Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

René Labounek; Zhuolin Wu; David A Bridwell; Milan Brázdil; Jiří Jan; Igor Nestrašil

doi:10.3389/fneur.2021.644874

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

Front Neurol. 2021 Apr 26:12:644874. doi: 10.3389/fneur.2021.644874. eCollection 2021.

Authors

René Labounek¹, Zhuolin Wu^{1

2}, David A Bridwell³, Milan Brázdil⁴, Jiří Jan⁵, Igor Nestrašil^{1

6}

Affiliations

¹ Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States.
² Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
³ Mind Research Network, Albuquerque, NM, United States.
⁴ Central European Institute of Technology, Masaryk University, Brno, Czechia.
⁵ Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia.
⁶ Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States.

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.

Keywords: GLM; general linear model; independent component analysis; simultaneous EEG-fMRI; spectral and spatiospectral models; task-related network visualization; visual oddball paradigm.