scholarly journals Reliable Measurements of theβ-Amyloid Pool in Blood Could Help in the Early Diagnosis of AD

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Pedro Pesini ◽  
Virginia Pérez-Grijalba ◽  
Inmaculada Monleón ◽  
Mercè Boada ◽  
Lluís Tárraga ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
Small Proportion ◽  
Blood Cells ◽  
Plasma Proteins ◽  
Blood Samples ◽  
Striking Result ◽  
Healthy Control ◽  
Amnesic Mild Cognitive Impairment

The present study was aimed at assessing the capability of Aβ1-40 and Aβ1-42 levels in undiluted plasma (UP), diluted plasma (DP), and cell bound (CB) to distinguish between early stages of Alzheimer's disease (AD), amnesic mild cognitive impairment (MCI), and healthy control (HC). Four blood samples from each participant were collected during one month and the levels of Aβ1-40 and Aβ1-42 were determined by a blinded proprietary ELISA sandwich (Araclon Biotech. Zaragoza, Spain). First striking result was that the amount of Aβ1-40 and Aβ1-42 in UP represented only a small proportion (~15%) of the total beta-amyloid pool in blood (βAPB) described here as the sum of Aβ1-40 and Aβ1-42 in blood where they are free in plasma, bound to plasma proteins, and bound to blood cells. Furthermore, we found that levels of Aβ1-40 and Aβ1-42 in UP, DP, and CB were significantly higher in MCI when compared to HC. On average, the totalβAPB was 1.8 times higher in MCI than in HC (P=0.03) and allowed to discriminate between MCI and HC with a sensitivity and specificity over 80%. Thus, quantification of several markers of theβAPB could be useful and reliable in the discrimination between MCI and HC.

scholarly journals Entorhinal cortex volume is associated with episodic memory related brain activation in normal aging and amnesic mild cognitive impairment

2011 ◽  
Vol 5 (2) ◽  
pp. 126-136 ◽  
Author(s):  
Mehul A. Trivedi ◽  
Travis R. Stoub ◽  
Christopher M. Murphy ◽  
Sarah George ◽  
Leyla deToledo-Morrell ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Episodic Memory ◽  
Entorhinal Cortex ◽  
Brain Activation ◽  
Normal Aging ◽  
Amnesic Mild Cognitive Impairment

Differences in and correlations between cognitive abilities and brain volumes in healthy control, mild cognitive impairment, and Alzheimer disease groups

2016 ◽  
Vol 29 (4) ◽  
pp. 473-480 ◽  
Author(s):  
Soon-Cheol Chung ◽  
Mi-Hyun Choi ◽  
Hyung-Sik Kim ◽  
Jung-Chul Lee ◽  
Sung-Jun Park ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Alzheimer Disease ◽  
Cognitive Abilities ◽  
Brain Volumes ◽  
Healthy Control

scholarly journals Early diagnosis of mild cognitive impairment and Alzheimer's disease based on salivary lactoferrin

2017 ◽  
Vol 8 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Eva Carro ◽  
Fernando Bartolomé ◽  
Félix Bermejo‐Pareja ◽  
Alberto Villarejo‐Galende ◽  
José Antonio Molina ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis

scholarly journals Quantitative MRI of Perivascular Spaces at 3T for Early Diagnosis of Mild Cognitive Impairment

2018 ◽  
Vol 39 (9) ◽  
pp. 1622-1628 ◽  
Author(s):  
M. Niazi ◽  
M. Karaman ◽  
S. Das ◽  
X.J. Zhou ◽  
P. Yushkevich ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
Quantitative Mri ◽  
Perivascular Spaces

scholarly journals A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

2019 ◽  
Vol 9 (9) ◽  
pp. 217 ◽  
Author(s):  
Gorji ◽  
Kaabouch
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
Brain Structure ◽  
Neurodegenerative Disorder ◽  
Healthy People ◽  
Learning Approach ◽  
Brain Structure And Function ◽  
Percent Accuracy

Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer’s disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease’s progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.

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