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- 1. The Developing HumanConnectome Project (dHCP): Towards a dynamic map of evolving brain connectivity, reflecting fetal and early neonatal periods Dr Emma C. Robinson @emrobSci emma.robinson@kcl.ac.uk https://emmarobinson01.com/
- 2. The Developing HumanConnectome Project • Mapping the emergence of brain connectivity from 20-44 weeks PMA • ~1500 scans • Acquisitions (MRI): Resting state fMRI Multi-shell HARDI Structural T1 and T2 • Supported by Genetic samples Cognitive test scores/eye tracking Demographics http://www.developingconnectome.org/ c/o Dr Bernard Kainz, Imperial College
- 3. The Developing HumanConnectome Project • Mapping the emergence of brain connectivity from 20-44 weeks PMA • ~1500 scans • Acquisitions (MRI): Resting state fMRI Multi-shell HARDI Structural T1 and T2 • Supported by Genetic samples Cognitive test scores/eye tracking Demographics http://www.developingconnectome.org/ c/o Dr Bernard Kainz, Imperial College
- 4. The Developing HumanConnectome Project • Mapping the emergence of brain connectivity from 20-44 weeks PMA • ~1500 scans • Acquisitions (MRI): Resting state fMRI Multi-shell HARDI Structural T1 and T2 • Supported by Genetic samples Cognitive test scores/eye tracking Demographics http://www.developingconnectome.org/ c/o Dr Bernard Kainz, Imperial College
- 5. Challenges of working withdeveloping data
- 6. Challenges of working withdeveloping data • Developing data is affected by
- 7. Challenges of working withdeveloping data • Developing data is affected by Motion (severe cases account for < 2% )
- 8. Challenges of working withdeveloping data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times
- 9. Challenges of working withdeveloping data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution
- 10. Challenges of working withdeveloping data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution Inverted contrast
- 11. Challenges of working withdeveloping data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution Inverted contrast spatio-temporal evolution
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- 13. Neonatal Structural Pipeline •Reconstruction with motion correction • Turbo Spin Echo (TSE) T2
- 14. Neonatal Structural Pipeline •Reconstruction with motion correction • Turbo Spin Echo (TSE) T2 • Two stacks of 2D slices • 0.8x0.8x1.6 mm (image courtesy: M. Fogtmann, IEEE TMI 2014)
- 15. Neonatal Structural Pipeline •Reconstruction with motion correction • Turbo Spin Echo (TSE) T2 • Two stacks of 2D slices • 0.8x0.8x1.6 mm • Slice to volume -> 0.5mm3 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)”
- 16. Neonatal Structural Pipeline Makropoulosand Robinson et al. The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction. NeuroImage (2018) • Reconstruction with motion correction
- 17. Neonatal Structural Pipeline Makropoulosand 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 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.
- 18. Neonatal Structural Pipeline Brainextract Bias correct T1T2 Align White PialMid-thickness Inflated Very Inflated Sphere T1/T2w ratio A. Pre-Processing F G C H B DE I I Myelin MapSulcal DepthCurvatureThickness Segmentation 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 • Surface mesh modelling
- 19. Neonatal Structural Pipeline Brainextract Bias correct T1T2 Align White PialMid-thickness Inflated Very Inflated Sphere T1/T2w ratio A. Pre-Processing F G C H B DE I I Myelin MapSulcal DepthCurvatureThickness Segmentation 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 • Surface mesh modelling • Feature Extraction
- 20. Neonatal Surface QC 2raters 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
- 21. Neonatal fMRI Pipeline Fitzgibbon, SeanP., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 22. Neonatal fMRI Pipeline • FIELDMAP Fitzgibbon,Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 23. Neonatal fMRI Pipeline • FIELDMAP •Motion & Distortion Correction (MCDC) Fitzgibbon, Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 24. Neonatal fMRI Pipeline • FIELDMAP •Motion & Distortion Correction (MCDC) • REGISTRATION Fitzgibbon, Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 25. Neonatal fMRI Pipeline • FIELDMAP •Motion & Distortion Correction (MCDC) • REGISTRATION • ICA+FIX Fitzgibbon, Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 26. Neonatal fMRI Pipeline • FIELDMAP •Motion & Distortion Correction (MCDC) • REGISTRATION • ICA+FIX • NUISANCE REGRESSION Fitzgibbon, Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 27. Neonatal fMRI Pipeline • FIELDMAP •Motion & Distortion Correction (MCDC) • REGISTRATION • ICA+FIX • NUISANCE REGRESSION • SAMPLE TO SURFACE Fitzgibbon, Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 28. Neonatal fMRI Pipeline • FIELDMAP •Motion & Distortion Correction (MCDC) • REGISTRATION • ICA+FIX • NUISANCE REGRESSION • SAMPLE TO SURFACE • QC Fitzgibbon, Sean P., et al. "The developing Human Connectome Project (dHCP): minimal functional pre-processing pipeline for neonates." Fifth Biennial Conference on Resting State and Brain Connectivity. 2016.
