Journal of Clinical Neuroscience xxx (2017) xxx–xxx

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Journal of Clinical Neuroscience

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Commentary Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury ⇑ Yan Yan a,1, Jian Song a,1, Guozheng Xu a, , Shun Yao a, Chenglong Cao a, Chang Li b, Guibao Peng a, Hao Du a a Department of , Wuhan General Hospital of PLA, No. 627 Wuluo Road, Wuhan, China b Department of Radiology, Wuhan General Hospital of PLA, No. 627 Wuluo Road, Wuhan, China article info abstract

Article history: This study investigated the characteristics of the small-world brain network architecture of patients with Received 7 February 2017 mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network Accepted 22 May 2017 properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The Available online xxxx neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7 days after injury respectively. After Keywords: preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 Mild traumatic brain injury templates. The small-world network parameters and areas under the integral curves were computed Cerebral concussion in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these param- Small world Brain network eters between the MTBI and control group. Significantly different parameters were investigated for cor- Resting-state fMRI relations with SAC scores; those that correlated were chosen for further curve fitting. The clustering Correlation study coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local net- works, and strength of mean connectivity. These features may be associated with brain function compen- sation. The network parameters significantly correlated with SAC total and attention scores. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction dizziness, attention deficiency, and even depression [5–8]. These symptoms may persist and in some cases lead to permanent dis- Mild traumatic brain injury (MTBI), also referred to as brain ability that has been referred to as chronic traumatic encephalopa- injury with a score of 13–15, is the most com- thy [9]. There are studies indicating that athletes can develop fatal mon subtype of brain injury. Annually, about 42 million people brain swelling during a second concussion [10,11]. Thus, the worldwide suffer a MTBI [1,2] However, MTBI is difficult to diag- assessment of MTBI and the study of injury mechanisms are of nose because the brain often appears quite normal on conventional great importance. computed tomography (CT) and magnetic resonance imaging There have been multiple scales to evaluate MTBI. These include (MRI) scans. Furthermore, the clinical severity of MTBI is usually the King-Devick test, the Balance Error Scoring System, the Sport overlooked, since patients often present with slight symptoms that Concussion Assessment Tool-3, and the Standardized Assessment appear transient and resolve within days to weeks [2–4] Yet, in of Concussion (SAC) [12–15]. The SAC comprises evaluations of recent years studies have found that 15–30% of MTBI patients orientation, attention, and transient memory and delayed memory. can develop cognitive, physiological, and clinical symptoms that It is an outstanding assessment scale with high reliability and do not resolve by 3 months post-injury. Such symptoms have been validity [16–18]. termed post-concussive syndrome (PCS) and include headache, Some studies indicate that the brain functional network may be disrupted in patients with traumatic brain injury. Previous evi- dence indicated that severe traumatic brain injury can lengthen ⇑ Corresponding author at: No. 627, Wuluo Road, Wuhan, Hubei Province 430000, the shortest path and lower the efficiency of communication China. [19]. In addition, some scholars have proposed that some cortex E-mail address: [email protected] (G. Xu). nodes with high efficiency and connectivity are changed in coma 1 Yan Yan and Jian Song contributed equally to the study. http://dx.doi.org/10.1016/j.jocn.2017.05.010 0967-5868/Ó 2017 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010 2 Y. Yan et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx patients after brain injury [20]. There are also reports that the local Anatomical images of the whole head were acquired with a efficiency and connectivity in MTBI patients may be higher [21]. T1-weighted 3-dimensional fast fluid-attenuated inversion Thus, we speculate that the brain small-world functional network recovery (FLAIR) sequence (TR, 11.5 ms; TE, 5.1 ms; FOV, may be a sensitive index for the assessment of brain functional 240 mm 240 mm; slice thickness, 1.2 mm; inter slice gap, activity. 0 mm; 232 slices covering the whole brain). This study investigated the characteristics of the small-world brain network architecture of patients with MTBI, and a correlation 2.4. Resting-State fMRI preprocessing between brain functional connectivity network properties in the resting-state functional MRI (fMRI) and SAC parameters. The preprocessing of resting-state fMRI data was performed using MATLAB-based GRETNA_1.1.1 software (National Key Labo- 2. Methods ratory of Cognitive Neuroscience and Learning, Beijing Normal University, http://www.nitrc.org/projects/gretna). Data prepro- 2.1. Subjects cessing included: image format conversion from DICOM to NIFTI; removal of the initial 10 timepoints; time correction between the The Ethic Committee of Wuhan General Hospital of PLA layers; head motion correction; 3-dimensional data standardiza- approved this study. Twenty-two MTBI patients (17 men and 5 tion; imaging smoothing; removal of drift and filtering; and women) were enrolled; the ages of the patients ranged from 18 removal of covariates. The last included removal of the following to 60 y (median, 23.5 y). signals: whole brain, , cerebrospinal fluid, and head The diagnostic criteria for MTBI were: definite history of moving. During processing, data associated with head moving trauma; Glasgow Coma Scale score 13–15; coma duration >2 mm or flip angle >2° were excluded. <30 min; admitted within 12 h of injury; closed brain injury; treated with the standardized strategy. Criteria for inclusion in 2.5. Brain network construction the study were: aged 18–60 y; right handed; native language is Chinese; school education 5 y; normal neurological functions Two templates were used to mark the regions of interest (ROIs). (consciousness, vision, hearing, language ability); normal interper- First, the was considered to have 90 ROIs, based on the sonal communication skills; normal viscera functions; no metal Automated Anatomical Labeling brain atlas. Second, with reference implants; no lacunar infarct lesion on MRI; and no history of pre- to Dosenbach’s 160 functional ROIs [22], the network threshold vious trauma, stroke, brain tumor, psychiatric care, or craniocere- value was set at 0.3, and the differences between lengths of connec- bral surgery or general anesthesia or relevant family history of tion were defined as 75. Node time courses of fMRI signals for each the same. Patients with the following were excluded: unstable epoch were extracted from the preprocessed EPI images by averag- condition; needing decompressive craniectomy or intracranial ing the voxel time courses within the ROIs for each participant. pressure probe implantation; newly occurring complications, such To estimate functional connectivity, Pearson’s correlation coef- as organ failure or severe infection; or a subjective decision to ficients between all node pairs were calculated, resulting in withdraw from the study. 90 90 or 160 160 correlation matrices. The matrices were The conditions of the patients were assessed by 2 neurosur- weighted, and both positive and negative connections were used geons independently. Routine MRI scan was performed from the to construct the whole brain weighted connectivity networks. 3rd to the 7th day after injury. Seventeen healthy adults (13 men We set thresholds with reference to the connection density K and 4 women) were recruited as the normal control group; their (the ratio of the number of edges to all possible node pairs). We ages ranged from 20 to 59 y (median, 30 y). All subjects or their applied a range of thresholds (0.01 K 0.50, in 0.01 increments) relatives provided signed informed consent. to calculate the network parameters.

