Cortical morphometry in neurodevelopment and neurodegeneration
AC Evans
Montreal Neurological Institute
Brain Imaging Centre Seminars
Montreal
March 22th, 2010
Basic principles of cortical morphometry
Seed-based cortical correlation (MACACC)
Graph theory network modelling (GRETNA)
Applications in neurology
Talk outline
1
2 3
3
3
4
4
7 mm
0 mm
5
6
Cortical morphometry with CLASP
1- Raw images mapped into a stereotaxic coordinate using automatic, multi-scale - ANIMAL
2- Segmented into WM, GM, CSF, background and lesion using the T1, T2, PD images and INSECT
3- Inner/outer cortical surfaces extracted using CLASP, resulting in two surfaces with 81920 polygons
4-Cortical thickness was defined as the distance between the linked nodes of the inner and outer surfaces
5- Cortical thickness map blurred using a 20 millimeter surface-based diffusion smoothing (Chung et al., 2002)
6- GLM at every vertex: regress CT against independent variable (age, performance, lesion volume, disability score etc.)
Cortical Surface Analysis
Cortical Surface Analysis
CLASP surfaces
Kim JS et al, NeuroImage, 2005
Single pass Iteratively aligned
Lateral
N=152
Medial
CLASP surface average for N=152 normal adult subjects (ICBM data) with/without
iterative surface alignment. Note dramatic increase in detail in the average surface
Iterative surface alignment
Lyttelton O et al., 2007
Left-right asymmetry (surface-aligned)
Cortical Thickness
18
-12
tstat
Cortical surface area
Surface area calculated in native space
F1
Vj
Surface Area (Vj) = Σ(Fi)/3
F2
F3
F4F5
F6
Surface Area (Fi) =
(where a, b, c are the lengths of the sides of Fi)
L. asymmetry: supramarginal, planum temporale, anterior lateral temporal, lateral orbital frontal
R. asymmetry: anterior occipital lobe, cingulate, gyrus rectus
Rightward
Leftward
Left-right asymmetry in cortical surface area in ICBM152
Lyttelton et al., 2009
Computation for vertex-wise volume of cortical surface
Multiplication based method
Local cortical area x local cortical thickness at each vertex
Tetrahedronization (Maxime Boucher)
Each prism split into three tetrahedra (object having four triangle faces)
Tetrahedrons form simplicial complex #
1/4 of volume V of each tetrahedron assigned to each of its vertices
Volume at a vertex = sum of 1/4-V from 4 adjacent tetrahedra# http://en.wikipedia.org/wiki/Simplicial_complex
Numerical integration (Claude Lepage)
Triangles Tg and Tw have corresponding vertices at gray and white surfaces
Volume V of prism defined by Tg and Tw computed by Gaussian Quadrature *
1/3-V assigned to each vertex of corresponding triangle at mid-surface
Volume at a vertex = sum of 1/3-V of 3 adjacent prisms* http://www.cs.rpi.edu/~flaherje/pdf/fea6.pdf
Cortical volume gender effect versus Scale (FWHM)
FWHM 13.86 19.61 27.73 39.21 55.45 78.42 110.90
corrected significance level = 0.01
Boucher M, Zhao L et al. (2009)
Adult
Cortical volume asymmetry effect versus Scale (FWHM)
FWHM 13.86 19.61 27.73 39.21 55.45 78.42 110.90
Asymmetry = (VR – VL)/[0.5(VR+VL)]
Adult
corrected significance level = 0.01
Boucher M, Zhao L et al. (2009)
Cortical volume age effect versus Scale (FWHM)
FWHM 13.86 19.61 27.73 39.21 55.45 78.42 110.90
corrected significance level = 0.01
Boucher M, Zhao L et al. (2009)
Older
Statistical cost for scale-space search
Conclusions re. cortical volume analysis
Scale-space search reveals dramatic differences – matched filter
