Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

The Developing Human Connectome Project (dHCP): the power of Big Data

3 views

Published on

An overview of the second data release from the developing human connectome project

Published in: Science
  • Be the first to comment

  • Be the first to like this

The Developing Human Connectome Project (dHCP): the power of Big Data

  1. 1. The Developing Human Connectome Project (dHCP): the power of Big Data Dr Emma C. Robinson @emrobSci ecr05 emma.robinson@kcl.ac.uk https://emmarobinson01.com/
  2. 2. The Developing Human Connectome Project • Mapping the emergence of brain connectivity from 20-44 weeks PMA • 861 neonates (144 Pre-term), 266 foetuses (so far) • Acquisitions (MRI): Resting state fMRI Multi-shell HARDI Structural T1 and T2 • Supported by Genetics-> 4.3 million SNP array Cognitive test scores/eye tracking Demographics http://www.developingconnectome.org/ c/o Dr Bernard Kainz, Imperial College
  3. 3. Data Releases 3 • 1st Pilot data release • https://data.developingconnectome.org/app/template/Login .vm • 40 neonatal subjects: • T1, T2, fMRI and dMRI volumes (minimally processed) • output of surface extraction pipelines • Released as Torrent
  4. 4. Data Releases 4
  5. 5. Data Releases 5 • 2nd Data Release •558 session (505 subjects) of Neonatal data: • T1, T2, fMRI and dMRI volumes, original, cleaned, pre-processed and mapped to volumetric template space • Cortical surface meshes and features • http://www.developingconnectome.org/information-registration- and-download/ • Subject to open access terms of use (data agreement) • Released as Torrent • Troubleshooting documents will be available Support via https://neurostars.org/tags/developing-hcp
  6. 6. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution Inverted T1/T2 contrast spatio-temporal evolution
  7. 7. Neonatal Structural Pipeline Makropoulos and Robinson et al. The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction. NeuroImage (2018) • Reconstruction with motion correction • Tissue segmentation with DRAW-EM • Surface mesh modelling • Feature Extraction • Visually QC’d (sub-set) High intensity white matter correction Makropoulos, Antonios, et al. "Automatic whole brain MRI segmentation of the developing neonatal brain." IEEE transactions on medical imaging 33.9 (2014): 1818-1831. Cordero-Grande, Lucilio, et al. "Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice MRI: Application to neonatal brain imaging." MRM (2018)”
  8. 8. Neonatal fMRI Pipeline • Inspired by the HCP pipelines and FSL FEAT pipeline • But optimised to address the challenges of neonatal data Head motion
  9. 9. Neonatal fMRI Pipeline • Inspired by the HCP pipelines and FSL FEAT pipeline • But optimised to address the challenges of neonatal data Head motion motion by susceptibility
  10. 10. Neonatal fMRI Pipeline: Key features fMRI EDDY (FREDDY) Integrated dynamic distortion correction and slice-to-volume motion correction Pre- Eddy Post- Eddy Correction of intra-volume motion Andersson, Jesper LR, et al. "Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement." Neuroimage 152 (2017): 450-466.
  11. 11. fMRI EDDY (FREDDY) Integrated dynamic distortion correction and slice-to-volume motion correction Correction of motion-by-susceptibility distortions Neonatal fMRI Pipeline: Key features Andersson, Jesper LR, et al. "Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement." Neuroimage 152 (2017): 450-466.
  12. 12. Correction of motion-by-susceptibility distortions Bespoke ICA-based denoising (FIX) Neonatal fMRI Pipeline: Key features
  13. 13. Neonatal fMRI Pipeline *Harrison, Samuel J., et al. "Large-scale probabilistic functional modes from resting state fMRI." NeuroImage 109 (2015): 217-231. 16 PROFUMO* modes qualitatively assessed as corresponding to adult resting-state networks.
  14. 14. Neonatal dMRI Pipeline • 300 diffusion volumes (20 b0) • b = 400 s/mm2 (64) • b = 1000 s/mm2 (88) • b = 2600 s/mm2 (128) • Correction for eddy currents, susceptibility and motion performed with FSL’s Eddy • Virtual dissection (atlas based) Bastiani et al., Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. NeuroImage 185 (2019): 750- 763. Jesper L. R. et al. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125:1063-1078, 2016.
  15. 15. • Micro-structural parameter estimates using NODDI (Zhang et al 2012) Microstructure Tracts (virtual dissection) Neonatal dMRI Pipeline Bastiani et al., Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. NeuroImage 185 (2019): 750-763. 38 39 40 4138 39 40 41