2.2. Neurological assessments 2.6. Small-world organization

Physical examinations and radiological evaluations were per- Two parameters were used to quantify the small-world organi- formed by 2 neurosurgeons and one radiologist, respectively. SAC zation of the brain functional network: the characteristic path assessments were performed upon admission and ±12 h the time length (L) and clustering coefficient (C). L is the average of the of MRI examination by the same neurosurgeon. The words used shortest path length between all pairs of nodes, which was calcu- in instantaneous memory tests were selected randomly from the lated according to the following formula (di,j is the shortest path modern Chinese vocabulary. SAC assessment was performed twice length between nodes i and j): for each subject, and the mean was calculated for further analyses. X 1 L ¼½0:5NðN 1Þ di;j i;j;i

Department of Radiology at Wuhan General Hospital of PLA. Ci was set as the clustering coefficient of the node During functional runs, subjects were required to keep alert with i:Ci ¼ Ei=½0:5Ki ðKi 1Þ, where Ei is the number of edges their eyes closed, and instructed to avoid to think anything. The between the neighbors of node i, and Ki is the number of edges resting-state fMRI measurements were performed using a 1.5-T connected to node i. scanner (GE Signa HDxt scanner, GE, USA). The average shortest path length Lp and the average clustering Whole-brain T2-weighted images were acquired using an echo coefficient Cp were calculated. According to the definition for planar imaging (EPI) sequence, and the following parameters were small-world network, when used: repetition time (TR), 2000 ms; echo time (TE), 40 ms; field of c = Cp/Crand > 1, then k = Lp/Lrand 1, and r = c/k > 1, where view (FOV), 240 mm 240 mm; matrix, 64 64; slice thickness, under similar connection density Cp is the clustering coefficient 5 mm; interslice gap, 1 mm; flip angle, 90°; number of excitations, of the studied network, Crand is the clustering coefficient of the 1; scanning, 186 times (2 s time; total scanning time, 6 min 12 s). random network, Lp is the average shortest path length of the

Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010 Y. Yan et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx 3 studied network and Lrand is the average shortest path length of all level of parameters. The areas were documented as aCp, aLp, the random network. aEg, aEl, and aDeg.

