Individual-level morphological hippocampal networks in patients with Alzheimer’s disease

2021 ◽  
Vol 151 ◽  
pp. 105748
Author(s):  
Chunlan Yang ◽  
Jiechuan Ren ◽  
Wan Li ◽  
Min Lu ◽  
Shuicai Wu ◽  
...  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene B. Meier ◽  
Max Buegler ◽  
Robbert Harms ◽  
Azizi Seixas ◽  
Arzu Çöltekin ◽  
...  

AbstractConventional neuropsychological assessments for Alzheimer’s disease are burdensome and inaccurate at detecting mild cognitive impairment and predicting Alzheimer’s disease risk. Altoida’s Digital Neuro Signature (DNS), a longitudinal cognitive test consisting of two active digital biomarker metrics, alleviates these limitations. By comparison to conventional neuropsychological assessments, DNS results in faster evaluations (10 min vs 45–120 min), and generates higher test-retest in intraindividual assessment, as well as higher accuracy at detecting abnormal cognition. This study comparatively evaluates the performance of Altoida’s DNS and conventional neuropsychological assessments in intraindividual assessments of cognition and function by means of two semi-naturalistic observational experiments with 525 participants in laboratory and clinical settings. The results show that DNS is consistently more sensitive than conventional neuropsychological assessments at capturing longitudinal individual-level change, both with respect to intraindividual variability and dispersion (intraindividual variability across multiple tests), across three participant groups: healthy controls, mild cognitive impairment, and Alzheimer’s disease. Dispersion differences between DNS and conventional neuropsychological assessments were more pronounced with more advanced disease stages, and DNS-intraindividual variability was able to predict conversion from mild cognitive impairment to Alzheimer’s disease. These findings are instrumental for patient monitoring and management, remote clinical trial assessment, and timely interventions, and will hopefully contribute to a better understanding of Alzheimer’s disease.


2021 ◽  
Author(s):  
Elizabeth Levitis ◽  
Jacob W Vogel ◽  
Thomas Funck ◽  
Vladimir Halchinski ◽  
Serge Gauthier ◽  
...  

Amyloid-beta (Aβ) deposition is one of the hallmark pathologies in both sporadic Alzheimer's disease (sAD) and autosomal dominant Alzheimer's disease (ADAD), the latter of which is caused by mutations in genes involved in Aβ processing. Despite Aβ deposition being a centerpiece to both sAD and ADAD, some differences between these AD subtypes have been observed with respect to the spatial pattern of Aβ. Previous work has shown that the spatial pattern of Aβ in individuals spanning the sAD spectrum can be reproduced with high accuracy using an epidemic spreading model (ESM), which simulates the diffusion of Aβ across neuronal connections and is constrained by individual rates of Aβ production and clearance. However, it has not been investigated whether Aβ deposition in the rarer ADAD can be modeled in the same way, and if so, how congruent the spreading patterns of Aβ across sAD and ADAD are. We leverage the ESM as a data-driven approach to probe individual-level variation in the spreading patterns of Aβ across three different large-scale imaging datasets (2 SAD, 1 ADAD). We applied the ESM separately to the Alzheimer's Disease Neuroimaging initiative (N=737), the Open Access Series of Imaging Studies (N=510), and the Dominantly Inherited Alzheimer's Network (N=249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately, and further identified the most likely epicenter of Aβ spread for each individual. Using epicenters defined in previous work in sAD, the ESM provided moderate prediction of the regional pattern of Aβ deposition across all three datasets. We further find that, while the most likely epicenter for most Aβ-positive subjects overlaps with the default mode network, 13% of ADAD individuals were best characterized by a striatal origin of Aβ spread. These subjects were also distinguished by being younger than ADAD subjects with a DMN Aβ origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most ADAD patients express Aβ spreading patters similar to those of sAD, but that there may be a subset of ADAD patients with a separate, striatal phenotype.


2021 ◽  
Author(s):  
Michael David ◽  
Paresh A. Malhotra

There is clear and early noradrenergic dysfunction in Alzheimer’s disease. This is likely secondary to pathological tau deposition in the locus coeruleus, the pontine nucleus that produces and releases noradrenaline, which occurs prior to involvement of cortical brain regions. Disruption of noradrenergic pathways affects cognition, especially attention, which impacts on memory and broader functioning in daily life. Additionally, it leads to the autonomic and neuropsychiatric symptoms that are frequently observed with disease progression.Despite the strong evidence of noradrenergic involvement in Alzheimer’s, there are no clear trial data supporting the clinical use of any noradrenergic treatments. Several approaches have been tried, including both proof-of-principle studies and randomised controlled trials, although most of the latter have been relatively small scale. Treatments have included drug therapies as well as stimulation modalities thought to modulate noradrenergic transmission. The lack of clear positive findings is likely secondary to limited capacity for gauging locus coeruleus integrity and noradrenergic dysfunction at an individual level. However, the recent development of several novel biomarkers holds potential and should allow quantification of dysfunction. This in turn may inform inclusion criteria and stratification for future trials. Imaging approaches in particular have improved greatly following the development of neuromelanin-sensitive sequences, enabling the use of structural MRI scans to estimate locus coeruleus integrity. Additionally, functional MRI and PET scanning have the potential to quantify network dysfunction. As well as neuroimaging, EEG and pupillometry techniques may prove useful in assessing noradrenergic tone and dynamic response to stimulation.Here we review the development of these biomarkers and discuss how they might augment clinical studies, particularly randomised trials, through stratification and identification of patients most likely to benefit from treatment. We outline the biomarkers with most potential, and how they hold promise as a means of transforming symptomatic therapy for people living with Alzheimer’s disease.


