scholarly journals Deterioration from healthy to mild cognitive impairment and Alzheimer’s disease mirrored in corresponding loss of centrality in directed brain networks

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sinan Zhao ◽  
D. Rangaprakash ◽  
Peipeng Liang ◽  
Gopikrishna Deshpande
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Information Flow ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Directed Networks ◽  
Single Node ◽  
Potential Biomarker

Abstract Objective It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks. Methods Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network. Results MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC. Conclusion Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

scholarly journals To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Justine Staal ◽  
Francesco Mattace-Raso ◽  
Hennie A. M. Daniels ◽  
Johannes van der Steen ◽  
Johan J. M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Support Vector ◽  
Potential Biomarker

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

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 Analysis of 27 vascular-related proteins reveals that NT-proBNP is a potential biomarker for Alzheimer's disease and mild cognitive impairment: A pilot-study

2014 ◽  
Vol 50 ◽  
pp. 114-121 ◽  
Author(s):  
Josef Marksteiner ◽  
Douglas Imarhiagbe ◽  
Michaela Defrancesco ◽  
Eberhard A. Deisenhammer ◽  
Georg Kemmler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Pilot Study ◽  
Potential Biomarker ◽  
Related Proteins

scholarly journals YKL-40 as a Potential Biomarker for the Differential Diagnosis of Alzheimer’s Disease

Medicina ◽  
2021 ◽  
Vol 58 (1) ◽  
pp. 60
Author(s):  
Ioannis Mavroudis ◽  
Rumana Chowdhury ◽  
Foivos Petridis ◽  
Eleni Karantali ◽  
Symela Chatzikonstantinou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neurodegenerative Disorder ◽  
Neuronal Loss ◽  
Cochrane Library ◽  
Potential Biomarker ◽  
The Cochrane Library

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, associated with extensive neuronal loss, dendritic and synaptic changes resulting in significant cognitive impairment. An increased number of studies have given rise to the neuroinflammatory hypothesis in AD. It is widely accepted that AD brains show chronic inflammation, probably triggered by the presence of insoluble amyloid beta deposits and neurofibrillary tangles (NFT) and is also related to the activation of neuronal death cascade. In the present study we aimed to investigate the role of YKL-40 levels in the cerebrospinal fluid (CSF) in the diagnosis of AD, and to discuss whether there are further potential roles of this protein in the management and treatment of AD. We conducted an online search on PubMed, Web of Science, and the Cochrane library databases from 1990 to 2021. The quantitative analysis showed that the levels of YKL-40 were significantly higher in Alzheimer’s disease compared to controls, to mild cognitive impairment (MCI) AD (MCI-AD) and to stable MCI. They were also increased in MCI-AD compared to stable MCI. The present study shows that the CSF levels of YKL-40 could be potentially used as a biomarker for the prognosis of mild cognitive impairment and the likelihood of progression to AD, as well as for the differential diagnosis between AD and MCI.

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.

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