scholarly journals ANALYSES OF BRAIN CORTICAL CHANGES OF ALZHEIMER’S DISEASE

2021 ◽  
pp. 2140025
Author(s):  
YUTING LV ◽  
WENSHUO ZHAO ◽  
XUFENG YAO ◽  
SONG XU ◽  
ZHIXIAN TANG ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Brain Regions ◽  
Morphological Description ◽  
Gyrification Index ◽  
Morphological Differences ◽  
The Brain ◽  
Normal Controls

Alzheimer’s disease (AD) produces complicated cortical changes in gray matter (GM) of the human brain. However, alterations in the brain cortex have not been clearly addressed. In our study, a cohort of 236 cases MR data enrolled from the ADNI database was categorized into three groups of normal controls (NCs), mild cognitive impairment (MCI) and AD. The GM morphological differences were investigated among the three groups using the magnetic resonance (MR) GM characteristics of gray matter volume (GMV), cortical thickness (CT), cortical surface area (CSA) and local gyrification index (LGI) at the three levels of whole brain, bilateral hemispheres and critical brain regions. Totally, there were six critical brain regions for GMV, 11 for CT, 2 for CSA and 59 for LGI among the three groups for the no-division groups. Also, there were 11 critical brain regions for GMV, 15 for CT, 8 for CSA, 3 for LGI for female sub-groups and 4 critical brain regions for GMV, 11 for CT, 1 for CSA, 3 for LGI for male sub-groups. The four measured cortical characteristics showed reliable capability in the morphological description of GM changes of AD. In conclusion, the cortical characteristics of GMV, CT, CSA and LGI of critical brain regions showed valuable indications for GM changes of AD, and those characteristics could be used as imaging markers for AD prediction.

scholarly journals Topographic patterns of white matter hyperintensities are associated with multimodal neuroimaging biomarkers of Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Malo Gaubert ◽  
Catharina Lange ◽  
Antoine Garnier-Crussard ◽  
Theresa Köbe ◽  
Salma Bougacha ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Glucose Metabolism ◽  
Corpus Callosum ◽  
Gray Matter ◽  
Fdg Pet ◽  
White Matter Hyperintensities ◽  
Gray Matter Volume ◽  
Matter Volume

Abstract Background White matter hyperintensities (WMH) are frequently found in Alzheimer’s disease (AD). Commonly considered as a marker of cerebrovascular disease, regional WMH may be related to pathological hallmarks of AD, including beta-amyloid (Aβ) plaques and neurodegeneration. The aim of this study was to examine the regional distribution of WMH associated with Aβ burden, glucose hypometabolism, and gray matter volume reduction. Methods In a total of 155 participants (IMAP+ cohort) across the cognitive continuum from normal cognition to AD dementia, FLAIR MRI, AV45-PET, FDG-PET, and T1 MRI were acquired. WMH were automatically segmented from FLAIR images. Mean levels of neocortical Aβ deposition (AV45-PET), temporo-parietal glucose metabolism (FDG-PET), and medial-temporal gray matter volume (GMV) were extracted from processed images using established AD meta-signature templates. Associations between AD brain biomarkers and WMH, as assessed in region-of-interest and voxel-wise, were examined, adjusting for age, sex, education, and systolic blood pressure. Results There were no significant associations between global Aβ burden and region-specific WMH. Voxel-wise WMH in the splenium of the corpus callosum correlated with greater Aβ deposition at a more liberal threshold. Region- and voxel-based WMH in the posterior corpus callosum, along with parietal, occipital, and frontal areas, were associated with lower temporo-parietal glucose metabolism. Similarly, lower medial-temporal GMV correlated with WMH in the posterior corpus callosum in addition to parietal, occipital, and fontal areas. Conclusions This study demonstrates that local white matter damage is correlated with multimodal brain biomarkers of AD. Our results highlight modality-specific topographic patterns of WMH, which converged in the posterior white matter. Overall, these cross-sectional findings corroborate associations of regional WMH with AD-typical Aß deposition and neurodegeneration.

