preliminary study: hierarchical bayesian cognitive

Click here to load reader

Post on 20-Feb-2022

0 views

Category:

Documents

0 download

Embed Size (px)

TRANSCRIPT

P2-072_AmyloidPrediction_SmallPRELIMINARY STUDY: HIERARCHICAL BAYESIAN COGNITIVE PROCESSING MODEL CLASSIFICATION OF CSF AND PET AMYLOID POSITIVITY AND NEGATIVITY FROM COGNITIVE ASSESSMENT DATA
Jason R Bock1, Michael D. Lee2, Junko Hara1,3, Dennis Fortier2, Tushar Mangrola2, William R. Shankle1,2,3
1Medical Care Corporation; 2Department of Cognitive Sciences, University of California at Irvine,; 3Pickup Family Neurosciences Institute, Hoag Memorial Hospital Presbyterian
P2-072
Current identification of patients with Alzheimer’s disease (AD) in pre-clinical or mild cognitive impairment (MCI) stages requires CSF and/or PET imaging in clinical trials and care settings. These methods, however, are invasive and costly, and there are urgent needs for establishing more pragmatic and cost-effective methods.
In this preliminary study, we examined whether patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset can be classified as amyloid positive/negative from baseline cognitive assessment data with parameters generated by hierarchical Bayesian cognitive processing (HBCP) models (Figure 1).
- Apply hierarchical Bayesian processing models to cognitive assessment item response data - Establish classification accuracy for amyloid positivity/negativity with latent cognitive processing parameters
Figure 1: Hierarchical Bayesian Cognitive Processing Model
Unlearned (U)
Intermediate (I)
Learned (L)
Correctly Retrieved
Correctly Retrieved
Correctly Retrieved
Correctly Retrieved
Delay p
0.0 t
L1 Lp
Figure 1. HBCP Hidden Markov Model of Latent Cognitive Processes Underlying WLM Task Performance. Consisting of unlearned (U), intermediate (I), and learned (L) states connected by encoding/storage processes (green arrows) and retrieval processes (pink arrows) from states I and L.
Fifty patients (either Aβ+ or Aβ-) from the ADNI dataset (Table 1a and 1b) were randomly sampled for each analysis: a) Cognitively Normal (NL) with CSF; b) MCI with CSF; c) NL with PET; d) MCI with PET. ADNI criteria were used to determine amyloid positivity/negativity.
The HBCP model estimated cognitive processing parameters for individuals and Aβ groups with demographic information (age, gender, and education, with and without ApoE status) and ADAS-Cog wordlist memory (WLM) test item responses. Estimated amyloid positive/negative group means and standard deviations for each cognitive processing parameter were used to generate posterior distributions of probable cognitive processing parameters for a typical, representative, individual. Sample individuals’ cognitive processing parameter values were compared against cutoffs along this posterior representative distribution for classification. To calculate classification accuracy, we then compared our classification of individuals to their observed amyloid status.
Classification was generally better with CSF measurement, MCI patients, and when including ApoE as a covariate. This may be explained by greater CSF amyloid prominence in pre-clinical or MCI AD compared to amyloid accumulation in the brain.
CSF Results For CSF amyloid, the parameters, r (encoding/storage from unlearned state into learned state) and t (retrieval from intermediate state), or t and L2 (retrieval from learned state with delay), demonstrated potential for amyloid group classification, with t declining in positive individuals and r showing a compensatory increase in cognitively normal subjects. Predictive powers (Table 2a) ranged from 0.70 to 0.82 (PPV), 0.48 to 1.00 (NPV), 0.60 to 0.90 (accuracy).
PET Results For PET amyloid, the parameters, t and L2, representing memory retrieval, declined with amyloid positivity. Predictive powers (Table 2b) ranged from 0.35 to 0.70 (PPV), 0.57 to 0.76 (NPV), 0.56 to 0.68 (accuracy).
This preliminary study demonstrates the ability for HBCP models to accurately classify amyloid positivity with noninvasive cognitive assessment responses.
BACKGROUND
OBJECTIVES
METHOD
NL MCI Amyloid Status + - + - N 19 31 31 19 Age, M yrs. (SD) 75.48 (6.10) 71.45 (7.61) 73.47 (6.63) 75.71 (7.17) Gender, proportion female 0.79 0.65 0.61 0.42 Education, M yrs. (SD) 16.32 (2.87) 17.23 (2.06) 15.61 (2.84) 16.21 (2.72) ApoE genotype, group proportion e2e2 0.00 0.00 0.00 0.00 e2e3 0.05 0.13 0.19 0.11 e3e3 0.53 0.89 0.39 0.63 e2e4 0.11 0.03 0.00 0.00 e3e4 0.32 0.39 0.29 0.26 e4e4 0.00 0.06 0.13 0.00
NL MCI Amyloid Status + - + - N 16 34 28 22 Age, M yrs. (SD) 74.29 (9.29) 70.71 (8.54) 73.18 (6.61) 71.54 (6.92) Gender, proportion female 0.63 0.56 0.57 0.45 Education, M yrs. (SD) 16.38 (3.32) 15.97 (3.10) 15.96 (2.99) 15.86 (2.88) ApoE genotype, group proportion e2e2 0.00 0.00 0.00 0.00 e2e3 0.19 0.12 0.14 0.23 e3e3 0.56 0.50 0.46 0.45 e2e4 0.00 0.00 0.00 0.00 e3e4 0.19 0.32 0.29 0.18 e4e4 0.06 0.06 0.11 0.14
RESULT
SUMMARY
NL MCI
Sample (Amyloid + / Amyloid -) 19 / 31 19 / 31 31 / 19 31 / 19
ApoE Status Included N Y N Y
Parameters Used r, t r, t t, L2 t, L2
PPV 0.78 0.79 0.7 0.82
NPV 0.96 1 0.48 1
Accuracy 0.88 0.9 0.6 0.86
NL MCI
Sample (Amyloid + / Amyloid -) 16 / 34 16 / 34 28 / 22 28 / 22
ApoE Status Included N Y N Y
Parameters Used t, L2 t, L2 t, L2 t, L2
PPV 0.43 0.35 0.67 0.7
NPV 0.76 0.7 0.57 0.65
Accuracy 0.62 0.56 0.62 0.68
Table 2b: Classification Summary of ADNI PET Amyloid
Table 2a: Classification Summary of ADNI CSF Amyloid