scholarly journals EUROPEAN CONGRESS ON THE EARLY DETECTION OF ALZHEIMER'S DISEASE: BIOLOGICAL MARKERS AND LONGITUDINAL RESEARCH

1989 ◽  
Vol 18 (4) ◽  
pp. 1011-1012
2009 ◽  
Vol 11 (2) ◽  
pp. 141-157 ◽  

The introduction of biological markers in the clinical management of Alzheimer's disease (AD) will not only improve diagnosis relating to early detection of neuropathology with underlying molecular mechanisms, but also provides tools for the assessment of objective treatment benefits. In this review, we identify a number of in vivo neurochemistry and neuroimaging techniques, which can reliably assess aspects of physiology, pathology, chemistry, and neuroanatomy of AD, and hold promise as meaningful biomarkers in the early diagnostic process, as well as for the tracking of disease-modifying pharmacological effects. These neurobiological measures appear to relate closely to pathophysiological, neuropathological, and clinical data, such as hyperphosphorylation of tau, abeta metabolism, lipid peroxidation, pattern and rate of atrophy, loss of neuronal integrity, and functional and cognitive decline, as well as risk of future decline. As a perspective, the important role of biomarkers in the development of innovative drug treatments for AD and the related regulatory process is discussed.


2015 ◽  
Vol 3 (2) ◽  
pp. 58-65 ◽  
Author(s):  
Jiajia Yang ◽  
Mohd Usairy Syafiq ◽  
Yinghua Yu ◽  
Satoshi Takahashi ◽  
Zhenxin Zhang ◽  
...  

2021 ◽  
Vol 11 (4) ◽  
pp. 1574
Author(s):  
Shabana Urooj ◽  
Satya P. Singh ◽  
Areej Malibari ◽  
Fadwa Alrowais ◽  
Shaeen Kalathil

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).


2019 ◽  
Vol 184 ◽  
pp. 111175 ◽  
Author(s):  
Tao-Ran Li ◽  
Xiao-Ni Wang ◽  
Can Sheng ◽  
Yu-Xia Li ◽  
Frederic Zhen-Tao Li ◽  
...  

2008 ◽  
Vol 21 (3) ◽  
pp. 11-16 ◽  
Author(s):  
Z. Zhong ◽  
D. Bennett ◽  
D. Chapman ◽  
J. Chen ◽  
D. Connor ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document