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Change of Brain Functional Connectivity in Patients With Spinal Cord Injury: Graph Theory Based Approach.

Min YS, Chang Y, Park JW, Lee JM, Cha J, Yang JJ, Kim CH, Hwang JM, Yoo JN, Jung TD - Ann Rehabil Med (2015)

Bottom Line: Clustering coefficient, global efficiency and small-worldness did not show any difference between controls and SCIs in all density ranges.These findings imply that patients with SCI can build on preserved competent brain control.Further analyses, such as topological rearrangement and hub region identification, will be needed for better understanding of neuroplasticity in patients with SCI.

View Article: PubMed Central - PubMed

Affiliation: Department of Rehabilitation Medicine, Kyungpook National University Hospital, Daegu, Korea.

ABSTRACT

Objective: To investigate the global functional reorganization of the brain following spinal cord injury with graph theory based approach by creating whole brain functional connectivity networks from resting state-functional magnetic resonance imaging (rs-fMRI), characterizing the reorganization of these networks using graph theoretical metrics and to compare these metrics between patients with spinal cord injury (SCI) and age-matched controls.

Methods: Twenty patients with incomplete cervical SCI (14 males, 6 females; age, 55±14.1 years) and 20 healthy subjects (10 males, 10 females; age, 52.9±13.6 years) participated in this study. To analyze the characteristics of the whole brain network constructed with functional connectivity using rs-fMRI, graph theoretical measures were calculated including clustering coefficient, characteristic path length, global efficiency and small-worldness.

Results: Clustering coefficient, global efficiency and small-worldness did not show any difference between controls and SCIs in all density ranges. The normalized characteristic path length to random network was higher in SCI patients than in controls and reached statistical significance at 12%-13% of density (p<0.05, uncorrected).

Conclusion: The graph theoretical approach in brain functional connectivity might be helpful to reveal the information processing after SCI. These findings imply that patients with SCI can build on preserved competent brain control. Further analyses, such as topological rearrangement and hub region identification, will be needed for better understanding of neuroplasticity in patients with SCI.

No MeSH data available.


Related in: MedlinePlus

Consecutive steps of functional connectivity analysis using resting state-functional magnetic resonance imaging (rs-fMRI) with graph theoretical approach. The whole brain was parcellated into 90 regions according to automated anatomical labeling (AAL) atlas. The correlations between rs-fMRI time-series were computed. The weighted correlation matrix per subject was constructed for the controls and the spinal cord injuries (SCIs). The weighted correlation matrix was converted into binarized matrix by density thresholding from 0.06 to 0.4 (increase 1%). Random networks were also generated. Graph-theoretical metrics such as clustering coefficient, characteristic path length, global efficiency, small-worldness were measured.
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Figure 1: Consecutive steps of functional connectivity analysis using resting state-functional magnetic resonance imaging (rs-fMRI) with graph theoretical approach. The whole brain was parcellated into 90 regions according to automated anatomical labeling (AAL) atlas. The correlations between rs-fMRI time-series were computed. The weighted correlation matrix per subject was constructed for the controls and the spinal cord injuries (SCIs). The weighted correlation matrix was converted into binarized matrix by density thresholding from 0.06 to 0.4 (increase 1%). Random networks were also generated. Graph-theoretical metrics such as clustering coefficient, characteristic path length, global efficiency, small-worldness were measured.

Mentions: Four network measures-clustering coefficient, Cp; characteristic path length, Lp; small world-ness parameters, σ; and global efficiency, Eglob were calculated to analyze the differences between normal and SCI patients. Clustering coefficient of a node i (Ci) is defined as the ratio of the number of connections between the neighbors of ROI i and the total number of possible connections between its neighbors. Clustering coefficient for a network (Cp) is defined as the average Ci from entire nodes in the network and characteristic path length (Lp) is the mean the shortest distance between any two nodes in the network [22]. Global efficiency of the network (Eglob) was defined as the average inverse shortest path length for all node-node pairs in the network [23]. To examine the small-world properties, the normalized parameters γ=Cpreal/Cprand and λ=Lpreal/Lprand were computed [22]. A network is considered as a small-world network if it shows much higher Cp (γ>1) while similar Lp (λ≈1) in comparison with the matched random network. That is, small-world index σ=γ/λ is greater than 1 [24]. In the random networks, each edge was rewired 1,000 times and an average of 100. Small-worldness tests were done repeatedly over a range of density (Fig. 1). The comparison of network parameters between controls and SCIs was performed using a two-tailed two-sample t test (p<0.05). We did not make any correction for multiple comparisons because we tried to explore the general trends of between-group differences through the wide range of density level.


