Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science

Latent Variable Modeling of Neuropathology Data:

Implications for Collaborative Science

Dan MungasUniversity of California, Davis

Friday Harbor Psychometrics, 2013

Acknowledgements

• Funded in part by Grant R13 AG030995 from the National Institute on Aging

• The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.


Collaborative Science




Latent Variable Modeling


• Now what is the message there? The message is that there are no "knowns." There are things we know that we know. There are known unknowns. That is to say there are things that we now know we don't know. But there are also unknown unknowns. There are things we do not know we don't know. So when we do the best we can and we pull all this information together, and we then say well that's basically what we see as the situation, that is really only the known knowns and the known unknowns. And each year, we discover a few more of those unknown unknowns. ~ D. Rumsfeld, June 6, 2002


The Essence of Latent Variable Modeling

Neuropathology





Neurofibrillary tangles and neuritic plaques

Neuritic Plaques

Neurofibrillarytangles



Measurement Challenges in Neuropathology

• Sampling of brain regions• Reliability and standardization of methods for

quantitation• Distribution of variables• Relation to clinical and cognitive outcomes


Distribution Issues




Sophisticated Tools for Item Scaling

Neurofibrillary tangles and neuritic plaques

Neuritic Plaques

Neurofibrillarytangles



Practical Approaches to Modeling Neuropathology

• Many modeling approaches are based on assumption of multivariate normality

• Modeling neuropathology counts as continuous variables can be problematic Use of robust distribution free estimators does

not solve problem• Latent variable modeling approaches for

categorical/ordinal data can be helpful



Categorical Variable Modeling Example

Categorical Data Issues

• Recoding of data required to create “manageable” number of categories Does this result in loss of information? Are there other/better approaches?

• Count variables modeled using different distributional assumptions

• Bayesian estimation


Applications of Latent Variable Modeling to Neuropathology Studies


CFI = .988TLI = .994RMSEA = .076WRMR =.738

mfrnp

mtmpnp

inparnp

hipponp

entonp

mfrdp

mtmpdp

inpardp

hippodp

entodp

mfrnft

mtmpnft

inparnft

hipponft

entonft

Neur-Plq

Diff-Plq

Cort-NFT

MT-NFT

.89

.87

.92

.75

.87

.83

.83

.91

.94

.91

.89

.93

.85

.89

.73

.80

.58

.71

.68

.48

.77

2 = 124.9, df = 39

4 Dimension Measurement Model – AD NeuropathologyReligious Order Study


MedialTemporalTangles

NeoCorticalTangles

NeuriticPlaques

ENT

HC MF

IP

MT

ENT

HC

MF

IP

MT

DiffusePlaques

ENT

HC MF

IP

MTFriday Harbor Psychometrics, 2013


NeoCorticalTangles

NeuriticPlaques

ENT

HC MF

IP

MT

ENT

HC

MF

IP

MT

DiffusePlaques

ENT

HC MF

IP

MT

Age

APOE


HC

MF


NeoCorticalTangles

NeuriticPlaques

Age

APOE

ENT

HC MF

IP

MT

ENT

IP

MT

DiffusePlaques

ENT

HC MF

IP

MT

0.84

0.58

0.32

0.26

0.40

0.18

0.36

0.77



Study 2 - MAS

Study 1 - ROS

Neuropathology and Cognition – Religious Order Study & Memory and Aging Project

N = 652, Dowling et al., 2011Friday Harbor Psychometrics, 2013

GWMSUBCMIC

KDPCBRALWM

GWMCMIC

KWMCIMIC

KGMCPAFMI

KGMCUNFMI

KGMCMUFMI

KGMSUBCM

WM_ISCH

KWMPERIV

KGMSUBCL

KWMCICYS

KWMCILAC

GWMSUBCLAC

KGMCMUCIV

KGMCUNCIV

KGMCPACIV

GWMCCYS

White MatterIncomplete Infarction

CorticalInfarcts

MicroInfarcts

Sub-CorticalInfarcts

0.80

0.77

0.84

0.78

0.91

0.64

Model Fit: CFI: 0.994 RMSEA: .022


Mixed Effects Modeling of Neuropathology Effects on Longitudinal Trajectories


CASI and NeuropathologyHonolulu Asian Aging Study

• Random Effects Model• Dependent Variable

CASI• Estimated score at death• Rate of change preceding death

• Independent Variables Neuritic Plaque Factor Score Neurofibrillary Tangle - Neocortical Factor Score Neurofibrillary Tangle - Medial Temporal Factor Score Estimated Brain Atrophy


Estimated CASI at Death

Effect Coef. S.E. p

Intercept 75.92 0.84 .001

NPL -2.01 1.26 .11

NFT-NC -3.10 1.25 .01

NFT-MT -.44 1.07 .68

Brain Atrophy -6.43 .83 .001


Estimated CASI Change

Effect Coef. S.E. p

Intercept 75.92 0.84 .001

NPL .44 .18 .01

NFT-NC .19 .19 .32

NFT-MT -.35 .15 .02

Brain Atrophy .31 .12 .009


Braak and Vascular Risk TrajectoriesEpisodic Memory


Braak and Vascular Risk TrajectoriesExecutive Function


The Internet

• A global to-do list that anyone in the world can add to, especially Rich Jones.


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Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science