cortical morphometry in neurodevelopment and neurodegeneration

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

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7 mm

0 mm

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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

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



Cortical volume age effect versus Scale (FWHM)

FWHM 13.86 19.61 27.73 39.21 55.45 78.42 110.90



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


left superior frontal


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)


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


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


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)


(266)


(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)

http://www.oasis-brains.org/



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

cortical morphometry in neurodevelopment and neurodegeneration

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