2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hui Dong ◽  
Jialu Li ◽  
Lei Huang ◽  
Xi Chen ◽  
Donghai Li ◽  
...  

Alzheimer’s disease (AD) is the most common type of dementia, and promptly diagnosis of AD is crucial for delaying the development of disease and improving patient quality of life. However, AD detection, particularly in the early stages, remains a substantial challenge due to the lack of specific biomarkers. The present study was undertaken to identify and validate the potential of circulating miRNAs as novel biomarkers for AD. Solexa sequencing was employed to screen the expression profile of serum miRNAs in AD and controls. RT-qPCR was used to confirm the altered miRNAs at the individual level. Moreover, candidate miRNAs were examined in the serum samples of patients with mild cognitive impairment (MCI) and vascular dementia (VD). The results showed that four miRNAs (miR-31, miR-93, miR-143, and miR-146a) were markedly decreased in AD patients’ serum compared with controls. Receiver operating characteristic curve analysis demonstrated that this panel of four miRNAs could be used as potential biomarker for AD. Furthermore, miR-93, and miR-146a were significantly elevated in MCI compared with controls, and the panel of miR-31, miR-93 and miR-146a can be used to discriminate AD from VD. We established a panel of four serum miRNAs as a novel noninvasive biomarker for AD diagnosis.


Author(s):  
Nasim S. Sabounchi

ABSTRACT ObjectivesAs estimated there are about 5.3 million who suffer from Alzheimer’s disease in United States. The incidence is increasing as the population is aging. Due to the increasing trend of Alzheimer’s disease, there is a lot of discussion on prevention efforts or slowing the incidence. Also, models that could predict individual risk of cognitive impairment are needed to assist in prevention efforts.  In general dementia development has been associated with growth in various vascular, lifestyle and other risk factors. Epidemiological research provides evidence of some vascular, lifestyle and psychological risk factors that are modifiable and protective of disease incidence either independently or while interacting with other factors. However, as reported by National Institute of Aging, it is not yet clear whether health or lifestyle factors can prevent Alzheimer’s disease. The objective of this research project is to adopt a system dynamics modeling approach to study the interaction of several key factors including vascular, lifestyle and psychological aspects over the life course of individuals, to gain further understanding of Alzheimer’s disease incidence and evaluate prevention strategies. Both datasets of ‘Alzheimer's Disease Neuroimaging Initiative (ADNI)’ and ‘Health and Retirement Study (HRS)’ will be used for model development and validation. ApproachA system dynamics approach is an optimal choice for addressing the goal of this proposal because different key factors interact over time and make Alzheimer’s disease incidence a complex problem. Furthermore, system dynamics approaches focus on understanding the relationship between the structure of a system and the resulting dynamic behaviors generated through multiple interacting feedback loops. Such an approach could be invaluable in studying dynamic problems arising in complex health, social, economic, or ecological systems. ResultsFor the purpose of the proposal, the following stages are planned:1. Develop a system dynamics simulation model at individual level that predicts the Alzheimer’s disease incidence over the life course, and aggregates individual level models to predict population level trends 2. Calibrate the resulting simulation model based upon longitudinal data trends employed from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Both cohorts with Alzheimer’s disease and control subjects from this database will be used to fine-tune the simulation model. ConclusionThe final validated model would be used to test different hypotheses and evaluate various strategies and/or their combinations to help evaluate the efficacy of prevention strategies on Alzheimer’s disease incidence and its growth.


2019 ◽  
Author(s):  
Kun Zhao ◽  
Yanhui Ding ◽  
Ying Han ◽  
Yong Fan ◽  
Aaron F. Alexander-Bloch ◽  
...  

Abstract Background: Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). The primary aim of this study is to explore whether radiomics feature is a robust biomarker for AD and further explore the biological basis of those features.Methods: Hippocampal radiomic features were extracted for classification and prediction using a support vector machine (SVM) with 1943 subjects (multi-site).Results: Multivariate classifier-based SVM analysis provided individual-level predictions for distinguishing AD patients (N=261) from normal controls (NCs; N=231) with accuracy=88.21% with inter-site cross-validation. Further analyses of a large, independent ADNI dataset (N=1228) reinforced these findings. In mild cognitive impairment (MCI) groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features have a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.Conclusion: These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical applications in AD/MCI.


2016 ◽  
Vol 12 ◽  
pp. P353-P354
Author(s):  
Daniel J. Blackburn ◽  
Yifan Zhou ◽  
Simon Bell ◽  
Matteo De Marco ◽  
Iain Wilkinson ◽  
...  

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 116 ◽  
Author(s):  
Ignacio Echegoyen ◽  
David López-Sanz ◽  
Johann H. Martínez ◽  
Fernando Maestú ◽  
Javier M. Buldú

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.


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