2.7. Network efficiency analysis 2.10. Statistical analysis

Global efficiency (E ) reflects the ability of the entire net- global P The data were analyzed using Statistical Package for the Social ¼ ½: ð Þ 1 ð Þ1 work to integrate information:Eglobal 0 5N N 1 i;j;i–j dij , Sciences (SPSS) version 20.0 software (Chicago, IL, USA). The independent-samples t-test was used to compare the mean age of where di,j is the shortest path length between node i and j. the 2 groups. The Fisher exact probability test was used for compar- The local efficiency (Elocal) of each node [Elocal (i)] is the mean of ison of the gender distribution between the 2 groups. To analyze the the inverse of the shortest path length from node i to all other nodes: network parameters, we used the independent-samples t-test to compare the integrated parameters (aCp, aLp, aEg, aEl, and aDeg). ElocalðiÞ¼EglobalðGiÞ For a further study of correlations, we selected the parameters with significant differences to perform a bivariate correlation anal- 2.8. Nodal connectivity analysis ysis with the SAC scores. Pearson’s and Spearman’s correlation analysis methods were used. Nodal connectivity (k) of the node i (ki) represents the number As the strength of connectivity has a better neurobiological of edges connected to node i, which was calculated as: explanation than other parameters, we used a curve-fitting method to predict the quantitative link between SAC scores and 1 X k ¼ K aDeg. P < 0.05 was considered statistically significant. N i i 3. Results 2.9. Integration of parameters 3.1. Demographic and assessment results To identify differences of the parameters between the 2 groups, we performed an integral calculation of the parameters, and then The MTBI and normal control groups were statistically similar calculated the area under the curve, which can represent the over- in age (p = 0.345) and gender (p = 0.953) distribution. The average

Fig. 1. Brain network matrices of MTBI patients (left) and normal control individuals (right), showing differences according to the AAL90 template (upper row) and Dosenbach160 template (lower row).

Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010 4 Y. Yan et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx

SAC scores of the MTBI patients ranged from 17 to 30, with a mean the following parameters were higher in the MTBI patients than 23.86 ± 3.08. the normal control group: aCp (p = 0.008), aEl (p = 0.020), and aDeg (p = 0.035). According to the Dosenbach160 template, the 3.2. Resting-state small-world parameter analyses following parameters were higher in the MTBI patients than the normal control group: aCp (p = 0.001), aEg (p = 0.006), aEl With a range of thresholds (0.01 K 0.50, in 0.01 incre- (p = 0.001), and aDeg (p < 0.001). Comprehensive analyses ments), 49 weighted matrices were calculated according to the showed that aCp, aEl, and aDeg in the MTBI patients were two templates for every participant (Fig. 1). We calculated c, k, significantly higher than that of the normal control group and r. Every subject met the criteria: c >1, k 1, and r >1 (Fig. 3). (Fig. 2). Thus, both the MTBI patients and normal control individu- als were consistent with regard to small-world characteristics. 3.4. Correlation analyses

3.3. Statistical results of network parameters The parameters aCP, aEl, and aDeg were selected for correlation analyses with SAC scores. Using the Dosenbach160 template, The parameters were compared according to two templates, Pearson’s correlation analysis and Spearman’s correlation analysis using the two-sample t-test. According to the AAL90 template, showed that aCp, aEl, and aDeg each significantly correlated

Fig. 2. Resting-state small-world parameter analyses showing that both the MTBI patients and normal control individuals were consistent with small-world characteristics (c>1,k 1, and r > 1).

Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010 Y. Yan et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx 5 positively with the total SAC score, and the attention measurement function the attention measurement score (Table 2; Fig. 4). The 2 3 score (Table 1). fitting equation is: ytotal = 99.007 4.782x + 0.456x and 2 The curve fitting analysis showed that the network parameter yattention = 50.25 13.749x + 1.006x . aDeg may be a cubic function of the total SAC score and a quadratic

Fig. 3. Statistical analysis showing that aCp, aEl, and aDeg in MTBI patients are significantly higher than that of the normal control group.

Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010 6 Y. Yan et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx

Table 1 Statistical analysis results based on the AAL90 and Dosenbach160 templates.

AAL90 Dosenbach160 aCp aEl aDeg aCp aEl aDeg Pearson Spearman Pearson Spearman Pearson Spearman Pearson Spearman Pearson Spearman Pearson Spearman Orientation r 0.313 0.301 0.371 0.542* 0.291 0.163 0.492* 0.612 0.417 0629* 0.408 0.593* P 0.156 0.173 0.086 0.009 0.189 0.468 0.02 0.002 0.053 0.002 0.059 0.004 TM r 0.156 0.122 0.136 0.142 0.075 -0.037 0.416 0.405 0.416 0.399 0.332 0.495* P 0.489 0.589 0.545 0.529 0.74 0.871 0.054 0.061 0.054 0.066 0.131 0.019 Attention r 0.246 0.289* 0.169 0.273 0.131 0.093 0.651* 0.685* 0.550* 0.635* 0.573* 0.670* P 0.27 0.192 0.453 0.219 0.561 0.679 0.001 < 0.001 0.008 0.001 0.005 0.001 DM r 0.343 0.319 0.369 0.295 0.361 0.239 0.385 0.352 0.375 0.262 0.414 0.378 P 0.118 0.148 0.091 0.182 0.099 0.285 0.077 0.108 0.086 0.239 0.056 0.082 Total score r 0.349 0.345 0.347 0.456* 0.28 0.18 0.648* 0.665* 0.556* 0.604* 0.570* 0.689* P 0.112 0.116 0.114 0.033 0.207 0.423 0.001 0.001 0.007 0.003 0.006 < 0.001

DM, delayed memory; TM, transient memory. * P value <0.05.

Table 2 Results of curve fitting analysis between function and SAC degree of connectivity and attention scores.

Degree & attention Degree & total score R2 PR2 P Linear function 0.328 0.005 0.325 0.006 Logarithmic function 0.32 0.006 0.313 0.007 Reciprocal function 0.311 0.007 0.302 0.008 Quadratic function 0.384 0.01 0.441 0.004 Cubic function 0.383 0.01 0.442 0.004 Coincidence function 0.271 0.013 0.308 0.007 Power function 0.263 0.015 0.297 0.009 Sigmoid Curve 0.255 0.016 0.287 0.01 Growth curve 0.271 0.013 0.308 0.007 Exponential function 0.271 0.013 0.308 0.007 Logistic curve 0.271 0.013 0.308 0.007

R2, determination coefficient.

Fig. 4. The fitted curve showing the cubic-function correlation between connectivity degree and the total score of SAC and the quadratic-function correlation between connectivity degree and score of attention measurement.

Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010 Y. Yan et al. / Journal of Clinical Neuroscience xxx (2017) xxx–xxx 7