- 29. Neonatal fMRI Pipeline 17ProfumoModes Harrison, SamuelJ., et al. "Large-scale probabilistic functional modes from resting state fMRI." NeuroImage 109 (2015): 217-231. (n=242)
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- 31. Neonatal dMRI Pipeline •300 diffusion volumes (20 b0) • b = 400 s/mm2 (64) • b = 1000 s/mm2 (88) • b = 2600 s/mm2 (128)
- 32. 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 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.
- 33. 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 (under review).
- 34. • Micro-structural parameterestimates 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 (under review). 38 39 40 4138 39 40 41
- 35. 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 (under review)
- 36. 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 (under revision)
- 37. 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 (under revision)
- 38. 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 (under revision)
- 39. Surface-based alignment ofneonatal cortical surfaces • Spherical framework for cortical surface registration: MSM • Use low resolution control point grids to constrain the deformation • Optimised using discrete methods Robinson, Emma C., et al. "MSM: a new flexible framework for Multimodal Surface Matching." Neuroimage (2014)
- 40. Surface-based alignment ofneonatal cortical surfaces • Spherical framework for cortical surface registration: MSM • Use low resolution control point grids to constrain the deformation • Optimised using discrete methods Robinson, Emma C., et al. "MSM: a new flexible framework for Multimodal Surface Matching." Neuroimage (2014)
- 41. Surface-based alignment ofneonatal cortical surfaces • Spherical framework for cortical surface registration: MSM • Use low resolution control point grids to constrain the deformation • Optimised using discrete methods data cost: i.e. correlation, MNI, SSD Regularisation cost to encourage smoother warp Robinson, Emma C., et al. "MSM: a new flexible framework for Multimodal Surface Matching." Neuroimage (2014)
- 42. SAS SSS a) e) b) x LABEL POINT OPTIMAL LABEL CONTROL POINT c) f) d) MSS SSS+ G TSS DAS TAS g) F Finding trends in longitudinal cortical development MSM now also allows smooth deformation of cortical anatomies Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints." NeuroImage (2018).
- 43. New MSM: findingtrends 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)
- 44. Deep Learning forBrain Imaging Transfer learning: Applied to motion artefact detection of neonatal diffusion MRI (dMRI) Data from Developing Human Connectome Project (dHCP) Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection.
- 45. Deep Learning forBrain Imaging Transfer learning: Applied to motion artefact detection of neonatal diffusion MRI (dMRI) •Like fMRI highly sensitive to motion Data from Developing Human Connectome Project (dHCP) Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection. Red boxed highlight motion artifacted slices
- 46. Deep Learning forBrain 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 Data from Developing Human Connectome Project (dHCP) Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection. Red boxed highlight motion artifacted slices
- 47. Deep Learning forBrain 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., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection. Red boxed highlight motion artifacted slices
- 48. Deep Learning forMedical Imaging Transfer learning: Applied to motion artefact detection of neonatal diffusion MRI (dMRI) Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection.
- 49. Deep Learning forMedical Imaging Transfer learning: Applied to motion artefact detection of neonatal diffusion MRI (dMRI) •Trained on 36 subjects Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection.
- 50. Deep Learning forMedical 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 Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection.
- 51. Deep Learning forMedical 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.