M > F cortical volume at inferior frontal and temporal lobes
Volume decreases with age in frontal and temporal lobes
Consistent with thickness (Luders, Cer Cor, 2005) & area (Lyttelton, NeuroImage, 2009)
MRI Study of Normal Brain Development
(N=500)
Create a database of behavioral and brain MRI
development data for 0-18 years
Analyze structural-behavioural relationships
Develop technique for dissemination of results
The National Institute for Drug Abuse
Problems with previous studies
Sample sizes too small
Heterogeneity of subject population
Little longitudinal data
Lack of demographic
representativeness
Limited behavioral data
Limited MRI data (typically T1 only)
Usually limited analysis techniques
NIH MRI Study of Normal Brain DevelopmentObjective 2 – Infants and toddlers
T1 T2 PD
Normal brain growth from 0-48 months (N=69)
MRI Study of normal brain development
Evolution of hemispheric asymmetry from 0-54 months
Colours show hemispheric difference in surface position (L > R)
15mm
7.5mm
0mm
(N=90)
NIH MRI Study of Normal Brain Development
Cortical thickness development from birth to 54 mos (N=90)
6 mm
4.5 mm
3 mm
1.5 mm
1 mm
NIH MRI Study of Normal Brain Development
Cortical thickness development from birth to 18 yrs
6 mm
4.5 mm
3 mm
1.5 mm
1 mm
Frontal Lobe I
Regression line
Confidence band
Tolerance band
Left hemisphere Right hemisphere
Cortical Correlation and Network Modelling
+
+
+
+
+
+
+
+
+
+
Linear, Intercept = 0 (y = mx)
Linear, Intercept ≠ 0 (y=mx+c)
Mapping Anatomical Correlation across Cerebral Cortex
(MACACC)
Plot goodness of fit index
(e.g. r-value) at every
voxel (i,j,k) in volume
Each “+” represents one subject
Morphometric Variable
at voxel (i,j,k) in volume
(e.g. grey matter density
or regional volume
or cortical thickness)
Morphometric Variable
at “seed” voxel in volume
(e.g. grey matter density
or regional volume
or cortical thickness)
MACACC for BA44
(N=292)
1.0
0.8
Cross-cortical correlation
DTI probability map
Parker G et al. (2005)
Lerch J et al., 2006
Catani M et al. (2005)
Fiber tracts
Cortical thickness correlation vs. RS functional connectivity
Resting fMRI data(Fox et al., PNAS 2006)
ICBM data
r > 0.5
Cortical thickness correlations could partly reflect intrinsic or spontaneous
functional connectivity measured by resting-state fMRI
IFG
IPS
He et al., 2009
MACACC database
• Pre-processing
• Vertex Number: 40,962 per hemisphere X 2 = 81,924
• MACACC database: 81924 x 81924 correlation
• For each vertex
• Statistics (3): T-map, P-map (vertex level), P-map (cluster level)
• Scale space (9): 0mm, 5mm, 10mm, … , 35mm, 40mm
• Cortical measures (3): thickness, area, volume
• For all vertices
• Text files: 81,924 x 3 x 9 x 3 = 6,635,844
• Disk space: 6,635,844 x (~400KB) = 2.654 GB
• If more than one group, then 2.654 GB x N … …
MACACC interface layout
MACACC database interface – Surface inflation
MACACC database interface – Scale space
Example MACACC maps - ICBM152
right occipital pole
Cortical Thickness Local Cortical Area
Cortical Thickness Local Cortical Area
Example MACACC maps - ICBM152
left superior frontal
Example MACACC maps - ICBM152
asymmetry of cortical thickness correlation
Graph Theory Network Analysis
(GRETNA)
Are there cortical thickness couplings across entire cortex ?
How to describe the coordinated variation in brain morphology ?
What properties does the cortical network comprise ?
