EEG Stochastic Nonlinear Oscillator Models for Alzheimer’s Disease

2015 ◽  
Author(s):  
Parham Ghorbanian ◽  
Subramanian Ramakrishnan ◽  
Hashem Ashrafiuon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nonlinear Oscillator ◽  
Model Performance ◽  
Model Parameters ◽  
Decision Variable ◽  
Eeg Signals ◽  
Brain Disorder ◽  
Eyes Closed ◽  
Oscillator Models

In this article, we derive unique stochastic nonlinear coupled oscillator models of EEG signals from an Alzheimer’s Disease (AD) study. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing - van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects. The selected decision variable are the model parameters and noise intensity. While, the selected signal characteristics are power spectral densities in major brain frequency bands and Shannon and sample entropies to match the signal information content and complexity. It is shown that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. Moreover, the inclusion of sample entropy in the optimization process significantly enhances the stochastic nonlinear oscillator model performance. The study suggests that EEG signals recorded under different brain states as well as those belonging to a brain disorder such as Alzheimer’s disease can be uniquely represented by stochastic nonlinear oscillators paving the way for identification of new discriminants.

scholarly journals Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
François-B. Vialatte ◽  
Justin Dauwels ◽  
Monique Maurice ◽  
Toshimitsu Musha ◽  
Andrzej Cichocki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frequency Domain ◽  
Matched Control ◽  
Cost Effective ◽  
Eeg Signals ◽  
Time Frequency ◽  
Eyes Closed ◽  
Eeg Data ◽  
Rest Condition

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients.Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest wereθ(3.5–7.5 Hz),α1(7.5–9.5 Hz),α2(9.5–12.5 Hz), andβ(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models.Results. Enhanced EEG power in theθrange is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies.Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Natural Anti-inflammatory compounds as Drug candidates in Alzheimer’s disease

2020 ◽  
Vol 27 ◽  
Author(s):  
Reyaz Hassan Mir ◽  
Abdul Jalil Shah ◽  
Roohi Mohi-ud-din ◽  
Faheem Hyder Potoo ◽  
Mohd. Akbar Dar ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Natural Products ◽  
Protective Action ◽  
New Drugs ◽  
Huperzine A ◽  
Brain Disorder ◽  
Anti Inflammatory

: Alzheimer's disease (AD) is a chronic neurodegenerative brain disorder characterized by memory impairment, dementia, oxidative stress in elderly people. Currently, only a few drugs are available in the market with various adverse effects. So to develop new drugs with protective action against the disease, research is turning to the identification of plant products as a remedy. Natural compounds with anti-inflammatory activity could be good candidates for developing effective therapeutic strategies. Phytochemicals including Curcumin, Resveratrol, Quercetin, Huperzine-A, Rosmarinic acid, genistein, obovatol, and Oxyresvertarol were reported molecules for the treatment of AD. Several alkaloids such as galantamine, oridonin, glaucocalyxin B, tetrandrine, berberine, anatabine have been shown anti-inflammatory effects in AD models in vitro as well as in-vivo. In conclusion, natural products from plants represent interesting candidates for the treatment of AD. This review highlights the potential of specific compounds from natural products along with their synthetic derivatives to counteract AD in the CNS.

Exploring the Role of Aggregated Proteomes in the Pathogenesis of Alzheimer’s Disease

2020 ◽  
Vol 21 (12) ◽  
pp. 1164-1173
Author(s):  
Siju Ellickal Narayanan ◽  
Nikhila Sekhar ◽  
Rajalakshmi Ganesan Rajamma ◽  
Akash Marathakam ◽  
Abdullah Al Mamun ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Precursor Protein ◽  
Early Onset ◽  
Late Onset ◽  
Precursor Protein ◽  
Brain Disorder ◽  
Amyloid Aβ ◽  
T Tau

