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The Human Connectome Project multimodal cortical parcellation: new avenues for brain research.

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Description of the methodological advances that went into the development of the HCP brain parcellation, and the challenges that remain

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The Human Connectome Project multimodal cortical parcellation: new avenues for brain research.

  1. 1. The Human Connectome Project multimodal cortical parcellation: new avenues for brain research. Dr Emma. C. Robinson Biomedical Engineering, Kings College emma.robinson@kcl.ac.uk, emmarobinson01.com Twitter: @emrobSci
  2. 2. Overview
  3. 3. Overview 1. The Human Connectome Project (HCP) Goals Neuroimaging approach (surface-based processing)
  4. 4. Overview 1. The Human Connectome Project (HCP) Goals Neuroimaging approach (surface-based processing) 2.The HCP v1 multi-modal parcellation: Prospects and Future Challenges
  5. 5. Overview 1. The Human Connectome Project (HCP) Goals Neuroimaging approach (surface-based processing) 2.The HCP v1 multi-modal parcellation: Prospects and Future Challenges 3.Translating HCP processing to developing data: The dHCP project
  6. 6. The Human Connectome Project (HCP) Goal: Build the most accurate model to date of the adult* human Connectome Capture high spatial and temporal resolution functional, diffusion and structural MRI Deliver enhanced image processing pipelines and methods Improve understanding of the functional organisation of the human brain
  7. 7. The Human Connectome Project (HCP) Goal: Build the most accurate model to date of the adult* human Connectome Capture high spatial and temporal resolution functional, diffusion and structural MRI Deliver enhanced image processing pipelines and methods Improve understanding of the functional organisation of the human brain
  8. 8. The Human Connectome Project (HCP) Goal: Build the most accurate model to date of the adult* human Connectome Capture high spatial and temporal resolution functional, diffusion and structural MRI Deliver enhanced image processing pipelines and methods Improve understanding of the functional organisation of the human brain * the HCP is now acquiring new data sets across the lifespan
  9. 9. The Human Connectome Project (HCP) HCP young adult study: 1206 healthy adult subjects (aged 22-35yrs) Twins and non-twin siblings Acquisitions: 0.7mm T1 and T2 1hr resting state functional MRI (rfMRI) 7 tasks fMRI experiments including: language, emotional, relational, gambling, motor, social cognition, working memory HARDI diffusion data ***COMPLETED*** •
  10. 10. The HCP’s neuroimaging approach Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  11. 11. The HCP’s neuroimaging approach 7 Tenets: Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  12. 12. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  13. 13. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  14. 14. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution 3. Preprocess data to minimize distortions, blurring and temporal artifacts Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  15. 15. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution 3. Preprocess data to minimize distortions, blurring and temporal artifacts 4. Perform cortical surface-based processing Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  16. 16. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution 3. Preprocess data to minimize distortions, blurring and temporal artifacts 4. Perform cortical surface-based processing 5. Accurately align corresponding brain areas across subjects and studies - surface-based alignment Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  17. 17. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution 3. Preprocess data to minimize distortions, blurring and temporal artifacts 4. Perform cortical surface-based processing 5. Accurately align corresponding brain areas across subjects and studies - surface-based alignment 6. Analyze data using neurobiologically accurate brain parcellations Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  18. 18. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution 3. Preprocess data to minimize distortions, blurring and temporal artifacts 4. Perform cortical surface-based processing 5. Accurately align corresponding brain areas across subjects and studies - surface-based alignment 6. Analyze data using neurobiologically accurate brain parcellations 7. Open data-sharing via user-friendly databases. Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  19. 19. The HCP’s neuroimaging approach 7 Tenets: 1. Collect multimodal imaging data from many subjects 2. Acquire data at high spatial and temporal resolution 3. Preprocess data to minimize distortions, blurring and temporal artifacts 4. Perform cortical surface-based processing 5. Accurately align corresponding brain areas across subjects and studies - surface-based alignment 6. Analyze data using neurobiologically accurate brain parcellations 7. Open data-sharing via user-friendly databases. Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