  16. 16. dHCP spatio-temporal atlases • New volumetric and surface templates spanning 36-44 weeks gestation Andreas Schuh et al. Unbiased construction of a temporally consistent morphological atlas of neonatal brain development bioRxiv (2018): 251512.
  17. 17. dHCP spatio-temporal atlases • New volumetric and surface templates spanning 36-44 weeks gestation Jelena Bozek et al. Construction of a Neonatal Cortical Surface Atlas Using Multimodal Surface Matching in the Developing Human Connectome Project NeuroImage 179 (2018): 11- 29.
  18. 18. Surface-template alignment • Optimised Multi-modal Surface matching • Scripts available at https://github.com/ecr05/dHCP_template_alignment Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints." NeuroImage (2018). Robinson, Emma C., et al. "MSM: a new flexible framework for Multimodal Surface Matching." Neuroimage (2014)
  19. 19. Some recent developments in MSM MSM now also allows smooth deformation of cortical anatomies Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints." NeuroImage (2 018).
  20. 20. New MSM: finding trends in longitudinal cortical development • 24 very preterm infants (born <30 weeks PMA, 15 male, 15 female) • scanned 2-4 times before or at term-equivalent (36-40 weeks PMA) Garcia, Kara E., Robinson E.C. et al. "Dynamic patterns of cortical expansion during folding of the preterm human brain." PNAS (2018)
  21. 21. The power of Big Data • 505 subjects (558) sessions • Higher statistical power • More robust models • Sufficient to train Deep Networks (?)
  22. 22. Deep Learning for Brain Imaging Transfer learning: Applied to motion artefact detection of neonatal diffusion MRI (dMRI) •Like fMRI highly sensitive to motion •Standard practice to remove noisy slices •Train CNN classifier using transfer learning Data from Developing Human Connectome Project (dHCP) Kelly, C., et al. Transfer learning and convolutional neural net fusion for motion artefact detection. Red boxed highlight motion artifacted slices
  23. 23. Deep Learning for Medical Imaging Transfer learning: Applied to motion artefact detection of neonatal diffusion MRI (dMRI) •Trained on 36 subjects •Multiple CNNs trained on different categories of dMRI data •Output of predictions merged by random forest 94.8%-99.8% accuracy Human level ~99.25% Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection.
  24. 24. Predicting gestational age from Neonatal Structural connectome • Training a neural network to predict* • GA at birth • GA at scan • From dMRI connectomes • Mae: • 1.5 weeks (GA at birth) • 0.65 (GA at scan) *replication of method in Girault, Jessica B., et al. "White matter connectomes at birth accurately predict cognitive abilities at age 2." NeuroImage192 (2019): 145-155.
  25. 25. Geometric (surface) deep learning • Train CNNs on spatial filters fit to the cortical surface e.g. Seong, Si-Baek, et al Frontiers in Neuroinformatics 12 (2018): 42. Zhao, Fenqiang, et al. "Spherical U- Net on Cortical Surfaces: Methods and Applications." IPMI 2019.
  26. 26. • 3 channels: cortical thickness, curvature and myelin • Projected to 2D(via sphere) • ResNet - 5 blocks of residual layers (2 units per block o Accuracy for prem vs term classification = 100% o GA at scan mae=0.493 x Train mae=0.198; Test mae= 0.493 Geometric (surface) deep learning Regression of Age at scan
  27. 27. • Outperforms ROI analysis • 100 Voronoi parcels • Average data for each parcel • GA regression Test mae= 0.95 x Train mae=0.41; Test mae= 0.95 Geometric (surface) deep learning
  28. 28. • Features visualised using Grad CAM Increasing GA 32 37 41 44 Means Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." Proceedings of the IEEE International Conference on Computer Vision. 2017. Geometric (surface) deep learning
  29. 29. Modelling brain Development with Gaussian Process Regression • 446 dHCP neonates scanned cross sectionally • Aligned using 2-channel registration of T2 and cortical mantel • Input variables, GA, PMA, sex • Gaussian Process regression estimated • brain tissue intensity on T1 and T2 • local tissue shape (dx,dy,dz deformation maps) • x GP model of brain growth J O’Muircheartaigh, EC Robinson et al, Modelling brain development: investigating white matter injury in term and preterm born neonates, submitted
  30. 30. Modelling brain Development with Gaussian Process Regression • 446 dHCP neonates scanned cross sectionally • Aligned using 2-channel registration of T2 and cortical mantel • Gaussian Process regression estimated • brain tissue intensity on T1 and T2 • local tissue shape (dx,dy,dz deformation maps) GP model of brain growth J O’Muircheartaigh, EC Robinson et al, Modelling brain development: investigating white matter injury in term and preterm born neonates, submitted
