Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
Normative modelling is a method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD), by quantifying how each patient deviates from the expected normative pattern that has been learned from a healthy control distribution. Existing deep learning based normative models have been applied on only single modality Magnetic Resonance Imaging (MRI) neuroimaging data. However, these do not take into account the complementary information offered b y m ultimodal M RI, w hich i s e ssential for understanding a multifactorial disease like AD. To address this limitation, we propose a multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities to identify abnormal brain volume deviations due to AD. Our multi-modal framework takes as input Freesurfer processed brain region volumes from T1-weighted (cortical and subcortical) and T2-weighed (hippocampal) scans of cognitively normal participants to learn the morphological characteristics of the healthy brain. The estimated normative model is then applied on AD patients to quantify the deviation in brain volumes and identify abnormal brain pattern deviations due to the progressive stages of AD. We compared our proposed mmVAE with a baseline unimodal VAE having a single encoder and decoder and the two modalities concatenated as unimodal input. Our experimental results show that deviation maps generated by mmVAE are more sensitive to disease staging within AD, have a better correlation with patient cognition and result in higher number of brain regions with statistically significant d eviations c ompared t o t he u nimodal b aseline model.
The risk of Alzheimer's disease (AD) in women is about 2 times greater than in men. The estrogen hypothesis is being accepted as the essential sex factor causing the sex difference in AD. Also, the recent meta-analysis using large-scale medical records data indicated estrogen replacement therapy. However, the underlying molecular targets and mechanisms explaining this sex difference in AD disease development remain unclear. In this study, we identified that estrogen treatment can strongly inhibition of neuro-inflammation signaling targets, using the systems pharmacology model; and identified ESR1/ESR2 (the receptors of estrogen) are topologically close to the neuroinflammation biomarker genes using signaling network analysis. Moreover, the estrogen level in women decreased to an extremely lower level than in men after age 55. Pooling together the multiple pieces of evidence, it is concluded that the loss of estrogen unleashing neuro-inflammation increases the women's risk of Alzheimer's disease. These analysis results provide novel supporting evidence explaining the potential mechanism of the anti-neuroinflammation role of estrogen causing the sex difference of AD. Medications boosting the direct downstream signaling of ESR1/ESR2, or inhibiting upstream signaling targets of neuroinflammation, like JAK2 inhibitors, on the signaling network can be potentially effective or synergistic combined with estrogen for AD prevention and treatment.
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