4. Discussion second MTBI requires more energy to compensate for the defi- ciency of brain function. This study used resting-state fMRI for brain functional small- In the correlation analyses of the present study, we found that world network construction. We found that the MTBI patients the network parameters that were higher in the MTBI patients rel- had retained small-world characteristics, but nevertheless the glo- ative to the controls were positively associated with SAC evalua- bal efficiency, local efficiency, and degree of connectivity were all tion scores, including the total and attention scores. We think higher than that of the normal control individuals. In addition, this is also due to brain functional compensation. Increased infor- we noted that these increased network parameters positively cor- mation transfer efficiency can reflect a high level of compensation, related with the SAC evaluation scores, including total score and which is also helpful for attention [32]. To date, studies focusing on attention score, and curve-fitting analysis showed an exponential attention have been limited. One study showed that the efficiency factor of 3. In the current study, the average SAC score of the MTBI of hyperactive children was lower, and this change was related to patients was 20.9, which is in accord with other reports. the severity of the condition [38]. Another study showed that brain The manifestations of patients with MTBI are usually mild, network connectivity negatively correlated with switching task however complications can be persistent, and a second injury performance [21]. These findings do not contradict the current can even be fatal. In clinical practice, definitive diagnostic criteria study, since the resting-state fMRI is different from task-state fMRI for MTBI are still under debate [23]. Scholars have recommended in reflecting the brain network—the resting-state fMRI is more multiple assessment scales for evaluating the severity of MTBI specific in studying brain introspection and environmental [24]. Tests for diagnosis and evaluation include the eye movement sensitivity. test [24], susceptibility weighted imaging (SWI) [25], diffusion ten- We also found a correlation between network parameters and sor imaging (DTI) [26], and magnetic resonance spectroscopy SAC assessment scores based on the Dosenbach160 ROI template, (MRS) [27]. There are studies suggesting that resting-state fMRI which was more significant than that based on the AAL90 tem- can be used to identify late-stage MTBI [28]. In the current study, plate. This may be related to differences in definitions. The we found the brain functional network parameters in early-stage AAL90 is a hemispheric anatomy template that uses anatomical MTBI patients are different from those in normal control group. landmarks rather than brain function [39]. The Dosenbach160 These differences are related to SAC assessment, indicating that template is based on whole brain functional regions proposed by resting-state fMRI can also be used in the identification of early- Dosenbach et al. [27]. In 2010; the cortex is divided into 6 func- stage MTBI. tional domains, including 160 ROIs. The Dosenbach160 template The brain functional network in patients with brain injury has focuses more on the integrality, globality, and functions of the been studied for a long time, but definitive changes are still brain than does the AAL90 template. MTBI usually involves the unclear. In the current study, we found that the clustering coeffi- entire brain, and thus the Dosenbach160 ROI template may be cient, local efficiency, and degree of connectivity were all higher more suitable for brain function analyses. in the MTBI patients than in the normal control individuals. We speculate that these findings may be associated with brain func- 4.1. Limitations tional compensation. This is consistent with some previous studies regarding MTBI and brain tumor patients [21,28–30]. Brain func- This study is limited by a small sample size, due to the strict tional compensation can cause an increase in connectivity effi- inclusion and exclusion criteria, which may affect the reliability ciency, and an increase in the degree of connectivity may be a of the results. There are differences between MTBI and brain con- result of the balance of brain energy consumption between differ- cussion. However, there is still no specific assessment scale for ent regions [31]. Some scholars have proposed that local efficiency MTBI. Thus, we selected SAC as the evaluation method. Addition- can reflect the integration of Cp [32], and thus we speculate that an ally, we speculate the injury and compensation of brain functions increase in local efficiency can also be attributed to brain func- are dynamic, which needs further research in the future. tional compensation. However, there are still studies suggesting that small-world characteristics can be weakened in late-stage 5. Conclusions MTBI patients [33]. We think that this may be due to fading of brain functional compensation at the end of stress and repair of Our findings indicate that MTBI patients do have small-world injury. Previous studies support this hypothesis [34,35]. In fact, brain network properties, and the clustering coefficient, local effi- MTBI, especially concussion, should be a mild subtype of axonal ciency, and degree of connectivity were all higher compared with injury. However, we found that the specific brain network param- the normal subjects. We conjecture that this may be associated eters that were different in MTBI patients relative to normal con- with brain function compensation. Furthermore, the increased net- trols were not those that have been observed in axonal injury work parameters positively correlated with SAC evaluation scores, patients. In studies involving primates after corpus callosotomy, including total score and attention score, and the curve-fitting the interhemispheric connectivity and local efficiency were low- analysis showed an exponential factor of 3 and 2 either. This study ered, nevertheless the global efficiency was not significantly chan- provides new insight into the mechanism of brain injury in MTBI ged [36] which was similar with our study. Some studies found patients. that axonal injury patients with long-term conscious disturbance have a longer average path and lower communication efficiency, Disclosure of conflict of interest indicating weakened small-world characteristics [19]. We think these differences may be related to differences in injury severities. The authors have no conflict of interest associated with this The injury is more severe in axonal injury patients, such as scat- article. tered hemorrhage in cerebral white matter and fracture of fiber connectivity, and the brain functional network is not able to com- pensate [37]. Or findings in the present study, that the brain func- Conflict of interest tional network parameters were higher in MTBI patients than the normal control, support the above hypothesis. This can also explain The authors declare that they have no conflict of interest. We why a second MTBI can result in serious brain swelling [10], if the certify that this manuscript is a unique submission and is not being

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Please cite this article in press as: Yan Y et al. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury. J Clin Neurosci (2017), http://dx.doi.org/10.1016/j.jocn.2017.05.010