: Alzheimer’s disease (AD) is a progressive brain disorder and one of the most common causes of dementia and death. AD can be of two types; early-onset and late-onset, where late-onset AD occurs sporadically while early-onset AD results from a mutation in any of the three genes that include amyloid precursor protein (APP), presenilin 1 (PSEN 1) and presenilin 2 (PSEN 2). Biologically, AD is defined by the presence of the distinct neuropathological profile that consists of the extracellular β-amyloid (Aβ) deposition in the form of diffuse neuritic plaques, intraneuronal neurofibrillary tangles (NFTs) and neuropil threads; in dystrophic neuritis, consisting of aggregated hyperphosphorylated tau protein. Elevated levels of (Aβ), total tau (t-tau) and phosphorylated tau (ptau) in cerebrospinal fluid (CSF) have become an important biomarker for the identification of this neurodegenerative disease. The aggregation of Aβ peptide derived from amyloid precursor protein initiates a series of events that involve inflammation, tau hyperphosphorylation and its deposition, in addition to synaptic dysfunction and neurodegeneration, ultimately resulting in dementia. The current review focuses on the role of proteomes in the pathogenesis of AD.

scholarly journals Resting State Alpha Electroencephalographic Rhythms Are Differently Related to Aging in Cognitively Unimpaired Seniors and Patients with Alzheimer’s Disease and Amnesic Mild Cognitive Impairment

2021 ◽  
pp. 1-30
Author(s):  
Claudio Babiloni ◽  
Raffaele Ferri ◽  
Giuseppe Noce ◽  
Roberta Lizio ◽  
Susanna Lopez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Diagnostic Biomarkers ◽  
Eyes Closed ◽  
Topographical Effects ◽  
And Gender ◽  
International Archive

Background: In relaxed adults, staying in quiet wakefulness at eyes closed is related to the so-called resting state electroencephalographic (rsEEG) rhythms, showing the highest amplitude in posterior areas at alpha frequencies (8–13 Hz). Objective: Here we tested the hypothesis that age may affect rsEEG alpha (8–12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with mild cognitive impairment due to Alzheimer’s disease (ADMCI). Methods: Clinical and rsEEG datasets in 63 ADMCI and 60 Nold individuals (matched for demography, education, and gender) were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands, as well as fixed beta (14–30 Hz) and gamma (30–40 Hz) bands. Each group was stratified into three subgroups based on age ranges (i.e., tertiles). Results: As compared to the younger Nold subgroups, the older one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions. On the contrary, in relation to the younger ADMCI subgroups, the older one displayed a lesser reduction in those rhythms. Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions revealed by neuroimaging, and clinical and neuropsychological scores. Conclusion: The present results suggest that age may represent a deranging factor for dominant rsEEG alpha rhythms in Nold seniors, while rsEEG alpha rhythms in ADMCI patients may be more affected by the disease variants related to earlier versus later onset of the AD.

scholarly journals Marine-Derived Compounds with Anti-Alzheimer’s Disease Activities

Marine Drugs ◽  
2021 ◽  
Vol 19 (8) ◽  
pp. 410
Author(s):  
Salar Hafez Ghoran ◽  
Anake Kijjoa
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Thinking Skills ◽  
Mechanisms Of Action ◽  
Brain Disorder ◽  
Brain Functioning ◽  
Pharmacologically Active ◽  
Pharmacologically Active Compounds

Alzheimer’s disease (AD) is an irreversible and progressive brain disorder that slowly destroys memory and thinking skills, and, eventually, the ability to perform simple tasks. As the aging population continues to increase exponentially, AD has become a big concern for society. Therefore, neuroprotective compounds are in the spotlight, as a means to tackle this problem. On the other hand, since it is believed—in many cultures—that marine organisms in an individual diet cannot only improve brain functioning, but also slow down its dysfunction, many researchers have focused on identifying neuroprotective compounds from marine resources. The fact that the marine environment is a rich source of structurally unique and biologically and pharmacologically active compounds, with unprecedented mechanisms of action, marine macroorganisms, such as tunicates, corals, sponges, algae, as well as microorganisms, such as marine-derived bacteria, actinomycetes, and fungi, have been the target sources of these compounds. Therefore, this literature review summarizes and categorizes various classes of marine-derived compounds that are able to inhibit key enzymes involved in AD, including acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), β-secretase (BACE-1), and different kinases, together with the related pathways involved in the pathogenesis of AD. The compounds discussed herein are emerging as promising anti-AD activities for further in-depth in vitro and in vivo investigations, to gain more insight of their mechanisms of action and for the development of potential anti-AD drug leads.

