Real-time functional magnetic resonance imaging (rtfMRI) technology has been widely used to train subjects to actively regulate the activity of specific brain regions. Although many previous studies have demonstrated that neurofeedback training alters the functional connectivity between brain regions in the task state and resting state, it is unclear how the regulation of the key hub of the default mode network (DMN) affects the topological properties of the resting-state brain network. The current study aimed to investigate what topological changes would occur in the large-scale intrinsic organization of the resting state after the real-time down-regulation of the posterior cingulate cortex (PCC). The results indicated that the down-regulation of the PCC in the DMN reduced the functional connectivity of the PCC with the nodes outside of the DMN and reduced functional connectivity between the superior medial frontal gyrus (SFGmed) and parahippocampal gyrus (PHG) in the experimental group. Moreover, the nodal graph properties of the SFGmed were reduced, while that of the PHG showed the opposite alteration after the down-regulation of the PCC. These findings possibly suggest that the regulation of the key hub of the DMN, the PCC, mainly changed the information transfer of the SFGmed and PHG.
Functional magnetic resonance imaging (fMRI) is a noninvasive technique that can be used to assess brain function by measuring blood-oxygen-level-depen- dent signal changes [
Resting-state functional connectivity (RSFC) research has revealed a number of consistent resting networks that represent specific patterns of synchronous activity from healthy subjects [
Recently, real-time functional magnetic resonance imaging (rtfMRI) technology has been used to train subjects to actively control their brain activity [
In our previous study, the down-regulation of the activity of the PCC was demonstrated to alter the activity of the DMN in the resting state using the rtfMRI technique and the Independent Component Analysis (ICA) method [
The data used in this study were collected in our previous study [
Thirty-two healthy right-handed individuals with normal vision participated in the experiment. The experimental group included eight females and eight males (age: 21.00 ± 2.00 years) and the control group included eight females and eight males (age: 21.60 ± 2.00 years). The two groups had no significant difference in age (p = 0.18). All participants agreed to sign informed consent before scanning.
The brain scans were performed at the MRI Center of Beijing Normal University using a 3.0-T Siemens whole-body MRI scanner. A single-shot T2 -weighted gradient-echo, EPI sequence was used for functional imaging acquisition with the following parameters: TR/TE/flip angle = 2000 ms/30 ms/90˚, matrix = 64 × 64, in-plane resolution = 3.125 × 3.125 mm2, slice = 33, slice thickness = 4 mm, and slice gap = 0.6 mm.
The whole experiment included a task familiarization exercise, a pre-training resting run, a region of interest (ROI) localizer run, two training runs, a post-training resting run and a questionnaire interview outside of the scanner. Our previous study demonstrated that the task of moving and imaging the right fingers according to the sequence 4-2-3-1-3-4-2 can make the region in PCC deactivated stably [
In the task familiarization exercise, all subjects were told that the four fingers of their right hand from their index to little finger represented one, two, three, and four, respectively. Then, they were required to perform a right-hand finger movement for 30-s according to the sequence 1-2-3-4 with a metronome set to 4 Hz and imagine the movement of the right fingers for 30 s according to the sequence 1-2-3-4 without a metronome.
All subjects were instructed to remain still for 10 min with eyesclosed in this run.
The 3.5-min localizer run consisted of three 30-s task blocks alternating with four 30-s rest blocks. During the task blocks, subjects performed the right-hand finger movement according to a new sequence4-2-3-1-3-4-2 at 4 Hz without a metronome. The ROI was selected as a rectangular zone in one slice centered on the area of deactivation of the PCC. The volume and location of the ROI varied across subjects (mean volume: 15.90 ± 3.20; mean slice: 13.20 ± 0.80). The volume represented the number of voxels in the ROI.
