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Li W, Zhang W, Jiang Z, Zhou T, Xu S, Zou L. Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration. Front Hum Neurosci 2022; 16:960784. [PMID: 36034109 PMCID: PMC9411793 DOI: 10.3389/fnhum.2022.960784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. Fused priori weighted minimum norm estimation (FWMNE) with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index (PLI) was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders.
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Affiliation(s)
- Wenjie Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Wei Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhongyi Jiang
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Tiantong Zhou
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Shoukun Xu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- *Correspondence: Shoukun Xu
| | - Ling Zou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, China
- Ling Zou
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Zhuang W, Wang J, Chu C, Wei X, Yi G, Dong Y, Cai L. Disrupted Control Architecture of Brain Network in Disorder of Consciousness. IEEE Trans Neural Syst Rehabil Eng 2022; 30:400-409. [PMID: 35143400 DOI: 10.1109/tnsre.2022.3150834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The human brain controls various cognitive functions via the functional coordination of multiple brain regions in an efficient and robust way. However, the relationship between consciousness state and the control mode of brain networks is poorly explored. Using multi-channel EEG, the present study aimed to characterize the abnormal control architecture of functional brain networks in the patients with disorders of consciousness (DOC). Resting state EEG data were collected from 40 DOC patients with different consciousness levels and 24 healthy subjects. Functional brain networks were constructed in five different EEG frequency bands and the broadband in the source level. Subsequently, a control architecture framework based on the minimum dominating set was applied to investigate the of control mode of functional brain networks for the subjects with different conscious states. Results showed that regardless of the consciousness levels, the functional networks of human brain operate in a distributed and overlapping control architecture different from that of random networks. Compared to the healthy controls, the patients have a higher control cost manifested by more minimum dominating nodes and increased degree of distributed control, especially in the alpha band. The ability to withstand network attack for the control architecture is positive correlated with the consciousness levels. The distributed of control increased correlation levels with Coma Recovery Scale-Revised score and improved separation between unresponsive wakefulness syndrome and minimal consciousness state. These findings may benefit our understanding of consciousness and provide potential biomarkers for the assessment of consciousness levels.
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Daniel Arzate-Mena J, Abela E, Olguín-Rodríguez PV, Ríos-Herrera W, Alcauter S, Schindler K, Wiest R, Müller MF, Rummel C. Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales. Neuroimage 2021; 246:118763. [PMID: 34863961 DOI: 10.1016/j.neuroimage.2021.118763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022] Open
Abstract
Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.
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Affiliation(s)
- J Daniel Arzate-Mena
- Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos,Cuernavaca Morelos, Mexico
| | - Eugenio Abela
- Center for Neuropsychiatrics, Psychiatric Services Aargau AG, Windisch, Switzerland
| | | | - Wady Ríos-Herrera
- Facultad de Psicología Universidad Nacional Autónoma de México, Mexico City, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus F Müller
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Morelos, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico; Centro Internacional de Ciencias A. C., Cuernavaca, México
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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Labounek R, Wu Z, Bridwell DA, Brázdil M, Jan J, Nestrašil I. Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? Front Neurol 2021; 12:644874. [PMID: 33981283 PMCID: PMC8107237 DOI: 10.3389/fneur.2021.644874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/22/2021] [Indexed: 02/01/2023] Open
Abstract
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low β 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, β 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
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Affiliation(s)
- René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
| | - Zhuolin Wu
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milan Brázdil
- Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Jiří Jan
- Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Igor Nestrašil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
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