Disrupted modular organization of resting-state cortical functional connectivity in U.S. military personnel following concussive 'mild' blast-related traumatic brain injury

Abstract

Blast-related traumatic brain injury (TBI) has been one of the "signature injuries" of the wars in Iraq and Afghanistan. However, neuroimaging studies in concussive 'mild' blast-related TBI have been challenging due to the absence of abnormalities in computed tomography or conventional magnetic resonance imaging (MRI) and the heterogeneity of the blast-related injury mechanisms. The goal of this study was to address these challenges utilizing single-subject, module-based graph theoretic analysis of resting-state functional MRI (fMRI) data. We acquired 20min of resting-state fMRI in 63 U.S. military personnel clinically diagnosed with concussive blast-related TBI and 21 U.S. military controls who had blast exposures but no diagnosis of TBI. All subjects underwent an initial scan within 90days post-injury and 65 subjects underwent a follow-up scan 6 to 12months later. A second independent cohort of 40 U.S. military personnel with concussive blast-related TBI served as a validation dataset. The second independent cohort underwent an initial scan within 30days post-injury. 75% of the scans were of good quality, with exclusions primarily due to excessive subject motion. Network analysis of the subset of these subjects in the first cohort with good quality scans revealed spatially localized reductions in the participation coefficient, a measure of between-module connectivity, in the TBI patients relative to the controls at the time of the initial scan. These group differences were less prominent on the follow-up scans. The 15 brain areas with the most prominent reductions in the participation coefficient were next used as regions of interest (ROIs) for single-subject analyses. In the first TBI cohort, more subjects than would be expected by chance (27/47 versus 2/47 expected, p<0.0001) had 3 or more brain regions with abnormally low between-module connectivity relative to the controls on the initial scans. On the follow-up scans, more subjects than expected by chance (5/37, p=0.044) but fewer subjects than on the initial scans had 3 or more brain regions with abnormally low between-module connectivity. Analysis of the second TBI cohort validation dataset with no free parameters provided a partial replication; again more subjects than expected by chance (8/31, p=0.006) had 3 or more brain regions with abnormally low between-module connectivity on the initial scans, but the numbers were not significant (2/27, p=0.276) on the follow-up scans. A single-subject, multivariate analysis by probabilistic principal component analysis of the between-module connectivity in the 15 identified ROIs, showed that 31/47 subjects in the first TBI cohort were found to be abnormal relative to the controls on the initial scans. In the second TBI cohort, 9/31 patients were found to be abnormal in identical multivariate analysis with no free parameters. Again, there were not substantial differences on the follow-up scans. Taken together, these results indicate that single-subject, module-based graph theoretic analysis of resting-state fMRI provides potentially useful information for concussive blast-related TBI if high quality scans can be obtained. The underlying biological mechanisms and consequences of disrupted between-module connectivity are unknown, thus further studies are required.

Keywords: Blast injury; Functional connectivity; Functional magnetic resonance imaging (fMRI); Graph theory; Modularity; Traumatic brain injury.

© 2013.

Figures

Figure 1. An illustration of the analysis procedure

For each subject, with volumetric structural MRI data (a), cortical surface (b) was reconstructed. Subsequently, the surface underwent the inter-subject alignment and spatial resampling close to the spatial resolution of resting-state BOLD fMRI (c) to allow surface-based, node-by-node cross-subject analyses. The preprocessed resting-state BOLD fMRI data (d) were converted to surface-based BOLD signal data (f) aligned to the individual cortical surface (e). BOLD fluctuation correlation coefficients between every pair of nodes in the brain (e.g., the gray square from red and cyan nodes in (e ) ) were obtained to yield a correlation matrix (g). A connectivity matrix (h) was derived by thresholding the correlation matrix, and a brain network (i) was constructed. In this illustration, yellow lines indicate connection between nodes. With the identified modules (three modules delineated by dashed lines in this example) in (j), modularity, within-module degree z-score (e.g., five magenta lines for the red-colored node in (k)) and participation coefficient (e.g., the distribution of magenta, cyan and olive lines for the red-colored node in (l)) were obtained for each node.

Figure 2. Group module assignments of each of the controls and the first TBI (TBI I) cohort

The identified modules from group averaged correlation matrices were color-coded as a function of tie densities (densities of the retained strongest correlations): 3% (a), 2% (b) and 1.5% (c). Only modules of size greater than 1% of total number of nodes were displayed.

