Presented by Hyeong Ryeol Kam

Transcription

1 Presented by Hyeong Ryeol Kam 2013/02/20

2 Contents Abstract + Introduction Theory Methods + Results Discussion Hyeong Ryeol Kam 2013/02/20 # 2

3 Abstract 목표 1. Volumetric data 로부터 brain network 구성 2. Network 를이용한 measure 구하기 TLE 인지아닌지 classify 가능 (thinning 현상 ) 3. 추가적인통계자료얻기 TLE Temporal lobe epilepsy ( 측두엽간질 ) 발생장소 : medial temporal lobe 뇌졸중 (Seizure) Temporal lobe+ 다른영역도관련있음 Hyeong Ryeol Kam 2013/02/20 # 3

4 Abstract Mean cortical thickness and curvature FreeSurfer 를통해계산되어구해짐 이값들로 graph, network 로만듦 Node : brain region Edge(link) : disease progression path를가질확률 통계자료 Correlation 과 classification 을통해구해짐 Contribution : Network peakiness values 도입 TLE-MTS : peaky / Healthy : not peaky Hyeong Ryeol Kam 2013/02/20 # 4

5 Abstract T1 weighted, 4T magnet 13 TLE-MTS patients TLE-MTS : TLE-Mesial temporal lobe sclerosis Severe neuronal loss 14 TLE-no patients TLE-no : TLE-with normal appearing hippocampus on MRI Mild or no neuronal loss Inferior and lateral temporal lobe 30 Controls (healthy) Hyeong Ryeol Kam 2013/02/20 # 5

6 Introduction Previous Work Cortical thickness를사용하여 Cortical region간의 connectivity를보일수있음을증명했으나, Network-level architecture를고려하지않음 This Paper 일반화된 Gibbs probability model 을제안 Network-level 정보를이용하여 correlation과 classification 등을수행할 method 제안 Network feature들을체크 Degree distribution, clustering, network entropy, complexity, etc. Hyeong Ryeol Kam 2013/02/20 # 6

7 Theory A Generalized Gibbs Probability Distribution Model Groups Control, TLE-MTS, TLE-no Input 68 cortical ROIs 4 subcortical ROIs from hippocampal volumetry Hyeong Ryeol Kam 2013/02/20 # 7

8 Theory A Generalized Gibbs Probability Distribution Model Normalized Z-score Volume : subcortical structure 에서 thickness 얻기힘들기에사용 k = subject # i = ROI # Hyeong Ryeol Kam 2013/02/20 # 8

9 Theory A Generalized Gibbs Probability Distribution Model Figure 1 (a) TLE (b) Healthy negative correlation no correlation Hyeong Ryeol Kam 2013/02/20 # 9

10 Theory A Generalized Gibbs Probability Distribution Model The probability of a disease progression path The distribution over the entire brain network z : the normalizing constant (Partition function) Hyeong Ryeol Kam 2013/02/20 # 10

11 Theory A New Proposal To Create Networks Characteristic of TLE Pearson correlation : Highest classifying power Subcortical : Pruning : noise 제거위해 Subcortical : Hyeong Ryeol Kam 2013/02/20 # 11

12 ROC Curves Classification Using Individual Networks Accuracy increases When using both thickness and curvature Hyeong Ryeol Kam 2013/02/20 # 12

13 Theory Graph Theoretical Features of an Individual Network Network Markovian transition matrix The sum of the connectivity = 1 edge weight = 다른노드로이동할확률을의미 New matrix C k (row 의합이 1 이되게 normalize) p i =v 1 (i) / v 1 = the left-most eigenvector of C k p 를이용하여다음페이지의 feature 들을계산 Hyeong Ryeol Kam 2013/02/20 # 13

14 Theory Graph Theoretical Features of an Individual Network Network Entropy : Low = peaky Network Statistical Complexity : Low = peaky (complexity = uniformity) Mono-exponential decay constant τ of sorted degree curve : Small τ = peaky Hyeong Ryeol Kam 2013/02/20 # 14

15 Summary Network Measures Results Summary Network Measures (a) Connectivity sum를제외하고는특별한차이없음 (b) TLE-MTS : peaky / TLE-no : reduced peakiness (a) (b) Hyeong Ryeol Kam 2013/02/20 # 15

16 A Proposal For Classifying Networks A Proposal For Classifying Networks G k ={V k, E k } Edge, node, topology 등의개수가다를수있음 Insert, delete, modify edges Graph Edit Distance Hyeong Ryeol Kam 2013/02/20 # 16

17 ROC Curves Classification Using Individual Networks (a) 2-way : control vs TLE (b) 3-way : control vs TLE-MTS vs TLE-no Hyeong Ryeol Kam 2013/02/20 # 17

18 TLE-Specific Measure Theory New TLE-Specific Measures: Dispersion : Logit transform make its range from 0 to 1 Severity : TL-Specificity : Hyeong Ryeol Kam 2013/02/20 # 18

19 TLE-Specific Measure Results TLE-Specific Measures TLE-MTS High severity Low dispersion TLE-no More dispersion Less severity Hyeong Ryeol Kam 2013/02/20 # 19

20 TLE-Specific Measure Results TLE-Specific Measures TLE-MTS High TL specificity Low dispersion Hyeong Ryeol Kam 2013/02/20 # 20

21 Summary Group Network How frequently the edges appear? TLE-MTS has a few very strong edges TLE-no has more, welldistributed, but weaker edges Hyeong Ryeol Kam 2013/02/20 # 21

22 Results More or less thinning Regions Thickness and Curvature 이용해서구함 TLE-MTS Prominent thinning Ipsilateral medial posterior temporal, Lateral prestriatal Less thinning TLE-no Superior frontal, Pre/postcentral, Superior temporal regions Prominent thinning Ipsilateral anterior inferior, lateral and superior temporal, opercular and insular region Less thinning Superior frontal, pre/postcentral regions Hyeong Ryeol Kam 2013/02/20 # 22

23 Discussion Summary of findings TLE-MTS : ipsilateral temporal lobe 에서 thinning The Individual TLE-Specific Network Approach Noisy but enough to perform classification Why? 1) z-scores pruned by thickness and curvature constraints 2) 전체 network를사용했기때문 Hyeong Ryeol Kam 2013/02/20 # 23

24 Thanks for attention! Got any question? Hyeong Ryeol Kam 2013/02/20 # 24