john g. csernansky, m.d. washington university school of medicine computational anatomy and...

John G. Csernansky, M.D.

Washington University School of Medicine

Computational Anatomy and Neuropsychiatric Disease

Probabilistic Assessment of Variation and Statistical Inference of Group Difference, Hemispheric Asymmetry,

and Time-Dependent Change

Rationale for Assessing Neuroanatomy as a Disease Biomarker

• Neuroanatomical changes are characteristic of neuropsychiatric diseases and may be discoverable before clinical symptoms occur (preclinical diagnosis)

• Ongoing changes in neuroanatomy may occur during the disease process and may be modified by treatment (monitoring of treatment response)

Challenges in Assessing Neuroanatomy as a Disease Biomarkers

• Small sample sizes

• Normative variability (age, gender, etc.)

• Disease heterogeneity

• Abnormalities may be specific a particular stages of illness

Approaches to Hypothesis Testing: Using a ROI Approach

• Group comparisons of individual structures - volumes and shapes

• Group comparisons of the relationship between structures - hemispheric asymmetries

• Group comparisons of the rate of change in the volume and shape of structures over time

Rationale for Using a ROI Approach

• Problems encountered in structural analysis may be region specific

• Different regions may have different tissue characteristics and be susceptible to different sources of measurement error

• Hypothesis generation versus hypothesis testing - taking advantage of prior knowledge about a disease

Time (years)

Pro

gre

ssio

nPresymptomatic Clinical Dementia

CDR 0.5 CDR 1 CDR 2 CDR 3

Neuropsychological

Functional Status

AD Disease Process

Adapted from: Daffner & Scinto, 2000

Threshold for

Clinical Detection

Dementia of the Alzheimer Type (DAT)

Distribution of Neuropathology in Alzheimer Disease is Not Uniform

From: Arnold SE, et al. (1991) Cerebral Cortex 1:103-116.

Structure/Function Relationships in DAT Subjects

In patients with very mild DAT (MMSE = 25, N = 8), glucose metabolism (18F-FDG uptake) is reduced in the lateral medial cerebral cortex. From: Minoshima, et al (1997) Ann Neurol 42:85-94.

Group Comparisons of Individual Structures in DAT Subjects

• Hippocampus (subcortical gray matter structure - volume enclosed by a single surface)

• Cingulate gyrus (cortical mantle structure - subregion of gray matter layered between CSF and white matter)

The Circuit of Papez (Limbic Lobe)

Picture of limbic lobe here

AC

PC

H

PHG

24

32

23

AT

EC

S

M

F

• Cingulate efferents (from 32 and 23) project to the entorhinal cortex and subiculum• Hippocampal efferents project to the anterior thalamic nucleus and mammillary body• Afferents from the anterior thalamic nucleus project throughout the cingulate gyrus

From: Nieuwenhuys, Voogd and Huijzen (1998) The Human Central Nervous System, Springer-Verlag

Conventional Neuromorphometry: Manual Segmentation

R L

• Labor intensive

• Difficult to maintain reliability

• Difficult to share neuroanatomical knowledge across sites

• Overemphasis on simple measures (volumes)

Large Deformation High Dimensional Brain Mapping

High Dimensional Large Deformation Transformation

Coarse Registration PatientTemplate

Landmark-based Low Dimensional Transformation

Miller, et al.

Transformation Vector Fields and Shape Change

Transformation

Template

A B

C

Transformed

AB

C

Template TransformedTransformation

Eigenvectors Derived from Vector Fields Using Singular Value Decomposition

• Latent variables representing dimensions of shape variation within a population

• Use first n eigenvectors and MANOVA to test basic “shape” hypothesis

• Logistic regression is used to select most informative eigenvectors, and a leave-one-out analysis to test power of classification

Selecting Brain Regions to Look for Early Changes in Alzheimer Disease

• Hippocampus (CA1 and subiculum)

• Cingulate gyrus (posterior > anterior)

Hippocampal Volume Changes in Early AD

Variable (SD) CDR 0.5 CDR 0 Young

N 18 18 15

Sex (M/F) 9/9 9/9 11/4

Age 74.1 (4.8) 74.2 (5.3) 30.9 (9.0)

Sum of Boxes 2.0 (1.3) 0.02 (0.1) -

Trailmaking A (sec) 49.1 (14.9) 37.5 (12.0) -

Total Intracranial Volume (cm 3) 1,307 (144) 1,393 (131) -

Total Cerebral Volume (cm 3) 940 (95) 986 (92) -

From: Csernansky, et al (2000) Neurology 55:1636-1643.

