Comparison of the Unified Segmentation Method and the New Segmentation Method on Detection of Grey Matter and White Matter Changes in Alzheimer’s Disease Based on Voxel-Based Morphometry

Voxel-based morphometry method (VBM) has been widely applied to detect the brain atrophy and achieved promising results; however, the effect of the segmentation step in VBM is not clear and the new segmentation method in SPM8 hasn’t been used in Alzheimer’s disease (AD) studies. The aim of this study is to investigate the locations and degrees of grey matter (GM), white matter (WM) atrophy and evaluate the results derived from two segmentation methods. Magnetic resonance imaging (MRI) was collected in 16 AD patients and 16 healthy controls (HC). Using two segmentation methods respectively, several reduction clusters of GM and WM were detected but the locations and degrees of reduction volumes were discrepant resulted from different segmentation methods. Our results suggest that VBM is an effective tool to analyze AD brain atrophy and based on VBM, the comparison of the locations and degrees of volume reduction among AD researches through different segmentation methods should be cautious.

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A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

10.1101/2020.08.18.256321 ◽

2020 ◽

Author(s):

Jafar Zamani ◽

Ali Sadr ◽

Amir-Homayoun Javadi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Large Scale ◽

Automated Segmentation ◽

Resonance Imaging ◽

Brain Areas ◽

Segmentation Methods ◽

Magnetic Resonance Imaging Mri ◽

Underlying Mechanisms ◽

The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

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White Matter Network Alterations in Alzheimer’s Disease Patients

Applied Sciences ◽

10.3390/app10030919 ◽

2020 ◽

Vol 10 (3) ◽

pp. 919

Author(s):

Ramesh Kumar Lama ◽

Sang-Woong Lee

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Grey Matter ◽

Small World ◽

Brain Network ◽

Topological Properties ◽

Graph Theoretical Analysis ◽

Small World Property ◽

The Brain

Previous studies have revealed the occurrence of alterations of white matter (WM) and grey matter (GM) microstructures in Alzheimer’s disease (AD) and their prodromal state amnestic mild cognitive impairment (MCI). In general, these alterations can be studied comprehensively by modeling the brain as a complex network, which describes many important topological properties, such as the small-world property, modularity, and efficiency. In this study, we systematically investigated white matter abnormalities using unbiased whole brain network analysis. We compared regional and network related WM features between groups of 19 AD and 25 MCI patients and 22 healthy controls (HC) using tract-based spatial statistics (TBSS), network based statistics (NBS) and graph theoretical analysis. We did not find significant differences in fractional anisotropy (FA) between two groups on TBSS analysis. However, observable alterations were noticed at a network level. Brain network measures such as global efficiency and small world properties were low in AD patients compared to HCs.

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Mechanical property alterations across the cerebral cortex due to Alzheimer’s disease

Brain Communications ◽

10.1093/braincomms/fcz049 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 7

Author(s):

Lucy V Hiscox ◽

Curtis L Johnson ◽

Matthew D J McGarry ◽

Helen Marshall ◽

Craig W Ritchie ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Mechanical Property ◽

