GENERALIZED EEG-FMRI SPECTRAL AND SPATIOSPECTRAL HEURISTIC MODELS
René Labounek
1,2,3
, David Janeček
1
, Radek Mareček
2
, Martin Lamoš
1,2
, Tomáš Slavíček
1,2
, Michal Mikl
2
,
Jaromír Baštinec
4
, Petr Bednařík
2,3,5
, David Bridwell
6
, Milan Brázdil
2
, Jiří Jan
1
1
Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic
2
Central European Institute of Technology, Masaryk University, Brno, Czech Republic
3
Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
4
Department of Mathematics, Brno University of Technology, Brno, Czech Republic
5
Division of Endocrinology and Diabetes, University of Minnesota, Minneapolis, USA
6
Mind Research Network, Albuquerque, NM 87106, USA
ABSTRACT
The aim of the current study is visualization of task-related
variability in EEG-fMRI data, performed as a blind-search
analysis without stimulus timings, using a methodology that
is based on Kilner’s et al. heuristic approach [2]. We show
that filters of the relative EEG spectra with different fre-
quency responses visualize different task-related brain net-
works. The effect is more pronounced within an event-
related oddball paradigm (i.e. detecting rare visual targets)
than within a block-design semantic decision paradigm (i.e.
detecting semantic errors). The mutual information between
different EEG-fMRI activation maps calculated with filters
of different frequency responses appears stable between the
different paradigms. We also introduce preliminary results
implementing the heuristic analysis with spatiospectral EEG
components, where the filter response has two dimensions
and depends on frequency and channels.
Index Terms— Simultaneous EEG-fMRI, heuristic
model, GLM, ICA
1. INTRODUCTION
In the current study we extended current models used to
analyze simultaneously measured EEG-fMRI signals. The
ultimate goal of work was to reconstruct task-related net-
works from EEG-fMRI data without prior knowledge of
stimulation timing (see also [1-3]).
Recently, we proposed that relative (i.e. normalized)
EEG power [p(ω), eq. 1] is more powerful than absolute
power for visualizing task-related EEG-fMRI networks [1].
The strongest task-related correlates between relative EEG
power and fMRI-BOLD [4] signal were observed within the
α (8-12Hz) and γ (20-40Hz) bands.
() =
()
∫ ()
(1)
In eq. 1, s(ω) represents the power spectral density for
frequency ω in the acquisition time duration of the n-th
fMRI scan. The numerator and denominator fluctuate over
time, sometimes asynchronously across frequencies.
Kilner et al. [2] introduced the EEG-fMRI heuristic
model in 2005 (eq. 2). This approach fuses the data based on
models of trans-membrane voltage and current changes, i.e.
directly at the level of neuronal changes.
[
]
2
∝ (1 + )
2
∝
∫
2
()
∫
2
()
(2)
This simplest model declares that changes in BOLD
signal b are proportional to neuronal activation a which is
proportional to changes in root mean square frequency of
whole normalized (relative) EEG power spectrum p(ω). The
character ~ indicates variables during increased activity,
while variables without ~ represent signal values during
rest.
Rosa et al. (2010) [3] considered the denominators in
equation (2) to have a constant baseline, and incorporated
general linear model (GLM) fitting (eq. 3).
∝ √∫
2
()
(3)
Fig. 1 shows the function ω
2
(in eq. 3) is only different
frequency response of the relative EEG spectra filter in
contrast to the response of a band of interest.
In our previous study [1], we calculated task-related ac-
tivation maps for different frequency bands of interest, sug-
Fig. 1. Different weighting functions g(ω) for classic heuristic
model and different frequency bands of interest
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