scholarly journals Precuneus Structure Changes in Amnestic Mild Cognitive Impairment

2016 ◽  
Vol 32 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Robert Haussmann ◽  
Annett Werner ◽  
Antonia Gruschwitz ◽  
Antje Osterrath ◽  
Jan Lange ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cortical Thickness ◽  
Structural Changes ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Network Characteristics ◽  
Structure Changes ◽  
Positron Emission ◽  
Higher Cognitive Functions

Patients with amnestic mild cognitive impairment (aMCI) are at risk for developing Alzheimer’s disease. Due to their prominent memory impairment, structural magnetic resonance imaging (MRI) often focuses on the hippocampal region. However, recent positron-emission tomography data suggest that within a network of frontal and temporal changes, patients with aMCI show metabolic alterations in the precuneus, a key region for higher cognitive functions. Using high-resolution MRI and whole-brain cortical thickness analyses in 28 patients with aMCI and 25 healthy individuals, we wanted to investigate whether structural changes in the precuneus would be associated with cortical thickness reductions in frontal and temporal brain regions in patients with aMCI. In contrast to healthy people, patients with aMCI showed an association of cortical thinning in the precuneus with predominantly left-hemispheric thickness reductions in medial temporal and frontal cortices. Our data highlight structural neuronal network characteristics among patients with aMCI.

Cortical thickness in single- versus multiple-domain amnestic mild cognitive impairment

NeuroImage ◽  
2007 ◽  
Vol 36 (2) ◽  
pp. 289-297 ◽  
Author(s):  
Sang Won Seo ◽  
Kiho Im ◽  
Jong-Min Lee ◽  
Yun-Hee Kim ◽  
Sung Tae Kim ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cortical Thickness ◽  
Amnestic Mild Cognitive Impairment ◽  
Single Versus ◽  
Multiple Domain

Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
pp. 1-14
Author(s):  
Fangmei He ◽  
Yuchen Zhang ◽  
Xiaofeng Wu ◽  
Youjun Li ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Microstructure Changes ◽  
Disease Trajectory ◽  
Development Trajectory ◽  
The Right

Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer’s disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient’s Mini-Mental State Examination scores. Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

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 Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer’s disease

2020 ◽  
Author(s):  
Ruaridh Clark ◽  
Niia Nikolova ◽  
William J. McGeown ◽  
Malcolm Macdonald
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Structural Changes ◽  
Amnestic Mild Cognitive Impairment ◽  
Functional Networks ◽  
Limited Information ◽  
The Impact

AbstractEigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer’s disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network’s dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain’s functional networks develop and adapt when challenged by disease processes such as AD.

Reduced Thickness of the Anterior Cingulate Cortex as a Predictor of Amnestic-Mild Cognitive Impairment Conversion to Alzheimer’s Disease with Psychosis

2021 ◽  
pp. 1-9
Author(s):  
Hee-Jeong Jeong ◽  
Young-Min Lee ◽  
Je-Min Park ◽  
Byung-Dae Lee ◽  
Eunsoo Moon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cortical Thickness ◽  
Cingulate Cortex ◽  
Amnestic Mild Cognitive Impairment ◽  
Anterior Cingulate ◽  
Prospective Longitudinal Study ◽  
Prospective Longitudinal

Background: A long-term follow-up study in patients with amnestic mild cognitive impairment (aMCI) is needed to elucidate the association between regional brain volume and psychopathological mechanisms of Alzheimer’s disease with psychosis (AD + P). Objective: The purpose of this study was to investigate the effect of the thickness of the angular cingulate cortex (ACC) on the risk of AD + P conversion in patients with aMCI. Methods: This was a hospital-based prospective longitudinal study including 174 patients with aMCI. The main outcome measure was time-to-progression from aMCI to AD + P. Subregions of the ACC (rostral ACC, rACC; caudal ACC, cACC) and hippocampus (HC) were measured as regions of interest with magnetic resonance imaging and the Freesurfer analysis at baseline. Survival analysis with time to incident AD + P as an event variable was calculated with Cox proportional hazards models using the subregions of the ACC and HC as a continuous variable. Results: Cox proportional hazard analyses showed that the risk of AD + P was associated with sub-regional ACC thickness but not HC volume: reduced cortical thickness of the left cACC (HR [95%CI], 0.224 [0.087–0.575], p = 0.002), right cACC (HR [95%CI], 0.318 [0.132–0.768], p = 0.011). This association of the cACC with the risk of AD also remained significant when adjusted for HC volume. Conclusion: We found that reduced cortical thickness of the cACC is a predictor of aMCI conversion to AD + P, independent of HC, suggesting that the ACC plays a vital role in the underlying pathogenesis of AD + P.

scholarly journals β-Amyloid is Associated with Aberrant Metabolic Connectivity in Subjects with Mild Cognitive Impairment

2014 ◽  
Vol 34 (7) ◽  
pp. 1169-1179 ◽  
Author(s):  
Felix Carbonell ◽  
Arnaud Charil ◽  
Alex P Zijdenbos ◽  
Alan C Evans ◽  
Barry J Bedell ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Process ◽  
Brain Regions ◽  
Apoe Ε4 ◽  
Positron Emission ◽  
Β Amyloid ◽  
Glucose Hypometabolism

Positron emission tomography (PET) studies using [18F]2-fluoro-2-deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein ε4 (APOE ε4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ε4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ε4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns.

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