Magnetic resonance imaging showing parietal atrophy of the brain in late-onset Alzheimer’s disease

Early- and late-onset Alzheimer’s disease (AD) patients often exhibit distinct features. We sought to compare overall white matter connectivity and evaluate the pathological factors (amyloid, tau, and vascular pathologies) that affect the disruption of connectivity in these two groups. A total of 50 early- and 38 late-onset AD patients, as well as age-matched cognitively normal participants, were enrolled and underwent diffusion-weighted magnetic resonance imaging to construct fractional anisotropy-weighted white matter connectivity maps. [18F]-THK5351 PET, [18F]-Flutemetamol PET, and magnetic resonance imaging were used for the evaluation of tau and related astrogliosis, amyloid, and small vessel disease markers (lacunes and white matter hyperintensities). Cluster-based statistics was performed for connectivity comparisons and correlation analysis between connectivity disruption and the pathological markers. Both patient groups exhibited significantly disrupted connectivity compared to their control counterparts with distinct patterns. Only THK retention was related to connectivity disruption in early-onset AD patients, and this disruption showed correlations with most cognitive scores, while late-onset AD patients had disrupted connectivity correlated with amyloid deposition, white matter hyperintensities, and lacunes in which only a few cognitive scores showed associations. These findings suggest that the pathogenesis of connectivity disruption and its effects on cognition are distinct between EOAD and LOAD.

Download Full-text

Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network

Applied Bionics and Biomechanics ◽

10.1155/2021/6690539 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Fanar E. K. Al-Khuzaie ◽

Oguz Bayat ◽

Adil D. Duru

Keyword(s):

Neural Network ◽

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Convolutional Neural Network ◽

Dropout Rate ◽

Resonance Imaging ◽

Data Set ◽

The Brain

There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer’s patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer’s patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet.

Download Full-text

Easy Identification of Optimal Coronal Slice on Brain Magnetic Resonance Imaging to Measure Hippocampal Area in Alzheimer’s Disease Patients

BioMed Research International ◽

10.1155/2020/5894021 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

P. Zach ◽

A. Bartoš ◽

A. Lagutina ◽

Z. Wurst ◽

P. Gallina ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Brain Magnetic Resonance Imaging ◽

Control Group ◽

Resonance Imaging ◽

Hippocampal Area ◽

Left And Right ◽

The Brain

Introduction. Measurement of an- hippocampal area or volume is useful in clinical practice as a supportive aid for diagnosis of Alzheimer’s disease. Since it is time-consuming and not simple, it is not being used very often. We present a simplified protocol for hippocampal atrophy evaluation based on a single optimal slice in Alzheimer’s disease. Methods. We defined a single optimal slice for hippocampal measurement on brain magnetic resonance imaging (MRI) at the plane where the amygdala disappears and only the hippocampus is present. We compared an absolute area and volume of the hippocampus on this optimal slice between 40 patients with Alzheimer disease and 40 age-, education- and gender-mateched elderly controls. Furthermore, we compared these results with those relative to the size of the brain or the skull: the area of the optimal slice normalized to the area of the brain at anterior commissure and the volume of the hippocampus normalized to the total intracranial volume. Results. Hippocampal areas on the single optimal slice and hippocampal volumes on the left and right in the control group were significantly higher than those in the AD group. Normalized hippocampal areas and volumes on the left and right in the control group were significantly higher compared to the AD group. Absolute hippocampal areas and volumes did not significantly differ from corresponding normalized hippocampal areas as well as normalized hippocampal volumes using comparisons of areas under the receiver operating characteristic curves. Conclusion. The hippocampal area on the well-defined optimal slice of brain MRI can reliably substitute a complicated measurement of the hippocampal volume. Surprisingly, brain or skull normalization of these variables does not add any incremental differentiation between Alzheimer disease patients and controls or give better results.

Download Full-text