Longitudinal Changes In White Matter
Disease and Cognition in the First Year of
the Alzheimer’s Disease Neuroimaging
Initiative
Owen Carmichael1, Christopher Schwarz1, David Drucker1, Evan Fletcher1,
Danielle Harvey1, Laurel Beckett1, Clifford R. Jack Jr.2, Michael Weiner3,
Charles DeCarli1, and the Alzheimer’s Disease Neuroimaging Initiative.
University of California, Davis 1; Mayo Clinic 2; University of California, San
Francisco
In press, Archives of Neurology
Overview
• Investigate WMH
– In association with diagnosis and cognition at
baseline
– As a predictor of subsequent cognitive
trajectory
• Examine relation between WMH
– Markers of AD pathology
– Vascular risk
Demographics
Variable
Baseline
Normal
All
Number of subjects
Number of measurements per
subject
804
2.7 +/- 0.57
Baseline Age
224
Baseline MCI
Baseline AD
391
189
2.8 +/- 0.52
2.7 +/- 0.57
2.6 +/- 0.63
76 +/- 6.9
76 +/- 4.8
75 +/- 7.5
76 +/- 7.5
CV Risk
1.7 +/- 1
1.6 +/- 1
1.7 +/- 1.1
1.7 +/- 1
Years of Education
16 +/- 3.1
16 +/- 2.9
16 +/- 3.1
15 +/- 3.1
Gender (# male; % male)
446 ; 59%
112 ; 52%
240 ; 65%
94 ; 53%
Race (White; Other)
APOE genotype (2-2;2-3;2-4;33;3-4;4-4)
709; 95
2 ; 52 ; 18 ;
354 ; 290 ; 88
198 ; 26
2 ; 31 ; 3 ; 132 ;
51 ; 5
346 ; 45
0 ; 16 ; 11 ;
164 ; 153 ; 47
165 ; 24
0 ; 5 ; 4 ; 58 ;
86 ; 36
MMSE Baseline
27 +/- 2.7
29 +/- 1
27 +/- 1.8
23 +/- 2.1
MMSE Change rate per year
-0.93 +/- 3.1
0.0029 +/- 1.3
-0.75 +/- 2.8
-2.4 +/- 4.4
ADAS-Cog Baseline
19 +/- 9.2
9.5 +/- 4.3
19 +/- 6.3
29 +/- 7.7
ADAS-Cog Change rate per year
1.8 +/- 6.2
-0.4 +/- 3.9
1.5 +/- 5.8
5.2 +/- 7.5
CDR Sum Baseline
1.8 +/- 1.8
0.03 +/- 0.12
1.6 +/- 0.89
4.3 +/- 1.7
CDR Sum Change rate per year
0.7 +/- 1.5
0.12 +/- 0.61
0.66 +/- 1.4
1.5 +/- 2.2
WMH Baseline (in cm3)
WMH Change rate per year
(change in cm3 per year)
0.72 +/- 1.4
0.51 +/- 1.1
0.66 +/- 1.2
1.1 +/- 2
0.2 +/- 1.2
0.082 +/- 0.92
0.24 +/- 1.2
0.24 +/- 1.5
Methods: WMH Detection from MRI
Bayesian Inference Model
Use two key sources of information to determine whether there is a
white matter hyperintensity at each voxel:
?
?
Prior knowledge
The image signal
Do WMHs tend to
occur at this voxel
in general?
Does it look like a
WMH on PD, T1,
and T2 MRI?
Combine these two sources of information in a Bayesian inference
framework.
Example UCD ADC Result
PD
Spatial Prior:
Prior Probability
of WMH
Gold Standard
WMH Map from
FLAIR
T1
T2
Likelihood of
WMH from
PD, T1, T2
Posterior
Probability of
WMH From PD,
T1, T2 and
Spatial Prior
Results: Baseline WMH Burden
•ADNI subjects had WMH burden at baseline that is comparable to that
of population-based studies
•Normal and MCI had similar WMH distributions; increased WMH
burden in AD with suggestions of anterior-posterior progression (Agrees
with Yoshita et al. Neurology 2006)
Normal
MCI
AD
Prediction of MMSE
Baseline Cognition and WMH
MMSE
ADAS-Cog
CDR Sum

p

p

p
Baseline WMH
--
--
0.056
0.022
0.06
<0.001
Race
--
--
--
--
--
--
0.021
0.004
--
--
--
--
Baseline Age
--
--
--
--
--
--
APOE
--
--
0.078
0.037
--
--
CV Risk
--
--
--
--
--
--
Education
Baseline
Diagnosis
MCI
-0.531
AD
-1.487
<0.001
0.831
1.757
<0.00
1
1.321
2.244
<0.001
Baseline WMH and Longitudinal
Differences in Cognition
MMSE
ADAS-Cog
CDR Sum

