | — | 2018 | → |
| Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI. | Mangalathu-Arumana J et al. | — | 2018 | → |
| Advances in Electrophysiological Research. | Kamarajan C et al. | — | 2015 | → |
| Multimodal EEG Recordings, Psychometrics and Behavioural Analysis. | Boeijinga PH | — | 2015 | → |
| The Roles of Dopamine and Hypocretin in Reward: A Electroencephalographic Study. | Mensen A et al. | — | 2015 | → |
| Brain circuits implicated in psychogenic paralysis in conversion disorders and hypnosis. | Vuilleumier P | — | 2014 | → |
| Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. | Anderson A et al. | — | 2014 | → |
| Neurophysiological techniques in the study of the excitability, connectivity, and plasticity of the human brain. | Rossinia PM et al. | — | 2013 | → |
| Decoding covert spatial attention using electrocorticographic (ECoG) signals in humans. | Gunduz A et al. | — | 2012 | → |
| Neural correlates of visual-spatial attention in electrocorticographic signals in humans. | Gunduz A et al. | — | 2011 | → |
| From the analysis of the brain images to the study of brain networks using functional connectivity and multimodal brain signals. | Babiloni F | — | 2010 | → |