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Visualizing Human Neuroimaging Data
Jennifer Ann Stark Ph.D., B.Sc. Information Visualization (MPS)
Exploring transient ne...
Project Concept: Brains!
• Neuroscience B.Sc.
• Neuroimaging Ph.D. and post-doctoral work
• Goal: identify a project that
...
Project Concept: Brains!
• Prof. Luiz Pessoa: “Dynamic Network Mapping”
– Current neuroimaging analysis assumes stationari...
Project Concept: Data
• Additional Goals:
– To assess two versions of a new data collection method
(“multiband” imaging se...
Project Concept: Summary
Visualize dynamic brain activity (networks)
Determine the better of two novel methods of data
col...
Project Development
Project Development
Project Approach
• To make a web page describing neuroimaging data
analysis at both individual (quality checks) and group
...
Project Development
Project Development
Project Approach
Audience-driven Design Goals
• Mainly scientists in brain imaging research
– Content should be comprehended by general aud...
Landing Page
Landing Page: Scroll down
Neuroimaging Background
What is neuroimaging? What is the data?
Neuroimaging Data: resting-state MRI
• Functional MRI = spatial + temporal
• Resting state: no task, just chill and “think...
Neuroimaging Data: resting-state MRI
• Functional MRI = spatial + temporal
• Resting state: no task, just chill and “think...
Neuroimaging Data: resting-state MRI
• Functional MRI = spatial + temporal
• Resting state: no task, just chill and “think...
Neuroimaging Data: resting-state MRI
• Functional MRI = spatial + temporal
• Resting state: no task, just chill and “think...
Neuroimaging Data: resting-state MRI
• Space: different brain regions
– Anatomical shape and size
– Sphere
– Individual vo...
Neuroimaging Data
& Analysis
Neuroimaging Data
• Pre-existing dataset (convenient)
• Afforded the added dimension of comparing the 2 data
sets, as well...
Data Pipeline: AFNI, Matlab
Remove noise from data
(cardiac and respiratory
noise, motion, white matter
and CSF
Despike
Mo...
Neuroimaging Data: ROIs
Default mode network (DMN)
Salience Network (SN)
Executive Control Network (ECN)
Posterior Cingula...
Neuroimaging Data: Analysis
• Export .txt for each subject containing 1 column of data
for each ROI (19 columns), and 1 ro...
Analysis Pipeline (Python)
Correlation coefficient
fisher transform to z-
score -> 19 x 19
Add Network label key
to each n...
Audience-driven Project Goals
• Data quality:
– Do I need to exclude any subjects?
• Based on what?
– Are my regions of in...
Data Quality
EPI2
ECN
Are my ROIs in good places?
Do the ROIs in the networks correlate well together?
Subject 108
Subject...
Landing Page: Scroll down
Individual Data: mean network z-scores
Group Data: mean network z-scores
Audience-driven Design Goals
• Represent network graphs in consistent, replicable
manner (not force-layout)
Group Data: network graphs
Audience-driven Project Goals
• Data quality:
– Do I need to exclude any subjects?
• Based on what?
– Are my regions of in...
Cluster Pipeline (Python)
Windows
Contain 60 seconds each
1-time-frame step size
Resulting dimensions:
19 x 19 x 496
19 x ...
Cluster Pipeline (Python)
EPI1_Subject1.txt 560 seconds x 19 nodes
EPI2_Subject1.txt 280 seconds x 19 nodes
Windows
Contai...
Cluster Pipeline (Python)
• Number of Clusters range:
1-10
• Number of iterations: 100
• Alpha: 1
• Akaike Information Cri...
Cluster Data: network graphs
Group Data: Adjacency matrices
Application Wish List
• Networks:
– Curvilinear links
• Adjacency matrices:
– The color range is set manually, but would l...
Application Wish List
• App development: User can load own data
• Complete time courses, temporal duration and
temporal or...
Cluster: Temporal order/duration
• Design goals
– Should not require too much prior knowledge to understand the
research, ...
Acknowledgements
• MICA Students: Marianne Siblini, Kevin Ripka, Olga Cooper,
Brittney Younger
• UMD colleagues/students: ...
Thesis presentation
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Thesis presentation

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Master of Professional Studies (MPS) in Information Visualization (INVIZ, Maryland Institute College of Art, Baltimore) Final Project. Visualizing neuroimaging data quality and for evidence of dynamic networks.

