Emotional tasks may result in a strong blood oxygen level-dependent (BOLD) signal in the amygdala in 5-
HTTLRP short-allele. Reduced anterior cingulate cortex (ACC)-amygdala connectivity in short-allele
provides a potential mechanistic account for the observed increase in amygdala activity. In our study, fearful
and threatening facial expressions were presented to two groups of 12 subjects with long- and short-allele
carriers. The BOLD signals of the left amygdala of each group were averaged to increase the signal-to-noise
ratio. A Bayesian approach was used to estimate the model parameters to elucidate the underlying
hemodynamic mechanism. Our results showed a positive BOLD signal in the left amygdala for short-allele
individuals, and a negative BOLD signal in the same region for long-allele individuals. This is due to the fact
that short-allele is associated with lower availability of serotonin transporter (5-HTT) and this leads to an
increase of serotonin (5-HT) concentration in the cACC-amygdala synapse.
KEYWORDS: Amygdala, Functional magnetic resonance imaging, Sensors, Signal processing, Hemodynamics, Neurotransmitters, Solids, Brain, Signal to noise ratio, Data acquisition
A negative blood oxygen level - dependent (BOLD) has been associated with a high concentration of GABA using Magnetic Resonance Spectroscopy and fMRI. Subjects with long-allele carriers have seen with high concentration of serotonin in Rostral Subgenual portion of the anterior cingulate cortex (rACC). In this paper, we investigate the effect of serotonin concentration on hemodynamic responses. Our results show a negative BOLD signal in rACC in the subjects with long-allele carriers. In contrast, the subjects with short-allele carriers showed positive BOLD signals in rACC. These results suggest that the serotonin transporter gene impacts the neuronal activity and eventually the BOLD signal similar to GABA.
Functional magnetic resonance imaging (fMRI) has complementary spatiotemporal resolution
compared to Electroencephalography (EEG) as well as Magnetoencephalography (MEG). Thus,
their integrated analysis should improve the overall resolution. To integrate analysis of E/MEG and
fMRI, we extend our previously proposed integrated E/MEG and fMRI neural mass model to a
multi-area model by defining two types of connections: the Short-Range Connections (SRCs)
between minicolumns within the areas and Long-Range Connections (LRCs) between inter-areas
minicolumns. The nonlinear input/output relationship in the proposed model is derived from the
state space representation of the multi-area model. The E/MEG signals are originated from the
overall synaptic activities of the pyramidal cells of all minicolumns and can be calculated using the
lead field matrix (i.e., forward electromagnetic model). The fMRI signal is extracted from the
proposed integrated model by calculating the overall neural activities in the areas and using it as the
input of the extended balloon model (EBM). Using the simulation results, the capabilities of the
proposed model to generate E/MEG and fMRI signals is shown. In addition, changes in the
dynamics of the model to variations of its parameters were evaluated and lead us to the appropriate
ranges for the parameters. In conclusion, this work proposes an effective method to integrate E/MEG
and fMRI for the more effective use of these techniques in functional neuroimaging.
KEYWORDS: Magnetoencephalography, Signal to noise ratio, Signal detection, Sensors, Iterative methods, Brain activation, Brain, Head, Magnetic resonance imaging, Inverse problems
Magnetoencephalography (MEG) is a neuroimaging technique for brain activation detection. This
technique does not provide a unique solution due to ill-posedness of its inverse solution. Several
methods are proposed to improve the MEG inverse solution. Minimum Norm (MN) is a simple
method whose solution is distributed and biased toward the superficial sources. In addition, its
solution is sensitive to the noise. Several methods are proposed to improve performance of the MN
method. In this paper, we propose a method whose solution is less sensitive to the noise and spatially
unbiased toward the superficial sources. Control of focal solution properties is achieved by
specifying a parameter in the proposed method. Performance of the proposed method is compared to
others using simulation studies consisting of single and multiple dipole sources as well as an
extended source model. Proposed method has superior performance compared to non-iterative
methods. Its performance is similar to the iterative methods but its computational load is lower.
The integrated analysis of the Electroencephalography (EEG), Magnetoencephalography (MEG),
and functional magnetic resonance imaging (fMRI) are instrumental for functional neuroimaging of
the brain. A bottom-up integrated E/MEG and fMRI model based on physiology as well as a method
for estimating its parameters are keys to the integrated analysis. We propose the variational Bayesian
expectation maximization (VBEM) method to estimate parameters of our proposed integrated
model. VBEM method iteratively optimizes a lower bound on the marginal likelihood. An iteration
of the VBEM consists of two steps: a variational Bayesian expectation step implemented using the
extended Kalman smoother (EKS) and the posterior probability of the parameters in the previous
step, and a variational Bayesian maximization step to estimate the posterior distributions of the
parameters. For a given external stimulus, a variety of multi-area models can be considered in which
the number of areas and the configuration and strength of connections between the areas are
different. The proposed VBEM method can be used to select an optimal model as well as estimate its
parameters. The efficiency of the proposed VBEM method is illustrated using simulation and real
datasets. The proposed VBEM method can be used to estimate parameters of other non-linear
dynamical systems. This study proposes an effective method to integrate E/MEG and fMRI and
plans to use these techniques in functional neuroimaging.
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