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BIOMAG2018 - Darren Price - CamCAN


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These are the slides presented by Darren Price in the Open Science Panel discussion at the BIOMAG 2018 meeting in Philadelphia. See also

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BIOMAG2018 - Darren Price - CamCAN

  1. 1. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository Darren Price Rik Henson, Lorraine Tyler, and the CamCAN team!
  2. 2. Overview • Overview • What’s available? –Pipelines: MEG & MRI • Application process • Recent Studies
  3. 3. Overview
  4. 4. CC700 (~100 per decade 18-88, selected from CC3000) ~7 hours of cognitive tests, 1 hour MRI, 30mins MEG Shafto et al. (2014). BMC Neurology, 14(204) Invitation Letters to 18yrs+ 10,525 3000 700 280 Refusals/ Exclusions 2300 Removed 420 excluded END? Stage 3. Three fMRI and MEG sessions Attention Language, Motor Learning, Memory and Emotion Stage 2. Three sessions MRI, MEG, cognitive tasks outside scanner. Saliva sample Home Interview. Health and Lifestyle, Demographics, etc. Available Now 2010
  5. 5. What’s Available?
  6. 6. MEG Data Pipeline MRI Anatomicals MRI FunctionalMEG MaxFilter 2.2CONVERT SPM NO MOVECOMPMOVECOMP CONVERTCONVERT Artefacts not removed ICA ComponentsICA Components N=~650 See website
  7. 7. CC700 MRI database and pipelines Taylor et al. (2016). NeuroImage
  8. 8. Current Users (>300)
  9. 9. Application Process
  10. 10.
  11. 11. Application Step 1. Browse the website Imaging Data N Data Sets Unprocessed data (de-faced, converted to NIFTI format, Gzipped 4D NIfTI) Structural Data T1 653 T2 653 Diffusion Weighted Imaging (DWI) 650 Functional Field Map 649 Resting State 649 Movie Watching 649 Sensori-motor task 649 Processed (DARTEL warped to sample template and then to MNI space in SPM12) Segmented images (GM, WM) from T1 + T2 (NIFTI / ROI values) Diffusion Metrics (NIFTI images / ROI values) Fractional Anisotropy (FA) Mean Diffusivity (MD) Diffusion Kurtosis (DKI) Magnetisation Transfer Ratio (MTR) Functional (movement and slice-time corrected, smoothed) Resting State Movie Watching Sensori-motor task Magnetoencephalography (MEG) Resting State 652 Sensori-Motor task 652 Sensori (passive) task 652
  12. 12. Dataset N Behavioural datafromnon-imagingsessions Bentonfaces 657 Cardiovascularmeasures 587 Cattell 660 Emotional expressionrecogn. 665 Emotional memory 330 Emotionregulation 316 Famousfaces 660 Forcemtching 328 Hotel task 658 Motorlearning 318 Picturepriming 652 Proverbs 655 RTchoice 682 RTsimple 686 Synsem 660 TOT 656 VSTMcolour 656 Behavioural datafromimagingsessions MRISensori-motortask 657 MEGSensori-motortask(RTs) 655 Application Step 1. Browse the website behavioural data
  13. 13. Application Step 2. Develop Proposal • Do – Develop a well thought out research question – Include a hypothesis – Request only what is needed – Refer to the data requested in the proposal – Read Terms and Conditions • Don’t – Data mining discouraged – Apply for “all variables”
  14. 14. Bad Example Data requested: ALL IMAGING AND BEHAVIOURAL DATA Proposal: “I like big datasets and I cannot lie”
  15. 15. Too broad and unfocused
  16. 16. Good Example • We request access to subsets of the neuroimaging, behavioral and home interview data to undertake two retrospective studies: • 1. Analysis of T1w and T2w volumes using radiomic feature analysis ( and correlation of derived features with behavioral, memory, and cardiovascular risk factor scores, correcting for educational and social economic status. Here we aim to decode age-related radiographic changes in the brain from basic MRI images, in particular subtle changes in image texture that can not be detected using standard 'atrophy- based' techniques (i.e. Voxel Based Morphometry). Observed changes will be compared against those occurring in small vessel disease, vascular dementia, and Alzheimer's disease cohorts to build a radiographic signature of age- and dementia related cognitive decline. • 2. Apply microstructural modeling to the multi-angle diffusion MRI data. Three models will be applied and compared for sensitivity: a) NODDI (Neurite Orientation and Dispersion Imaging; b) ODI (Orientation Dispersion Index) c) Parametric Spherical Deconvolution These techniques will provide highly specific microstructural measurements and possibly enable more subtle age-related GM and WM changes to be detected. As in the radiomic study above, we will correlate measures against behavioral, memory, and cardiovascular risk factor scores, correcting for educational and social economic status. • We have recently validated such microstructural models against immunohistochemistry in animal models and have shown strong relationships of derived measures with cell density. Therefore, by applying such models to this dataset we hope to gain insight into the causes of atrophy in the aging brain.
  17. 17. • Unfortunately, this takes some time. • Applications must be approved by the CamCAN PIs, which takes at least two weeks. • If rejected, don’t be dejected. – Normally revise and resubmit Application Step 4. Wait
  18. 18. Application Step 5. Access • Access details supplied by email (password sent separately) • Instructions are in the email – Rsync – WinSCP • 99% of problems are with the user’s university firewall. Check on another computer (i.e. at home) first.
  19. 19. Disclaimer • Data comes without a warranty • It is the user’s responsibility to check data • Please report back any problems • Home interview forms will be made available on request
  20. 20. Future Data Releases FreeSurfer – recon-all Genetic data coming soon, thanks to Max Plank research centre Hypothesis especially important here Stage 3 Data with cognitive tasks
  21. 21. Managing Access - Technology HTML/CSS SQLite Python Website User PHP CSV File Email User WinSCP / rsync / scp Cron Job creates temporary account Secure
  22. 22. Research at CamCAN
  23. 23. Price et al. (2017) Nature Communications Grey/White Matter Explains Age- related Evoked Response Delays
  24. 24. Complexity and Ageing Cognitive Reserve Drowsiness / Complexity StandardisedScore(σ) Complexity Drowsiness
  25. 25. Age vs. ΔComplexity (LZ) Spearman’sR Age vs. Condition Interaction Increase in LZ is greater in older subjects Complexity and Ageing
  26. 26. Hidden Markov Model Dr. Roni Tibon Figure reproduced from: Baker et al. (2014) eLife How do these state dynamics change with age?
  27. 27. Summary • ~650 MRI/MEG datasets available – Resting – Sensorimotor – Passive audio-visual • Demographics / lifestyle data • Apply at
  28. 28. Thank you! …and these people Rik Henson Lorraine Tyler Meredith Shafto Jason Taylor