p

p

p
-0.096
<0.001
0.034
0.048
--
--
Race
--
--
--
--
--
--
Education
--
--
--
--
0.013
0.012
0.008
0.016
--
--
0.005
0.017
APOE
--
--
--
--
--
--
CV Risk
--
--
--
--
0.033
0.011
Baseline WMH
Baseline Age
Baseline
Diagnosis
MCI
-0.147
AD
-0.648
<0.001
0.137
0.444
<0.001
0.109
0.251
<0.001
Change in WMH and Change
in Cognition
MMSE
ADAS-Cog
CDR Sum

p

p

p
WMH
-0.1
<0.001
0.036
0.05
--
--
Race
--
--
--
--
--
--
Education
--
--
--
--
0.012
0.016
0.008
0.026
--
--
--
--
APOE
--
--
--
--
--
--
CV Risk
--
--
--
--
0.036
0.005
Baseline Age
Baseline
Diagnosis
MCI
-0.139
AD
-0.634
<0.001
0.131
0.435
<0.001
0.103
0.242
<0.001
Summary
• AD was associated with significantly greater
baseline WMH and rate of WMH accretion
was lowest for normal and highest for AD
• Vascular risk was significantly associated
with baseline WMH and accretion in WMH
• Baseline WMH volume was significantly
associated with change in MMSE and
ADAS-Cog including adjustment for brain
and hippocampal volume
Summary II
• Change is WMH volume was
significantly associated with worsening
scores in MMSE and ADAS-Cog
independent of:
– Age
– ApoE4
– Vascular risk
– Diagnosis
Conclusion I
• ADNI subjects have substantial CVD burden that is
increasing over time and negatively impacting
cognitive function
• The effects of CVD on cognition should be accounted
for during biomarker evaluation in ADNI
PERIVENTRICULAR WMH ARE
RELATED TO VASCULAR
RISK—EVEN IN ADNI
Charles DeCarli, Danielle Harvey, Laurel Beckett,
Christopher Schwarz, David Drucker, Evan Fletcher
and Owen Carmichael
Imaging of Dementia and Aging laboratory, Department of Neurology and
Center for Neuroscience, University of California at Davis. Davis,
California, USA
Goal
• Assess whether regional WMH is a
viable biological marker for vascular
brain injury in ADNI, a clinical trial
cohort with an extremely mild profile of
vascular risk
Study Design
• ADNI
– 409 subjects
– MRI, Hachinski, CSF Measures
– 75.6 + 7 years,
– 40% Female
– Diagnosis
• 25% AD,
• 48% MCI
• 28% normal controls
MRI WMH Mapping Method
Target
Cubic Spline
Affine
T1
Warping
WMH
Mapping
Registration
WMH Replacement
WMH
Segmentation
DSE
Periventricular Regions
Pm
Pp
Pp
Pa
Po
Pm
Po
Pa
Cg
Cs
Cg
A
Cs
B
Periventricular WMH and HSS
HSS = 0
HSS = 1
HSS = 2
Red indicates brain regions where at least 5% of the subjects had WMH
HSS = 3
Predictors of Anterior
Periventricular WMH
Variable
F Ratio
P value
Age
4.73
0.03
Gender
1.52
0.22
ApoE
0.61
0.65
Diagnosis
1.56
0.20
CSF Tau
3.69
0.06
CSF A-beta
1.58
0.21
Hachinski
3.69
0.0058
Predictors of Middle
Periventricular WMH
Variable
F Ratio
P value
Age
7.75
0.0056
Gender
0.001
0.98
ApoE
0.62
0.65
Diagnosis
0.53
0.59
CSF Tau
4.9
0.03
CSF A-beta
2.67
0.1
Hachinski
3.69
0.029
Conclusions II
• Vascular disease, even when crudely
measured by the HSS, is associated
with WMH even in ADNI
• Periventricular WMH are associated
with HSS independent of typical AD
markers such as ApoE4 genotype, CSF
Tau and amyloid beta, and diagnosis
Supported by NIH:
U01 AG024904, P30 AG10129,
R01 AG021028, R01 NS 29993,
P01 AG12435, P01 AG0027232,
R01 AG111101, R01 AG08122,
R01 AG16495, R01 AG09029,
ocarmichael@ucdavis.edu
http://rope.ucdavis.edu/~owenc
http://neuroscience.ucdavis.edu/idealab/