Reproducibility of Brain Volume Changes in Longitudinal Voxel-Based Morphometry Between Non-Accelerated and Accelerated Magnetic Resonance Imaging

2021 ◽  
pp. 1-10
Author(s):  
Hidemasa Takao ◽  
Shiori Amemiya ◽  
Osamu Abe ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Elderly Subjects ◽  
Voxel Based Morphometry ◽  
Volume Changes ◽  
Matter Volume ◽  
Healthy Elderly

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Angela M. Crist ◽  
Kelly M. Hinkle ◽  
Xue Wang ◽  
Christina M. Moloney ◽  
Billie J. Matchett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Digital Pathology ◽  
Selective Vulnerability ◽  
Brain Regions ◽  
Biochemical Analyses ◽  
The Brain ◽  
Relevant Gene ◽  
Tau Expression

AbstractSelective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

Intranasal administration of mitochondria improves spatial memory in olfactory bulbectomized mice

2021 ◽  
pp. 153537022110568
Author(s):  
Natalia V Bobkova ◽  
Daria Y Zhdanova ◽  
Natalia V Belosludtseva ◽  
Nikita V Penkov ◽  
Galina D Mironova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Memory ◽  
Intranasal Administration ◽  
Brain Structures ◽  
Brain Regions ◽  
Dependent Manner ◽  
Behavioral Experiments ◽  
Hippocampal Cell Culture ◽  
The Brain

Here, we found that functionally active mitochondria isolated from the brain of NMRI donor mice and administrated intranasally to recipient mice penetrated the brain structures in a dose-dependent manner. The injected mitochondria labeled with the MitoTracker Red localized in different brain regions, including the neocortex and hippocampus, which are responsible for memory and affected by degeneration in patients with Alzheimer's disease. In behavioral experiments, intranasal microinjections of brain mitochondria of native NMRI mice improved spatial memory in the olfactory bulbectomized (OBX) mice with Alzheimer’s type degeneration. Control OBX mice demonstrated loss of spatial memory tested in the Morris water maze. Immunocytochemical analysis revealed that allogeneic mitochondria colocalized with the markers of astrocytes and neurons in hippocampal cell culture. The results suggest that a non-invasive route intranasal administration of mitochondria may be a promising approach to the treatment of neurodegenerative diseases characterized, like Alzheimer's disease, by mitochondrial dysfunction.

scholarly journals Ceramide and Related-Sphingolipid Levels Are Not Altered in Disease-Associated Brain Regions of APPSLand APPSL/PS1M146LMouse Models of Alzheimer's Disease: Relationship with the Lack of Neurodegeneration?

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Laurence Barrier ◽  
Bernard Fauconneau ◽  
Anastasia Noël ◽  
Sabrina Ingrand
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Models ◽  
Neuronal Death ◽  
Time Course ◽  
Neuronal Loss ◽  
Brain Regions ◽  
App Processing ◽  
Ceramide Accumulation ◽  
The Brain

There is evidence linking sphingolipid abnormalities, APP processing, and neuronal death in Alzheimer's disease (AD). We previously reported a strong elevation of ceramide levels in the brain of the APPSL/PS1Ki mouse model of AD, preceding the neuronal death. To extend these findings, we analyzed ceramide and related-sphingolipid contents in brain from two other mouse models (i.e., APPSLand APPSL/PS1M146L) in which the time-course of pathology is closer to that seen in most currently available models. Conversely to our previous work, ceramides did not accumulate in disease-associated brain regions (cortex and hippocampus) from both models. However, the APPSL/PS1Ki model is unique for its drastic neuronal loss coinciding with strong accumulation of neurotoxic Aβisoforms, not observed in other animal models of AD. Since there are neither neuronal loss nor toxic Aβspecies accumulation in APPSLmice, we hypothesized that it might explain the lack of ceramide accumulation, at least in this model.

scholarly journals Moderating Effect of Insulin Resistance on the Relationship between Gray Matter Volumes and Cognitive Function

2018 ◽  
Vol 7 (11) ◽  
pp. 413 ◽  
Author(s):  
Jiyeon Lee ◽  
Jihyeon Kim ◽  
Seong Shin ◽  
Soowon Park ◽  
Dong Yoon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Insulin Resistance ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Gray Matter ◽  
Structural Changes ◽  
Gray Matter Volume ◽  
Volume Loss ◽  
Matter Volume

Background: It is controversial whether exposure to insulin resistance accelerates cognitive deterioration. The present study aimed to investigate the association between insulin resistance and gray matter volume loss to predict the cognitive decline. Methods: We recruited 160 participants (78 with Alzheimer’s disease and 82 without Alzheimer’s disease). Insulin resistance, regional gray matter volume, and cognitive function were assessed. A hierarchical moderated multiple regression (MMR) model was used to determine any associations among insulin resistance, structural changes in the brain, and cognitive decline. Results: The volumes of 7 regions in the gray matter were negatively related to insulin resistance in Alzheimer’s disease (p =0.032). Hierarchical MMR analysis indicated that insulin resistance did not directly affect the cognitive decline but moderated the cognitive decline through the decrease in gray matter volume in the key brain regions, i.e., inferior orbitofrontal gyrus (left), middle cingulate gyrus (right), hippocampus (right), and precuneus (right) (p < 0.05 in each case). Conclusion: Insulin resistance appears to exacerbate the cognitive decline associated with several gray matter volume loss.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

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