Figure 3. Whole brain modularity of the controls and the first TBI (TBI I) cohort

Each symbol represents a single individual’s modularity. The I bars indicate the means and standard deviation of the control, the dotted horizontal bar is two standard deviations from the mean of the control and the solid horizontal bar in the TBI I cohort is the mean of the TBI I cohort. Filled triangles represent TBI patients with relatively ‘abnormal’ modularity, located outside of the dotted horizontal bars. The number of relatively ‘abnormal’ TBI patients for modularity was labeled in parentheses, and the p-values were obtained from permutation tests (10,000 permutations) on group mean difference. Because the quality control procedures for inclusion or exclusion were performed on each scan individually, the subjects whose modularity data is shown for the follow-up scans were not an exact subset of subjects whose data is shown for the initial scans.

Figure 4. Node-specific network properties of the controls and TBI I cohort

Group mean comparison maps (puncorr < 0.05) of within-module degree z-score (a) and participation coefficient (b), respectively. All color maps were superimposed on the averaged cortical surface from all participants in the controls and TBI I cohort. R G_precentral: right precentral gyrus, R G_pariet_inf-Supramar: right supramarginal gyrus, R G_front_inf-Opercular: right opercular part of the inferior frontal gyrus, R G_front_sup-medial : right medial superior frontal gyrus, R G_front_sup-dorsomedial: right dorsomedial superior frontal gyrus.

Figure 5. Count of the TBI patients from the TBI I cohort with ‘abnormal’ node-specific network properties relative to the controls

Color maps of the number of TBI patients whose measures (within-module degree z-score and participation coefficient, respectively) were outside two standard deviations from the mean of the control. All color maps were superimposed on the averaged cortical surface from all participants in the controls and TBI I cohort.

Figure 6. Regions of interest (ROIs) for participation coefficient

15 ROIs are colored and labeled (1: central sulcus, 2: left anterior transverse temporal gyrus, 3: right long insular gyrus and central sulcus of the insula, 4: superior frontal sulcus, 5: medial superior frontal gyrus, 6: anterior superior frontal gyrus, 7: deep anterior part of the cingulate gyrus and sulcus, 8: superficial anterior part of the cingulate gyrus and sulcus, 9: right superior part of the precentral sulcus, 10: right superior temporal sulcus, 11: right orbital gyri, 12: posterior-ventral part of the cingulate gyrus, 13: lingual gyrus, 14: right parieto-occipital sulcus and 15: left cuneus). See Table III for the coordinates and areas of these ROIs.

Figure 7. ROI analysis of participation coefficient of the controls and patients with TBI

Scatter plots for averaged participation coefficients within each of three selected ROIs. See Fig. 3 for the details of the scatter plots, Table III for abbreviations and Table IV for results from all ROIs. Again, because the quality control procedures for inclusion or exclusion were performed on each scan individually, the subjects whose modularity data is shown for the follow-up scans were not an exact subset of subjects whose data is shown for the initial scans.

Figure 8. Bar graphs for observed and expected ‘abnormal’ ROIs in the patients with TBI relative to the controls

The distribution of expected relatively ‘abnormal’ ROIs was calculated from the binomial distribution with the probability that one region deems ‘abnormal’ by chance (0.0455; the probability that participation coefficient for a TBI patient falls outside two standard deviations from the mean of the controls). The p-values were obtained by the one-sided z-test (TBI I > controls in the number of ‘abnormal’ ROIs).

Figure 9. Scatter plots for multivariate analysis of participation coefficients within the ROIs after dimensionality reduction by probabilistic principal component analysis

Solid triangles represent relatively ‘abnormal’ TBI patients whose values are locating within the lower and upper tail, accounting for 2.5% in each tail, of the estimated multivariate normal distribution from the control. The label in each panel indicates the number of relatively ‘abnormal’ TBI patients and the number of TBI patients on the corresponding scan.

Figure 10. Bar graphs for observed and expected proportion of ‘abnormal’ nodes in the patients with TBI relative to the controls without ROI selection

The distributions of expected relatively ‘abnormal’ ROIs were calculated from the permutation of group memberships (10,000 permutations). The p-values were obtained by the one-sided z-test (TBI I > controls in the proportion of ‘a bnormal ’ nodes).