Early Diagnosis Of Alzheimer's Disease Research Articles

Resting-State fMRI and Machine Learning as Diagnostic Tools for Alzheimer's Disease

: Alzheimer's disease (AD) presents a significant challenge in healthcare, necessitating accurate and timely diagnosis for effective management. Resting-state functional magnetic resonance imaging (Rs-fMRI) has emerged as a valuable tool for understanding neural correlates and the early detection of AD. This article reviews recent advancements in utilizing Rs-fMRI in combination with machine learning (ML) techniques for early AD diagnosis. First, we discuss the underlying principles of Rs-fMRI, highlighting its ability to detect alterations in brain functional connectivity (FC) patterns associated with AD. We then explore the potential of ML algorithms, particularly support vector machines (SVMs), in analyzing Rs-fMRI data and discriminating between AD patients and healthy controls. We indicate the challenges and opportunities in integrating Rs-fMRI and ML, such as in data preprocessing, feature selection, and model interpretation. We also address the importance of large-scale, multi-site studies to validate the robustness and generalizability of the proposed approaches. Overall, the integration of Rs-fMRI and ML holds great promise as a non-invasive, objective, and sensitive diagnostic tool for AD, potentially enabling early detection and personalized treatment strategies. However, further studies are warranted to optimize methodologies, enhance interpretability, and facilitate clinical translation.

Nanotechnology Breakthroughs in the Early Diagnosis of Alzheimer's Disease

Dear Sir, Alzheimer's Disease (AD) is an advancing neurological condition marked by diminishing cognitive functions, memory deterioration, and diminished functional skills. It stands as the primary contributor to dementia, encompassing roughly 60-80% of dementia occurrences. Generally affecting the elderly population, particularly those aged 65 and above (10%), and even more prominently in individuals aged 85 and older (30%), the likelihood of developing AD escalates with advancing age. The Key pathological features of AD include the accumulation of amyloid-beta (Aβ) plaques, tau protein build-up, oxidative stress, and immune inflammation. The complexity and unclear nature of AD's pathogenesis make early detection and timely treatment difficult. Nanotechnology, with its distinct physical, electrical, magnetic, and optical properties, shows significant promise in AD detection and treatment. We have explored the recent advancements in applications of nanoparticles (NPs) for AD detection. Additionally, we emphasize significant progress in nanotechnology-based treatment strategies, and challenges and propose future directions for nanotechnology in AD diagnosis and intervention1.

Novel β-amyloid PET Imaging Study of [18F]92 in Patients with Cognitive Decline.

[18F]-4-((E)-(((E)-4-(2-(2-(2-Fluoroethoxy)ethoxy)ethoxy)benzylidene)-hydrazono)methyl)-N-methylaniline ([18F]92) is a novel positron emission tomography (PET) tracer previously reported to exhibit high binding affinity to aggregated β-amyloid (Aβ). This study aims to report a fully automated radiosynthesis procedure for [18F]92, explore its radioactive distribution in the brains of healthy subjects, and investigate its potential application value in the early diagnosis of Alzheimer's disease (AD). The fully automated radiosynthesis of [18F]92 was performed on the AllinOne module. Thirty one participants were recruited for this study. Dynamic [18F]92 PET imaging was conducted over 0-90 min period to assess time-activity curves (TAC) and standardized uptake value ratio (SUVR) curves in cognitively normal (CN) subjects. All participants were visually classified as either positive (+) or negative (-). Semiquantitative analyses of [18F]92 were performed by calculating SUVRs in different regions of interest. Furthermore, the study analyzed the relationships between global SUVR and plasma AD biomarkers, including Aβ42, Aβ40, P-tau181, and T-tau. The automated radiosynthesis of [18F]92 was completed within 50 min, yielding a radiochemical purity of greater than 95% and a radiochemical yield of 36 ± 3% (nondecay-corrected). Among the participants, 15 were estimated as Aβ (-) and 16 as Aβ (+). TACs indicated that [18F]92 rapidly crossed the blood-brain barrier within 10 min, followed by a rapid decrease, which then slowed down in the last 50-90 min. SUVR curves revealed that SUVR values stabilized around 60-70 min after injection and reached an equilibrium between 70 and 90 min, primarily in the cerebral cortex. SUVRs of Aβ (+) participants were significantly higher than those of Aβ (-) individuals within the cerebral cortex. In addition, Aβ42 and the Aβ42/Aβ40 ratio exhibited negative correlations with global SUVR, while plasma P-tau181 and the P-tau181/T-tau ratio displayed positive correlations with global SUVR. [18F]92 exhibits excellent pharmacokinetic properties in the human brain and can be synthesized automatically on a large scale. [18F]92 is a promising and reliable radiotracer for estimating Aβ pathology accumulation, providing valuable assistance in AD diagnosis and guiding clinical trials of therapeutic drugs.

