Detailed Information on Publication Record
2021
Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?
LABOUNEK, R., Z.L. WU, D.A. BRIDWELL, Milan BRÁZDIL, J. JAN et. al.Basic information
Original name
Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?
Authors
LABOUNEK, R., Z.L. WU, D.A. BRIDWELL, Milan BRÁZDIL (203 Czech Republic, guarantor, belonging to the institution), J. JAN and I. NESTRASIL
Edition
Frontiers in Neurology, Lausanne, Frontiers, 2021, 1664-2295
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
30103 Neurosciences
Country of publisher
Switzerland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 4.086
RIV identification code
RIV/00216224:14740/21:00124305
Organization unit
Central European Institute of Technology
UT WoS
000648447500001
Keywords in English
simultaneous EEG-fMRI; task-related network visualization; spectral and spatiospectral models; visual oddball paradigm; general linear model; GLM; independent component analysis
Tags
Tags
International impact, Reviewed
Změněno: 26/2/2022 14:41, Mgr. Pavla Foltynová, Ph.D.
Abstract
V originále
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 delta(4) band and low beta(1) band) demonstrated significant negative linear relationship (p(FWE) < 0.05) to the frequent stimulus and three patterns (two low delta(2) and delta(3) bands, and narrow theta(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 delta(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 delta(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 beta(1) patterns visualized less significant and distinct suprathreshold spatial associations. Each theta(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 theta(1) model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For delta(4), beta(1), and theta(1) bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.