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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
Overview
Overview
1. The Human Connectome Project (HCP)
Goals
Neuroimaging approach (surface-based processing)
Overview
1. The Human Connectome Project (HCP)
Goals
Neuroimaging approach (surface-based processing)
2.The HCP v1 multi-modal parcellation:
Prospects and Future Challenges
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
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 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 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
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***
•
The HCP’s neuroimaging approach
Glasser, Matthew F., et al. "The human connectome project's neuroimaging approach." Nature Neuroscience 19.9 (2016): 1175-1187.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Cortical Surface-based image
processing
• HCP cortical data is projected to the cortical surface for
two reasons:
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
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
Surface extraction
The HCP uses a refinement of the FreeSurfer Pipeline
Surface extraction
The HCP uses a refinement of the FreeSurfer Pipeline
• Higher resolution
Surface extraction
The HCP uses a refinement of the FreeSurfer Pipeline
• Higher resolution
• Pial surface
extraction uses both
T1 and T2
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
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
CIFTI: A new file format
for surface imaging data
CIFTI: A new file format
for surface imaging data
• Surface AND Volume data
“Grayordinates”
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
Surface-based registration:
FreeSurfer
• Simplifies alignment of the complex 2D cortical sheet through
projection to a sphere
• Folding based alignment only
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
Limitations of morphological
alignment
• Alignment by cortical folds can lead to high residual
variability of functional regions across subjects
Nenning et al, Neuroimage 2017
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
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
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
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
M
R {
V {
MSM: improves alignment of areal
features
• Alignment driven
multivariate feature
vectors
myelin (M)
rfMRI (R)
visuotopic (V))
M
R {
V {
MSM: improves alignment of areal
features
• 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
• 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.
MSM: improves alignment of areal
features
• 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
• 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
• 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
• 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
• 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
Topological variation in the human
brain
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.
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).
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).
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
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
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
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
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
Features
• 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
• 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
• 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
• 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
• 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
• 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
The HCP Multi-modal Parcellation:
propagating the result to individuals
• Output from Classifier for 4 example datasets
Group Average
Classifier results for 4 subjects
The HCP Multi-modal Parcellation:
propagating the result to individuals
• Output from Classifier for 4 example datasets
Group Average
Classifier results for 4 subjects
Topological Variance of region 55b
55b
FEF
PEF
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)
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
The HCP Multi-modal Parcellation:
Prospects
Improves statistical significance
The HCP Multi-modal Parcellation:
Prospects
Improves statistical significance
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
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
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
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
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
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
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
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
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
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…?
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.
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
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
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
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)
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)
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)
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)
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
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).
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.
Examples of dHCP rfMRI ICA
30 dimensional ICA estimated from 114 subjects imaged across 36-42
weeks PMA
Examples of dHCP rfMRI ICA
Predicting gestational age at scan
prediction train score 0.636331204848 test score 0.212042264421
Examples of dHCP dMRI
Visualisation of anatomical connections derived from multi-shell
high angular resolution diffusion data.
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
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
• 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
Happy to help!
emma.robinson@kcl.ac.uk
https://emmarobinson01.com/
@emrobSci

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

  • 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
  • 3. Overview 1. The Human Connectome Project (HCP) Goals Neuroimaging approach (surface-based processing)
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Cortical Surface-based image processing • HCP cortical data is projected to the cortical surface for two reasons:
  • 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. 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. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline
  • 24. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline • Higher resolution
  • 25. Surface extraction The HCP uses a refinement of the FreeSurfer Pipeline • Higher resolution • Pial surface extraction uses both T1 and T2
  • 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. 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. CIFTI: A new file format for surface imaging data
  • 29. CIFTI: A new file format for surface imaging data • Surface AND Volume data “Grayordinates”
  • 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. Surface-based registration: FreeSurfer • Simplifies alignment of the complex 2D cortical sheet through projection to a sphere • Folding based alignment only
  • 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. 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. 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. 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. 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. 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. M R { V { MSM: improves alignment of areal features
  • 39. • Alignment driven multivariate feature vectors myelin (M) rfMRI (R) visuotopic (V)) M R { V { MSM: improves alignment of areal features
  • 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. • 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. MSM: improves alignment of areal features
  • 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. • 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. • 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. • 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. • 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. Topological variation in the human brain
  • 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. 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. 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. 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. 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. 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. 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. 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
  • 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. • 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. • 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. • 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. • 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. • 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. 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. 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. Topological Variance of region 55b 55b FEF PEF
  • 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. 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. The HCP Multi-modal Parcellation: Prospects Improves statistical significance
  • 70. The HCP Multi-modal Parcellation: Prospects Improves statistical significance
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Examples of dHCP rfMRI ICA 30 dimensional ICA estimated from 114 subjects imaged across 36-42 weeks PMA
  • 93. Examples of dHCP rfMRI ICA Predicting gestational age at scan prediction train score 0.636331204848 test score 0.212042264421
  • 94. Examples of dHCP dMRI Visualisation of anatomical connections derived from multi-shell high angular resolution diffusion data.
  • 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. 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. • 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