Alzheimer's Disease ◽

Cerebral Cortex ◽

White Matter ◽

Grey Matter ◽

Region Of Interest ◽

Voxel Based Morphometry ◽

Cortical Grey Matter ◽

Post Hoc

Abstract Alzheimer’s disease is a personally devastating neurodegenerative disorder and a major public health concern. There is an urgent need for medical imaging techniques that better characterize the early stages and monitor the progression of the disease. Magnetic resonance elastography (MRE) is a relatively new and highly sensitive MRI technique that can non-invasively assess tissue microstructural integrity via measurement of brain viscoelastic mechanical properties. For the first time, we use high-resolution MRE methods to conduct a voxel-wise MRE investigation and state-of-the-art post hoc region of interest analysis of the viscoelastic properties of the cerebral cortex in patients with Alzheimer’s disease (N = 11) compared with cognitively healthy older adults (N = 12). We replicated previous findings that have reported significant volume and stiffness reductions at the whole-brain level. Significant reductions in volume were also observed in Alzheimer’s disease when white matter, cortical grey matter and subcortical grey matter compartments were considered separately; lower stiffness was also observed in white matter and cortical grey matter, but not in subcortical grey matter. Voxel-based morphometry of both cortical and subcortical grey matter revealed localized reductions in volume due to Alzheimer’s disease in the hippocampus, fusiform, middle, superior temporal gyri and precuneus. Similarly, voxel-based MRE identified lower stiffness in the middle and superior temporal gyri and precuneus, although the spatial distribution of these effects was not identical to the pattern of volume reduction. Notably, MRE additionally identified stiffness deficits in the operculum and precentral gyrus located within the frontal lobe; regions that did not undergo volume loss identified through voxel-based morphometry. Voxel-based-morphometry and voxel-based MRE results were confirmed by a complementary post hoc region-of-interest approach in native space where the viscoelastic changes remained significant even after statistically controlling for regional volumes. The pattern of reduction in cortical stiffness observed in Alzheimer’s disease patients raises the possibility that MRE may provide unique insights regarding the neural mechanisms which underlie the development and progression of the disease. The measured mechanical property changes that we have observed warrant further exploration to investigate the diagnostic usefulness of MRE in cases of Alzheimer’s disease and other dementias.

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Compensatory reconfiguration of functional networks between white matter and grey matter in Alzheimer’s disease

10.1101/2021.08.30.458154 ◽

2021 ◽

Author(s):

Xingxing Zhang ◽

Qing Guan ◽

Debo Dong ◽

Fuyong Chen ◽

Jing Yi ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Euclidean Distance ◽

Grey Matter ◽

Temporal Cortex ◽

Resting State Fmri ◽

Functional Networks ◽

Compensatory Mechanisms ◽

The Brain

AbstractThe temporal synchronization of BOLD signals within white matter (WM) and between WM and grey matter (GM) exhibited intrinsic architecture and cognitive relevance. However, few studies examined the network property within- and between-tissue in Alzheimer’s disease (AD). The hub regions with high weighted degree (WD) were prone to the neuropathological damage of AD. To systematically investigate the changes of hubs within- and between-tissue functional networks in AD patients, we used the resting-state fMRI data of 30 AD patients and 37 normal older adults (NC) from the ADNI open database, and obtained four types of voxel-based WD metrics and four types of distant-dependent WD metrics (ddWD) based on a series of Euclidean distance ranges with a 20mm increment. We found that AD patients showed decreased within-tissue ddWD in the thalamic nucleus and increased between-tissue ddWD in the occipito-temporal cortex, posterior thalamic radiation, and sagittal stratum, compared to NC. We also found that AD patients showed the increased between-tissue FCs between the posterior thalamic radiation and occipito-temporal cortex, and between the sagittal stratum and the salience and executive networks. The dichotomy of decreased and increased ddWD metrics and their locations were consistent with previous studies on the neurodegnerative and compensatory mechanisms of AD, indicating that despite the disruptions, the brain still strived to compensate for the neural inefficiency by reorganizing functional circuits. Our findings also suggested the short-to-medium ranged ddWD metrics between WM and GM as useful biomarker to detect the compensatory changes of functional networks in AD.

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Abstract 44: Associations of Atrial Fibrillation, Neuroimaging Measures of Cerebrovascular Disease and Alzheimer’S Disease-related Pathology, and Cognitive Impairment

Stroke ◽

10.1161/str.46.suppl_1.44 ◽

2015 ◽

Vol 46 (suppl_1) ◽

Author(s):

Jonathan Graff-Radford ◽

Rosebud Roberts ◽

Malini Madhavan ◽

Alejandro Rabinstein ◽

Ruth Cha ◽

...

