Computational Anatomy and
Neuropsychiatric Disease
Probabilistic Assessment of Variation and Statistical
Inference of Group Difference, Hemispheric Asymmetry,
and Time-Dependent Change
John G. Csernansky, M.D.
Washington University School of Medicine
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
Dementia of the Alzheimer Type (DAT)
Presymptomatic
Clinical Dementia
CDR 0.5
CDR 1
Neuropsychological
Progression
Functional Status
Threshold
for
Clinical
Detection
Time (years)
Adapted from: Daffner & Scinto, 2000
CDR 2
CDR 3
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)
AC
23
24
PC
32
F
AT
Picture of limbic lobe here
H
M
S
PHG
EC
• 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
• Labor intensive
• Difficult to maintain
reliability
• Difficult to share
neuroanatomical
knowledge across sites
• Overemphasis on
simple measures
(volumes)
R
L
Large Deformation High Dimensional Brain Mapping
Template
Coarse Registration
Patient
Landmark-based Low Dimensional Transformation
Miller, et al.
High Dimensional Large Deformation Transformation
Transformation Vector Fields and Shape Change
C
A
C
B
A
B
Transformation
Template
Transformed
Transformation
Template
Transformed
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)
-
49.1 (14.9) 37.5 (12.0)
-
Total Intracranial Volume (cm3) 1,307 (144) 1,393 (131)
-
Total Cerebral Volume (cm3)
-
Trailmaking A (sec)
940 (95)
From: Csernansky, et al (2000) Neurology 55:1636-1643.
986 (92)
Comparison of CDR 0.5, CDR 0 and Young
Controls: Hippocampal Volume and Shape
VOLUME
4000
Group Effect:
F = 20.0, df = 2,48, p = .0001
Between Groups F
p
CDR 0/CDR 0.5 19.4
.0001
Young/CDR 0.5 37.1
.0001
Young/CDR 0
3.6
.065
Hippocampus volume (mm3)
3500
3000
2500
2000
SHAPE
1500
MANOVA (first five EVs)
F = 40.8, df = 10,88, p < .0001
1000
L
R
Young
L
R
CDR 0
L
R
CDR 0.5
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 0.5
Shape + Volume, Logistic Regression:
Left and Right volumes + EV 5
CDR 0.5 15/18 CDR 0 14/18
Log-likelihood ratio
Log-likelihood ratio
Shape Alone, Logistic Regression:
EVs 1 and 5
CDR 0.5 12/18 CDR 0 14/18
CDR 0
CDR 0.5
CDR 0
CDR 0.5
CDR 0 CDR 0.5
[ev1 and ev5]
Rank-order test
Outward, 1.8mm
Outward, p < 0.05
p > 0.05
Inward, p < 0.05
R
L
R
L
Inward, 1.8mm
Shape and Volume: CDR 0 vs Young
Shape + Volume, Logistic Regression:
Left and Right volumes + EVs 1 and 2
CDR 0 18/18 Young 15/15
Log-likelihood ratio
Log-likelihood ratio
Shape Alone, Logistic Regression:
EVs 1 and 2
CDR 0 18/18 Young 15/15
Young
CDR 0
Young
CDR 0
Young
Rank-order test
CDR 0
[ev1 and ev2]
Outward, 1.8mm
Outward, p < 0.05
p > 0.05
Inward, p < 0.05
R
L
R
L
Inward, 1.8mm
Shape Change May Reflect Changes in Internal
Structure of the Hippocampus
Top View
Bottom View
Tail
Henri M. Duvernoy (1988) The Human
Hippocampus: An Atlas of Applied Anatomy,
Springer-Verlag, New York.
Group Comparison of Rate of Change in
Hippocampal Volume and Shape
Variable (SD)
CDR 0.5
CDR 0
18
26
11/7
12/14
Age
74 (4.4)
73 (7.0)
Sum of Boxes
2.0 (1.3)
.02 (0.1)
2.0
range 1-2.6
2.2
range 1.4-4.1
N
Sex (M/F)
Mean Length of Follow-Up (years)
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
CDR 0
Left 8.7 %
Left 3.9 %
Right 9.8 %
Right 5.5 %
From: Wang, et al (2003) NeuroImage 20:667-682.
Group Effect
F = 7.81, p = .0078
Pattern of Surface Deformation Over Time
Distinguishes Groups
Baseline to Follow-up
*
CDR 0
*
*
CDR 0.5
*
15/18
22/26
ev 1 2, 4, 11
-1
0mm
1
In, p < .05
p > .05
From: Wang, et al (2003) NeuroImage 20:667-682.
Out, p < .05
Baseline
Spreading Deformation of the Hippocampal
Surface in Early AD
38%
CDR 0.5 vs CDR 0 rank order test
Follow-up
CDR 0.5 vs CDR 0
47%
-1
0mm
1
In, p < .05
From: Wang, et al (2003) NeuroImage 20:667-682.
p > .05
Out, p < .05
Progressive Deformation of CA1 and Subiculum in
Alzheimer Disease
Baseline
Follow-up
CA1
CA2
CA3
CA4
Gyrus Dentaus
Subiculum
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.
Labeled Cortical Depth Mapping: Automated
Tissue Segmentation
A
B
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
From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.
Labeled Cortical Depth Mapping (LCDM)
CSF
Cingulate
Surface
The gray-white surface is
generated from the automatic
tissue segmentation and then the
boundaries of the desired
cortical region are determined.
