Alzheimer’s disease: The state of the art in resting-state magnetoencephalography

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

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Neuroprotective Properties of Resveratrol and Its Derivatives—Influence on Potential Mechanisms Leading to the Development of Alzheimer’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms21082749 ◽

2020 ◽

Vol 21 (8) ◽

pp. 2749

Author(s):

Michał Wiciński ◽

Anna Domanowska ◽

Eryk Wódkiewicz ◽

Bartosz Malinowski

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

State Of The Art ◽

Therapeutic Option ◽

The State ◽

Future Research ◽

Disease Treatment ◽

Research Results

The lack of effective Alzheimer’s disease treatment is becoming a challenge for researchers and prompts numerous attempts to search for and develop better therapeutic solutions. Compounds that affect several routes of the neurodegeneration cascade leading to the development of disease are of particular interest. An example of such substances is resveratrol and its synthetic and natural derivatives, which have gained popularity in recent years and show promise as a possible new therapeutic option in the approach to Alzheimer’s disease treatment. In this article, the state of the art evidence on the role of resveratrol (RSV) in neuroprotection is presented; research results are summarized and the importance of resveratrol and its derivatives in the treatment of Alzheimer’s disease are underlined. It also focuses on various modifications of the resveratrol molecule that should be taken into account in the design of future research on drugs against Alzheimer’s disease.

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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

Journal of Alzheimer s Disease ◽

10.3233/jad-201271 ◽

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.

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APOE: The New Frontier in the Development of a Therapeutic Target towards Precision Medicine in Late-Onset Alzheimer’s

International Journal of Molecular Sciences ◽

10.3390/ijms22031244 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1244

Author(s):

Anna Yang ◽

Boris Kantor ◽

Ornit Chiba-Falek

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Precision Medicine ◽

Antisense Oligonucleotides ◽

State Of The Art ◽

Late Onset ◽

Base Editing ◽

Medical Need ◽

New Frontier ◽

Amyloid Cascade

Alzheimer’s disease (AD) has a critical unmet medical need. The consensus around the amyloid cascade hypothesis has been guiding pre-clinical and clinical research to focus mainly on targeting beta-amyloid for treating AD. Nevertheless, the vast majority of the clinical trials have repeatedly failed, prompting the urgent need to refocus on other targets and shifting the paradigm of AD drug development towards precision medicine. One such emerging target is apolipoprotein E (APOE), identified nearly 30 years ago as one of the strongest and most reproduceable genetic risk factor for late-onset Alzheimer’s disease (LOAD). An exploration of APOE as a new therapeutic culprit has produced some very encouraging results, proving that the protein holds promise in the context of LOAD therapies. Here, we review the strategies to target APOE based on state-of-the-art technologies such as antisense oligonucleotides, monoclonal antibodies, and gene/base editing. We discuss the potential of these initiatives in advancing the development of novel precision medicine therapies to LOAD.

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Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2019-0058 ◽

2020 ◽

Vol 19 (3) ◽

Author(s):

Yunlong Nie ◽

Eugene Opoku ◽

Laila Yasmin ◽

Yin Song ◽

Jie Wang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Default Mode Network ◽

Resting State ◽

Brain Connectivity ◽

Parametric Bootstrap ◽

Imaging Genetics ◽

Nucleotide Polymorphisms ◽

Mixed Effect ◽

Default Mode

AbstractWe conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

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