Covalent organic framework-based ratiometric electrochemical sensing platform for ultrasensitive determination of amyloid-β 42 oligomer

Accurate and sensitive detection of amyloid-β 42 oligomer (Aβ42O) is of great significance for early diagnosis of Alzheimer's disease (AD). Herein, a signal on-off ratiometric electrochemical immunosensor was developed for highly selective and quantitative determination of Aβ42O by using novel covalent organic frameworks (COFs) composites as the sensing platform. This immunosensor produced two independent electrochemical signals from the [Fe(CN)6]3−/4− and methylene blue (MB) probes at different potentials based on the electrocatalytic activity of gold nanoparticle−functionalized porphyrinyl COFs nanocomposites toward [Fe(CN)6]3−/4− and the signal probe of MB encapsulated in the aptamer-modified alkynyl COFs. Because the two signals of [Fe(CN)6]3−/4− and MB changed in opposite directions, a signal on−off mode was generated which can correct the results by introducing a reference signal and effectively eliminate background interference. Under optimal experimental conditions, the current ratio (IMB/I[Fe(CN)6]3−/4−) was well linearly related to the logarithmic value of Aβ42O concentrations in the range of 10 pM to 1 μM, and the detection limit was 5.1 pM (S/N = 3). Additionally, the immunosensor exhibited satisfactory performance in case of real cerebrospinal fluid samples. The designed ratiometric electrochemical immunosensor provides a valuable route for early diagnosis of AD and our results also pave the way for designing of sensing platforms using COF-based nanomaterials and extending their functions and applications to bioanalysis.

Insights from a 7-Year Dementia Cohort (VALCODIS): ApoE Genotype Evaluation.

Background: The VALCODIS (Valencian Cognitive Diseases Study) cohort was designed and studied at the Hospital Universitari i Politècnic La Fe (Valencia, Spain) for the research of cognitive diseases, especially in the search for new biomarkers of Alzheimer's disease (AD). Methods: Participants in the VALCODIS cohort had cerebrospinal fluid (CSF) and blood samples, neuroimaging, and neuropsychological tests. The ApoE genotype was evaluated to identify its relationship with CSF biomarkers and neuropsychological tests in AD and non-AD participants. Results: A total of 1249 participants were included. They were mainly AD patients (n = 547) but also patients with other dementias (frontotemporal lobar dementia (n = 61), Lewy body dementia without AD CSF signature (n = 10), vascular dementia (n = 24) and other specific causes of cognitive impairment (n = 442), and patients with subjective memory complaints (n = 165)). In the ApoE genotype evaluation, significant differences were found for Aβ42 levels between genotypes in both AD and non-AD patients, as well as a negative correlation between tau values and a cognitive test in non-carriers and ε4 heterozygous. Conclusions: The VALCODIS cohort provides biologically diagnosed patients with demographical, clinical and biochemical data, and biological samples for further studies on early AD diagnosis. Also, the ApoE genotype evaluation showed correlations between CSF biomarkers and neuropsychological tests.