Each training run lasted 782 s and consisted of eight 46-s task blocks alternating with nine 46-s rest blocks. The neurofeedback presented to the subjects was a continuously updated time course averaged across the voxels in the ROI. All subjects in the experimental group were required to lower the activity in the ROI during the task blocks and maximizing the difference between the activity of the ROI during the rest and task blocks. Apart from imagining, subjects were instructed to imagine the movement of the right fingers according to the sequence 4-2-3-1-3-4-2 with varied speed, strength and the mode of the movement during the imagery task. During the rest blocks, subjects were required to rest and not recall anything about the regulation. The subjects in the control group were required to imagine movements of the right fingers according to the sequence 4-2-3-1-3-4-2 without neurofeedback signal during the task blocks.
All subjects were asked to stay relaxed with eyes closed during the 10-min post-training resting run.
After the scan, a questionnaire was filled out by each subject. The questionnaire mainly addressed whether the subjects performed the tasks according to the experimenter’s instruction, the detailed strategies they used to regulate the activity and any difficulties they encountered during the experiment.
For the pre-training and post-training resting data of each subject, the preprocessing, brain network construction and the calculation of global network parameters and regional nodal parameters were performed using graph theoretical network analysis software (GRETNA, http://www.nitrc.org/projects/gretna).
The preprocessing steps included removal of the first 10 volumes, slice timing correction, head movement correction, spatial normalization (EPI template provided by the Montreal Neurological Institute, MNI, with a final resolution of 3 × 3 × 3 mm), removal of linear trend, temporal band-pass filtering (0.01 - 0.08 Hz) and nuisance signal regression (6 head motion parameters, the cerebrospinal fluid signal and the white matter signals). Two subjects in the experimental group were eliminated because the target ROI could not be defined. In addition, two subjects in the experimental group and four in the control group were further removed from the analysis because the translation of head movement was larger than one voxel during training. As a result, a total of 24 subjects consisting of 12 in the experimental group and 12 in the control group underwent the subsequent brain network analysis.
Each participant’s brain was parceled into 90 cortical and subcortical regions using the AAL atlas. Then, the time series of each ROI was acquired by averaging the signals of all voxels within each ROI region. Pearson’s correlation coefficients of time series between any pair of brain regions were calculated, and a Fisher’s r-to-z transformation [
The network analysis, including the global network parameters and regional nodal parameters, was performed in the large-scale brain functional networks.
Global network parameters. Graph theory has been widely used to quantitatively characterize the brain functional networks [
C p = 1 N ∑ i ∈ G K i D n o d ( i ) ( D n o d ( i ) − 1 ) / 2 (1)
where N is the number of all nodes of a network G, D n o d ( i ) is the degree of node i, and K i is the number of edges in the subgraph G i that consists of the neighbors of node i. C p measures the local cliquishness of a network G,
L p = 1 1 N ( N − 1 ) ( ∑ j ≠ i ∈ G 1 L i j ) (2)
where L i j denotes the shortest path length between nodes i and j. L p measures the overall routing efficiency of a network G.
E g l o b = 1 N ( N − 1 ) ∑ j ≠ i ∈ G 1 L i j (3)
E g l o b measures the extent of information propagation through the whole network.
E l o c = 1 N ∑ i ∈ G E g l o b ( i ) (4)
E l o c measures the capability of parallel information transfer in the local scope of a network.
To examine the small-world properties, the normalized clustering coefficient γ = C p r e a l / C p r a n d and the normalized shortest pathlength λ = L p r e a l / L p r a n d were computed [
Regional nodal parameters of DMN nodes. Among the various resting networks, the DMN is a prominent one that reflects a default state of neuronal activity of the human brain. Moreover, the PCC that was down-regulated during neurofeedback training was a key hub in the DMN [
D n o d ( i ) = ∑ j ≠ i ∈ G e i j (5)
where e i j is the (i, j)th element in the formerly obtained binarized correlation matrix. D n o d ( i ) measures the connectivity of node i with the rest of the nodes in a network.
E n o d ( i ) = 1 N − 1 ∑ j ≠ i ∈ G 1 L i j (6)
E n o d ( i ) measures the ability of information transmission of node i in the network.
