Large-scale functional brain network for attentional regulation of encoding: Oscillatory interactions based on bipartite graph filtration
부호화에 대한 주의 조절의 뇌 기능적 신경망: 양분 그래프 필트레이션 기반의 오실레이션 관계 연구

DC Field Value Language
dc.description학위논문 (박사)-- 서울대학교 대학원 : 협동과정 인지과학전공, 2015. 2. 이동수.-
dc.description.abstractTo encode relevant information and also suppress irrelevant one is suggested to be important mechanism for efficient memory functioning in the brain. The neural correlates of such memory regulation has been suggested as intra-regional interactions between alpha and gamma cross-frequency power. The current study expanded the cross-frequency interactions into a large-scale regional network for memory regulation. I analyzed the data wherein twenty three healthy subjects were instructed to remember (Remember) or not to remember (No-Remember) the following picture item by a cue during magnetoencephalography (MEG) recording. In this setting, network was modeled by correlation between regions for alpha power during cue presentation and regions for gamma power during item presentation, yielding a bipartite graph. Then, graph filtration method was applied to bipartite graph, wherein the changes of connected components between two sets of nodes (i.e., region) were quantified into two invariant measures: a barcode and single linkage distance. This procedure was called bipartite graph filtration in this study. As a result, as for the global connectivity reflected by the barcode, task condition was significantly different for gamma power during item presentation. In addition, subjects with steeper barcode in Remember than in No-Remember condition showed higher compliance to task instruction. Also, for Remember condition, subjects with fast merging pattern in alpha power during cue presentation than the pattern in gamma power during item presentation showed better memory performance. These findings suggested that encoding regulation is achieved by large-scale regional interactions, and the cognitive function is captured by distinctive pattern of network. As for the local connectivity reflected by single linkage distance, the connectivity between the left dorsolateral superior frontal gyrus for alpha power and the left insula for gamma power was significantly closer in Remember than in No-Remember condition. The superior frontal gyrus was included in dorsal attention network, suggesting dorsal attentional regulation of later long-term memory in the insular cortex. In conclusion, encoding regulation was investigated by large-scale brain interactions using bipartite graph filtration, showing the method allows to examine the time-lagged interactions between different frequency powers in an MEG network study for the first time.-
dc.description.tableofcontentsAbstract i
Contents iv
List of Figures vii
List of Tables viii

1. Introduction 1
1.1. The control mechanism for efficient memory function 1
1.2. Brain oscillation in encoding regulation 2
1.2.1. The previous study of encoding regulation in terms of long-term memory 3
1.2.2. The proposal from the previous study 9
1.3. The distributed network organization for cognitive function 13
1.4. Large-scale brain network based on bipartite graph filtration 14
1.5. The aim and hypothesis the present study 17

2. Materials and Methods 19
2.1. Participants 19
2.2. Experimental paradigm and procedure 20
2.3. Behavioral measures 21
2.4. MEG measurement 22
2.5. Structural MR image acquisition 23
2.6. Data analysis 23
2.7. Spectral analysis 24
2.8. Source analysis 25
2.9. Network analysis 27
2.9.1. Network construction 27
2.9.2. Graph filtration 33
2.10. Statistical analysis 36

3. Results 40
3.1. Behavioral performance 40
3.2. Global network property measured by barcode 40
3.3. Local network property measured by single linkage distance 50

4. Discussion 56
4.1. Multi-scale connectivity pattern for encoding regulation is distinctive of functional state and task performance 57
4.2. Top-down controls for encoding in insular cortex is predictive of better performance 59
4.3. The estimation of interaction between alpha and gamma band power 62
4.4. Graph filtration method for investigating brain network structure 64
4.5. Modeling a large-scale network in cerebral cortex using electrophysiological data 65

5. Conclusion 69

References 76

국문 초록 86
dc.format.extent2495461 bytes-
dc.publisher서울대학교 대학원-
dc.subjectfunctional brain network-
dc.subjectalpha oscillation-
dc.subjectgamma oscillation-
dc.subjectmagnetoencephalography (MEG)-
dc.subjectgraph filtration-
dc.titleLarge-scale functional brain network for attentional regulation of encoding: Oscillatory interactions based on bipartite graph filtration-
dc.title.alternative부호화에 대한 주의 조절의 뇌 기능적 신경망: 양분 그래프 필트레이션 기반의 오실레이션 관계 연구-
dc.contributor.AlternativeAuthorJarang Hahm-
dc.contributor.affiliation인문대학 협동과정 인지과학전공-
Appears in Collections:
College of Humanities (인문대학)Program in Cognitive Science (협동과정-인지과학전공)Theses (Ph.D. / Sc.D._협동과정-인지과학전공)
Files in This Item:
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.