Practical algorithms for amyloid β probability in subjective or mild cognitive impairment

Abstract

Introduction Practical algorithms predicting the probability of amyloid pathology among patients with subjective cognitive decline or mild cognitive impairment may help clinical decisions regarding confirmatory biomarker testing for Alzheimer's disease. Methods Algorithm feature selection was conducted with Alzheimer's Disease Neuroimaging Initiative and Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing data. Probability algorithms were developed in Alzheimer's Disease Neuroimaging Initiative using nested cross‐validation accompanied by stratified subsampling to obtain 1000 internally validated decision trees. Semi‐independent validation was conducted using Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Independent external validation was conducted in the population‐based Mayo Clinic Study of Aging. Results Two algorithms were developed using age and normalized immediate recall z‐scores, with or without apolipoprotein E ε4 carrier status. Both algorithms had robust performance across data sets and when substituting different recall memory tests. Discussion The statistical framework resulted in robust probability estimation. Application of these algorithms may assist in clinical decision‐making for further testing to diagnose amyloid pathology.

Detecting Alzheimer’s disease biomarkers with a brief tablet-based cognitive battery: sensitivity to Aβ and tau PET

“…Specifically, we found significant associations between individual BHA tests of associative memory and processing speed and executive functions and Aβ positivity in both MCI and dementia. These findings are largely consistent with prior reports on the associations of memory and executive measures with Aβ burden [ 19 – 21 ] in clinically mixed samples, and with our prior results on these tests being associated with regional gray matter volumes typically affected in the early symptomatic stages of AD [ 29 ]. While not directly comparable due to differences in cognitive measures used in the analyses, the accuracy of classification in our study is similar to or better than previously published findings on Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) [ 19 ] and ADNI cognitive battery [ 20 , 21 ].…”

“…These findings are largely consistent with prior reports on the associations of memory and executive measures with Aβ burden [ 19 – 21 ] in clinically mixed samples, and with our prior results on these tests being associated with regional gray matter volumes typically affected in the early symptomatic stages of AD [ 29 ]. While not directly comparable due to differences in cognitive measures used in the analyses, the accuracy of classification in our study is similar to or better than previously published findings on Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) [ 19 ] and ADNI cognitive battery [ 20 , 21 ]. Additionally, we found that the BHA measures were associated with Aβ positivity after controlling for age, sex, education, time gap, disease severity, and amnestic phenotype, which supports the notion that these measures are sensitive to Aβ deposition beyond the effects of demographic and clinical characteristics.…”

Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

“…Therefore, there has been great interest in developing low cost, minimally invasive methods to detect AD Aβ pathology compared to PET scans and or CSF as the ‘gold standard’. Many publications (reviewed in Ashford et al ) have evaluated the role of demographics ( Insel et al , 2016 ; Tosun et al , 2016 ; Jansen et al , 2018 ; Buckley et al , 2019 ; Ko et al , 2019 ; Maserejian et al , 2019 ), APOE ε4 ( de Rojas et al , 2018 ; Jansen et al , 2018 ; Ten Kate et al , 2018 ; Ba et al , 2019 ; Buckley et al , 2019 ), cognition ( Mielke et al , 2012 ; Burnham et al , 2014 ; Kandel et al , 2015 ; Burnham et al , 2016 ; Insel et al , 2016 ; Kim et al , 2018 ; Lee et al , 2018 ; Ba et al , 2019 ; Brunet et al , 2019 ; Maserejian et al , 2019 ; Ansart et al , 2020 ) and MRI measures ( Tosun et al , 2013 , 2014 , 2016 ; Ten Kate et al , 2018 ; Petrone et al , 2019 ; Ansart et al , 2020 ; Ezzati et al , 2020 ) to detect AD Aβ pathology. More recently, there has been considerable excitement concerning the value of assays of plasma Aβ species and related proteins ( Burnham et al , 2014 , 2016 ; Kaneko et al , 2014 ; Fandos et al , 2017 ; Ovod et al , 2017 ; Park et al , 2017 ; de Rojas et al , 2018 ; Nakamura et al , 2018 ; Verberk et al , 2018 ; Westwood et al , 2018 ; Chatterjee et al , 2019 ;…”

Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

“…However, the most previous studies were performed without proper validation in a test set, which may have led to over tted results, especially in highdimensional datasets with machine learning studies. (34) A recent study applying data-driven algorithm with clinical features with validation showed an AUC of 0.71 at the test set, (35) showing only fair performance, unlike that in previous studies. This gives another line of evidence of potential over tted results of the previous studies.…”