Comparison of CDR 0.5, CDR 0 and Young Controls: Hippocampal Volume and Shape

4000

3500

3000

2500

2000

1500

1000

Hip

poca

mpu

s vo

lum

e (m

m3 )

Young CDR 0 CDR 0.5

L R L R L R

VOLUME

Group Effect:F = 20.0, df = 2,48, p = .0001Between Groups F pCDR 0/CDR 0.5 19.4 .0001Young/CDR 0.5 37.1 .0001Young/CDR 0 3.6 .065

SHAPE

MANOVA (first five EVs)F = 40.8, df = 10,88, p < .0001

SHAPE + VOLUME

MANOVA (vols + first 5 EVs)F = 28.6, df = 14,84, p < .0001

From: Csernansky, et al (2000) Neurology 55:1636-1643.

Shape and Volume: CDR 0 vs CDR 0.5Shape Alone, Logistic Regression: EVs 1 and 5CDR 0.5 12/18 CDR 0 14/18

Shape + Volume, Logistic Regression: Left and Right volumes + EV 5CDR 0.5 15/18 CDR 0 14/18

Log

-lik

elih

ood

rati

o

CDR 0.5CDR 0

Log

-lik

elih

ood

rati

o

CDR 0.5CDR 0

R L

Outward, p < 0.05

Inward, p < 0.05

p > 0.05

Rank-order test

Inward, 1.8mm

Outward, 1.8mm

R L

CDR 0 CDR 0.5[ev1 and ev5]

Shape and Volume: CDR 0 vs YoungShape Alone, Logistic Regression: EVs 1 and 2CDR 0 18/18 Young 15/15

Shape + Volume, Logistic Regression: Left and Right volumes + EVs 1 and 2CDR 0 18/18 Young 15/15

Log

-lik

elih

ood

rati

o

CDR 0Young

Log

-lik

elih

ood

rati

o

CDR 0Young

R L

Outward, p < 0.05

Inward, p < 0.05

p > 0.05

Rank-order test

R L Inward, 1.8mm

Outward, 1.8mm

Young CDR 0

[ev1 and ev2]

Shape Change May Reflect Changes in Internal Structure of the Hippocampus

Henri M. Duvernoy (1988) The Human Hippocampus: An Atlas of Applied Anatomy, Springer-Verlag, New York.

Top View Bottom View

Tail

Group Comparison of Rate of Change in Hippocampal Volume and Shape

Variable (SD) CDR 0.5 CDR 0

N 18 26

Sex (M/F) 11/7 12/14

Age 74 (4.4) 73 (7.0)

Sum of Boxes 2.0 (1.3) .02 (0.1)

Mean Length of Follow-Up (years) 2.0range 1-2.6

2.2range 1.4-4.1

From: Wang, et al (2003) NeuroImage 20:667-682.

Progression of Hippocampal Volume Loss in Early AD (CDR 0.5)


Groups Change in Hippocampal Volume (~ two years)

CDR 0.5 Left 8.7 % Right 9.8 % Group EffectCDR 0 Left 3.9 % Right 5.5 % F = 7.81, p = .0078

Pattern of Surface Deformation Over Time Distinguishes Groups

In, p < .05 Out, p < .05p > .05-1 0mm 1

CD

R 0

.5

CD

R 0


ev 1 2, 4, 11

*

**

*

15/18

22/26

Baseline to Follow-up

Spreading Deformation of the Hippocampal Surface in Early AD

From: Wang, et al (2003) NeuroImage 20:667-682.In, p < .05 Out, p < .05p > .05-1 0mm 1

Fol

low

-up

Bas

elin

e

38%

47%

CDR 0.5 vs CDR 0 CDR 0.5 vs CDR 0 rank order test

Progressive Deformation of CA1 and Subiculum in

Alzheimer Disease

CA1 CA2 CA3 CA4 Gyrus Dentaus SubiculumBaseline

Follow-up

Selecting Brain Regions to Look for Early Changes in Alzheimer Disease

• Hippocampus (CA1 and subiculum)

• Cingulate gyrus (posterior > anterior)