W
G
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 graywhite surface.
From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.
LCDM: Generating Metrics Related to
Volume and Depth
1
.9x
Distance from cortical surface
0
d’
Distance from cortical surface
Gray matter profile
Cumulative probability
Volume
Depth (thickness)
From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.
Validity of Cortical Depth Mapping
Gray
White
CSF
Agreement between
surfaces derived from
automated segmentations
and hand contouring in 3
subjects: 75% of all voxels
are within 0.5 mm
Cingulate Volumes in CDR 1, CDR 0.5,
CDR 0 and Young Controls
Left
Right
VOLUME
F=3.68, p=.02
Anterior
F=1.22, df=3,33, p=.32
*
+
Anterior/Left
YC ~ 0 ~ 0.5 > 1
Anterior/Right
YC ~ 0 > 0.5 ~ 1
Posterior/Left
YC ~ 0 > 0.5 ~ 1
Posterior
F=7.10, p=.0008
F=4.92, p=.0006
*
*
+
+
Posterior/Right
YC ~ 0 > 0.5 ~ 1
Between-group
comparisons vs
Young Controls:
* p < .05
+ p < .01
Cingulate Depths in CDR 1, CDR 0.5,
CDR 0 and Young Controls
Anterior
Posterior
Stochastic Ordering
Left
Right
Left
Right
CDR 0.5
Young
Subjects
vs
CDR 0
vs
CDR 1
DEPTH
. 021
. 001
. 005
. 002
CDR 0.5
Right Posterior
Left Posterior
CDR 1
. 016
CDF
. 007
. 022
Anterior/Left
YC (~ 0 ~ 0.5) > 1
Anterior/Right
YC ~ 0 (~ 0.5) > 1
Posterior/Left
YC ~ 0 (~ 0.5) > 1
CDF
Posterior/Right
YC ~ 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
Age
Gender (M/F)
Race (Cau/Afr-Amer/Other)
Parental SES
Age of Illness Onset
Total SAPS Score
Total SANS Score
52
38.0 (1.74 [20-63])
30/22
22/30/2
4.1 (0.12 [2-5])
22.8 (1.18 [13-54])
19.7 (2.41 [0-67])
19.7 (1.76 [0-52])
65
40.0 (1.78 [20-67])
33/32
34/18/0
3.6 (0.13 [1.5-5])
-------------
From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006
Hippocampal Volume and Shape in Schizophrenia
Volume Scatter Plots
F = 7.9, df = 1,115, p = .006
F = 2.5, df = 1,114, p = .12 (covaried for total brain volume)
Log-Likelihood Plot
F = 2.7, df = 15,101, p = .002 (first fifteen EV)
Logistic regression - EV 1, 5, 14 (70.9% classified)
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
From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006
Pattern of Hippocampal Shape Deformity
Outward
Top View
+1.4mm
-1.4mm
Inward
R
L
Difference Mapped on Mean Control
Positive
Reconstructed from the
Eigenvector Solution
+0.3
From: Csernansky, et al (2002) Am J
Psychiatry 159:2000-2006
-0.3
Negative
Z-Scores Mapped on Mean Control
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
Eigenvector Maps
+1.5
0 mm
Point-by-Point Maps
-1.5
+1.5
0 mm
-1.5
Control
Schizophrenia
From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006
Group Difference
Thalamic Volume (mm3)
Log-likelihood Ratio Values
Thalamic Volume and Shape in Schizophrenia
Schizophrenia
Controls
Schizophrenia
Volume Scatter Plots
F = 6.6, df = 1,115, p = .011
F = 1.3, df = 1,114, p = .26 (covaried for total brain volume)
Controls
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.
Pattern of Thalamic Shape Deformity
B
Anterior View
Posterior View
S
S
R
L
L
R
I
I
Superior View
Magnitude of Displacement (mm)
0.5
P
0.0
-0.5
R
L
A
From: Csernansky, et al (2003) Am J Psychiatry In press.
S–
I –
A–
P–
R–
L–
superior
inferior
anterior
posterior
right
left
Nuclei Within the Human Thalamic Complex
S
Anterior
Ventral Anterior
P
A
Ventral Lateral
Dorsal Lateral
Ventral Posterior
Lateral
Lateral View
I
Pulvinar
S
Dorsal Medial
Central Medial
A
P
Ventral Posterior
Medial
Lateral Geniculate
Medial Geniculate
Medial View
I
Eigenvector Maps
Exaggerated Thalamic Asymmetry
1.5
S
A
0.0
P
I
-1.5
Point-by-Point Maps
Right Thalamus
Left Thalamus
1.5
0.0
-1.5
Control
Schizophrenia
From: Csernansky, et al (2003) Am J Psychiatry In press.
Group Difference
Improving Subject Classification by Combining
Shape Information
Combined assessment - sensitivity = 73%, specificity = 83%
Evidence for neuroanatomical heterogeneity in schizophrenia ?
From: Csernansky, et al (2003) Am J Psychiatry In press.
Acknowledgments
Collaborators
Support
Deanna Barch, Ph.D.
C. Robert Cloninger, M.D.
J. Philip Miller
Paul A. Thompson, Ph.D.
John C. Morris, M.D.
Lei Wang, Ph.D.
Thomas Conturo, M.D.
Mokhtar Gado, M.D.
MH 62130/071616 (Conte)
MH 56584
MH 60883
NARSAD
AHAF
AG 05681 (ADRC)
AG 03991
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.