He Y. Chen Z, Evans AC (2007) Cerebral Cortex 17(10):2407-19
-150 -100 -50 0 50-50
0
50
e) AnteriorPosterior
mm
Superior
inferior
Cortical thickness network of the human brain
0 50 100 150-0.4
-0.2
0
0.2
0.4
Subjects
Resid
uals
Whole brain segmented into N (2x27) cortical regions (a) and regional cortical thickness measured in native space
Anatomical correlation in cortical thickness across subjects after removing effects of gender, age and mean thickness (b)
Pearson correlation matrix (c) constructed and thresholded (FDR) to get binarized connection matrix (d)
Thresholded matrix visualized in anatomical space (e)
a)
b)
mm
10 20 30 40 50
10
20
30
40
50-0.4
-0.2
0
0.2
0.4
0.6
c)
Brain regions
10 20 30 40 50
10
20
30
40
50
d)
Brain regions
Superior frontal gyrus (red = right)
He Y. Chen Z, Evans AC (2007) Cerebral Cortex 17(10):2407-19
Brain networks as graphs
Node: brain region
Link: connection
Shortest path length from i to j: 3
N = 10
K = 15
i
3C =
4*(4-1)/2
Random: low Cp
low Lp
Watts & Strogatz (1998) Nature
Small-world:
high Cp
low Lp
Regular: high Cp
high Lp
Small-world networks contain predominantly local
links and a few long-distance links (“shortcuts”)
i
i
j
Clustering coefficient
Cp: average clustering of a network
Lp: average shortest path length of a network
He Y. Chen Z, Evans AC (2007) Cerebral Cortex 17(10):2407-19
Network models
Latora and Marchiori 2001
1( )loc i
i G
E E GN
1 1
( 1) / 2glob
i j G ij
EN N l
1 1( )
( 1) / 2i
i
j k Gi i jk
E GN N l
Global efficiency:
Local efficiency:
Watts and Strogatz 1998
Global efficiency: Eglob ~ 1/Lp
- Quantifies global integration of brain network
- Associated with long cortico-cortical tracts (e.g. superior longitudinal fasciculus)
Local efficiency: Eloc ~ Cp
- Quantifies local specialization of brain network
- Associated with short white matter tracts (e.g. U-fibers)
Small-world:
high Eloc
high Eglob
Random:
low Eloc
high Eglob
Regular: high Eloc
low Eglob
Human structural cortical network is small-world
Cpregular > Cp
brain > Cp random
Lpregular > Lp
brain ~ Lp random
0
0.1
0.2
0.3
0.4
0.5
Clu
ste
rin
g, C
p
0
1.0
2.0
3.0
4.0
5.0
Path
Len
gth
, L
p
Cpbrain/Cprand=2.36 >1 Lpbrain/Lprand=1.15 ~1
brain randomregular
Cortical thickness has small-world topology, i.e. high clustering/ short mean path
He Y. Chen Z, Evans AC (2007) Cerebral Cortex 17(10):2407-19
Corpus callosum
Short-distance fibers
Superior longitudinal
fasciculus
Cortical thickness correlation and DTI connectivity
A
A
B
C
(Corrected P<1.0×10-05)
White matter tracts
Wakana et al (2004) Radiology
Cortical thickness correlations (top 15)
He et al (2007) Cereb Cortex
Correspondence between cortical correlation, DTI tractography
Gong G et al.
266 edges for
CC and DTI
MC network
(266)
Agreement Disagreement MC network
(266)
Agreement MC network
(266)
Agreement MC network
(266)
Agreement MC network
(266)
Agreement (A)MC network (266) Disagreement (D)
159 +ve (60%): 97 (61%) in A 62 (39%) in D
107 -ve (40%): 1 ( 1%) in A 106 (99%) in D
98 in total: 97 (99%) +ve
1 ( 1%) -ve
168 total: 62 (37%) +ve
106 (63%) -ve
AAL template (39 regions/hemisphere)ICBM aging (N=95)
Cortical correlation (CC) and fibre connectivity (FC)
1. Pre-programmed (genetic)
2. Trophic (CCs , mostly +ve, with matched FCs)
having underlying FC
Not all FCs will lead to detectable CCs
1) Linking fibers (density, concentration or number) are not strong enough
2) Methodological bias , i.e. our regional ROI definition
3. Common experience dependent/functional plasticity (CCs ,both +ve ,-ve, with no matched FCs)
Reflect functional correlation between regions - not necessary for direct FC.
Functional correlation must be strong enough to induce morphological correlation.
Correlated regions communicate through indirect FCs.
