Multi-scale visual analysis of time-varying ECoG data via clustering of brain regions
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The document discusses a hierarchical spatio-temporal visual analysis system for electrocorticography (ECoG) data to explore dynamic brain networks. It addresses challenges such as matching temporal scales and aims for better understanding of brain network changes, particularly in relation to epileptic seizures. Future work includes developing semi-supervised consensus clustering and scalable visual analysis techniques.
![Introduction
• Human brain
• Massively connected
• Dynamically
reconfiguring
• Complex communication
patterns
3
Communication patterns between brain regions[1]
[1] J. Bottger et al., Conexel visualization: a software implementation of glyphs and
edge-bundling for dense connectivity data using BrainGL, J. Neuroscience](https://image.slidesharecdn.com/finalbrainkdd-170531214957/85/Multi-scale-visual-analysis-of-time-varying-ECoG-data-via-clustering-of-brain-regions-3-320.jpg)
![Electrocorticography
● ECoG
○ High temporal
and spatial
resolution
○ High signal-to-
noise ratio Human ECoG Data, 4mm
spatial resolution, [1]
Edward Chang, UCSF
Micro-electrode array
200μm spatial resolution,
[2] Kristofer Bouchard, LBNL
4](https://image.slidesharecdn.com/finalbrainkdd-170531214957/85/Multi-scale-visual-analysis-of-time-varying-ECoG-data-via-clustering-of-brain-regions-4-320.jpg)
![Choosing K, Consensus Clustering
7
Maximum value, K =2
Final
Result
For one
time step
Time-series
1 2
[1] Monti et al., Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data](https://image.slidesharecdn.com/finalbrainkdd-170531214957/85/Multi-scale-visual-analysis-of-time-varying-ECoG-data-via-clustering-of-brain-regions-7-320.jpg)
![Cluster Evolution View
8
[1] Vehlow, Corinna, et al. "Visualizing the evolution of communities in dynamic graphs." Computer Graphics Forum. Vol. 34. No. 1. 2015.](https://image.slidesharecdn.com/finalbrainkdd-170531214957/85/Multi-scale-visual-analysis-of-time-varying-ECoG-data-via-clustering-of-brain-regions-8-320.jpg)
![Analysis of connectivity data
Graph theoretic methods
Modular organization
Hierarchical modularity
Challenges
Time-varying 22[2] Complex network measures of brain connectivity: uses and interpretations
Functional connectivity of brain
networks](https://image.slidesharecdn.com/finalbrainkdd-170531214957/85/Multi-scale-visual-analysis-of-time-varying-ECoG-data-via-clustering-of-brain-regions-22-320.jpg)
![Electrocorticography
36
● ECoG:
○ High temporal
and spatial
resolution
○ High signal-to-
noise ratio Human ECoG Data, 4mm
spatial resolution, [1]
Edward Chang, UCSF
Micro-electrode array
200μm spatial resolution,
[2] Kristofer Bouchard, LBNL](https://image.slidesharecdn.com/finalbrainkdd-170531214957/85/Multi-scale-visual-analysis-of-time-varying-ECoG-data-via-clustering-of-brain-regions-36-320.jpg)
Multi-scale visual analysis of time-varying ECoG data via clustering of brain regions
- 1. Hierarchical Spatio-Temporal Visual Analysis of Electrocorticography (ECoG) Data Sugeerth Murugesan1,2, Kristofer Bouchard2, Edward Chang3, Max Dougherty2, Bernd Hamann1, Gunther Weber1,2 1 1UC Davis,2Lawrence Berkeley National Lab,3UC San Francisco
- 2. Visual Analysis of Dynamic Functional Brain Networks On-going Work 2
- 3. Introduction • Human brain • Massively connected • Dynamically reconfiguring • Complex communication patterns 3 Communication patterns between brain regions[1] [1] J. Bottger et al., Conexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using BrainGL, J. Neuroscience
- 4. Electrocorticography ● ECoG ○ High temporal and spatial resolution ○ High signal-to- noise ratio Human ECoG Data, 4mm spatial resolution, [1] Edward Chang, UCSF Micro-electrode array 200μm spatial resolution, [2] Kristofer Bouchard, LBNL 4
- 5. Challenges • Matching of temporal scales • Appropriate scales not obvious a priori 5
- 7. Choosing K, Consensus Clustering 7 Maximum value, K =2 Final Result For one time step Time-series 1 2 [1] Monti et al., Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data
- 8. Cluster Evolution View 8 [1] Vehlow, Corinna, et al. "Visualizing the evolution of communities in dynamic graphs." Computer Graphics Forum. Vol. 34. No. 1. 2015.