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Thesis presentation

  1. 1. Visualizing Human Neuroimaging Data Jennifer Ann Stark Ph.D., B.Sc. Information Visualization (MPS) Exploring transient networks in the brain using novel neuroimaging techniques
  2. 2. Project Concept: Brains! • Neuroscience B.Sc. • Neuroimaging Ph.D. and post-doctoral work • Goal: identify a project that – Would be in keeping with my brains background – Would benefit science (like real research!) – Is in line with research interests of the Director of the Maryland Neuroimaging Center, Dr. Luiz Pessoa – Would benefit graduate students (educational)
  3. 3. Project Concept: Brains! • Prof. Luiz Pessoa: “Dynamic Network Mapping” – Current neuroimaging analysis assumes stationarity – New research demonstrates several stable brain configurations occur over the course of time (the scan) – What do they mean? What do they do? How many are there? Can they predict disease or cognition? • Students / Post-Docs new to imaging struggle with the analysis pipeline and identifying errors (data quality, sanity checks)
  4. 4. Project Concept: Data • Additional Goals: – To assess two versions of a new data collection method (“multiband” imaging sequence) to determine which version is best at detecting transient networks. • This comparison/assessment has not been done before • Would benefit Principle Investigators at work by providing information that can help them determine the methods they will use in their experiments.
  5. 5. Project Concept: Summary Visualize dynamic brain activity (networks) Determine the better of two novel methods of data collection Present the data (and some process) in the simplest way to educate graduate students involved in neuroimaging: create a prototype app for quick data quality assessment, using this data set as a working example
  6. 6. Project Development
  7. 7. Project Development
  8. 8. Project Approach • To make a web page describing neuroimaging data analysis at both individual (quality checks) and group level to determine existence of brain states, and describes their characteristics – HTML/CSS/Javascript • Web page may serve as working example/tutorial for future web App where people can upload their own data.
  9. 9. Project Development
  10. 10. Project Development
  11. 11. Project Approach
  12. 12. Audience-driven Design Goals • Mainly scientists in brain imaging research – Content should be comprehended by general audience • Facilitated by non-linear story-telling approach – Users can skip parts and focus on others – Each part is independent from others • Encourage exploration (particularly of individual subject data)
  13. 13. Landing Page
  14. 14. Landing Page: Scroll down
  15. 15. Neuroimaging Background What is neuroimaging? What is the data?
  16. 16. Neuroimaging Data: resting-state MRI • Functional MRI = spatial + temporal • Resting state: no task, just chill and “think of nothing in particular” for 10 minutes
  17. 17. Neuroimaging Data: resting-state MRI • Functional MRI = spatial + temporal • Resting state: no task, just chill and “think of nothing in particular” for 10 minutes +
  18. 18. Neuroimaging Data: resting-state MRI • Functional MRI = spatial + temporal • Resting state: no task, just chill and “think of nothing in particular” for 10 minutes – Day dream / Self-referential thinking B A SignalIntensity/brainactivity Time
  19. 19. Neuroimaging Data: resting-state MRI • Functional MRI = spatial + temporal • Resting state: no task, just chill and “think of nothing in particular” for 10 minutes – Day dream / Self-referential thinking B A Time b ß å a Network “A” Network “B”
  20. 20. Neuroimaging Data: resting-state MRI • Space: different brain regions – Anatomical shape and size – Sphere – Individual voxels Klein and Tourville, Frontiers in Neuroscience, 2012
  21. 21. Neuroimaging Data & Analysis
  22. 22. Neuroimaging Data • Pre-existing dataset (convenient) • Afforded the added dimension of comparing the 2 data sets, as well as the novel data collection – multiband – method itself. • 12 participants (“pilot” dataset) • Two multiband EPIs collected in counterbalanced order EPI1: 1 second temporal resolution 2.2mm3 voxel size EPI2: 2 second temporal resolution 1.5mm3 voxel size EPI = Echo-Planar imaging…the physics that defines how the data is acquired Multiband = collect multiple slices at once (much quicker!) Typical EPI: 2-2.5 seconds temporal resolution 3mm3 voxel size
  23. 23. Data Pipeline: AFNI, Matlab Remove noise from data (cardiac and respiratory noise, motion, white matter and CSF Despike Motion correct Bandpass filter: 0.01 – 0.1Hz Quadratic trend filtering Align and warp brain to the Talairach standard brain template Create .1D file of brain region coordinates. Apply with 6mm radius to each subject to get ROI x time signal intensity values as .txt Resample tissue segments to make masks for EPI1 and EPI2 Segment anatomical scans into tissue types: white, grey, and CSF.