Keyword(s):

Atrial Fibrillation ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

White Matter ◽

Cerebrovascular Disease ◽

Regression Models ◽

Grey Matter ◽

White Matter Hyperintensity ◽

Carrier Status

The objective of this study was to investigate the cross-sectional associations of atrial fibrillation with neuroimaging measures of cerebrovascular disease and Alzheimer’s disease-related pathology, and their interaction with cognitive impairment. MRI scans of non-demented individuals (n=1044) from the population-based Mayo Clinic Study of Aging were analyzed for infarctions, total grey matter, hippocampal and white matter hyperintensity volumes. A subset of 496 individuals underwent FDG and C-11 Pittsburgh compound B (PiB) PET scans. We assessed the associations of atrial fibrillation with i) categorical MRI measures (cortical and subcortical infarctions) using multivariable logistic regression models, and with ii) continuous MRI measures ( hippocampal, total grey matter, and white matter hyperintensity volumes) and FDG-PET and PiB-PET measures using multivariable linear regression models, and adjusting for confounders. Among participants who underwent MRI (median age, 77.8, 51.6% male), 13.5% had atrial fibrillation. Presence of atrial fibrillation was associated with subcortical infarctions (odds ratio [OR], 1.83; p=0.002), cortical infarctions (OR, 1.91; p=0.03), total grey matter volume (Beta [β], -.025, p<.0001) after controlling for age, education, gender, APOE e4 carrier status, coronary artery disease, diabetes, history of clinical stroke, and hypertension. However, atrial fibrillation was not associated with white matter hyperintensity volume, hippocampal volume, Alzheimer’s pattern of FDG hypometabolism or PiB uptake. There was a significant interaction of cortical infarction (p for interaction=0.004) and subcortical infarction (p for interaction =0.015) with atrial fibrillation with regards to odds of mild cognitive impairment (MCI). Using subjects with no atrial fibrillation and no infarction as the reference, the OR (95% confidence intervals [CI]) for MCI was 2.98 (1.66, 5.35;p = 0.0002) among participants with atrial fibrillation and any infarction, 0.69 (0.36, 1.33;p= 0.27) for atrial fibrillation and no infarction, and 1.50 (0.96, 2.32;p = 0.07) for no atrial fibrillation and any infarction. These data highlight that atrial fibrillation is associated with MCI in the presence of infarctions.

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The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

Fuzzy Systems and Data Mining VI - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200689 ◽

2020 ◽

Author(s):

Jingyan Qiu ◽

Linjian Li ◽

Yida Liu ◽

Yingjun Ou ◽

Yubei Lin

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Large Scale ◽

Early Stage ◽

Training Transfer ◽

Detection And Diagnosis ◽

Magnetic Resonance Imaging Mri ◽

Deep Learning Model ◽

The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

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Magnetic Nanoparticles as In Vivo Tracers for Alzheimer’s Disease

Magnetochemistry ◽

10.3390/magnetochemistry6010013 ◽

2020 ◽

Vol 6 (1) ◽

pp. 13

Author(s):

Bhargy Sharma ◽

Konstantin Pervushin

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Diagnosis ◽

Contrast Agents ◽

Neurological Diseases ◽

Pulse Sequences ◽

Experimental Conditions ◽

Magnetic Resonance Imaging Mri ◽

The Brain

Drug formulations and suitable methods for their detection play a very crucial role in the development of therapeutics towards degenerative neurological diseases. For diseases such as Alzheimer’s disease, magnetic resonance imaging (MRI) is a non-invasive clinical technique suitable for early diagnosis. In this review, we will discuss the different experimental conditions which can push MRI as the technique of choice and the gold standard for early diagnosis of Alzheimer’s disease. Here, we describe and compare various techniques for administration of nanoparticles targeted to the brain and suitable formulations of nanoparticles for use as magnetically active therapeutic probes in drug delivery targeting the brain. We explore different physiological pathways involved in the transport of such nanoparticles for successful entry in the brain. In our lab, we have used different formulations of iron oxide nanoparticles (IONPs) and protein nanocages as contrast agents in anatomical MRI of an Alzheimer’s disease (AD) brain. We compare these coatings and their benefits to provide the best contrast in addition to biocompatibility properties to be used as sustainable drug-release systems. In the later sections, the contrast enhancement techniques in MRI studies are discussed. Examples of contrast-enhanced imaging using advanced pulse sequences are discussed with the main focus on important studies in the field of neurological diseases. In addition, T1 contrast agents such as gadolinium chelates are compared with the T2 contrast agents mainly made of superparamagnetic inorganic metal nanoparticles.

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