Predicting conversion from MCI to AD using partially ordered models of cog t e u ct o g
cognitive functioning
Alan Lerner, MD(1)
Hui Yun Tseng, MS (2)
J di h J
Judith Jaeger, PhD (3,4)
PhD (3 4)
Curtis Tatsuoka, PhD (1)
1.
2.
3.
4
4.
Case Western Reserve University
Columbia University
Albert Einstein College of Medicine
AstraZeneca
MCI to AD conversion
MCI to AD conversion
• Can we stratify risk using baseline variables?
Can we stratify risk using baseline variables?
• Problem of non‐specificity of tests
bl
f
ifi i
f
– Neuropsychological test are polyfactorial
Partially Ordered Sets (POSETS)
Partially Ordered Sets (POSETS)
• Bayesian
Bayesian statistical methods designed to statistical methods designed to
mimic the expert analysis of a neuropsychologist. neuropsychologist
• Classifies a group of patients with MCI into discrete groupings or “states”
discrete groupings or states each having a each having a
unique cognitive profile. • The collection of such states is termed a Th
ll i
f
h
i
d
“partially ordered set” (poset).
ABC
BC
AB
AC
B
A
NONE
THREE STATE MODEL
THREE
STATE MODEL‐Eight
Eight possible states
possible states
‐then compare subsets of states
C
item
ADAS-delayed recall subscale
ADAS-word recognition
subscale
AVLT -Trial 6(After List B recall
AVLT-List B
Boston Naming Test
Category Fluency (average of
Vegetable and Animal)
ADAS-Number
ADAS
Number Cancellation
Trail Making Test A
Trail Making Test B
WAIS-R
WAIS
R Digit Symbol
Substitution
Med High
Cons Sem Sel
Work Work
ol Mem Att
Mem Mem
X
X
X
X
Percep
Speed
p
X
X
X
X
Cog
Flex
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
The poset consists of a collection of cognitive states which are associated with profiles of functionality.**
FUNCT→ Attention
STATE↓
Medium Working Memory
High Working Memory
Consoli‐
dation
Semantic Memory
Cognitive Perception Flexibility Speed
1
√
√
√
√
√
√
2
√
√
√
√
√
√
3
√
√
√
√
√
9
√
√
√
√
10
√
√
√
√
11
√
√
√
√
14
√
√
√
21
√
√
27
√
√
√
√
√
*
*
*
28
√ = has functionality, * = undetermined
**Includes MCI and AD only
Only 28 states delineated, due to
g
g
Confounding of cognitive variables
(limit of resolution from NP battery)
Comparison of MCI to AD converters to non‐converters
For those with high cognitive flexibility and high consolidation scores (N=34) conversion rate 14 7%; 40% for those with lower scores
conversion rate 14.7%; 40% for those with lower scores.
Chi‐ square 8.2 p=.004
Comparison of MCI to AD converters to non‐converters
For those with low baseline working memory and any APO E ε4 (N=30) 66% conversion rate
Chi square 14.4 p<.0005
Future directions
Future directions
• Enlarge sample size and time frame
Enlarge sample size and time frame
• Convergence with MCI subtypes
• Add neuroimaging derived variables
dd
i
i d i d i bl
– Regional brain volumes, Boundary shift integral, etc.
• Add to ADNI dataset?
Acknowledgements
• ADNI
ADNI investigators
investigators
• The G. R. Lincoln Family Foundation
• University Hospitals Case Medical Center
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