  20. 20. Cortical Surface-based image processing • HCP cortical data is projected to the cortical surface for two reasons:
  21. 21. Cortical Surface-based image processing • HCP cortical data is projected to the cortical surface for two reasons: Volumetric smoothing mixes signals Surface-constrained smoothing averages only GM signals 1. Surface based smoothing improves SNR
  22. 22. Cortical Surface-based image processing • HCP cortical data is projected to the cortical surface for two reasons: 1. Surface based smoothing improves SNR 2. Surface-based registration improves alignment of cortical folds Small mis-registraions in 3D can have a large impact on the alignment of the cortical sheet
  23. 23. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline
  24. 24. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline • Higher resolution
  25. 25. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline • Higher resolution • Pial surface extraction uses both T1 and T2
  26. 26. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline TISSUE SEGMENTATION MESH EXTRACTION FEATURE PROJECTION* *Using partial volume weighted ribbon-constrained volume to surface mapping
  27. 27. Multimodal cortical features from the HCP Curvature Task fMRI Myelin Structural Connectivity Sotiropoulos et al NeuroImage (In press) Functional Connectivity Glasser, 2011. J. Neurosci, 31 11597-11616
  28. 28. CIFTI: A new file format for surface imaging data
  29. 29. CIFTI: A new file format for surface imaging data • Surface AND Volume data “Grayordinates”
  30. 30. CIFTI: A new file format for surface imaging data • Surface AND Volume data “Grayordinates” • Compact representation: Left/right hemispheres (without medial wall) Deep grey-matter only Natural sub-space for representation of fMRI
  31. 31. Surface-based registration: FreeSurfer • Simplifies alignment of the complex 2D cortical sheet through projection to a sphere • Folding based alignment only
  32. 32. Limitations of morphological alignment • Cortical folding patterns are highly variable across subjects • Example: Cingulate (blue arrows) • Some subjects have one fold where others have two • Courtesy of Van Essen, NeuroImage 28 (2005) 635 – 662
  33. 33. Limitations of morphological alignment • Alignment by cortical folds can lead to high residual variability of functional regions across subjects Nenning et al, Neuroimage 2017
  34. 34. Limitations of morphological alignment • Alignment by cortical folds can lead to high residual variability of functional regions across subjects Nenning et al, Neuroimage 2017 Solution - drive surface alignment with ‘areal’ features such as rfMRI/tfMRI, cortical myelination
  35. 35. Surface-based alignment: MSM • Spherical framework for cortical surface registration • Use low resolution control point grids to constrain the deformation • Optimised using discrete methods • Modular
  36. 36. Surface-based alignment: MSM • Spherical framework for cortical surface registration • Use low resolution control point grids to constrain the deformation • Optimised using discrete methods • Modular
  37. 37. Surface-based alignment: MSM • Spherical framework for cortical surface registration • Use low resolution control point grids to constrain the deformation • Optimised using discrete methods • Modular data cost: i.e. correlation, MNI, SSD Regularisation cost to encourage smoother warp
  38. 38. M R { V { MSM: improves alignment of areal features
  39. 39. • Alignment driven multivariate feature vectors myelin (M) rfMRI (R) visuotopic (V)) M R { V { MSM: improves alignment of areal features
  40. 40. • Alignment driven multivariate feature vectors myelin (M) rfMRI (R) visuotopic (V)) • Improves alignment of task activations and correspondence across functional networks Smith, Stephen M., et al. "Functional connectomics from resting-state fMRI." Trends in cognitive sciences 17.12 (2013): 666-682. MSM: improves alignment of areal features
  41. 41. • Alignment driven multivariate feature vectors myelin (M) rfMRI (R) visuotopic (V)) • Improves alignment of task activations and correspondence across functional networks MSM: improves alignment of areal features Robinson, Emma C., et al. "MSM: a new flexible framework for multimodal surface matching." Neuroimage 100 (2014): 414-426.
