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.

scholarly journals Spatial inhibition of return is impaired in mild cognitive impairment and mild Alzheimer’s disease

2020 ◽  
Author(s):  
Xiong Jiang ◽  
James H. Howard ◽  
G. Wiliam Rebeck ◽  
R. Scott Turner
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Medial Temporal Lobe ◽  
Inhibition Of Return ◽  
Memory Performance ◽  
Brain Regions ◽  
List Type ◽  
Older Individuals

ABSTRACTSpatial inhibition of return (IOR) refers to the phenomenon by which individuals are slower to respond to stimuli appearing at a previously cued location compared to un-cued locations. Here we provide evidence supporting that spatial IOR is mildly impaired in individuals with mild cognitive impairment (MCI) or mild Alzheimer’s disease (AD), and the impairment is readily detectable using a novel double cue paradigm. Furthermore, reduced spatial IOR in high-risk healthy older individuals is associated with reduced memory and other neurocognitive task performance, suggesting that the novel double cue spatial IOR paradigm may be useful in detecting MCI and early AD.SIGNIFICANCE STATEMENTNovel double cue spatial inhibition of return (IOR) paradigm revealed a robust effect IOR deficits in individuals with mild cognitive impairment (MCI) or mild Alzheimer’s disease (AD)Spatial IOR effect correlates with memory performance in healthy older adults at a elevated risk of Alzheimer’s disease (with a family history or APOE e4 allele)The data suggests that double cue spatial IOR may be sensitive to detect early AD pathological changes, which may be linked to disease progress at the posterior brain regions (rather than the medial temporal lobe)

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 The Use Of The Hungarian Test Your Memory (Tym-Hun), Mmse And Adas-Cog Tests For Patients With Mild Cognitive Impairment (Mci) In A Hungarian Population: A Cross-Sectional Study

2020 ◽  
Author(s):  
Szabolcs Garbóczy ◽  
Éva Magócs ◽  
Gergő Szőllősi ◽  
Szilvia Harsányi ◽  
Égerházi Anikó ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Recognition ◽  
Early Stage ◽  
Disease Process ◽  
Disease Assessment ◽  
Cross Sectional ◽  
Hungarian Population

Abstract BACKGROUND Alzheimer's Disease (AD) is a growing disease process with aging. If we could recognize the disease at an early stage and increase the number of years spent in a better condition through preventive and treatment measures, we could reduce the pressure both directly on families and indirectly on society. There is a need for testing methods that are easy to perform even in general practitioner’s office, inexpensive and non-invasive, which could help early recognition of mental decline. We have selected Test Your Memory (TYM), which has proven to be reliable for detecting AD and mild cognitive impairment (MCI) in several countries. Our study was designed to test the usability of the TYM-HUN comparing with the ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive Subscale) in MCI recognition in the Hungarian population. METHODS TYM test was translated and validated into Hungarian (TYM-HUN). The TYM-HUN test was used in conjunction with and compared with the Mini-Mental State Examination (MMSE) and the ADAS-Cog. For our study, 50 subjects were selected, 25 MCI patients and 25 healthy controls. Spearman’s rank correlation was used to analyze the correlation between the scores of MMSE and ADAS-Cog with TYM-HUN. RESULTS MCI can be distinguished from AD and normal aging using ADAS-Cog and MMSE is a useful tool to detect dementia. We established a 'cut-off' point of TYM-HUN (44/45points) where optimal sensitivity and specificity values were obtained to screen MCI. The total TYM-HUN scores significantly correlated with the MMSE scores (ρ=0.626; p<0.001) and ADAS-Cog scores (ρ=-0.723; p<0.001). CONCLUSIONS Our results showed that the Hungarian version of TYM (TYM-HUN) is an easy, fast, self-administered questionnaire with the right low threshold regarding MCI and can be used for the early diagnosis of cognitive impairment.

scholarly journals Evaluation of Imaging Windows for Tau PET Imaging Using 18F-PI2620 in Cognitively Normal Individuals, Mild Cognitive Impairment, and Alzheimer’s Disease Patients