Methodological Challenges in the Assessment of Cortical Structures

• Segmentation of tissue subtypes (gray, white and mixed)

• Definition of a reference surface (gray/CSF vs gray/white)

• Definition of boundaries with neighboring cortical regions (gross anatomy, histology, function)

• Definition and calculation of distinct metrics (volume, thickness, surface area)

Labeled Cortical Depth Mapping: Outlining the Structure in a Template Scan

Manual outlining is used as a basis for the validation of Bayesian (automated) segmentation. Ten brains were manually segmented (cingulate region) into three compartments: CSF, Gray, and White. These hand segmentations were used to determine optimal thresholds for partial volume compartments (CSF/Gray and Gray/White).

From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.

A C

A Original T-1 weighted, MR image of anterior cingulate gyrus (coronal view)

B Tissue histogram generated by Bayesian segmentation (5 compartments) - selection of optimal G/W matter threshold guided by results of expert segmentation

C Tissue segmentation overlaid on MR image

B


Labeled Cortical Depth Mapping: Automated Tissue Segmentation

The gray-white surface is generated from the automatic tissue segmentation and then the boundaries of the desired cortical region are determined.

The extent of gray matter is estimated using the conditional probabilities of the occurrence of the gray matter tissue type as a function of distance from the gray-white surface.

G

CSF

W


Cingulate Surface

Labeled Cortical Depth Mapping (LCDM)

Distance from cortical surface

Nu

mb

er o

f vo

xels

Gray matter profile

Volume

Cumulative probability

Distance from cortical surface

1

0

LCDM: Generating Metrics Related to Volume and Depth


Depth (thickness)

d’

.9x

White

Validity of Cortical Depth Mapping

Agreement between surfaces derived from automated segmentations and hand contouring in 3 subjects: 75% of all voxels are within 0.5 mm

Gray CSF

Left

Post

erio

r

Between-group comparisons vs Young Controls: * p < .05 + p < .01

Right

Cingulate Volumes in CDR 1, CDR 0.5, CDR 0 and Young Controls

Ant

erio

r

VOLUME

Anterior/LeftYC ~ 0 ~ 0.5 > 1

Anterior/RightYC ~ 0 > 0.5 ~ 1

Posterior/LeftYC ~ 0 > 0.5 ~ 1

Posterior/RightYC ~ 0 > 0.5 ~ 1

F=1.22, df=3,33, p=.32 F=3.68, p=.02

F=7.10, p=.0008 F=4.92, p=.0006

*

* *

+

++

Lef

t P

oste

rior

Rig

ht

Pos

teri

or

Stochastic OrderingAnterior Posterior

Left Right Left Right

Young Subjects

vs CDR 0.5

CDR 1 . 021 . 001 . 005 . 002

CDR 0 vsCDR 0.5

CDR 1 . 016 . 007 . 022

Cingulate Depths in CDR 1, CDR 0.5, CDR 0 and Young Controls

CDF

CDF

DEPTH

Anterior/LeftYC (~ 0 ~ 0.5) > 1

Anterior/RightYC ~ 0 (~ 0.5) > 1

Posterior/LeftYC ~ 0 (~ 0.5) > 1

Posterior/RightYC ~ 0 (~ 0.5) > 1

Summary of Findings in AD

• Hippocampus - Smaller volumes and patterns of shape deformation consistent with damage to the CA1 subfield are present in very mildly demented subjects and progress in parallel with the worsening of dementia. Little change with healthy aging.

• Cingulate gyrus (posterior/anterior) - Smaller volumes and thinning are present in mildly demented subjects. Little change with healthy aging. Volume loss may precede thinning (shrinkage of surface area?)

Analysis of Neuroanatomical Structure in Schizophrenia

• Group comparisons of individual structures

• Analysis of structural asymmetries

• Combining information from more than one brain structure

Subcortical Neuroanatomical Abnormalities in Schizophrenia

From: Roberts (1990) TINS 13:207-211

Hippocampal Deformities in Schizophrenia

Variables (mean +/- SEM [range]) Schizophrenia Subjects Healthy Controls

N 52 65

Age 38.0 (1.74 [20-63]) 40.0 (1.78 [20-67])

Gender (M/F) 30/22 33/32

Race (Cau/Afr-Amer/Other) 22/30/2 34/18/0

Parental SES 4.1 (0.12 [2-5]) 3.6 (0.13 [1.5-5])