- 9. Electrode View 9 Time Step Time Step Start Point Time Steps Varying granularity Chosen Design Time Step Start Point
- 14. Conclusion • Fast and effective scientific exploration of brain networks • Better comprehension of multi-scale changes • Better characterization of genesis and dynamic propagation of epileptic seizures 14
- 15. Future Work • Semi-supervised consensus clustering • Scalable visual analysis • Quantitative evaluation of flexibility and stability of clusters 15
- 16. Acknowledgement This work is supported by the U.S. Department of Energy under Contract No. DE- AC02-05CH11231 through •LBNL Laboratory Directed Research and Development (LDRD) grant “Towards Exascale: High Performance Visualization and Analytics Program”, program manager Dr. Lucy Nowell. We thank the members of the •LBNL Vis Group •LBNL Data Science & Technology Department •LBNL Bouchard Group 16
- 17. Thank you 17
- 18. Backup Slides 18
- 19. ECoG Cluster Flow Overview 19 ● Cluster evolution view: summarizes evolution of clusters over time ● Electrode view: depicts propagation of clusters in their spatial domain
- 20. Community Changes over Space and Time 20
- 21. ECoG Functional Networks Derivation of Functional Networks 21 Graph Quantification Graph- theoretical Analysis Colors represent modules
- 22. Analysis of connectivity data Graph theoretic methods Modular organization Hierarchical modularity Challenges Time-varying 22[2] Complex network measures of brain connectivity: uses and interpretations Functional connectivity of brain networks
- 23. Major Visual Analysis Tasks in Brain Connectivity Analysis • How can we start to identify brain states? • How does brain transition into different states ? • How can we easily compare patterns associated with different brain states? 23
- 24. Brain Networks Properties Modular organization Hierarchical organization on multiple scales Small- worldness 24 Spatial Scales Temporal Scales
- 25. Challenges Networks Change over time Shifting structural properties Shifting topological attributes Additional data attributes Scalability Visual Scalability 25
- 26. Interactivity 26
- 27. Cluster Matching L1,1 = 11 L1,2 = 11 L2,1 = 10 L2,2 = 2 • All possible matching • Maximum cost value for each time 27
- 28. Lessons Learnt • Static timeline based representations • Careful with Visual Designs • Bottom-up vs Top-down 28
- 29. Graph-Theoretical Analysis • Network structure – Hierarchical structure – Multiple Scales – Modular • Visual Analysis – Neurological Disease progression – Normal Functioning 29 Community Pattern Detection Source: Stanford School of Medicine
- 30. Graph-Theoretical Analysis • Network structure – Hierarchical structure – Multiple Scales – Modular • Visual Analysis – Neurological Disease progression – Normal Functioning 30 Community Pattern Detection Source: Stanford School of Medicine
- 32. Electrode Spatial Views • Multiple Temporal Scales – Glyphs – User-defined aggregation – Hierarhical 32
- 34. Multi-scale Spatio-Temporal Visual Patterns 34 Spatial Scales Temporal Scales
- 36. Electrocorticography 36 ● ECoG: ○ High temporal and spatial resolution ○ High signal-to- noise ratio Human ECoG Data, 4mm spatial resolution, [1] Edward Chang, UCSF Micro-electrode array 200μm spatial resolution, [2] Kristofer Bouchard, LBNL
- 37. Notes 37