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A primer for the upcoming developing Human Connectome Project data release; presented at the Big Data Little Brains conference in Chapel Hill, May 2018

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  1. 1. The Developing Human Connectome 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. 2. The Developing Human Connectome 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. 3. The Developing Human Connectome 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. 4. The Developing Human Connectome 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. 5. Challenges of working with developing data
  6. 6. Challenges of working with developing data • Developing data is affected by
  7. 7. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% )
  8. 8. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times
  9. 9. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution
  10. 10. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution Inverted contrast
  11. 11. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution Inverted contrast spatio-temporal evolution
  12. 12. Neonatal Structural Pipeline
  13. 13. Neonatal Structural Pipeline • Reconstruction with motion correction • Turbo Spin Echo (TSE) T2
  14. 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. 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. 16. 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
  17. 17. 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 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. 18. Neonatal Structural Pipeline Brain extract 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. 19. Neonatal Structural Pipeline Brain extract 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. 20. 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
  21. 21. Neonatal fMRI Pipeline 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.
  22. 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. 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. 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. 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. 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. 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. 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. 29. Neonatal fMRI Pipeline 17ProfumoModes Harrison, Samuel J., et al. "Large-scale probabilistic functional modes from resting state fMRI." NeuroImage 109 (2015): 217-231. (n=242)
  30. 30. Neonatal dMRI Pipeline
  31. 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. 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. 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. 34. • 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 (under review). 38 39 40 4138 39 40 41
  35. 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. 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. 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. 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. 39. Surface-based alignment of neonatal 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. 40. Surface-based alignment of neonatal 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. 41. Surface-based alignment of neonatal 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. 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. 43. 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)
  44. 44. Deep Learning for Brain 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. 45. Deep Learning for Brain 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. 46. 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 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. 47. 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., 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. 48. Deep Learning for Medical 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. 49. Deep Learning for Medical 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. 50. 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 Kelly, C., Pietsch, M., Counsell, S., & Tournier, J. D. Transfer learning and convolutional neural net fusion for motion artefact detection.
  51. 51. 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.
  52. 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. 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. 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. 55. • SLICE-WISE MOTION CORRECTION AND 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. 56. Fetal Pipeline: fMRI Piloting !20
  57. 57. Fetal Pipeline: fMRI Piloting !20 • RSNs • Lower SNR/Higher motion than neonates • BUT similar structure at matched ages
  58. 58. Fetal Pipeline: fMRI Piloting !20 • RSNs • Lower SNR/Higher motion than neonates • BUT similar structure at matched ages • Comparable Netmats
  59. 59. Fetal Pipeline: dMRI Piloting !21 INPUT SHARD RECONSTRUCTION
  60. 60. Fetal Pipeline: dMRI Piloting !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. 61. Fetal Pipeline: dMRI Piloting !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. 62. Data Releases !22 •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 •2nd Major data release • Expected summer 2018 • For queries on data releases and pipelines see https:// neurostars.org/tags/developing-hcp
  63. 63. Data Releases !23 •dHCP structural pipeline • https://github.com/BioMedIA/dhcp-structural- pipeline • Includes docker installation • Contact j.cupitt@imperial.ac.uk
  64. 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

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