B C ( i ) = ∑ j ≠ i ≠ k ∈ G δ j k ( i ) δ j k (7)
δ j k is the number of the shortest paths from node j to node k, and δ j k ( i ) is the number of the shortest paths from node j to node k that passes through node i within the network G. B C ( i ) measures the influence of node i over information flow between other nodes in the brain network. Moreover, the nodal characteristics of the brain networks measure the extent to which a given node connects to all other nodes of a network, which may indicate the importance of special brain areas in the brain network [
In the present work, the DMN regions were determined from the AAL-at-a- sprimarily according to the coordinates of the peak foci of all the “task-negative” regions [
For each of the global network parameters and regional nodal parameters over the sparsity range of 0.1 - 0.5, a two-way repeated-measures analysis of variance (ANOVA) using group (experimental group and control group) as a between- subject factor and state (pre-training and post-training) as a repeated-measures factor was conducted in SPSS 20.0. Moreover, the same two-way repeated-mea- sures ANOVA was performed on each functional connectivity of the global DMN topography. If any parameter showed a significant interaction effect (p < 0.05), tests of simple effect were further carried out and were corrected by the false discovery rate (FDR) method [
Regions of interest in the AAL | Talairach coordinates (x, y, z) | Common names |
---|---|---|
Frontal_Sup_L | (−14, 38, 52) | Superior frontal gyrus, dorsolateral |
Frontal_Sup_R | (17, 37, 52) | |
Frontal_Sup_Medial_L | (-5, 49, 31) | Superior frontal gyrus, medial |
Frontal_Sup_Medial_R | (9,50,30) | |
Cingulum_Ant_L | (−3, 39, −2) | Anterior cingulate and paracingulate gyri |
Cingulum_Ant_R | (8,37,15) | |
Cingulum_Post_L | (−2, −36, 37) | Posterior cingulate gyrus |
Cingulum_Post_R | (3, −51, 8) | |
ParaHippocampal_L | (−22, −26, −16) | Parahippocampal gyrus |
ParaHippocampal_R | (25, −26, −14) | |
Angular_L | (−47, −67, 36) | Angular |
Angular_R | (53, −67, 36) | |
Temporal_Mid_R | (65, −17, −15) | Middle temporal gyrus |
Temporal_Inf_L | (−61, −33, −15) | Inferior temporal gyrus |
proached 1 for the pre-training and post-training resting states of each group (see Figures 1(a)-(d)). According to Watts and Strogatz (1998), all four sets of networks exhibited small-worldness (γ > 1 and λ ≈ 1) in the range of 0.1 ≤ sparsity ≤ 0.5. From the efficiency perspective, the local efficiencies of these networks were larger than the matched random networks ( E l o c / E l o c − r a n d > 1 ), whereas the global efficiencies of these networks approached that of the matched random networks ( E g l o b / E g l o b − r a n d ≈ 1 ) (see
The degree, nodal efficiency and betweenness centrality of the SFGmed. L/R, the betweenness centrality of the cingulum_Post_L (PCC.L), the nodal efficiency and betweenness centrality of the cingulum_Post_R (PCC.R) and the betweenness centrality of the PHG.R showed a significant interaction effect (p < 0.05) within some ranges of sparsity levels.