- 52. Fetal Pipeline !18 • T1/T2 •0.75 mm isotropic • 141 Diffusion gradients • b = 0 s/mm2 (15) • b = 400 s/mm2 (46) • b = 1000 s/mm2 (80) • 2mm isotropic • fMRI • 12mins 35 secs • 2.2mm isotropic Example T2
- 53. Fetal Pipeline !18 • T1/T2 •0.75 mm isotropic • 141 Diffusion gradients • b = 0 s/mm2 (15) • b = 400 s/mm2 (46) • b = 1000 s/mm2 (80) • 2mm isotropic • fMRI • 12mins 35 secs • 2.2mm isotropic Example T2
- 54. Fetal Pipeline !18 • T1/T2 •0.75 mm isotropic • 141 Diffusion gradients • b = 0 s/mm2 (15) • b = 400 s/mm2 (46) • b = 1000 s/mm2 (80) • 2mm isotropic • fMRI • 12mins 35 secs • 2.2mm isotropic Example T2
- 55. • SLICE-WISE MOTION CORRECTIONAND DYNAMIC DISTORTION CORRECTION • SEGMENTATION / REGISTRATION • ICA+FIX • SAMPLE TO SURFACE • NUISANCE REGRESSION • QC Fetal Pipeline: fMRI Piloting !19 Neonatal 22 - 37 week gestational age scans Group Mean SNR
- 56. Fetal Pipeline: fMRIPiloting !20
- 57. Fetal Pipeline: fMRIPiloting !20 • RSNs • Lower SNR/Higher motion than neonates • BUT similar structure at matched ages
- 58. Fetal Pipeline: fMRIPiloting !20 • RSNs • Lower SNR/Higher motion than neonates • BUT similar structure at matched ages • Comparable Netmats
- 59. Fetal Pipeline: dMRIPiloting !21 INPUT SHARD RECONSTRUCTION
- 60. Fetal Pipeline: dMRIPiloting !21 • Slice to Volume Reconstruction (with motion correction) • “Multi-shell SHARD reconstruction from scattered slice diffusion MRI data in the neonatal brain.” Daan Christiaens et al ISMRM 2018 • Deprez, Maria, et al. "Higher order spherical harmonics reconstruction of fetal diffusion MRI with intensity correction." bioRxiv (2018): 297341. INPUT SHARD RECONSTRUCTION
- 61. Fetal Pipeline: dMRIPiloting !21 • Slice to Volume Reconstruction (with motion correction) • “Multi-shell SHARD reconstruction from scattered slice diffusion MRI data in the neonatal brain.” Daan Christiaens et al ISMRM 2018 • Deprez, Maria, et al. "Higher order spherical harmonics reconstruction of fetal diffusion MRI with intensity correction." bioRxiv (2018): 297341. • Spherical Deconvolution fit • Constrained • b 1000
- 62. Data Releases !22 •1st Pilotdata 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 •2nd Major data release • Expected summer 2018 • For queries on data releases and pipelines see https:// neurostars.org/tags/developing-hcp
- 63. Data Releases !23 •dHCP structuralpipeline • https://github.com/BioMedIA/dhcp-structural- pipeline • Includes docker installation • Contact j.cupitt@imperial.ac.uk
- 64. Acknowledgements !24 • Professor A.David Edwards (PI) • Professor Jo Hajnal (PI) • Dr Lucillio Cordero Grande • Dr Anthony Price • Dr Maria Deprez • Dr Chris Kelly • Max Pietsch • Daan Christiaens • Dr Donald Tournier • Dr Emer Hughes http://www.developingconnectome.org/teams-and-collaborators-v2/ • Professor Daniel Rueckert (PI) • Dr Antonios Makropoulos • Dr Andreas Schuh • Dr Jonathan Palmbach-Passerat • Dr John Cupitt • Dr Jianling Gao • Professor Steve Smith (PI) • Professor Mark Jenkinson • Dr Eugene Duff • Dr Matteo Bastiani • Dr Sean Fitzgibbon • Dr Saad Jbabdi • Dr Stam Sotiropoulos • Dr Jelena Bozek