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Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science. Dan Mungas University of California, Davis. Acknowledgements. Funded in part by Grant R13 AG030995 from the National Institute on Aging

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Latent variable modeling of neuropathology data implications for collaborative science

Latent Variable Modeling of Neuropathology Data: Implications for Collaborative Science

Dan Mungas

University 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.

Friday Harbor Psychometrics, 2013


Collaborative science
Collaborative Science

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Latent variable modeling
Latent Variable Modeling

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The essence of latent variable modeling
The Essence of Latent Variable Modeling

Friday Harbor Psychometrics, 2013

  • 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


Neuropathology
Neuropathology

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Neurofibrillary tangles and neuritic plaques
Neurofibrillary tangles and neuritic plaques

Neurofibrillary

tangles

Neuritic Plaques

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Measurement challenges in neuropathology
Measurement Challenges in Neuropathology

Sampling of brain regions

Reliability and standardization of methods for quantitation

Distribution of variables

Relation to clinical and cognitive outcomes

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Distribution issues
Distribution Issues

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Sophisticated Tools for Item Scaling

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Neurofibrillary tangles and neuritic plaques1
Neurofibrillary tangles and neuritic plaques

Neurofibrillary

tangles

Neuritic Plaques

Friday Harbor Psychometrics, 2013



Practical approaches to modeling neuropathology
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

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Categorical Variable Modeling Example

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Categorical data issues
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

Friday Harbor Psychometrics, 2013


Applications of latent variable modeling to neuropathology studies
Applications of Latent Variable Modeling to Neuropathology Studies

Friday Harbor Psychometrics, 2013


mfrnp Studies

mtmpnp

Neur-Plq

inparnp

hipponp

entonp

mfrdp

mtmpdp

Diff-Plq

inpardp

hippodp

entodp

mfrnft

Cort-NFT

mtmpnft

2

c

inparnft

hipponft

entonft

4 Dimension Measurement Model – AD Neuropathology

Religious Order Study

.89

.93

.89

.85

.87

.80

.92

.89

.77

.91

.68

.73

.75

.58

.87

.48

.94

= 124.9, df = 39

.91

.71

CFI = .988

TLI = .994

RMSEA = .076

WRMR =.738

.83

MT-NFT

.83

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ENT Studies

ENT

MT

MT

MT

ENT

MF

MF

MF

HC

HC

HC

IP

IP

Neuritic

Plaques

Diffuse

Plaques

NeoCortical

Tangles

Medial

Temporal

Tangles

IP

Friday Harbor Psychometrics, 2013


APOE Studies

Age

ENT

ENT

MT

MT

MT

ENT

MF

MF

MF

HC

HC

HC

IP

IP

Diffuse

Plaques

Neuritic

Plaques

NeoCortical

Tangles

Medial

Temporal

Tangles

IP

Friday Harbor Psychometrics, 2013


APOE Studies

Age

ENT

ENT

MT

MT

MT

ENT

MF

MF

MF

HC

HC

HC

IP

IP

0.40

0.84

Diffuse

Plaques

Neuritic

Plaques

0.26

0.77

NeoCortical

Tangles

0.58

0.32

0.36

Medial

Temporal

Tangles

0.18

IP

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Study 1 - ROS Studies

Study 2 - MAS

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

N = 652, Dowling et al., 2011

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Model Fit: Memory and Aging Project

CFI: 0.994

RMSEA: .022

0.84

0.77

0.78

0.80

0.91

GWMSUBCLAC

GWMSUBCMIC

KDPCBRALWM

KGMCMUFMI

KGMCMUCIV

KGMCUNFMI

KGMCUNCIV

KGMCPAFMI

KGMCPACIV

KGMSUBCM

KGMSUBCL

KWMCILAC

KWMCICYS

KWMPERIV

KWMCIMIC

GWMCCYS

GWMCMIC

WM_ISCH

0.64

White Matter

Incomplete Infarction

Sub-Cortical

Infarcts

Cortical

Infarcts

Micro

Infarcts

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Mixed effects modeling of neuropathology effects on longitudinal trajectories
Mixed Effects Modeling of Neuropathology Effects on Longitudinal Trajectories

Friday Harbor Psychometrics, 2013


Casi and neuropathology honolulu asian aging study
CASI and Longitudinal TrajectoriesNeuropathologyHonolulu 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

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Estimated casi at death
Estimated CASI at Death Longitudinal Trajectories

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Estimated casi change
Estimated CASI Change Longitudinal Trajectories

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Braak and vascular risk trajectories episodic memory
Braak Longitudinal Trajectories and Vascular Risk TrajectoriesEpisodic Memory

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Braak and vascular risk trajectories executive function
Braak Longitudinal Trajectories and Vascular Risk TrajectoriesExecutive Function

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The internet
The Internet Longitudinal Trajectories

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

Friday Harbor Psychometrics, 2013


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