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Neuronuclear Imaging of Alzheimer's and Other Neurodegenerative Diseases
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Neuronuclear Imaging of Alzheimer's and Other Neurodegenerative Diseases

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (10 October 2021) | Viewed by 2375

Special Issue Editor

Prof. Daniel H. Silverman

E-Mail Website
Guest Editor
Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, 200 Medical Plaza, Los Angeles, CA 90095, USA
Interests: positron emission tomography; cognition; neurodegenerative disease; chemotherapy

Special Issue Information

Dear Colleagues,

In a rapidly expanding field, marked by the development, commercialization, and impending availability of many new radiotracers with which to explore the biochemistry of the living human brain in early and late stages of neurodegenerative decline—and with 2020 marking the beginning of a new decade during which the pace of such developments are poised to dramatically accelerate—the time is ripe for a Special Issue on this topic. This Special Issue will take stock of where we are now and how we have arrived here, as well as point the way to the imminent future of where the field is headed.

Guest Editor

Prof. Daniel H. Silverman

Keywords

  • Alzheimer's disease
  • Mild cognitive impairment
  • MCI
  • Mild decline in cognition
  • MDC
  • Parkinson's disease
  • dementia
  • PET
  • SPECT
  • amyloid
  • tau
  • regional cerebral metabolism
  • Fluorodeoxyglucose
  • FDG

Published Papers (1 paper)

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Research

11 pages, 576 KiB  
Article
Exploring a Cost-Efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort
by Hyunwoong Ko, Seho Park, Seyul Kwak, Jungjoon Ihm and for the ADNI Research Group
J. Pers. Med. 2020, 10(4), 197; https://doi.org/10.3390/jpm10040197 - 27 Oct 2020
Viewed by 1932
Abstract
Many studies have focused on the early detection of Alzheimer’s disease (AD). Cerebral amyloid beta (Aβ) is a hallmark of AD and can be observed in vivo via positron emission tomography imaging using an amyloid tracer or cerebrospinal fluid assessment. However, these methods [...] Read more.
Many studies have focused on the early detection of Alzheimer’s disease (AD). Cerebral amyloid beta (Aβ) is a hallmark of AD and can be observed in vivo via positron emission tomography imaging using an amyloid tracer or cerebrospinal fluid assessment. However, these methods are expensive. The current study aimed to identify and compare the ability of magnetic resonance imaging (MRI) markers and neuropsychological markers to predict cerebral Aβ status in an AD cohort using machine learning (ML) approaches. The prediction ability of candidate markers for cerebral Aβ status was examined by analyzing 724 participants from the ADNI-2 cohort. Demographic variables, structural MRI markers, and neuropsychological test scores were used as input in several ML algorithms to predict cerebral Aβ positivity. Out of five combinations of candidate markers, neuropsychological markers with demographics showed the most cost-efficient result. The selected model could distinguish abnormal levels of Aβ with a prediction ability of 0.85, which is the same as that for MRI-based models. In this study, we identified the prediction ability of MRI markers using ML approaches and showed that the neuropsychological model with demographics can predict Aβ positivity, suggesting a more cost-efficient method for detecting cerebral Aβ status compared to MRI markers. Full article
(This article belongs to the Special Issue Neuronuclear Imaging of Alzheimer's and Other Neurodegenerative Diseases)
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