  42. 42. MSM: improves alignment of areal features
  43. 43. • Recent improvements to MSM regularisation: Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints."  NeuroImage (2017) MSM: improves alignment of areal features
  44. 44. • Recent improvements to MSM regularisation: Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints."  NeuroImage (2017) • Even greater improvements in alignment 0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 %improvementoverFS FS SD MSMSulc MSMAll sMSMP AIR MSMAll sMSMST R MSM: improves alignment of areal features
  45. 45. • Recent improvements to MSM regularisation: Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints."  NeuroImage (2017) • Even greater improvements in alignment 0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 %improvementoverFS FS SD MSMSulc MSMAll sMSMP AIR MSMAll sMSMST R MSM: improves alignment of areal features • Lower peak distortions
  46. 46. • Recent improvements to MSM regularisation: Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints."  NeuroImage (2017) • Even greater improvements in alignment 0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 %improvementoverFS FS SD MSMSulc MSMAll sMSMP AIR MSMAll sMSMST R MSM: improves alignment of areal features
  47. 47. • Recent improvements to MSM regularisation: Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints."  NeuroImage (2017) • Even greater improvements in alignment 0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 %improvementoverFS FS SD MSMSulc MSMAll sMSMP AIR MSMAll sMSMST R MSM: improves alignment of areal features Full method behind HCP multimodal parcellation Increased robustness in the face of (sometimes extreme) variation in structural and function organisation across subjects
  48. 48. Topological variation in the human brain
  49. 49. Topological variation in the human brain Evidence from folding Van Essen, David C. "A population-average, landmark-and surface- based (PALS) atlas of human cerebral cortex." Neuroimage 28.3 (2005): 635-662.
  50. 50. Topological variation in the human brain Topological variability in the human brain A A C C B B Group 1 Group 2 Evidence from folding Van Essen, David C. "A population-average, landmark-and surface- based (PALS) atlas of human cerebral cortex." Neuroimage 28.3 (2005): 635-662. And function Amunts, K., A. Schleicher, and K. Zilles. "Cytoarchitecture of the cerebral cortex—more than localization." Neuroimage 37.4 (2007): 1061-1065. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, Langs G, Pan R, Qian T, Li K, Baker JT. Parcellating cortical functional networks in individuals. Nature neuroscience. 2015;18(12):1853. Gordon EM, Laumann TO, Adeyemo B, Petersen SE. Individual variability of the system-level organization of the human brain. Cerebral Cortex 2017;27(1):386-99. Glasser, Matthew F., et al. "A multi-modal parcellation of human cerebral cortex." Nature (2016).
  51. 51. Topological variation in the human brain Topological variability in the human brain A A C C B B Group 1 Group 2 Evidence from folding Van Essen, David C. "A population-average, landmark-and surface- based (PALS) atlas of human cerebral cortex." Neuroimage 28.3 (2005): 635-662. And function Amunts, K., A. Schleicher, and K. Zilles. "Cytoarchitecture of the cerebral cortex—more than localization." Neuroimage 37.4 (2007): 1061-1065. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, Langs G, Pan R, Qian T, Li K, Baker JT. Parcellating cortical functional networks in individuals. Nature neuroscience. 2015;18(12):1853. Gordon EM, Laumann TO, Adeyemo B, Petersen SE. Individual variability of the system-level organization of the human brain. Cerebral Cortex 2017;27(1):386-99. Glasser, Matthew F., et al. "A multi-modal parcellation of human cerebral cortex." Nature (2016).
  52. 52. The HCP Multi-modal Parcellation • Regional boundaries found by looking for imaging gradients in group average data • Looking for patterns common across multiple modalities • Informed by the literature where available
  53. 53. The HCP Multi-modal Parcellation • Regional boundaries found by looking for imaging gradients in group average data • Looking for patterns common across multiple modalities • Informed by the literature where available
  54. 54. The HCP Multi-modal Parcellation • Expert manual annotations of 180 functionally specialised regions (per hemisphere) on group average data • 97 entirely new areas • 83 areas previously reported by histological studies
  55. 55. The HCP Multi-modal Parcellation • Expert manual annotations of 180 functionally specialised regions (per hemisphere) on group average data • 97 entirely new areas • 83 areas previously reported by histological studies
  56. 56. The HCP Multi-modal Parcellation: propagating the result to individuals • Single subject parcellations were then obtained by training MLP classifiers • Binary classifications • Group average data propagated to training subjects Hacker, Carl D., et al. "Resting state network estimation in individual subjects." Neuroimage 82 (2013): 616-633. used to train classifier ONLY where subject data closely agrees with group
  57. 57. Features
  58. 58. • Training data = 110 D feature vectors • Cortical thickness • Cortical myelin • Cortical curvature • 20 task ICA + mean • 77 rest ICA • 5 hand engineered ‘visuotopic’ features Features