A Numerical Study of Sensitivity Coefficients for a Model of Amyloid Precursor Protein and Tubulin-Associated Unit Protein Transport and Agglomeration in Neurons at the Onset of Alzheimer's Disease

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
I. A. Kuznetsov ◽  
A. V. Kuznetsov
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Precursor Protein ◽  
Tau Protein ◽  
Protein Transport ◽  
Numerical Study ◽  
Amyloid Β ◽  
Precursor Protein ◽  
Model Parameters ◽  
In The Beginning

Modeling of intracellular processes occurring during the development of Alzheimer's disease (AD) can be instrumental in understanding the disease and can potentially contribute to finding treatments for the disease. The model of intracellular processes in AD, which we previously developed, contains a large number of parameters. To distinguish between more important and less important parameters, we performed a local sensitivity analysis of this model around the values of parameters that give the best fit with published experimental results. We show that the influence of model parameters on the total concentrations of amyloid precursor protein (APP) and tubulin-associated unit (tau) protein in the axon is reciprocal to the influence of the same parameters on the average velocities of the same proteins during their transport in the axon. The results of our analysis also suggest that in the beginning of AD the aggregation of amyloid-β and misfolded tau protein have little effect on transport of APP and tau in the axon, which suggests that early damage in AD may be reversible.

scholarly journals Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease

Entropy ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 31 ◽  
Author(s):  
Hamed Azami ◽  
Daniel Abásolo ◽  
Samantha Simons ◽  
Javier Escudero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multiscale Entropy ◽  
Eeg Signals

scholarly journals Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease

2021 ◽  
Author(s):  
Elizabeth Levitis ◽  
Jacob W Vogel ◽  
Thomas Funck ◽  
Vladimir Halchinski ◽  
Serge Gauthier ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Pattern ◽  
Amyloid Beta ◽  
Large Scale ◽  
Autosomal Dominant ◽  
Model Performance ◽  
Sporadic Alzheimer’S Disease ◽  
Individual Level ◽  
Data Driven Approach

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.

Genipin Attenuates Tau Phosphorylation and Aβ Levels in Cellular Models of Alzheimer's Disease

2021 ◽  
Author(s):  
Meiting Li ◽  
Nan Cai ◽  
Liang Gu ◽  
Lijun Yao ◽  
Decheng Bi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Tau Phosphorylation ◽  
Brain Disorder ◽  
Cellular Models ◽  
Aβ Generation ◽  
Aβ Production

Abstract Alzheimer’s disease (AD) is a devastating brain disorder characterized by neurofibrillary tangles and amyloid plaques. Inhibiting Tau protein and amyloid-beta (Aβ) production or removing these molecules are considered potential therapeutic strategies for AD. Genipin is an aglycone and is isolated from the extract of Gardenia jasminoides Ellis fruit. In this study, the effect and molecular mechanisms of genipin on the inhibition of Tau aggregation and Aβ generation were investigated. The results showed that genipin bound to Tau and protected against heparin-induced Tau fibril formation. Moreover, genipin suppressed Tau phosphorylation probably by downregulating the expression of CDK5 and GSK-3β, and activated mTOR-dependent autophagy via the SIRT1/LKB1/AMPK signaling pathway in Tau-overexpressing cells. In addition, genipin decreased Aβ production by inhibiting BACE1 expression through the PERK/eIF2α signaling pathway in N2a/SweAPP cells. These data indicated that genipin could effectively lead to a significant reduction of phosphorylated Tau level and Aβ generation in vitro, suggesting that genipin might be developed into an effective therapeutic complement or a potential nutraceutical for preventing AD.

PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer’s Disease Feature Fusion and Multimodal Classification

2021 ◽  
pp. 1-18
Author(s):  
Mehdi Shojaie ◽  
Solale Tabarestani ◽  
Mercedes Cabrerizo ◽  
Steven T. DeKosky ◽  
David E. Vaillancourt ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Mutual Information ◽  
Feature Fusion ◽  
Early Stage ◽  
Model Performance ◽  
Amyloid Β ◽  
Feature Ranking ◽  
Promising Tool

Background: Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer’s disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. Objective: This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Results: Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. Conclusion: The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

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