2020 ◽  
Vol 19 ◽  
pp. 153601212094758 ◽  
Author(s):  
Chanisa Chotipanich ◽  
Monchaya Nivorn ◽  
Anchisa Kunawudhi ◽  
Chetsadaporn Promteangtrong ◽  
Natphimol Boonkawin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Standardized Uptake Value ◽  
Brain Regions ◽  
Post Injection ◽  
Cognitively Normal ◽  
Scanning Time ◽  
Normal Individuals

Background: The study aimed to evaluate the appropriate uptake-timing in cognitively normal individuals, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) patients, using 18F-PI 2620 dynamic PET acquisition. Methods: Thirty-four MCI patients, 6 AD patients, and 24 cognitively normal individuals were enrolled in this study. A dynamic 18F-PI 2620 PET study was conducted at 30-75 minutes post-injection in these groups. Co-registration was applied between the dynamic acquisition PET and T1-weighted MRI to delineate various cortical regions. The standardized uptake value ratio (SUVR) was used for quantitative analysis. P-mod software with the Automated Anatomical Labeling (AAL)-merged atlas was employed to generate automatic volumes of interest for 11 brain regions. Results: The curves in most brain regions presented an average SUVR stability at 30-40 minutes post-injection in each group. The appropriate uptake-timing interval of 18F-PI 2620 was 30-75 minutes post injection for AD group and 30-40 minutes post injection for both cognitively normal individuals and MCI groups. Conclusion: Short uptake time around 30-40 minutes post-injection would be more comfortable and convenient for all patients, especially in those with dementia who were unable to stay motionless for long periods of scanning time in the scanner.

scholarly journals Prediction of conversion to Alzheimer’s disease using deep survival analysis of MRI images

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Tomonori Nakagawa ◽  
Manabu Ishida ◽  
Junpei Naito ◽  
Atsushi Nagai ◽  
Shuhei Yamaguchi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Analysis ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cox Model ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Regions ◽  
Survival Model ◽  
Imaging Data

Abstract The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer’s disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer’s disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distribution. Our model achieved a maximum concordance index of 0.835, which was higher than that yielded by the Cox model and comparable to that of the DeepHit model. To our best knowledge, this is the first report to describe the application of a deep survival model to brain magnetic resonance imaging data. Our results demonstrate that this type of analysis could successfully predict the time of an individual’s conversion to Alzheimer’s disease.

Apolipoprotein E allele 4 is not a sufficient or a necessary predictor of the development of Mild Cognitive Impairment

2010 ◽  
Vol 25 (1) ◽  
pp. 15-18 ◽  
Author(s):  
R. Heun ◽  
U. Gühne ◽  
T. Luck ◽  
M.C. Angermeyer ◽  
U. Ueberham ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apolipoprotein E ◽  
Target Population ◽  
Population Based ◽  
Initial Development ◽  
Apoe Ε4 ◽  
Ε4 Allele

AbstractThe presence of Mild Cognitive Impairment (MCI) and of an apolipoprotein E (apoE) ε4 allele both predict the development of Alzheimer's disease. However, the extent to which this allele also predicts the development of MCI is unclear even though MCI is an early transitional stage in the development of Alzheimer's disease. The present study investigates the prevalence of the apoE ε4 allele in incipient MCI. Participants were recruited from the population-based Leipzig Longitudinal Study of the Aged (LEILA75+). All subjects who were initially cognitively healthy, i.e. did not meet MCI criteria described by Petersen [Petersen RC. Mild cognitive impairment. J Intern Med 2004; 256(3): 183–94], and whose apoE status could be determined were followed-up. After 4.5 years, 15.5% of the cognitively healthy target population had developed MCI. The frequencies of the apoE ε4 genotype did not differ between individuals with incipient MCI (12.9%) and individuals who remained cognitively healthy during the study (18.4%, p > 0.5). Consequently, the apoE ε4 genotype is not a necessary or sufficient risk factor for MCI. Further studies need to investigate the influence of the whole range of genetic and environmental risk factors on the course of Alzheimer's disease including the initial development of MCI and the later conversion to Alzheimer's disease.

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