Power spectral density and coherence analysis of Alzheimer’s EEG

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer’s disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.     

Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease

An early and accurate diagnosis of Alzheimer’s disease (AD) has been progressively attracting more attention in recent years. One of the main problems of AD is the loss of language skills. This paper presents a computational framework for classifying AD patients compared to healthy control subjects using information from spontaneous speech signals. Spontaneous speech data are obtained from 30 AD patients and 30 healthy controls. Because of the nonlinear and dynamic nature of speech signals, higher order spectral features (specifically bispectrum) were used for analysis. Four classifiers (k-Nearest Neighbor, Support Vector Machine, Naïve Bayes and Decision tree) were used to classify subjects into three different levels of AD and healthy group based on their performance in terms of the HOS-based features. Ten-fold cross-validation method was used to test the reliability of the classifier results. The results showed that the proposed method had a good potential in AD diagnosis. The proposed method was also able to diagnose the earliest stage of AD with high accuracy. The method has the great advantage of being non-invasive, cost-effective, and associated with no side effects. Therefore, the proposed method can be a spontaneous speech directed test for pre-clinical evaluation of AD diagnosis.     

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease

The complexity change of brain activity in Alzheimer’s disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.     

Animal model of Alzheimer’s disease: characteristics of EEG and memory

We studied the effects of aggregated amyloid β-peptide Aβ_25–35 on spatial memory and the spectral-correlational characteristics of EEG of both the dorsal hippocampus and the frontal cortex both in adult and aged rats at the early stage of Aβ_25–35 action. Spatial memory was characterized using a novel cognitive test. A decrease in low-frequency theta band oscillations in the dorsal hippocampus and the frontal cortex was observed. The mean coefficient of EEG cross-correlation between these structures was significantly reduced at the early stage of Aβ_25–35 action both in adult and aged rats. In addition, we found that one month after Aβ_25–35 injection spatial memory was impaired. These results suggest that the decrease in low-frequency theta band oscillations and the weakening of binding between the dorsal hippocampus and the frontal cortex under the action of Aβ_25–35 may be an underlying cause of the typical memory breakdown associated with the Alzheimer’s disease.     

Changes in R–R variability before and after endurance training measured by power spectral analysis and by the effect of isometric muscle contraction

 The long-term conditioning effects of physical training on cardiorespiratory interaction in 11 young healthy males were studied. Significant increases in maximum oxygen uptake (V˙O_2max)(P<0.05) and decreases in heart rate (P<0.05) were achieved in all subjects following a 6-week training programme consisting of cycling for 25min each day at a work level that increased heart rate to 85% of maximum. Heart rate variability, measured as the differences between the maximum and minimum R–R interval in a respiratory cycle, increased in nine of the subjects and decreased in two. The respiratory-cycle-related high-frequency peak in the power spectral plot of R–R variability also showed significant increases in the same nine subjects and decreases in two. The latter result was similar after normalisation of the data for changes in heart rate by calculating the common coefficient of variance (CCV=HFR–R×<∮∮), where HF is the high-frequency component of the power spectral plots, using a further measure of vagal tone it was shown that, for all subjects, the R–R interval change in response to isometric contractions of the arm flexors in one respiratory cycle were significantly greater after training. These data suggest that cardiac vagal tone is increased by aerobic training for all subjects and that this is accompanied by a respiratory sinus arrhythmia (RSA) in most, but may be associated with a decrease in RSA in subjects with a very low (< 50 beats⋅min^-1 heart rate. Accepted: 23 April 1996     

Success of the test task performance by students with different spectral α-rhythm characteristics in the baseline electroencephalogram

The subjects were divided into two groups according to the α₁ and α₂ spectral powers in the occipital derivations of the EEG recorded in the initial state with their eyes closed. Group I included subjects whose α₁-rhythm spectral power (7–10 Hz) was more than 70% of the total α-rhythm band power. Group II included subjects whose α₂-rhythm spectral power (10–13 Hz) was more than 70% of the total α-rhythm band power. It was established that, in the tasks requiring prediction of the subsequent result (memorizing a certain sequence of signals and its subsequent reproduction on the monitor screen), group I subjects differed from group II subjects in fewer sequence errors and a greater number of accurate predictions. In group II subjects, a decrease in the ϑ-band spectral power in the EEG of the central and frontal cortical areas was observed compared to the baseline. Therefore, the EEG ϑ-rhythm power at the memorizing stage was lower in them than in group II subjects. The results suggest that the baseline characteristics of ϑ-activity can be regarded as prognostic criteria of similar types of activity.     