Advancing Alzheimer’s biomarker Detection: A hairpin Aptamer-Based competitive electrochemical biosensor for trace Amyloid beta 40 analysis

Accurate detection of Alzheimer’s disease (AD) core biomarkers in body fluid is critical for identifying latent asymptomatic AD patients and prediction AD progress. Amyloid beta 1–40 (Aβ-40) as one of the core biomarkers, is expected to be an early diagnostic indicator for AD. Here, we establish a competitive method based on hairpin-structured aptamer for rapid and highly sensitive detection of Aβ-40 using square wave voltammetry (SWV). A hairpin aptamer with redox-active moiety methylene blue (MB) are immobilized on a gold working electrode as a recognition element. In the detection, a short complementary oligonucleotide strand is first introduced to switch on the hairpin, whereafter the targets Aβ-40 join in and selectively react with the aptamer in a competitive manner with the complementary strand, leading to the changes in the proximity of MB to the electrode and thereby in the electrochemical signal. Various parameters such as aptamer concentration, complementary chain length, incubation time, and temperature were optimized to achieve maximum sensing performance. By introducing a complementary chain, the sensor significantly reduces the detection limit to 7.14 pg/mL, with a dynamic range of 20 pg/mL to 200 ng/mL. Additionally, high selectivity of the sensor for Aβ-40 monomer (Aβ-40 m) over other forms of Aβ peptides has been validated. The hairpin-mediated detection method enables rapid, facile and highly sensitive detection of Aβ-40 using low-cost screen-printed electrode (SPE) platform, providing a wide range of possibilities for the early diagnosis of AD in point-of-care technologies (POCT).

Effects of healthy aging on tongue-jaw kinematics during feeding behavior in rhesus macaques.

Several age-related oral health problems have been associated with neurodegenerative diseases such as Alzheimer's Disease (AD), yet how oromotor dysfunction in healthy aging differ from those found in pathological aging is still unknown. This is partly because changes in the cortical and biomechanical ("neuromechanical") control of oromotor behavior in healthy aging are poorly understood. To this end, we investigated the natural feeding behavior of young and aged rhesus macaques (Macaca mulatta) to understand the age-related differences in tongue and jaw kinematics. We tracked tongue and jaw movements in 3D using high-resolution biplanar videoradiography and X-ray Reconstruction of Moving Morphology (XROMM). Older subjects exhibited a reduced stereotypy in tongue movements during chews and a greater lag in tongue movements relative to jaw movements compared to younger subjects. Overall, our findings reveal age-related changes in tongue and jaw kinematics, which may indicate impaired tongue-jaw coordination. Our results have important implications for the discovery of potential neuromechanical biomarkers for early diagnosis of AD.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Alzheimer’s Disease Diagnosis via Intuitionistic Fuzzy Random Vector Functional Link Network

Alzheimer's disease (AD) is a prominent neurodegenerative disorder, which leads to memory loss and cognitive impairment. The progression is irreversible and shows atrophies in cerebral cortex. Multiple studies revealed that the early diagnosis and early treatment can slow the progress of dementia, and hence, further atrophies can be controlled. Brain imaging data, such as magnetic resonance imaging (MRI), have been prominently used for the diagnosis of AD. Multiple approaches have been proposed for the diagnosis of AD. We propose a novel intuitionistic fuzzy random vector functional link network (IFRVFL) for the diagnosis of AD. Unlike standard random vector functional link (RVFL) network, extreme learning machine (ELM), and kernel ridge regression (KRR), which uses a uniform weighting approach for generating the optimal classifier, the proposed IFRVFL uses a fuzzy weighted approach for generating the optimal classifier. A uniform weighting scheme assumes that all the data samples are equally important; however, in real-world scenarios, this assumption may not hold true due to the presence of outliers and noise. Hence, it results in lower generalization. The proposed IFRVFL assigns each sample an intuitionistic fuzzy number (IFN), which is a function of membership and nonmembership score of a sample. The membership score is a function of the sample distance from the centroid of its corresponding class and the nonmembership score is a function of sample distance from the centroid as well as the neighborhood of the given sample. The proposed IFRVFL effectively reduces the influence of outliers. To evaluate the efficiency of the proposed IFRVFL model, we employed it for the diagnosis of AD. Experimental results demonstrate that the proposed IFRVFL model is superior in mild cognitive impairment (MCI) versus AD case. Thus, IFRVFL can be used in the clinical setting for the early diagnosis of AD. Furthermore, to check the robustness of the proposed IFRVFL model, we also evaluated it on benchmark datasets. Experimental results and the statistical tests reveal that the proposed IFRVFL is better in comparison to baseline models. The source code of the proposed IFRVFL formulation is available at https://github.com/mtanveer1/.