  31. 31. Modelling brain Development with Gaussian Process Regression • 446 dHCP neonates scanned cross sectionally • Aligned using 2-channel registration of T2 and cortical mantel • Gaussian Process regression estimated • brain tissue intensity on T1 and T2 • local tissue shape GP model of intensity changes J O’Muircheartaigh, EC Robinson et al, Modelling brain development: investigating white matter injury in term and preterm born neonates, submitted
  32. 32. Modelling brain Development with Gaussian Process Regression What would a term-aged infant look like if they were born with varying degrees of prematurity? J O’Muircheartaigh, EC Robinson et al, Modelling brain development: investigating white matter injury in term and preterm born neonates, submitted
  33. 33. Modelling brain Development with Gaussian Process Regression Comparisons of subjects versus the group can be used to identify punctate lesions J O’Muircheartaigh, EC Robinson et al, Modelling brain development: investigating white matter injury in term and preterm born neonates, submitted Mean ROC AUC=0.894
  34. 34. Polygenic Risk for Neuropsychiatric Disease and Abnormal Deep Grey Matter Development • Polygenic Risk analysis • Using genetic risk scores estimated for 5 major psychiatric disorders (Smoller et al 2013) • Applied to 194 preterm eprime subjects • Linked to deep grey matter volume Cullen, Harriet, et al. "Polygenic risk for neuropsychiatric disease and vulnerability to abnormal deep grey matter development." Scientific reports 9.1 (2019): 1976.
  35. 35. • Increasing polygenic risk score (PRS) Ø Associated with reduced lentiform nucleus volume - In the full mixed-ancestral cohort (R2 = 0.06, p = 8 × 10−4) - In the subsample of European infants (R2=0.06, p = 8 × 10−3) ● ● ● ● ●0.00 0.05 0.10 0.15 0.001 0.01 0.05 0.1 0.5 P value cut−off VarianceExplainedbyPRS ● ● ● ● ● 0.00 0.05 0.10 0.15 0.001 0.01 0.05 * 0.1 * 0.5 P value cut−off VarianceExplainedbyPRS 0.15 byPRS 0.15 byPRS Full Mixed-ancestry cohort (n=194) Proportion of variance explained in the lentiform nucleus volumes by the PRS at five different P-value thresholds. Polygenic Risk for Neuropsychiatric Disease and Abnormal Deep Grey Matter Development
  36. 36. Future/ongoing work Fetal pipelines - Deep learning driven tissue segmentation and surface extraction Christiaens et al., TMI 2019 ; Christiaens et al., ISMRM 2018 Subject with severe moti
  37. 37. Future/ongoing work Fetal pipelines - Deep learning driven tissue segmentation and surface extraction - Fetal diffusion advanced motion correction through Spherical Harmonics And a Radial Decomposition (SHARD) Christiaens et al., TMI 2019 ; Christiaens et al., ISMRM 2018 Subject with severe moti Subject with severe motion
  38. 38. Acknowledgements: PIs 43 Professor David Edwards Professor Jo Hajnal Professor Daniel Rueckert Professor Stephen Smith
  39. 39. Acknowledgements: Contributors 44 • Abdulah Fawaz • Kyriaki Kaza My team: Cher Bass Jonathan O’Muircheartaigh Daan Christiaens Yassine Benchekroun Dr Chris Kelly • Harriet Cullen • Dr Lucillio Cordero Grande • Max Pietsch • Dr Maria Deprez • Dr Emer Hughes • Dr Serena Counsell • Dr Jana Hutter • Dr Adrian Price • Dr J-Donald Tournier http://www.developingconnectome.org/teams-and- collaborators-v2/ … And many more
  40. 40. Acknowledgements: Contributors 45 http://www.developingconnectome.org/teams-and- collaborators-v2/ … And many more Antonios Makropoulos Andreas Schuh • Dr Ahmed Fetit • Dr John Cuppitt • Dr Jianliang Gao • Dr Robert Wright Matteo Basitani Sean Fitzgibbon Jesper Andersson • Prof Saad Jbabdi • Prof Mark Jenkinson • Dr Eugene Duff • Dr Stam Sotiropoulos • Dr Slava Karolis Jelena Bozek Kara Garcia
  41. 41. Acknowledgements: 46 And of course the participants…..
  42. 42. Code and atlases 47 • dHCP structural pipeline • https://github.com/BioMedIA/dhcp-structural-pipeline • Includes docker installation - contact j.cupitt@imperial.ac.uk • DRAW-EM • MSM and surface-to-template alignment • https://github.com/ecr05/MSM_HOCR • https://github.com/ecr05/dHCP_template_alignment • Atlases • https://brain-development.org/brain-atlases/atlases-from-the-dhcp- project/ emma.robinson@kcl.ac.uk ecr05 @emrobSci https://emmarobinson01.com/
  43. 43. Neonatal Volume QC 2 raters rated • 160 images Example QC from single rater From left to right: poor quality to best quality
  44. 44. Neonatal Surface QC 2 raters rated • 43 images • Patches of size 50x50x50mm • White surface only Comparison of intensity-based surface refinement (green) to segmentation result (yellow) Example QC from single rater

×