Study on dynamic characteristics’ change of hippocampal neuron reduced models caused by the Alzheimer’s disease

In the paper, based on the electrophysiological experimental data, the Hippocampal neuron reduced model under the pathology condition of Alzheimer’s disease (AD) has been built by modifying parameters’ values. The reduced neuron model’s dynamic characteristics under effect of AD are comparatively studied. Under direct current stimulation, compared with the normal neuron model, the AD neuron model’s dynamic characteristics have obviously been changed. The neuron model under the AD condition undergoes supercritical Andronov–Hopf bifurcation from the rest state to the continuous discharge state. It is different from the neuron model under the normal condition, which undergoes saddle-node bifurcation. So, the neuron model changes into a resonator with monostable state from an integrator with bistable state under AD’s action. The research reveals the neuron model’s dynamic characteristics’ changing under effect of AD, and provides some theoretic basis for AD research by neurodynamics theory.     

Quantitative Proteomic Analysis of Human Substantia Nigra in Alzheimer’s Disease, Huntington’s Disease and Multiple Sclerosis

The substantia nigra plays important roles in the brain function and is critical in the development of many diseases, particularly Parkinson??s disease. Pathological changes of the substantia nigra have also been reported in other neurodegenerative diseases. Using a quantitative proteomic approach, we investigated protein expressions in the substantia nigra of Alzheimer??s disease, Huntington??s disease, and Multiple sclerosis. The expression level of one hundred and four proteins that were identified in at least three samples of each group were compared with the control group, with nineteen, twenty-two and thirteen proteins differentially expressed in Alzheimer??s diseases, Huntington??s disease and Multiple sclerosis respectively. The result indicates that the substantia nigra also undergoes functional adaption or damage in these diseases.     

A genome-wide association study of Alzheimer’s disease using random forests and enrichment analysis

Alzheimer??s disease (AD) is a serious neurodegenerative disorder and its cause remains largely elusive. In past years, genome-wide association (GWA) studies have provided an effective means for AD research. However, the univariate method that is commonly used in GWA studies cannot effectively detect the biological mechanisms associated with this disease. In this study, we propose a new strategy for the GWA analysis of AD that combines random forests with enrichment analysis. First, backward feature selection using random forests was performed on a GWA dataset of AD patients carrying the apolipoprotein gene (APOE?4) and 1058 susceptible single nucleotide polymorphisms (SNPs) were detected, including several known AD-associated SNPs. Next, the susceptible SNPs were investigated by enrichment analysis and significantly-associated gene functional annotations, such as ??alternative splicing??, ??glycoprotein??, and ??neuron development??, were successfully discovered, indicating that these biological mechanisms play important roles in the development of AD in APOE?4 carriers. These findings may provide insights into the pathogenesis of AD and helpful guidance for further studies. Furthermore, this strategy can easily be modified and applied to GWA studies of other complex diseases.     