Dynamic CC pattern. Many conditions (disease/development/age/training) may alter it.
a) Six modules of cortical network. Node size signifies the relative between-ness centrality of the node
b) Dendrogram of module-identification progress. Q maximized when network separates into 6 modules
c) Q as regions merge into modules for cortical network (blue) and average of 1000 random networks (dot)
Modular architecture of the human cortical anatomy network
Chen ZJ et al., 2008, Cerebral Cortex
MULTIPLE
SCLEROSIS
PD/T2/T1-weighted MRI pipelined for intensity NU correction, stereotaxic registration, lesion extraction (1)
Binary lesion volumes Gaussian-smoothed (FWHM=10 mm) to create lesion „density‟ map (2)
T1-weighted MRIs classified with lesions masked and fit with a WM surface (3,4)
GM surface is found by expanding out from WM surface (5)
Cortical thickness measured at every vertex and smoothed with 20 mm surface kernel (6,7)
Cortical thickness analysis in MS
Charil A et al, Neuroimage (2007)
MS predominantly affects white matter
Some GM loss in cortical regions
MS cortical network has small-world topology ?
How does it relate to disease progression ?
Averaged lesion density map
from 425 subjects
Small-world properties of cortical networks in MS
Groups Group 1 Group 2 Group 3 Group 4 Group 5 Group 6
TWMLL(cm3) 0-2 2-4 4-8 8-16 16-32 32+
N 55 55 55 55 55 55
Age (yr) 40.0 ±5.1 38.1±6.0 36.3±5.8 39.6±6.0 37.7±5.8 38.4±6.3
Gender (M/F) 30/25 26/29 33/22 36/19 30/25 22/33
Average TWMLL(cm3) 1.0 2.9 5.82 12.0 22.0 43.4
He Y et al
Network models
Small-world:
high Eloc
high Eglob
Random:
low Eloc
high Eglob
Regular: high Eloc
low Eglob
Latora and Marchiori 2001
1( )loc i
i G
E E GN
1 1
( 1) / 2glob
i j G ij
EN N l
1 1( )
( 1) / 2i
i
j k Gi i jk
E GN N l
Global efficiency:
Local efficiency:
Watts and Strogatz 1998
Global efficiency (~1/Lp):
- Quantifying the ability in global integration of brain networks
- Associated with long cortico-cortical tracts (e.g. superior longitudinal fasciculus)
Local efficiency (~ Cp):
- Quantifying the ability in local specialization of brain networks
- Associated with short white matter tracts (e.g. U-fibers)
Changes in absolute MS network efficiency with lesion load
Top: absolute local and global efficiency with TWMLL at r = 0.02
Bottom: Integrated absolute local and global efficiency with TWMLL
Anatomical regions
t-score (p value)
Correlation strength
(Snodal)
Absolute efficiency
(Enodal)
Right insula -9.10 (0.0008) -4.01 (0.016)
Right inferior frontal gyrus -5.91 (0.004) -2.52 (0.065)
Right precentral gyrus -4.52 (0.010) N.S.
Right middle frontal gyrus -3.93 (0.017) -2.64 (0.057)
Left middle temporal gyrus -3.23 (0.032) N.S.
Left insular -2.60 (0.060) N.S.
Right middle temporal gyrus N.S. -2.78 (0.050)
Right superior middle gyrus N.S. -2.64 (0.057)
Left parahippocampal gyrus N.S. 2.85 (0.047)
Right parahippocampal gyrus N.S. N.S.