  24. 24. Neuroimaging Data: ROIs Default mode network (DMN) Salience Network (SN) Executive Control Network (ECN) Posterior Cingulate Cortex Angular Gyrus Parahippocampal Cortex Anterior Cingulate Cortex Dorsal Posterior Parietal Cortex Dorsolateral Prefrontal Cortex Caudate Nucleus Posterior Cingulate Cortex Angular Gyrus Parahippocampal Cortex
  25. 25. Neuroimaging Data: Analysis • Export .txt for each subject containing 1 column of data for each ROI (19 columns), and 1 row per time point 560 or 280 [Linux server -> my mac] • Import to IPython Notebook • Python: Network means (individual and group) Network nodes/link (individual, group, cluster) Correlation/adjacency matrices (individual, group, cluster) Time courses (individual only) Temporal ordering (cluster only) Temporal duration (cluster only)
  26. 26. Analysis Pipeline (Python) Correlation coefficient fisher transform to z- score -> 19 x 19 Add Network label key to each node: DMN, ECN, SN Read into networkx to create network graph Create ROI (node) label list: add to networkx object Group nodes into networks Calculate average and SD using flattened lower triangle of each network, for each subject EPI1_Subject1.txt 560 x 19 EPI2_Subject1.txt 280 x 19 Create adjacency matrix JSON formatJSON format Mean z-scores and standard deviations Networks and Adjacency Matrices
  27. 27. Audience-driven Project Goals • Data quality: – Do I need to exclude any subjects? • Based on what? – Are my regions of interest (ROIs) well positioned? • Do the ROIs in the networks correlate well with each other? • Actual research question: – Can I find any transient networks? • What do they look like? • How many are there? • How long does each one last? • Is there an order to these transient networks? – Which multiband EPI is best at finding these networks?
  28. 28. Data Quality EPI2 ECN Are my ROIs in good places? Do the ROIs in the networks correlate well together? Subject 108 Subject 105 Subject 105 nodes are not well correlated compared with Subject 108
  29. 29. Landing Page: Scroll down
  30. 30. Individual Data: mean network z-scores
  31. 31. Group Data: mean network z-scores
  32. 32. Audience-driven Design Goals • Represent network graphs in consistent, replicable manner (not force-layout)
  33. 33. Group Data: network graphs
  34. 34. Audience-driven Project Goals • Data quality: – Do I need to exclude any subjects? • Based on what? – Are my regions of interest (ROIs) well positioned? • Do the ROIs in the networks correlate well with each other? • Actual research question: – Can I find any transient networks? • What do they look like? • How many are there? • How long does each one last? • Is there an order to these transient networks? – Which multiband EPI is best at finding these networks?
  35. 35. Cluster Pipeline (Python) Windows Contain 60 seconds each 1-time-frame step size Resulting dimensions: 19 x 19 x 496 19 x 19 x 246 TONS of reshaping… Concatenate Subjects along time… Convert to array… Normalize signal per subject… Transpose, reshape: 4960 x 361 (EPI1) 2460 x 361 (EPI2) EPI1_Subject1.txt 560 seconds x 19 nodes EPI2_Subject1.txt 280 seconds x 19 nodes
  36. 36. Cluster Pipeline (Python) EPI1_Subject1.txt 560 seconds x 19 nodes EPI2_Subject1.txt 280 seconds x 19 nodes Windows Contain 60 seconds each 1-time-frame step size Resulting dimensions: 19 x 19 x 496 19 x 19 x 246 TONS of reshaping… Concatenate Subjects along time… Convert to array… Normalize signal per subject (remove mean)… Transpose, reshape: 2480 x 361 (EPI1) 2460 x 361 (EPI2)
  37. 37. Cluster Pipeline (Python) • Number of Clusters range: 1-10 • Number of iterations: 100 • Alpha: 1 • Akaike Information Criterion (AIC, model selection) Dirichlet Process Gaussian Mixture Models: AIC: EPI1 best put in to two clusters EPI2 best put into one cluster Visualization: Replace mean per subject Networkx (Network graphs + adjacency matrices Temporal ordering/duration: Extract posterior probabilities Autocorrelation… Crosscorrelation…
  38. 38. Cluster Data: network graphs
  39. 39. Group Data: Adjacency matrices
  40. 40. Application Wish List • Networks: – Curvilinear links • Adjacency matrices: – The color range is set manually, but would like more colors and /or automatic normalization of range to deal with data skew? – Original design?
  41. 41. Application Wish List • App development: User can load own data • Complete time courses, temporal duration and temporal ordering sections • Separate the goals: address fewer of them!
  42. 42. Cluster: Temporal order/duration • Design goals – Should not require too much prior knowledge to understand the research, or how the site works K2 -> K3 K3 -> K2
  43. 43. Acknowledgements • MICA Students: Marianne Siblini, Kevin Ripka, Olga Cooper, Brittney Younger • UMD colleagues/students: Brenton McMenamin, Joshua Kinnison, Mahshid Najafi, Luiz Pessoa, Mihai Sirbu, Sarah Blankenship, Dustin Moraczewski,. • Funding: NIH, UMD

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