Bifurcations in coupled amyloid-β aggregation-inflammation systems

A complex interplay between various processes underlies the neuropathology of Alzheimer’s disease (AD) and its progressive course. Several lines of evidence point to the coupling between Aβ aggregation and neuroinflammation and its role in maintaining brain homeostasis during the long prodromal phase of AD. Little is however known about how this protective mechanism fails and as a result, an irreversible and progressive transition to clinical AD occurs. Here, we introduce a minimal model of a coupled system of Aβ aggregation and inflammation, numerically simulate its dynamical behavior, and analyze its bifurcation properties. The introduced model represents the following events: generation of Aβ monomers, aggregation of Aβ monomers into oligomers and fibrils, induction of inflammation by Aβ aggregates, and clearance of various Aβ species. Crucially, the rates of Aβ generation and clearance are modulated by inflammation level following a Hill-type response function. Despite its relative simplicity, the model exhibits enormously rich dynamics ranging from overdamped kinetics to sustained oscillations. We then specify the region of inflammation- and coupling-related parameters space where a transition to oscillatory dynamics occurs and demonstrate how changes in Aβ aggregation parameters could shift this oscillatory region in parameter space. Our results reveal the propensity of coupled Aβ aggregation-inflammation systems to oscillatory dynamics and propose prolonged sustained oscillations and their consequent immune system exhaustion as a potential mechanism underlying the transition to a more progressive phase of amyloid pathology in AD. The implications of our results in regard to early diagnosis of AD and anti-AD drug development are discussed.

Research progress on humoral biomarkers of Alzheimer's disease: A review.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory loss. The main pathological features are neuronal fibrillary tangles caused by amyloid beta deposition and hyperphosphorylation of tau protein, accompanied by neuronal death and loss of synaptic structure. Early diagnosis is the key to the treatment of AD. It is known that some small molecular components are related to the pathogenesis of AD. This article will summarize the common AD biomarkers in cerebrospinal fluid and blood and analyze the current status of AD biomarkers and future research directions. This review summarizes the promising biomarkers for the diagnosis of AD in the last decade and describes their changes in AD body fluids. The diagnostic biomarkers related to AD were mainly distributed in cerebrospinal fluid and blood. Significant changes in these molecules can be detected in cerebrospinal fluid and blood, and they are correlated with AD severity. These humoral molecules have necessary relationship with AD and can be used as AD biomarkers to assist early diagnosis of AD.

Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling.

This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer’s disease

Alzheimer’s disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Enhancing Feature Selection in MCI Diagnosis using FDG-PET Images: Leveraging Multiple Simple Autoencoder Architectures

Alzheimer’s Disease (AD) is the most common type of neurodegenerative brain disease in elderly people. Early diagnosis of AD is crucial for providing suitable care. Positron Emission Tomography (PET) images and machine learning can be used to support this purpose. In this paper, we present a method for ranking the effectiveness of brain regions of interest (ROIs) to distinguish between stable mild cognitive impairment (sMCI) from progressive mild cognitive impairment (pMCI) in brain PET images based on AutoEncoder (AE). Experiments on the ADNI dataset show that our proposed method significantly improves classifier performance when compared to other popular feature ranking methods such as Fisher score, t-score, and LASSO. Our results suggest that instead of focusing on designing a complex AE structure, we can also use simple-but-multiple AEs for feature ranking. The proposed method could be easily applied to any image dataset where a feature selection is needed.

