scholarly journals P3-070: CSF Total tau and gray matter density in Alzheimer's disease-specific brain regions: A study of normal aging

2008 ◽  
Vol 4 ◽  
pp. T538-T538
Author(s):  
Lidia Glodzik ◽  
Wai Tsui ◽  
Lisa Mosconi ◽  
Miroslaw Brys ◽  
Susan De Santi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gray Matter ◽  
Brain Regions ◽  
Normal Aging ◽  
Matter Density ◽  
Gray Matter Density ◽  
Total Tau ◽  
Disease Specific

scholarly journals Anosognosia in Amnestic Mild Cognitive Impairment Is Related to Diminished Hippocampal Volume Comparable to Alzheimer’s Disease Dementia: Preliminary MRI Findings

2021 ◽  
Vol 13 ◽  
Author(s):  
Juan Francisco Flores-Vázquez ◽  
Gabriel Ramírez-García ◽  
Oscar René Marrufo-Meléndez ◽  
Ruth Alcalá-Lozano ◽  
Morten Peter Lietz ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Gray Matter ◽  
Mean Diffusivity ◽  
Amnestic Mild Cognitive Impairment ◽  
Matter Density ◽  
Mri Findings ◽  
Gray Matter Density

Although the presence of anosognosia in amnestic mild cognitive impairment (aMCI) may be predictive of conversion to Alzheimer’s disease (AD), little is known about its neural correlates in AD and aMCI. Four different groups were compared using volumetric and diffusion magnetic resonance imaging metrics in regions of interest (hippocampus and cingulum cortex gray matter, cingulum bundle white matter): aMCI subjects with anosognosia (n = 6), aMCI subjects without anosognosia (n = 12), AD subjects with anosognosia (n = 6), and AD subjects without anosognosia (n = 9). aMCI subjects with anosognosia displayed a significantly lower gray matter density (GMD) in the bilateral hippocampus than aMCI subjects without anosognosia, which was accounted for by bilateral hippocampal differences. Furthermore, we identified that the mean hippocampal gray matter density of aMCI subjects with anosognosia was not statistically different than that of AD subjects. The groups of aMCI and AD subjects with anosognosia also displayed a lower GMD in the bilateral cingulum cortex compared to subjects without anosognosia, but these differences were not statistically significant. No statistically significant differences were found in the fractional anisotropy or mean diffusivity of the hippocampus or cingulum between subjects with and without anosognosia in aMCI or AD groups. While these findings are derived from a small population of subjects and are in need of replication, they suggest that anosognosia in aMCI might be a useful clinical marker to suspect brain changes associated with AD neuropathology.

scholarly journals Conditioned fear reactions are associated with gray matter density but not cortical microstructure

2020 ◽  
Author(s):  
Christoph Fraenz ◽  
Dorothea Metzen ◽  
Christian J. Merz ◽  
Helene Selpien ◽  
Nikolai Axmacher ◽  
...  
Keyword(s):  
Gray Matter ◽  
Conditioned Fear ◽  
Brain Networks ◽  
Brain Regions ◽  
Matter Density ◽  
Fear Learning ◽  
Future Research ◽  
Fear Reaction ◽  
Gray Matter Density ◽  
Diffusion Weighted Images

AbstractResearch has shown that fear acquisition, in reaction to potentially harmful stimuli or situations, is characterized by pronounced interindividual differences. It is likely that such differences are evoked by variability in the macro- and microstructural properties of brain regions involved in the processing of threat or safety signals from the environment. Indeed, previous studies have shown that the strength of conditioned fear reactions is associated with the cortical thickness or volume of various brain regions. However, respective studies were exclusively targeted at single brain regions instead of whole brain networks. Here, we tested 60 young and healthy individuals in a differential fear conditioning paradigm while they underwent fMRI scanning. In addition, we acquired T1-weighted and multi-shell diffusion-weighted images prior to testing. We used task-based fMRI data to define global brain networks which exhibited increased BOLD responses towards CS+ or CS- presentations, respectively. From these networks, we obtained mean values of gray matter density, neurite density, and neurite orientation dispersion. We found that mean gray matter density averaged across the CS+ network was significantly correlated with the strength of conditioned fear reactions quantified via skin conductance response. Measures of neurite architecture were not associated with conditioned fear reaction in any of the two networks. Our results extend previous findings on the relationship between brain morphometry and fear learning. Most importantly, our study is the first to introduce neurite imaging to fear learning research and discusses how its implementation can be improved in future research.

scholarly journals Cortical Microstructural Alterations in Mild Cognitive Impairment and Alzheimer’s Disease Dementia

2020 ◽  
Vol 30 (5) ◽  
pp. 2948-2960 ◽  
Author(s):  
Nicholas M Vogt ◽  
Jack F Hunt ◽  
Nagesh Adluru ◽  
Douglas C Dean ◽  
Sterling C Johnson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cortical Thickness ◽  
Gray Matter ◽  
Brain Regions ◽  
Cortical Atrophy ◽  
Microstructural Alterations ◽  
Additional Information

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.

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

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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