of the parameters that showed a significant interaction effect with the sparsity level. For the post-training versus the pre-training resting run, the experimental group showed a significant decrease in the degree, nodal efficiency and betweenness centrality of the SFGmed. L/R (see Figures 2(a)-(f)), in the betweenness centrality of the PCC.L, and in the nodal efficiency and betweenness centrality of the PCC.R (see Figures 3(a)-(c)) within some ranges of sparsity levels (FDR-corrected p < 0.05). Moreover, the experimental group produced significantly higher betweenness centrality of the PHG.R (see
Importantly, we further divided the degree of each DMN node into two parts, including the intra-DMN degree and extra-DMN degree. The intra-DMN degree of a DMN node is defined as the number of edges that connect the node with the other nodes within the DMN. The extra-DMN degree of a DMN node is defined as the number of edges that connect the node with the other nodes outside of the DMN. A two-way repeated-measures ANOVA using group as a between-subject factor and state as a repeated-measures factor revealed that the intra-DMN degree of the SFGmed. L/R, the extra-DMN degree of the PCC.R and the extra-DMN degree of the PHG.R displayed significant interaction effect within some ranges of sparsity levels (p < 0.05). The variations of the intra-DMN degree of the SFGmed. L/R, the extra-DMN degree of the PCC.R and PHG.R with the sparsity level are presented in
For each functional connectivity, a two-way repeated-measures ANOVA using group (experimental group and control group) as a between-subject factor and state (pre-training and post-training) as a repeated-measures factor was con- ducted in SPSS 20.0. If any functional connectivity showed a significant interac-
tion effect (p < 0.05), tests of simple effect were further carried out and were corrected by the false discovery rate (FDR) method to examine the differences between the pre-training and post-training resting states in each group. The differences of the global DMN topography between the two resting runs of each group are shown in
In the present study, we utilized the rtfMRI technique and graph theory analysis method to investigate neurofeedback training-related changes in the topological
properties of brain functional networks of the resting state. The main findings are as follows: 1) The brain network exhibited prominent small-world properties that cannot be changed by neurofeedback training; 2) The down-regulation of the PCC significantly reduced the functional connectivity between the SFGmed and PHG of the experimental group; and 3) the down-regulation of the PCC resulted in significant reductions in the nodal parameters of the SFGmed and PCC and significant increases in the nodal parameters of the PHG.
In this study, the whole brain network showed small-world properties during the pre-training and post-training resting run (see
Our previous study demonstrated that most subjects in the experimental group can successfully reduce the activity of the PCC [
Compared with the pre-training resting run, the degree, nodal efficiency and betweenness centrality of the SFGmed.R and SFGmed. L of the experimental group were significantly decreased during the post-training resting run (see Figures 2(a)-(f)). Moreover, the experimental group significantly reduced the intra-DMN rather than the extra-DMN degree of the SFGmed. R and SFGmed. L (see
Notably, several nodal graph properties of the SFGmed were significantly decreased, while the betweenness centrality of the PHG.R was significantly increased in the experimental group after the down-regulation of the PCC (see Figures 2(a)-(f),
No significant real-time training-related alterations were found in the global parameters of the brain functional network for both the experimental group and control group. The results indicated that the real-time neurofeedback training could not change the global properties of brain networks in the resting state. For the functional network of the brain, the global properties are more stable and robust than the regional nodal properties. This finding is consistent with previous brain functional network studies [
It should be noted that there are some limitations in this study. Firstly, this study did not include the control groups using neurofeedback from different brain regions as well as sham feedback. Thus, the differences of these two groups may come from not only neurofeedback but also working load and sensory input etc. Secondly, this study parceled each subject’s data into 90 cortical and subcortical regions using the AAL atlas to construct the brain network. Some other atlases can be used to parcel each subject’s data into more regions in future study.
To summarize, we investigated the impact of the down-regulation of the PCC on the topological properties of the brain functional network in the resting state using a graph theory analysis method. We observed that both the small-world properties of the brain functional network and their global network parameters remained stable and robust after the neurofeedback training. Moreover, the results indicated that the down-regulation of the core hub (PCC) in the DMN possibly reduced the information transfer of the PCC with the nodes outside of the DMN and reduced the functional connectivity between the SFGmed and PCC.
The authors are also grateful to Dr. Zhijiang Wang who kindly discussed some issues on graph theory method with us. This work is supported by Key Program of National Natural Science Foundation of China (61731003) and the National Natural Science Foundation of China (61671067).
Wang, Y.B., Zhang, J.P., Zhang, G.Y., Yao, L. and Long, Z.Y. (2017) Changes in the Brain’s Intrinsic Organization in the Resting State with Real-Time fMRI Neurofeedback Training of Posterior Cingulate Cortex Activity. Journal of Behavioral and Brain Science, 7, 655-673. https://doi.org/10.4236/jbbs.2017.713044