The effect of beta-amyloid and tau protein aggregations on magnetic susceptibility of anterior hippocampal laminae in Alzheimer's diseases

Zhao, Zhiyong; Zhang, Lei; Wen, Qingqing; Luo, Wanrong; Zheng, Weihao; Liu, Tingting; Zhang, Yi; Zhu, Keqing; Wu, Dan

doi:10.1016/j.neuroimage.2021.118584

Cited by 19 publications

(20 citation statements)

References 65 publications

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“…Although many patients had not shown symptoms of AD during MCI period, the temporal lobe located in the inner part of the brain had obvious symptoms. The atrophy of the hippocampus was the most obvious ( Adachi et al, 2021 ; Guo et al, 2021 ; Zhao et al, 2021 ). Located at the bottom of the brain, the amygdala was shaped like an almond and was the center of the brain to control and manage emotions.…”

Section: Discussionmentioning

confidence: 99%

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Meng

Liu

et al. 2022

Front. Neuroinform.

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Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

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Section: Discussionmentioning

confidence: 99%

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Meng

Liu

et al. 2022

Front. Neuroinform.

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“…The extent of elevated magnetic susceptibility in QSM and standardized uptake value ratios in amyloid and tau PET do not overlap, which may imply that more complicated factors contribute to these signal changes. When the anterior hippocampus was segmented into seven layers using highresolution ex vivo MRI, the molecular changes in Aβ and tau protein aggregations had specific effects on the magnetic susceptibilities of AD brain tissues (Zhao et al, 2021). However, layer-specific PET analysis is impractical due to its low resolution.…”

Section: Role Of Quantitative Susceptibility Mapping As Biomarker For...mentioning

confidence: 99%

Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations

et al. 2022

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Alzheimer’s disease (AD) is the most common type of dementia and a distressing diagnosis for individuals and caregivers. Researchers and clinical trials have mainly focused on β-amyloid plaques, which are hypothesized to be one of the most important factors for neurodegeneration in AD. Meanwhile, recent clinicopathological and radiological studies have shown closer associations of tau pathology rather than β-amyloid pathology with the onset and progression of Alzheimer’s symptoms. Toward a biological definition of biomarker-based research framework for AD, the 2018 National Institute on Aging–Alzheimer’s Association working group has updated the ATN classification system for stratifying disease status in accordance with relevant pathological biomarker profiles, such as cerebral β-amyloid deposition, hyperphosphorylated tau, and neurodegeneration. In addition, altered iron metabolism has been considered to interact with abnormal proteins related to AD pathology thorough generating oxidative stress, as some prior histochemical and histopathological studies supported this iron-mediated pathomechanism. Quantitative susceptibility mapping (QSM) has recently become more popular as a non-invasive magnetic resonance technique to quantify local tissue susceptibility with high spatial resolution, which is sensitive to the presence of iron. The association of cerebral susceptibility values with other pathological biomarkers for AD has been investigated using various QSM techniques; however, direct evidence of these associations remains elusive. In this review, we first briefly describe the principles of QSM. Second, we focus on a large variety of QSM applications, ranging from common applications, such as cerebral iron deposition, to more recent applications, such as the assessment of impaired myelination, quantification of venous oxygen saturation, and measurement of blood– brain barrier function in clinical settings for AD. Third, we mention the relationships among QSM, established biomarkers, and cognitive performance in AD. Finally, we discuss the role of QSM as an imaging biomarker as well as the expectations and limitations of clinically useful diagnostic and therapeutic implications for AD.

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“…The right hemisphere was fixed in 4% paraformaldehyde, and then dissected into small blocks. Next, the anterior hippocampal blocks (~5 mm thick block starting from the most anterior tip) were transferred to phosphate‐buffered saline (PBS) with 1 mM Gd‐DTPA (Berlex Imaging) for over 72 h. Finally, the specimens were placed in a 25‐mm diameter tube (which was filled with Fomblin) with supporting materials for stabilization during MRI scanning (Zhao et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…Then, we performed a landmark‐based coarse registration, followed by intensity‐based affine and nonlinear transformations. More details can be found in our previous study (Zhao et al, 2021 ). Lastly, the dMRI data were registered to the MGE image, as described in the last section.…”

Section: Methodsmentioning

confidence: 99%

Layer‐specific microstructural patterns of anterior hippocampus in Alzheimer's disease with ex vivo diffusion MRI at 14.1 T

Zhao

Zhang

Luo

et al. 2022

Human Brain Mapping

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High‐resolution ex vivo diffusion MRI (dMRI) can provide exquisite mesoscopic details and microstructural information of the human brain. Microstructural pattern of the anterior part of human hippocampus, however, has not been well elucidated with ex vivo dMRI, either in normal or disease conditions. The present study collected high‐resolution (0.1 mm isotropic) dMRI of post‐mortem anterior hippocampal tissues from four Alzheimer's diseases (AD), three primary age‐related tauopathy (PART), and three healthy control (HC) brains on a 14.1 T spectrometer. We evaluated how AD affected dMRI‐based microstructural features in different layers and subfields of anterior hippocampus. In the HC samples, we found higher anisotropy, lower diffusivity, and more streamlines in the layers within cornu ammonis (CA) than those within dentate gyrus (DG). Comparisons between disease groups showed that (1) anisotropy measurements in the CA layers of AD, especially stratum lacunosum (SL) and stratum radiatum (SR), had higher regional variability than the other two groups; (2) streamline density in the DG layers showed a gradually increased variance from HC to PART to AD; (3) AD also showed the higher variability in terms of inter‐layer connectivity than HC or PART. Moreover, voxelwise correlation analysis between the coregistered dMRI and histopathology images revealed significant correlations between dMRI measurements and the contents of amyloid beta (Aβ)/tau protein in specific layers of AD samples. These findings may reflect layer‐specific microstructural characteristics in different hippocampal subfields at the mesoscopic resolution, which were associated with protein deposition in the anterior hippocampus of AD patients.

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The effect of beta-amyloid and tau protein aggregations on magnetic susceptibility of anterior hippocampal laminae in Alzheimer's diseases

Cited by 19 publications

References 65 publications

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations

Layer‐specific microstructural patterns of anterior hippocampus in Alzheimer's disease with ex vivo diffusion MRI at 14.1 T

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