Left angular gyrus N.S. 2.59 (0.061)
Nodal characteristics versus TWMLLGji ij
nodaldN
iE1
1
1)(
Gji
ijnodal RN
iS1
1)(
Gji ijnodal
dNiE
1
1
1)(
A. Insular region mapped onto cortical surface
B. Decrease of insular correlation strength with TWMLL
C. Integrated absolute regional efficiency with TWMLL
D. Integrated relative regional efficiency with TWMLL
Insula: Nodal characteristics of versus lesion load
ALZHEIMER‟S
DISEASE
AD Hypotheses
Delacourte et. al, Neurology, 1999 Braak et al, 1999
Lerch et al., (2005) Cerebral Cortex 15:995-1001
Cortical thickness analysis in AD showing PHG atrophy
(a) probability maps of entorhinal/perirhinal ctx
(b) t-statistics of MMSE regression
(c) between-group analysis
(d) time difference from baseline
Altered cortical thickness correlations: NC vs. AD
Decreased cortical thickness correlation in AD
p<0.01 inter-hemispheric
Increased cortical thickness correlation in AD
p<0.01 “default-mode” regions
T1 images from OASIS database (www.oasis-brains.org) (Marcus et al., 2007)
Normal controls (97): F/M = 71/26 age: 60-94 yrs MMSE: 25 – 30 CDR: 0
Early-stage AD (92): F/M = 54/38 age: 62-96 yrs MMSE: 14 – 30 CDR: 0.5/ 1
He Y, Chen ZJ, Evans AC (J Neurosci 28(18):4756-66 (2008)
Small-world parameters in cortical networks: NC vs. AD
A
BAD Normal
Longer paths (Lp) + higher clustering (Cp) in AD
more regular, less optimal topological organization
AD-related changes in “betweenness centrality”
Quantifies importance of each node
NC > AD: angular(L,R) , superior temporal (R)
NC < AD: lingual (L), occipitotemporal (L), cingulate (R)
He Y, Chen ZJ, Evans AC (J Neurosci 28(18):4756-66 (2008)
Relative size of largest connected component vs. fraction of removed nodes (A) or links (B)
Robustness in AD network versus normal networkNC - Normal Controls (N=97)
AD - Alzheimer‟s Disease (N=92)
Networks sparsity value = 13%
Random failure Targeted attack
AD (red)
HC (blue)
Edge removal
Nodal removal
Alzheimer’s Disease
(He et al., 2008a)
“Grey matter disease”
Multiple Sclerosis
(He et al., 2008b)
“White matter disease”
Small-
world
efficiency
Increase in local efficiency
Decrease in global efficiency
More regular configuration
Decrease in local efficiency
Decrease in global efficiency
More random configuration
Nodal
efficiency
Decrease in temporal/parietal association areas
Increase in primary occipital areas
Decrease in insula, precentral, prefrontal, temporal association areas
Increase in parahippocampal, angular gyrus
Changes in network properties with respect to normal controls
Small-world:
high Eloc
high Eglob
Random:
low Eloc
high Eglob
Regular: high Eloc
low Eglob
Global efficiency (Eglob ~1/Lp)
Quantifies global integration via
long cortico-cortical tracts (e.g. SLF)
Local efficiency (Eloc ~ Cp)
Quantifies local specialization via
short white matter tracts (e.g. U-fibers)
Results
What happened to the “ideal” modular structures of NC in AD ?
Qnc vs. Qad
Speculation: loss of modularity causes reduced functional segregation in AD
Individual Modules (NC vs. AD)
MS = Modular weight / Total Network Weight
Chen Z et al., 2010
Results
Reduced inter-modular strength in ADExecutive (E) - Visual (V)
Auditory/language (AL) – Visual (V)
Auditory/language (AL) - Sensory integration (SI)
What happened to the inter-modular connections of NC in AD ?
Increased inter-modular strength in ADSensorimotor/spatial (SS)- Visual (V)
Cingulate/Right AL (C) – Visual (V)
E
C
SI
AL
V
VA
SS
P = 0.015
P = 0.002
P = 0.036
P = 0.05
P=0.047
Speculation:
a) Anterior/Posterior disconnection ?
b) Weak inter-module links reflect AD deficits ?
c) Compensatory mechanism ?Chen Z et al., 2010
Conclusion
Cortical networks in AD and MS show altered network
architecture (small-world parameters, nodal centrality and
network robustness) with less optimal topologies
Future
Network analysis of morphological indices (thickness, area, volume)
Network analysis with different modalities (MRI, fMRI, DTI, PET)
Network properties and behaviour during development
Network properties and genotype
Network breakdown in disorders (AD, MS, SCZ, Autism)
Disease duration/severity
Behavioural deficit
Gene dosage
Discriminate disease sub-types (AD: FTD, DAT, LBD ; MS: RR, CP)
Animal models/investigation of basis of cortical correlation
Acknowledgements
Cortical thickness algorithm: David MacDonald, June-Sik Kim, Claude Lepage
Cortical thickness analysis: Jason Lerch, Oliver Lyttelton, Junki Lee
Cortical volume analysis:/scale space: Lu Zhao, Maxime Boucher
Network Analysis: Yong He, Zhang Chen, Gaolang Gong
Applications: Arnaud Charil, Alain Dagher, Sherif Karama, Yasser Ad-Dab‟bagh