Verbal Memory Test Best Indicator of Who Will Have Alzheimer's Disease, New Study Says: Early Detection of Alzheimer's Could Lessen the Impact

Background: Studies have suggested that cognitive impairment in Alzheimer’s disease (AD) is associated with dendritic spine loss, especially in the hippocampus. Fluoxetine (FLX) has been shown to improve cognition in the early stage of AD and to be associated with diminishing synapse degeneration in the hippocampus. However, little is known about whether FLX affects the pathogenesis of AD in the middle-tolate stage and whether its effects are correlated with the amelioration of hippocampal dendritic dysfunction. Previously, it has been observed that FLX improves the spatial learning ability of middleaged APP/PS1 mice. Objective: In the present study, we further characterized the impact of FLX on dendritic spines in the hippocampus of middle-aged APP/PS1 mice. Results: It has been found that the numbers of dendritic spines in dentate gyrus (DG), CA1 and CA2/3 of hippocampus were significantly increased by FLX. Meanwhile, FLX effectively attenuated hyperphosphorylation of tau at Ser396 and elevated protein levels of postsynaptic density 95 (PSD-95) and synapsin-1 (SYN-1) in the hippocampus. Conclusion: These results indicated that the enhanced learning ability observed in FLX-treated middle-aged APP/PS1 mice might be associated with remarkable mitigation of hippocampal dendritic spine pathology by FLX and suggested that FLX might be explored as a new strategy for therapy of AD in the middle-to-late stage.

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Emerging Proof of Protein Misfolding and Interactions in Multifactorial Alzheimer's Disease

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200601161703 ◽

2020 ◽

Vol 20 (26) ◽

pp. 2380-2390 ◽

Cited By ~ 8

Author(s):

Md. Sahab Uddin ◽

Abdullah Al Mamun ◽

Md. Ataur Rahman ◽

Tapan Behl ◽

Asma Perveen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Protein Misfolding ◽

Neurodegenerative Disorder ◽

Senile Plaques ◽

Neuronal Cell Death ◽

Neuronal Cell ◽

Misfolded Proteins ◽

Cell Organelles ◽

The Impact

Objective: Alzheimer's disease (AD) is a devastating neurodegenerative disorder, characterized by the extracellular accumulations of amyloid beta (Aβ) as senile plaques and intracellular aggregations of tau in the form of neurofibrillary tangles (NFTs) in specific brain regions. In this review, we focus on the interaction of Aβ and tau with cytosolic proteins and several cell organelles as well as associated neurotoxicity in AD. Summary: Misfolded proteins present in cells accompanied by correctly folded, intermediately folded, as well as unfolded species. Misfolded proteins can be degraded or refolded properly with the aid of chaperone proteins, which are playing a pivotal role in protein folding, trafficking as well as intermediate stabilization in healthy cells. The continuous aggregation of misfolded proteins in the absence of their proper clearance could result in amyloid disease including AD. The neuropathological changes of AD brain include the atypical cellular accumulation of misfolded proteins as well as the loss of neurons and synapses in the cerebral cortex and certain subcortical regions. The mechanism of neurodegeneration in AD that leads to severe neuronal cell death and memory dysfunctions is not completely understood until now. Conclusion: Examining the impact, as well as the consequences of protein misfolding, could help to uncover the molecular etiologies behind the complicated AD pathogenesis.

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Development and Evaluation of a Tactile Cognitive Function Test Device for Alzheimer’s Disease Early Detection

Neuroscience and Biomedical Engineering ◽

10.2174/2213385203666150804001349 ◽

2015 ◽

Vol 3 (2) ◽

pp. 58-65 ◽

Cited By ~ 2

Author(s):

Jiajia Yang ◽

Mohd Usairy Syafiq ◽

Yinghua Yu ◽

Satoshi Takahashi ◽

Zhenxin Zhang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Function ◽

Early Detection ◽

Test Device ◽

Function Test ◽

Cognitive Function Test

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Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment

Brain Communications ◽

10.1093/braincomms/fcaa213 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Jiangyi Xia ◽

Ali Mazaheri ◽

Katrien Segaert ◽

David P Salmon ◽

Danielle Harvey ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Verbal Memory ◽

Verbal Learning ◽

Event Related Potential ◽

Single Trial ◽

Prodromal Alzheimer’S Disease ◽

Alpha Beta

Abstract Reliable biomarkers of memory decline are critical for the early detection of Alzheimer’s disease. Previous work has found three EEG measures, namely the event-related brain potential P600, suppression of oscillatory activity in the alpha frequency range (∼10 Hz) and cross-frequency coupling between low theta/high delta and alpha/beta activity, each of which correlates strongly with verbal learning and memory abilities in healthy elderly and patients with mild cognitive impairment or prodromal Alzheimer’s disease. In the present study, we address the question of whether event-related or oscillatory measures, or a combination thereof, best predict the decline of verbal memory in mild cognitive impairment and Alzheimer’s disease. Single-trial correlation analyses show that despite a similarity in their time courses and sensitivities to word repetition, the P600 and the alpha suppression components are minimally correlated with each other on a trial-by-trial basis (generally |rs| < 0.10). This suggests that they are unlikely to stem from the same neural mechanism. Furthermore, event-related brain potentials constructed from bandpass filtered (delta, theta, alpha, beta or gamma bands) single-trial data indicate that only delta band activity (1–4 Hz) is strongly correlated (r = 0.94, P < 0.001) with the canonical P600 repetition effect; event-related potentials in higher frequency bands are not. Importantly, stepwise multiple regression analyses reveal that the three event-related brain potential/oscillatory measures are complementary in predicting California Verbal Learning Test scores (overall R2’s in 0.45–0.63 range). The present study highlights the importance of combining EEG event-related potential and oscillatory measures to better characterize the multiple mechanisms of memory failure in individuals with mild cognitive impairment or prodromal Alzheimer’s disease.

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Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease

Alzheimer s Research & Therapy ◽

10.1186/s13195-020-00741-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Luke Whiley ◽

◽

Katie E. Chappell ◽

Ellie D’Hondt ◽

Matthew R. Lewis ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Systemic Inflammation ◽

Metabolic Phenotyping ◽

Acid Urine ◽

Metabolite Concentrations ◽

Ssri Medication ◽

The Impact ◽

Urine And Serum ◽

Urine Serum

Abstract Background Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine. Conclusions Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.

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Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Applied Sciences ◽

10.3390/app11041574 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1574

Author(s):

Shabana Urooj ◽

Satya P. Singh ◽

Areej Malibari ◽

Fadwa Alrowais ◽

Shaeen Kalathil

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Detection ◽

Differential Evolution ◽

Early Stage ◽

Selection Process ◽

Parameter Tuning ◽

Wavelet Neural Network ◽

Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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