Associating Cognition With Amyloid Status Using Partially Ordered Set Analysis

The presence of brain amyloid-beta positivity is associated with cognitive impairment and dementia, but whether there are specific aspects of cognition that are most linked to amyloid-beta is unclear. Analysis of neuropsychological test data presents challenges since a single test often requires dra...

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Published inFrontiers in neurology Vol. 10; p. 976
Main Authors Carr, Sarah J.A., Jaeger, Judith, Bian, Shijia, He, Ping, Maserejian, Nancy, Wang, Wenting, Maruff, Paul, Enayetallah, Ahmed, Wang, Yanming, Chen, Zhengyi, Lerner, Alan, Tatsuoka, Curtis
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 13.09.2019
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ISSN1664-2295
1664-2295
DOI10.3389/fneur.2019.00976

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Summary:The presence of brain amyloid-beta positivity is associated with cognitive impairment and dementia, but whether there are specific aspects of cognition that are most linked to amyloid-beta is unclear. Analysis of neuropsychological test data presents challenges since a single test often requires drawing upon multiple cognitive functions to perform well. It can thus be imprecise to link performance on a given test to a specific cognitive function. Our objective was to provide insight into how cognitive functions are associated with brain amyloid-beta positivity among samples consisting of cognitively normal and mild cognitively impaired (MCI) subjects, by using partially ordered set models (POSETs). We used POSET classification models of neuropsychological test data to classify samples to detailed cognitive profiles using ADNI2 and AIBL data. We considered 3 gradations of episodic memory, cognitive flexibility, verbal fluency, attention and perceptual motor speed, and performed group comparisons of cognitive functioning stratified by amyloid positivity (yes/no) and age (<70, 70-80, 81-90 years). We also employed random forest methods stratified by age to assess the effectiveness of cognitive testing in predicting amyloid positivity, in addition to demographic variables, and 4 allele count. In ADNI2, differences in episodic memory and attention by amyloid were found for <70, and 70-80 years groups. In AIBL, episodic memory differences were found in the 70-80 years age group. In both studies, no cognitive differences were found in the 81-90 years group. The random forest analysis indicates that variable importance in classification depends on age. Cognitive testing that targets an intermediate level of episodic memory and delayed recall, in addition to 4 allele count, are the most important variables in both studies. In the ADNI2 and AIBL samples, the associations between specific cognitive abilities and brain amyloid-beta positivity depended on age, but in general episodic memory was most consistently predictive of brain amyloid-beta positivity. Random forest methods and OOB error rates establish the feasibility of predicting the presence of brain beta-amyloid using cognitive testing, 4 genotyping and demographic variables.
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Reviewed by: Luke William Bonham, Johns Hopkins University, United States; Alberto Benussi, University of Brescia, Italy
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
This article was submitted to Dementia, a section of the journal Frontiers in Neurology
Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (www.loni.usc.edu/ADNI). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au
Edited by: Jennifer S. Yokoyama, University of San Francisco, United States
ISSN:1664-2295
1664-2295
DOI:10.3389/fneur.2019.00976