Age of Illness Onset 22.8 (1.18 [13-54]) -----

Total SAPS Score 19.7 (2.41 [0-67]) -----

Total SANS Score 19.7 (1.76 [0-52]) -----

From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006

Hippocampal Volume and Shape in Schizophrenia

Volume Scatter PlotsF = 7.9, df = 1,115, p = .006

F = 2.5, df = 1,114, p = .12 (covaried for total brain volume)


Log-Likelihood Plot

No correlations were observed between hippocampal volume or shape changes and clinical measures in the subjects with schizophrenia; hippocampal volume was correlated with general intelligence in both schizophrenia and control subjects

F = 2.7, df = 15,101, p = .002 (first fifteen EV)Logistic regression - EV 1, 5, 14 (70.9% classified)

Pattern of Hippocampal Shape Deformity

Positive

Negative

+0.3

-0.3

Difference Mapped on Mean Control

Z-Scores Mapped on Mean Control

Top View+1.4mm

-1.4mm

Outward

Inward

Reconstructed from the Eigenvector Solution

R L


Topography of Hippocampal

Projections to the Frontal Cortex

Summary diagram showing the relative density of labeled neurons in the hippocampal formation projecting to medial (A) and to orbital (B) prefrontal cortices. Each small symbol represents two neurons. Each large symbol represents 40 neurons.

From: Barbas and Blatt (1995) Hippocampus 5:511-533

Exaggerated Hippocampal Asymmetry

0 mm

-1.5

+1.5

0 mm

-1.5

+1.5


Poin

t-by

-Poi

nt M

aps

E

igen

vect

or M

aps

Control Schizophrenia Group Difference

Thalamic Volume and Shape in Schizophrenia

Volume Scatter PlotsF = 6.6, df = 1,115, p = .011

F = 1.3, df = 1,114, p = .26 (covaried for total brain volume)

Shape (log-likelihood)F = 2.8, df = 10,106, p = .004 (first ten EV)

Logistic regression - EV 1, 8, 10 (66.7% classified)

Correlations were observed between hippocampal volume and shape changes and a measure of visual spatial memory in the subjects with schizophrenia

From: Csernansky, et al (2003) Am J Psychiatry In press.

Tha

lam

ic V

olum

e (m

m3 )

Schizophrenia Controls

Log

-lik

elih

ood

Rat

io V

alue

s

Schizophrenia Controls

Pattern of Thalamic Shape Deformity

B

R L

Anterior View

S

I

L R

Posterior View

S

I

R L

Superior View

P

A

S – superiorI – inferiorA – anteriorP – posteriorR – rightL – left

0.0

-0.5

0.5

Mag

nitu

de o

f D

ispl

acem

ent (

mm

)


Nuclei Within the Human Thalamic Complex

Anterior

Ventral Anterior

Ventral Lateral

Dorsal Lateral

Ventral Posterior Lateral

Pulvinar

Dorsal Medial

Central Medial

Ventral Posterior Medial

Lateral Geniculate

Medial Geniculate

P

I

S

A

A

I

S

P

Lateral View

Medial View

Exaggerated Thalamic Asymmetry

0.0

-1.5

1.5S

I

PA

0.0

-1.5

1.5

Poin

t-by

-Poi

nt M

aps

E

igen

vect

or M

aps

Control Schizophrenia Group Difference

Right Thalamus Left Thalamus


Improving Subject Classification by Combining Shape Information

Combined assessment - sensitivity = 73%, specificity = 83%Evidence for neuroanatomical heterogeneity in schizophrenia ?


Acknowledgments

Collaborators Support

Deanna Barch, Ph.D. MH 62130/071616 (Conte)C. Robert Cloninger, M.D. MH 56584J. Philip Miller MH 60883Paul A. Thompson, Ph.D. NARSADJohn C. Morris, M.D. AHAFLei Wang, Ph.D. AG 05681 (ADRC)Thomas Conturo, M.D. AG 03991Mokhtar Gado, M.D.

Michael I. Miller, Ph.D. (JHU)Tilak Ratnanather, Ph.D. (JHU)Sarang Joshi, D.Sc. (UNC)

Computational Neuroanatomy

Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni G, Thompson PM. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet: Neurology 2:79-88, 2003.

john g. csernansky, m.d. washington university school of medicine computational anatomy and...

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