Expression of Novel Alzheimer’s Disease Risk Genes in Control and Alzheimer’s Disease Brains

Late onset Alzheimer’s disease (LOAD) etiology is influenced by complex interactions between genetic and environmental risk factors. Large-scale genome wide association studies (GWAS) for LOAD have identified 10 novel risk genes: ABCA7, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, MS4A6A, MS4A6E, and PICALM. We sought to measure the influence of GWAS single nucleotide polymorphisms (SNPs) and gene expression levels on clinical and pathological measures of AD in brain tissue from the parietal lobe of AD cases and age-matched, cognitively normal controls. We found that ABCA7, CD33, and CR1 expression levels were associated with clinical dementia rating (CDR), with higher expression being associated with more advanced cognitive decline. BIN1 expression levels were associated with disease progression, where higher expression was associated with a delayed age at onset. CD33, CLU, and CR1 expression levels were associated with disease status, where elevated expression levels were associated with AD. Additionally, MS4A6A expression levels were associated with Braak tangle and Braak plaque scores, with elevated expression levels being associated with more advanced brain pathology. We failed to detect an association between GWAS SNPs and gene expression levels in our brain series. The minor allele of rs3764650 in ABCA7 is associated with age at onset and disease duration, and the minor allele of rs670139 in MS4A6E was associated with Braak tangle and Braak plaque score. These findings suggest that expression of some GWAS genes, namely ABCA7, BIN1, CD33, CLU, CR1 and the MS4A family, are altered in AD brains.     

Suppression of Alzheimer’s Disease-Related Phenotypes by Geranylgeranylacetone in Mice

Amyloid-β peptide (Aβ) plays an important role in the pathogenesis of Alzheimer’s disease (AD). Aβ is generated by the secretase-mediated proteolysis of β-amyloid precursor protein (APP), and cleared by enzyme-mediated degradation and phagocytosis. Transforming growth factor (TGF)-β1 stimulates this phagocytosis. We recently reported that the APP23 mouse model for AD showed fewer AD-related phenotypes when these animals were crossed with transgenic mice expressing heat shock protein (HSP) 70. We here examined the effect of geranylgeranylacetone, an inducer of HSP70 expression, on the AD-related phenotypes. Repeated oral administration of geranylgeranylacetone to APP23 mice for 9 months not only improved cognitive function but also decreased levels of Aβ, Aβ plaque deposition and synaptic loss. The treatment also up-regulated the expression of an Aβ-degrading enzyme and TGF-β1 but did not affect the maturation of APP and secretase activities. These outcomes were similar to those observed in APP23 mice genetically modified to overexpress HSP70. Although the repeated oral administration of geranylgeranylacetone did not increase the level of HSP70 in the brain, a single oral administration of geranylgeranylacetone significantly increased the level of HSP70 when Aβ was concomitantly injected directly into the hippocampus. Since geranylgeranylacetone has already been approved for use as an anti-ulcer drug and its safety in humans has been confirmed, we propose that this drug be considered as a candidate drug for the prevention of AD.     

Semiquantitative proteomic analysis of human hippocampal tissues from Alzheimer’s disease and age-matched control brains

Background

Alzheimer’s disease (AD) is the most common type of dementia affecting people over 65 years of age. The hallmarks of AD are the extracellular deposits known as amyloid β plaques and the intracellular neurofibrillary tangles, both of which are the principal players involved in synaptic loss and neuronal cell death. Tau protein and Aβ fragment 1–42 have been investigated so far in cerebrospinal fluid as a potential AD biomarkers. However, an urgent need to identify novel biomarkers which will capture disease in the early stages and with better specificity remains. High-throughput proteomic and pathway analysis of hippocampal tissue provides a valuable source of disease-related proteins and biomarker candidates, since it represents one of the earliest affected brain regions in AD.

Results

In this study 2954 proteins were identified (with at least 2 peptides for 1203 proteins) from both control and AD brain tissues. Overall, 204 proteins were exclusively detected in AD and 600 proteins in control samples. Comparing AD and control exclusive proteins with cerebrospinal fluid (CSF) literature-based proteome, 40 out of 204 AD related proteins and 106 out of 600 control related proteins were also present in CSF. As most of these proteins were extracellular/secretory origin, we consider them as a potential source of candidate biomarkers that need to be further studied and verified in CSF samples.

Conclusions

Our semiquantitative proteomic analysis provides one of the largest human hippocampal proteome databases. The lists of AD and control related proteins represent a panel of proteins potentially involved in AD pathogenesis and could also serve as prospective AD diagnostic biomarkers.     