  59. 59. • Training data = 110 D feature vectors • Cortical thickness • Cortical myelin • Cortical curvature • 20 task ICA + mean • 77 rest ICA • 5 hand engineered ‘visuotopic’ features Features
  60. 60. • Training data = 110 D feature vectors • Cortical thickness • Cortical myelin • Cortical curvature • 20 task ICA + mean • 77 rest ICA • 5 hand engineered ‘visuotopic’ features Features
  61. 61. • Training data = 110 D feature vectors • Cortical thickness • Cortical myelin • Cortical curvature • 20 task ICA + mean • 77 rest ICA • 5 hand engineered ‘visuotopic’ features Features
  62. 62. • Training data = 110 D feature vectors • Cortical thickness • Cortical myelin • Cortical curvature • 20 task ICA + mean • 77 rest ICA • 5 hand engineered ‘visuotopic’ features • Trained on 210 subject training set Features
  63. 63. • Training data = 110 D feature vectors • Cortical thickness • Cortical myelin • Cortical curvature • 20 task ICA + mean • 77 rest ICA • 5 hand engineered ‘visuotopic’ features • Trained on 210 subject training set • Validated on 210 subject validation set Features
  64. 64. The HCP Multi-modal Parcellation: propagating the result to individuals • Output from Classifier for 4 example datasets Group Average Classifier results for 4 subjects
  65. 65. The HCP Multi-modal Parcellation: propagating the result to individuals • Output from Classifier for 4 example datasets Group Average Classifier results for 4 subjects
  66. 66. Topological Variance of region 55b 55b FEF PEF
  67. 67. The HCP Multi-modal Parcellation: Accurate detection of regions across validation subjects Top = Training Set; Bottom = Test Set Darker orange indicates regions that were not detected in all subjects (or were detected by with very low surface areas)
  68. 68. The HCP Multi-modal Parcellation: • high consistency in group average parcellation between training and test sets Top = manual annotation; Bottom = overlap of training and test set classifier results Blue borders= Train set; Red borders= Test set; Purple=overlap
  69. 69. The HCP Multi-modal Parcellation: Prospects Improves statistical significance
  70. 70. The HCP Multi-modal Parcellation: Prospects Improves statistical significance
  71. 71. The HCP Multi-modal Parcellation: Prospects 1. A more robust and accurate reference space for general adult imaging studies Enhances the sensitivity of statistical comparisons Consistent with known patterns of cellular organisation (cyto-architecture) Consistent with known patterns of functional organisation Generalisable to new subjects Requires only MSM alignment and application of pre- trained* MLP classifier
  72. 72. The HCP Multi-modal Parcellation: Prospects 2.Potential to map to new populations e.g. patients/developing neonates and fetuses Must tune the HCP analysis pipelines to new data sets Propagate HCP v1 parcellation to new data either through: Surface registration And/or retraining the MLP classifier
  73. 73. The HCP Multi-modal Parcellation: Prospects 3. Predicting Cognition and Behaviour • Prediction of age/gender/developmental outcome/disease progression • Using: Classification Regression Unsupervised Learning • HCP data comes with 280 behavioural and demographic measures
  74. 74. The HCP Multi-modal Parcellation: Prospects 2. Predicting Cognition and Behaviour e.g. Canonical Correlation Analysis (CCA) Functional netmats vs HCP demographics Smith et al. Nature NeuroScience 2015 Shared modes of variance Complex relationships between all 280 behaviours & all network connections
  75. 75. The HCP Multi-modal Parcellation: Prospects 2. Predicting Cognition and Behaviour e.g. Canonical Correlation Analysis (CCA) Functional netmats vs HCP demographics Smith et al. Nature NeuroScience 2015 Shared modes of variance Complex relationships between all 280 behaviours & all network connections
  76. 76. The HCP Multi-modal Parcellation: Prospects 2. Predicting Cognition and Behaviour Same analysis applied to HCP parcellation Bijsterbosch, Janine Diane, et al. "The relationship between spatial configuration and functional connectivity of brain regions." bioRxiv (2017): 210195. Regions most predictive of the strongest CCA mode
  77. 77. HCP Fluid Intelligence Predictions • 360*110 features mean myelin/thickness/folding/ tfMRI/rfMRI per parcel • R2 = 0.347 • Feature Importance mapped back to the image space Random Forest regression to predict fluid intelligence from HCP features
  78. 78. HCP Fluid Intelligence Predictions • 360*110 features mean myelin/thickness/folding/ tfMRI/rfMRI per parcel • R2 = 0.347 • Feature Importance mapped back to the image space L R Random Forest regression to predict fluid intelligence from HCP features
  79. 79. Future Challenges What to do about topological variability? • Does averaging (group ICA, during annotation) obscure true variability • Could different brains have different numbers of parcels? • How to compare across data sets if spatial averaging no longer valid? Q
  80. 80. Future Challenges What to do about topological variability? • Look for new ways to compare data that do not rely on spatial averaging • Revise methods for image registration that break topological constraints A…?
