Gray matter atrophy in patients with mild cognitive impairment/Alzheimer's disease over the course of developing delusions

Abstract In Alzheimer’s disease (AD), neurodegenerative processes are ongoing for years prior to the time that cortical atrophy can be reliably detected using conventional neuroimaging techniques. Recent advances in diffusion-weighted imaging have provided new techniques to study neural microstructure, which may provide additional information regarding neurodegeneration. In this study, we used neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion model, in order to investigate cortical microstructure along the clinical continuum of mild cognitive impairment (MCI) and AD dementia. Using gray matter-based spatial statistics (GBSS), we demonstrated that neurite density index (NDI) was significantly lower throughout temporal and parietal cortical regions in MCI, while both NDI and orientation dispersion index (ODI) were lower throughout parietal, temporal, and frontal regions in AD dementia. In follow-up ROI analyses comparing microstructure and cortical thickness (derived from T1-weighted MRI) within the same brain regions, differences in NODDI metrics remained, even after controlling for cortical thickness. Moreover, for participants with MCI, gray matter NDI—but not cortical thickness—was lower in temporal, parietal, and posterior cingulate regions. Taken together, our results highlight the utility of NODDI metrics in detecting cortical microstructural degeneration that occurs prior to measurable macrostructural changes and overt clinical dementia.

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Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) Found by Hierarchical Clustering

International Journal of Alzheimer s Disease ◽

10.1155/2020/2142854 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Mikko Kärkkäinen ◽

Mithilesh Prakash ◽

Marzieh Zare ◽

Jussi Tohka ◽

for the Alzheimer's Disease Neuroimaging Initiative

Keyword(s):

Alzheimer’S Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Hierarchical Clustering ◽

Gray Matter ◽

Brain Atrophy ◽

Matter Density ◽

Gray Matter Density ◽

Gray Matter Atrophy ◽

Density Maps

A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer’s disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD). Among these clusters, EOAD subjects displayed marked cortical gray matter atrophy and atrophy of the precuneus. Furthermore, EOAD subjects had the highest progression rates as measured with ADAS slopes during the longitudinal follow-up of 36 months. Striking heterogeneities in brain atrophy patterns were observed with MCI subjects. We found clusters of stable MCI, clusters of diffuse brain atrophy with fast progression, and MCI subjects displaying similar atrophy patterns as the typical or atypical AD subjects. Bidirectional differences in structural phenotypes were found with MCI subjects involving the anterior cerebellum and the frontal cortex. The diversity of the MCI subjects suggests that the structural phenotypes of MCI subjects would deserve a more detailed investigation with a significantly larger cohort. Our results demonstrate that the hierarchical agglomerative clustering method is an efficient tool in dividing a cohort of subjects with gray matter atrophy into coherent clusters manifesting different structural phenotypes.

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Diffusion tensor studies and voxel-based morphometry of the temporal lobe to determine the cognitive prognosis in cases of Alzheimer’s disease and mild cognitive impairment: Do white matter changes precede gray matter changes?

SpringerPlus ◽

10.1186/s40064-016-2692-5 ◽

2016 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Toshiaki Taoka ◽

Fumihiko Yasuno ◽

Masayuki Morikawa ◽

Makoto Inoue ◽

Kuniaki Kiuchi ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

White Matter ◽

Temporal Lobe ◽

Gray Matter ◽

Diffusion Tensor ◽

Voxel Based Morphometry ◽

White Matter Changes

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Detection of gray matter microstructural changes in Alzheimer’s disease continuum using fiber orientation

10.21203/rs.3.rs-17303/v6 ◽

2020 ◽

Author(s):

Peter Lee ◽

Hang-Rai Kim ◽

Yong Jeong ◽

Alzheimer's Disease Neuroimaging Initiative

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Gray Matter ◽

Fiber Orientation ◽

Diffusion Tensor ◽

Mean Diffusivity ◽

High Sensitivity ◽

Normal Vector

Abstract Background This study aimed to investigate feasible gray matter microstructural biomarkers with high sensitivity for early Alzheimer’s disease (AD) detection. We propose a diffusion tensor imaging (DTI) measure, “radiality”, as an early AD biomarker. It is the dot product of the normal vector of the cortical surface and primary diffusion direction, which reflects the fiber orientation within the cortical column. Methods We analyzed neuroimages from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including images from 78 cognitively normal (CN), 50 early mild cognitive impairment (EMCI), 34 late mild cognitive impairment (LMCI), and 39 AD patients. We then evaluated the cortical thickness (CTh), mean diffusivity (MD), which are conventional AD magnetic resonance imaging (MRI) biomarkers, and the amount of accumulated amyloid and tau using positron emission tomography (PET). Radiality was projected on the gray matter surface to compare and validate the changes with different stages alongside other neuroimage biomarkers.Results The results revealed decreased radiality primarily in the entorhinal, insula, frontal, and temporal cortex with further progression of disease. In particular, radiality could delineate the difference between the CN and EMCI groups, while the other biomarkers could not. We examined the relationship between radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed a high association with conventional biomarkers. Additional ROI analysis revealed the dynamics of AD-related changes as stages onward.Conclusion Radiality in cortical gray matter showed AD-specific changes and relevance with other conventional AD biomarkers with high sensitivity. Moreover, radiality could identify the group differences seen in EMCI, representative of changes in early AD, which supports its superiority in early diagnosis compared to that possible with conventional biomarkers. We provide evidence of structural changes with cognitive impairment and suggest radiality as a sensitive biomarker for identifying early AD.

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Disruption of gray matter microstructure in Alzheimer’s disease continuum using fiber orientation

10.21203/rs.3.rs-17303/v1 ◽

2020 ◽

Author(s):

Peter Lee ◽

Hang-Rai Kim ◽

Yong Jeong

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Diagnosis ◽

Gray Matter ◽

Fiber Orientation ◽

Imaging Biomarkers ◽

Normal Vector ◽

Higher Sensitivity

Abstract There have been several MR imaging biomarkers of Alzheimer’s disease (AD) for early diagnosis. Cortical mean diffusivity (MD) is one of them for the study of the cortical microstructural change in AD. However, several factors may overshadow the feasibility of MD as AD biomarker. Thus, current study investigated feasible gray matter microstructure biomarker with higher sensitivity for early AD detection. With the aim of facilitating the early detection of AD, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) proposed two stages based on the memory performance: early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). We propose single shell DTI measure, ‘ radiality ’, for early AD biomarker. It is a dot product between cortical surface normal vector and primary diffusion direction, which presumably reflects the fiber orientation within the cortical column. Here, we gathered images from ADNI phase 2 & 3; 78 cognitive normal, 51 EMCI, 34 LMCI, and 39 AD patients. Then, we evaluated cortical thickness (CTh), MD, amount of amyloid and tau accumulations using PET, which are conventional AD biomarkers. Radiality was projected on gray matter surface to compare and validate the changes along other neuroimage biomarkers. Results showed decreased radiality primarily in entorhinal, insula, frontal and temporal cortex as disease progress onward. Especially, radiality could delineate the difference between cognitive normal and EMCI group while other biomarkers could not. We looked into the relationship between the radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed high association with conventional biomarkers. Additional ROI analysis exhibit dynamics of AD related changes as stages onward. In conclusion, radiality in cortical gray matter showed AD specific changes and relevance with other conventional AD biomarkers with higher sensitivity. Besides, it could show group differences in early AD changes from EMCI which show advantage for early diagnosis than using conventional biomarkers. We provide the evidence of structure changes with cognitive impairment and suggest radiality as a sensitive biomarker for early diagnose and progress monitor AD.

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