A brief overview of amyloids and Alzheimer’s disease

Amyloid fibrils are self-assembled fibrous protein aggregates that are associated with a number of presently incurable diseases such as Alzheimer’s and Parkinson’s disease. Millions of people worldwide suffer from amyloid diseases. This review summarizes the unique cross-β structure of amyloid fibrils, morphological variations, the kinetics of amyloid fibril formation, and the cytotoxic effects of these fibrils and oligomers. Alzheimer’s disease is also explored as an example of an amyloid disease to show the various approaches to treat these amyloid diseases. Finally, this review investigates the nanotechnological and biological applications of amyloid fibrils; as well as a summary of the typical biological pathways involved in the disposal of amyloid fibrils and their precursors.     

SNP analysis of genes related to cholesterol metabolism and associated with late-onset Alzheimer’s disease

Late onset Alzheimer’s disease (LOAD) is the most common type of dementia and is characterized by impaired cholesterol homeostasis. Genome-wide association studies (GWAS) have shown that APOE, TOMM40, CLU, SORL1, PICALM, and BIN1 are related to cholesterol metabolism. To characterize the association between single-nucleotide polymorphisms (SNPs) and LOAD, we sequenced the SNP regions of the identified genes in a total of 11 LOAD cases and 12 healthy case controls in the Korean population. The SNP data showed a relatively high frequency in LOAD samples compared to the control samples. LOAD samples showed an average of 2.9 SNPs, whereas normal controls showed an average of 1.5 SNPs in the genes. Taken together, six genes associated with cholesterol metabolism using SNP analysis have shown frequent genetic variations in LOAD.     

Blood–brain-barriers in aging and in Alzheimer’s disease

The aging process correlates with a progressive failure in the normal cellular and organ functioning; these alterations are aggravated in Alzheimer’s disease (AD). In both aging and AD there is a general decrease in the capacity of the body to eliminate toxic compounds and, simultaneously, to supply the brain with relevant growth and nutritional factors. The barriers of the brain are targets of this age related dysfunction; both the endothelial cells of the blood–brain barrier and the choroid plexus epithelial cells of the blood-cerebrospinal fluid barrier decrease their secretory capacity towards the brain and their ability to remove toxic compounds from the brain. Additionally, during normal aging and in AD, the permeability of the brain barriers increase. As such, a greater contact of the brain parenchyma with the blood content alters the highly controlled neural environment, which impacts on neural function. Of interest, the brain barriers are more than mere obstacles to the passage of molecules and cells, and therefore active players in brain homeostasis, which is still to be further recognized and investigated in the context of health and disease. Herein, we provide a review on how the brain barriers change during aging and in AD and how these processes impact on brain function.     

Role of zinc and copper ions in the pathogenetic mechanisms of Alzheimer’s and Parkinson’s diseases

Disbalance of zinc (Zn²⁺) and copper (Cu²⁺) ions in the central nervous system is involved in the pathogenesis of numerous neurodegenerative disorders such as multisystem atrophy, amyotrophic lateral sclerosis, Creutzfeldt-Jakob disease, Wilson-Konovalov disease, Alzheimer’s disease, and Parkinson’s disease. Among these, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most frequent age-related neurodegenerative pathologies with disorders in Zn²⁺ and Cu²⁺ homeostasis playing a pivotal role in the mechanisms of pathogenesis. In this review we generalized and systematized current literature data concerning this problem. The interactions of Zn²⁺ and Cu²⁺ with amyloid precursor protein (APP), β-amyloid (Abeta), tau-protein, metallothioneins, and GSK3β are considered, as well as the role of these interactions in the generation of free radicals in AD and PD. Analysis of the literature suggests that the main factors of AD and PD pathogenesis (oxidative stress, structural disorders and aggregation of proteins, mitochondrial dysfunction, energy deficiency) that initiate a cascade of events resulting finally in the dysfunction of neuronal networks are mediated by the disbalance of Zn²⁺ and Cu²⁺.