Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects

COPYRIGHT © 2021 by the Society of Nuclear Medicine and Molecular Imaging.This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algo- rithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward

Amyloid-dependent and amyloid-independent effects of Tau in individuals without dementia

Objective: To investigate the relationship between the topography of amyloid-β plaques, tau neurofibrillary tangles, and the overlap between the two, with cognitive dysfunction in individuals without dementia. Methods: We evaluated 154 individuals who were assessed with amyloid-β PET with [18F]AZD4694, tau-PET with [18F]MK6240, structural MRI, and neuropsychological testing. We also evaluated an independent cohort of 240 individuals who were assessed with amyloid-β PET with [18F]Florbetapir, tau-PET with [18F]Flortaucipir, structural MRI, and neuropsychological testing. Using the VoxelStats toolbox, we conducted voxel-wise linear regressions between amyloid-PET, tau-PET, and their interaction with cognitive function, correcting for age, sex, and years of education. Results: In both cohorts, we observed that tau-PET standardized uptake value ratio in medial temporal lobes was associated with clinical dementia rating Sum of Boxes (CDR-SoB) scores independently of local amyloid-PET uptake (FWE corrected at p < 0.001). We also observed in both cohorts that in regions of the neocortex, associations between neocortical tau-PET and clinical function were dependent on local amyloid-PET (FWE corrected at p < 0.001). Interpretation: In medial temporal brain regions, characterized by the accumulation of tau pathology in the absence of amyloid-β, tau had direct associations with cognitive dysfunction. In brain regions characterized by the accumulation of both amyloid-β and tau pathologies such as the posterior cingulate and medial frontal cortices, tau’s relationship with cognitive dysfunction was dependent on local amyloid-β concentrations. Our results provide evidence that amyloid-β in Alzheimer’s disease influences cognition by potentiating the deleterious effects of tau pathology

Cerebrospinal fluid p-tau231 as an early indicator of emerging pathology in Alzheimer's disease

Background: Phosphorylated tau (p-tau) epitopes in cerebrospinal fluid (CSF) are accurate biomarkers for a pathological and clinical diagnosis of Alzheimer's disease (AD) and are seen to be increased in preclinical stage of the disease. However, it is unknown if these increases transpire earlier, prior to amyloid-beta (Aβ) positivity as determined by position emission tomography (PET), and if an ordinal sequence of p-tau epitopes occurs at this incipient phase. Methods: We measured CSF concentrations of p-tau181, p-tau217 and p-tau231 in 171 participants across the AD continuum who had undergone Aβ ([18F]AZD4694) and tau ([18F]MK6240) position emission tomography (PET) and clinical assessment. Findings: All CSF p-tau biomarkers were accurate predictors of cognitive impairment but CSF p-tau217 demonstrated the largest fold-changes in AD patients in comparison to non-AD dementias and cognitively unimpaired individuals. CSF p-tau231 and p-tau217 predicted Aβ and tau to a similar degree but p-tau231 attained abnormal levels first. P-tau231 was sensitive to the earliest changes of Aβ in the medial orbitofrontal, precuneus and posterior cingulate before global Aβ PET positivity was reached. Interpretation: We demonstrate that CSF p-tau231 increases early in development of AD pathology and is a principal candidate for detecting incipient Aβ pathology for therapeutic trial application

Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer's disease

Studies of rodent models of Alzheimer's disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Abeta), may serve as surrogate markers of brain Abeta levels. As Abeta has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Abeta. Significant differences in the retinal reflectance spectra are found between individuals with high Abeta burden on brain PET imaging and mild cognitive impairment (n = 15), and age-matched PET-negative controls (n = 20). Retinal imaging scores are correlated with brain Abeta loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Abeta in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Abeta load

Wrappers feature selection in alzheimer’s biomarkers using kNN and SMOTE oversampling

Biomarkers are characteristics that are objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The combination of different biomarker modalities often allows an accurate diagnosis classification. In Alzheimer’s disease (AD), biomarkers are indispensable to identify cognitively normal individuals destined to develop dementia symptoms.However, using the combination of canonicalAD biomarkers, studies have repeatedly shown poor classification rates to differentiate between AD, mild cognitive impairment and control individuals. Furthermore, the design of classifiers to access multiple biomarker combinations includes issues such as imbalance classes and missing data. Due to the number of biomarkers combinations wrappers are used to avoid multiple comparisons. Here, we compare the ability of three wrappers feature selection methods to obtain biomarker combinations which maximize classification rates. Also, as the criterion to the wrappers feature selection we use the k-nearest neighbor classifier with balance aids, random undersampling and SMOTE oversampling. Overall, our analyses showed how biomarkers combinations affect the classifier precision and how imbalance strategy improve it.We show that non-defining and non-cognitive biomarkers have less precision than cognitive measures when classifying AD. Our approach surpasses in average the support vector machine and the weighted k-nearest neighbor classifiers and reaches 94.34 ± 3.91% of precision reproducing class definitions

Machine Learning in the Nuclear Medicine: Part 1-Introduction

This research was originally published in JNM. Uribe, C.F., Mathotaarachchi, S., Gaudet, V., Smith, K.C, Rosa-Neto, P., Benard, F., Black, S.E. & Zukotynski, K. Machine Learning in Nuclear Medicine: Part 1 - Introduction. J Nucl Med. 2019;60:451-458. © SNMMI.This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when they can be helpful in nuclear medicine. Part 2 focuses on current contributions of ML to our field, addresses future expectations and limitations, and provides a critical appraisal of what ML can and cannot do

Constrained instruments and their application to Mendelian randomization with pleiotropy

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer’s disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55–66) to disentangle causal relationships of several biomarkers with AD progression

Constrained instruments and their application to Mendelian randomization with pleiotropy

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer’s disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55–66) to disentangle causal relationships of several biomarkers with AD progression

Regional Amyloid-β Load and White Matter Abnormalities Contribute to Hypometabolism in Alzheimer's Dementia

We investigated the association between amyloid-β deposition and white matter (WM) integrity as a determinant of brain glucose hypometabolism across the Alzheimer's disease (AD) spectrum. We assessed ninety-six subjects (27 cognitively normal, 49 mild cognitive impairment, and 20 AD dementia) who underwent [ F]FDG and [ F]Florbetapir positron emission tomography (PET) as well as magnetic resonance imaging (MRI) with diffusion tensor imaging. Among the regions with reduced fractional anisotropy (FA) in the AD group, we selected a voxel of interest in the angular bundle bilaterally for subsequent analyses. Using voxel-based interaction models at voxel level, we tested whether the regional hypometabolism is associated with FA in the angular bundle and regional amyloid-β deposition. In the AD patients, [ F]FDG hypometabolism in the striatum, mesiobasal temporal, orbitofrontal, precuneus, and cingulate cortices were associated with the interaction between high levels of [ F]Florbetapir standard uptake value ratios (SUVR) in these regions and low FA in the angular bundle. We found that the interaction between, rather than the independent effects of, high levels of amyloid-β deposition and WM integrity disruption determined limbic hypometabolism in patients with AD. This finding highlights a more integrative model for AD, where the interaction between partially independent processes determines the glucose hypometabolism