1. Disconnection of network hubs and cognitive impairment after traumatic brain injury Wen

2. References Fagerholm, E.D., Hellyer, P.J., Scott, G., Leech, R., Sharp, D.J. (2015) Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain.

3. Background Data Methods Results Conclusion

4. Traumatic Brain Injury An outside force impacts the head causing the brain to move A direct blow to the head A rapid acceleration and deceleration of the head

5. Symptoms of a person who might have TBI Dilated or unequal size of pupils Vision changes Respiratory failure Paralysis, difficulty moving body parts, weakness, poor coordination Vomiting Headache Confusion Ringing in the ears, or changes in ability to hear Trouble with balance Difficulty with thinking skills Difficulty speaking, slurred speech

6. Background Data Methods Results Conclusion

7. Data 30 healthy controls and 52 patients with TBI MRI: T1,T2-weight DTI(64 DWI+B 0 ) 3.0T

8. Background Data Methods Results Conclusion

9. Methods Probabilistic tractography DTI Network Freesurfer & FSL Graph Theory Analysis Brain ConnectivityToolbox (Rubinov and Sporns, 2010) Support vector machine LIBSVM classification library (Platt, 1999; Chang and Lin, 2011)

10. Overview of methods

13. Eigenvector centrality

14. Support vectors are points on the boundary planes. We maximize this margin by minimizing |w|. Maximum Margin Hyperplane Boundary plane. SVM Math Notation: w is a vector perpendicular to the plane. x is a point on the plane. b is the offset (from the origin) parameter

15. LIBSVM model = svmtrain(trainlabel,traindata,'-s 0 -t 2 -c 1.2 -g 2.8'); -s svm 类型： SVM 设置类型 ( 默认 0) 0 -- C-SVC 1 --v-SVC 2 – 一类 SVM 3 -- e -SVR 4 -- v-SVR -t 核函数类型：核函数设置类型 ( 默认 2) 0 – 线性： u'v 1 – 多项式： (r*u'v + coef0)^degree 2 – RBF 函数： exp(-r|u-v|^2) 3 –sigmoid ： tanh(r*u'v + coef0) -g r(gama) ：核函数中的 gamma 函数设置 ( 针对多项式 /rbf/sigmoid 核 函数 ) -c cost ：设置 C-SVC ， e -SVR 和 v-SVR 的参数 ( 损失函数 )( 默认 1)

16. Prediction of cognitive outcome Elastic net: combine ridge penalty and lasso penalty to select more predictors than the number of observations (Zou & Hastie, 2005)

17. Background Data Methods Results Conclusion

18. Prediction of traumatic axonal injury

19. Prediction of behaviour

20. Background Data Methods Results Conclusion

21. 1. accurately differentiate patients with a previous history of TBI from healthy age-matched controls. 2. predict the extent of clinically significant cognitive impairment commonly seen following TBI. 3. traumatic axonal injury would impact most severely on the structural connections of hubs. 4. graph metrics that describe the degree to which brain regions act as hubs as being the most discriminatory in patient/control classification

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