Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.
KEYWORDS: Brain, Microsoft Foundation Class Library, Functional magnetic resonance imaging, Neuroimaging, Statistical analysis, Magnetic resonance imaging, Brain mapping, Cognitive neuroscience, Cognition, Data processing
Real-time functional magnetic resonance imaging (rtfMRI) can be used to train the subjects to selectively control activity
of specific brain area so as to affect the activation in the target region and even to improve cognition and behavior. So
far, whether brain activity in posterior cingulate cortex (PCC) can be regulated by rtfMRI has not been reported. In the
present study, we aimed at investigating whether real-time regulation of activity in PCC can change the functional
connectivity between PCC and other brain regions. A total of 12 subjects underwent two training runs, each lasts 782s.
During the training, subjects were instructed to down regulate activity in PCC by imagining right hand finger movement
with the sequence of 4-2-3-1-3-4-2 during task and relax as possible as they can during rest. To control for any effects
induced by repeated practice, another 12 subjects in the control group received the same experiment procedure and
instruction except with no feedback during training. Experiment results show that increased functional connectivity of
PCC with medial frontal cortex (MFC) was observed in both groups during the two training runs. However, PCC of the
experimental group is correlated with larger areas in MFC than the control group. Because the positive correlation
between task performance and MFC to PCC connectivity has been demonstrated previously, we infer that the stronger
connectivity between PCC and MFC in the experimental group may suggest that the experimental group with
neurofeedback can more efficiently regulate PCC than the control group without neurofeedback.
KEYWORDS: Functional magnetic resonance imaging, Independent component analysis, Data modeling, Brain, Signal to noise ratio, Interference (communication), Data analysis, Computer simulations, Superposition, Data processing
General linear model (GLM) and independent component analysis (ICA) are widely used methods in the community of
functional magnetic resonance imaging (fMRI) data analysis. GLM and ICA are all assuming that fMRI components are
location locked. Here we extend the Differentially variable component analysis (dVCA) and introduce it into fMRI data
to analyze the transient changes during fMRI experiments which are ignored in GLM and ICA. We apply the extended
dVCA to model fMRI images as the linear combination of ongoing activity and multiple fMRI components. We test our
extended dVCA method on simulated images that mimicked the fMRI slice images containing two components, and
employ the iterative maximum a posteriori (MAP) solution succeed to estimate each component's time-invariant spatial
patterns, and its time-variant amplitude scaling factors and location shifts. The extended dVCA algorithm also identify
two fMRI components that reflect the fact of hemispheric asymmetry for motor area in another test with fMRI data
acquired with the block design task of right/left hand finger tapping alternately. This work demonstrates that our
extended dVCA method is robustness to detect the variability of the fMRI components that maybe existent during the
fMRI experiments.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.