  81. 81. The Developing Human Connectome Project (dHCP) • Model dynamic, emerging 4D connectomes ~ 1500 fetuses, preterm and term born neonates • Multimodal imaging HARDI rfMRI 0.5mm3 (reconstructed) T1 & T2 • New surface extraction 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. bioRxiv, 125526.
  82. 82. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution spatio-temporal evolution
  83. 83. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution spatio-temporal evolution
  84. 84. Challenges of working with developing data • Developing data is affected by Motion (severe cases account for < 2% ) Limited scan times Relatively low resolution spatio-temporal evolution
  85. 85. 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 (in preparation)
  86. 86. 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)
  87. 87. 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)
  88. 88. 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)
  89. 89. Finding trends in longitudinal cortical development • 10 preterm subjects • Imaged twice at • 32.7 ± 1.2 weeks • 41.5 ± 1.6 weeks • MSM generates smooth maps of deformation strain over this time period • Consistent and statistically significant across the population Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints." NeuroImage (2017). SAS SSS a) e) b) x LABEL POINT OPTIMAL LABEL CONTROL POINT c) f) d) MSS SSS + G TSS DAS TAS g) F
  90. 90. Finding trends in longitudinal cortical development • 10 preterm subjects • Imaged twice at • 32.7 ± 1.2 weeks • 41.5 ± 1.6 weeks • MSM generates smooth maps of deformation strain over this time period • Consistent and statistically significant across the population Robinson, Emma C., et al. "Multimodal surface matching with higher-order smoothness constraints." NeuroImage (2017).
  91. 91. 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., et al. "Dynamic patterns of cortical expansion during folding of the preterm human brain." bioRxiv (2017): 185389.
  92. 92. Examples of dHCP rfMRI ICA 30 dimensional ICA estimated from 114 subjects imaged across 36-42 weeks PMA
  93. 93. Examples of dHCP rfMRI ICA Predicting gestational age at scan prediction train score 0.636331204848 test score 0.212042264421
  94. 94. Examples of dHCP dMRI Visualisation of anatomical connections derived from multi-shell high angular resolution diffusion data.
  95. 95. dHCP release • http://www.developingconnectome.org/project/data- release-user-guide/ • 40 representative neonatal subjects • Structural, diffusion, functional fMRI (minimally pre- processed) • Cortical surfaces including cortical thickness, folding and T1/T2 ration estimates of cortical myelination
  96. 96. Conclusions 1. The HCP v 1.0 multi-modal parcellation is Cytoarchitecturally accurate Functionally consistent Sensitive & Robust 2. Future iterations will Map regions to diseased or developing populations Fully account for topological variations in the data 3. HCP processing has inspired the dHCP Leading to new insights wrt the functional and morphological development of the neonatal cortex
  97. 97. • Prof. Daniel Rueckert • Dr Bernhard Mainz • Dr Ben Glocker • Dr Antonis Makropoulos • Dr Andreas Schuh • Prof Jo Hajnal • Prof David Edwards • Prof Julia Schnabel Acknowledgements • Prof. David Van Essen • Matthew Glasser • Tim Coalson • Dr Carl Hacker • Kara Garcia • Prof. Mark Jenkinson • Prof. Steven Smith • Prof. Saad Jbadi • Dr Janine Bijsterbosch • Dr Samuel Harrison
  98. 98. Happy to help! emma.robinson@kcl.ac.uk https://emmarobinson01.com/ @emrobSci

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