An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Current trends on the early diagnosis of Alzheimer’s Disease by means of neural computation methods

Abstract—The prevalence of dementia is expected to increment in the next decades as the elderly population grows and ages. Hence, Alzheimer’s Disease (AD), as the most frequent dementia, will be more problematic from a socioeconomic point of view. Different diagnostic criteria have been proposed by clinicians for the early diagnosis of AD. After discarding the longitudinal and prognosis articles, a selection of articles from the last decade and based on Artificial Neural Networks (ANNs) was collated from the PubMed database, and complemented with researches extracted from others. The latest trends on this field were discovered in these selected articles, which were later discussed. Only articles based whether on shallow ANNs, Deep Learning (DL) or a mix of both were included. The total number of cross-sectional articles that complied with our selection criteria was 154. Convolutional Neural Networks (CNNs) combined with neuroimaging has been the most popular approach, yielding very good performance results. Approaches based on nonneuroimaging techniques, such as gait, genetics, speech and neuropsychological tests, were less common but have their own advantages. Multimodality solutions may become even more prevalent in the near future. Similarly, novel diagnostic criteria will appear and the popularity of currently not-so-common ones will expand. A new proposal emerged from these trends, which is based on ontogenetic ANNs.

The Crosstalk Between Amyloid-β, Retina, and Sleep for the Early Diagnosis of Alzheimer's Disease: A Narrative Review.

Alzheimer's disease (AD) is the most common type of dementia, which is characterised by progressive memory loss and accumulation of hallmark markers amyloid-β (Aβ) and neurofibrillary tangles in the diseased brain. The current gold standard diagnostic methods have limitations of being invasive, costly, and not easily accessible. Thus, there is a need for new avenues, such as imaging the retina for early AD diagnosis. Sleep disruption is symptomatically frequent across preclinical and AD subjects. As circadian activity, such as the sleep-wake cycle, is linked to the retina, analysis of their association may be useful additions for achieving predictive AD diagnosis. In this narrative review, we provide an overview of human retina studies concerning the deposition of Aβ, the role of the retina in sleep-wake cycle, the disruption of sleep in AD, and to gather evidence for the associations between Aβ, the retina, and sleep. Understanding the mechanisms behind the associations between Aβ, retina, and sleep could assist in the interpretation of retinal changes accurately in AD.

Corneal innervation changes ın Alzheimer's: implications for sensory dysfunction.

To compare, between Alzheimer's disease (AD) patients and healthy individuals, corneal subbasal nerve plexus (CSNP) parameters and corneal sensitivities. Twenty-two patients who were followed up with Alzheimer's disease (Alzheimer's group) and 18 age- and gender-matched healthy individuals (control group) were included in this cross-sectional study. CSNP parameters, including nerve fiber length (NFL), nerve fiber density (NFD), and nerve branch density (NBD), were evaluated using in vivo confocal microscopy. Corneal sensitivity was evaluated using a Cochet-Bonnet esthesiometer. The results were compared between the two groups. In the Alzheimer's group, NFL was 12.2 (2.4) mm/mm2, NFD was 12.5 [3.1] fibers/mm2, and NBD was 29.7 [9.37] branches/mm2. In the control group, NFL was 16.5 (2.0) mm/mm2, NFD was 25.0 [3.13] fibers/mm2, and NBD was 37.5 [10.9] branches/mm2. All three parameters were significantly lower in the Alzheimer's group compared to the control group (p < 0.001, p < 0.001, and p = 0.001, respectively). Similarly, corneal sensitivity was significantly lower in the Alzheimer's group (55.0 [5.0] mm) compared to the control group (60.0 [5.0] mm) (p < 0.001). We determined that, in AD, corneal sensitivity decreases significantly, in parallel with the decrease in corneal nerves. Changes in the corneal nerve plexus and a decrease in corneal sensitivity may be used in the early diagnosis and follow-up of AD. In addition, ocular surface problems secondary to these changes should also be kept in mind.

Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection

Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer’s disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer’s disease. The Alzheimer’s disease Neuroimaging Initiative I and Alzheimer’s disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer’s disease by registering an accuracy of 99.89 and 98.37 F1-score.

From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.