Change of Brain Functional Connectivity in Patients With Spinal Cord Injury: Graph Theory Based Approach.

Min YS, Chang Y, Park JW, Lee JM, Cha J, Yang JJ, Kim CH, Hwang JM, Yoo JN, Jung TD - Ann Rehabil Med (2015)

Consecutive steps of functional connectivity analysis using resting state-functional magnetic resonance imaging (rs-fMRI) with graph theoretical approach. The whole brain was parcellated into 90 regions according to automated anatomical labeling (AAL) atlas. The correlations between rs-fMRI time-series were computed. The weighted correlation matrix per subject was constructed for the controls and the spinal cord injuries (SCIs). The weighted correlation matrix was converted into binarized matrix by density thresholding from 0.06 to 0.4 (increase 1%). Random networks were also generated. Graph-theoretical metrics such as clustering coefficient, characteristic path length, global efficiency, small-worldness were measured.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496508&req=5

Figure 1: Consecutive steps of functional connectivity analysis using resting state-functional magnetic resonance imaging (rs-fMRI) with graph theoretical approach. The whole brain was parcellated into 90 regions according to automated anatomical labeling (AAL) atlas. The correlations between rs-fMRI time-series were computed. The weighted correlation matrix per subject was constructed for the controls and the spinal cord injuries (SCIs). The weighted correlation matrix was converted into binarized matrix by density thresholding from 0.06 to 0.4 (increase 1%). Random networks were also generated. Graph-theoretical metrics such as clustering coefficient, characteristic path length, global efficiency, small-worldness were measured.
Mentions: Four network measures-clustering coefficient, Cp; characteristic path length, Lp; small world-ness parameters, σ; and global efficiency, Eglob were calculated to analyze the differences between normal and SCI patients. Clustering coefficient of a node i (Ci) is defined as the ratio of the number of connections between the neighbors of ROI i and the total number of possible connections between its neighbors. Clustering coefficient for a network (Cp) is defined as the average Ci from entire nodes in the network and characteristic path length (Lp) is the mean the shortest distance between any two nodes in the network [22]. Global efficiency of the network (Eglob) was defined as the average inverse shortest path length for all node-node pairs in the network [23]. To examine the small-world properties, the normalized parameters γ=Cpreal/Cprand and λ=Lpreal/Lprand were computed [22]. A network is considered as a small-world network if it shows much higher Cp (γ>1) while similar Lp (λ≈1) in comparison with the matched random network. That is, small-world index σ=γ/λ is greater than 1 [24]. In the random networks, each edge was rewired 1,000 times and an average of 100. Small-worldness tests were done repeatedly over a range of density (Fig. 1). The comparison of network parameters between controls and SCIs was performed using a two-tailed two-sample t test (p<0.05). We did not make any correction for multiple comparisons because we tried to explore the general trends of between-group differences through the wide range of density level.

Bottom Line: Clustering coefficient, global efficiency and small-worldness did not show any difference between controls and SCIs in all density ranges.These findings imply that patients with SCI can build on preserved competent brain control.Further analyses, such as topological rearrangement and hub region identification, will be needed for better understanding of neuroplasticity in patients with SCI.

View Article: PubMed Central - PubMed

Affiliation: Department of Rehabilitation Medicine, Kyungpook National University Hospital, Daegu, Korea.

ABSTRACT

Objective: To investigate the global functional reorganization of the brain following spinal cord injury with graph theory based approach by creating whole brain functional connectivity networks from resting state-functional magnetic resonance imaging (rs-fMRI), characterizing the reorganization of these networks using graph theoretical metrics and to compare these metrics between patients with spinal cord injury (SCI) and age-matched controls.

Methods: Twenty patients with incomplete cervical SCI (14 males, 6 females; age, 55±14.1 years) and 20 healthy subjects (10 males, 10 females; age, 52.9±13.6 years) participated in this study. To analyze the characteristics of the whole brain network constructed with functional connectivity using rs-fMRI, graph theoretical measures were calculated including clustering coefficient, characteristic path length, global efficiency and small-worldness.

Results: Clustering coefficient, global efficiency and small-worldness did not show any difference between controls and SCIs in all density ranges. The normalized characteristic path length to random network was higher in SCI patients than in controls and reached statistical significance at 12%-13% of density (p<0.05, uncorrected).

Conclusion: The graph theoretical approach in brain functional connectivity might be helpful to reveal the information processing after SCI. These findings imply that patients with SCI can build on preserved competent brain control. Further analyses, such as topological rearrangement and hub region identification, will be needed for better understanding of neuroplasticity in patients with SCI.

No MeSH data available.


Related in: MedlinePlus