Early Diagnosis Of Alzheimer's Disease Research Articles

Glutamate Receptor Dysregulation and Platelet Glutamate Dynamics in Alzheimer's and Parkinson's Diseases: Insights into Current Medications.

Two of the most prevalent neurodegenerative disorders (NDDs), Alzheimer's disease (AD) and Parkinson's disease (PD), present significant challenges to healthcare systems worldwide. While the etiologies of AD and PD differ, both diseases share commonalities in synaptic dysfunction, thereby focusing attention on the role of neurotransmitters. The possible functions that platelets may play in neurodegenerative illnesses including PD and AD are becoming more acknowledged. In AD, platelets have been investigated for their ability to generate amyloid-ß (Aß) peptides, contributing to the formation of neurotoxic plaques. Moreover, platelets are considered biomarkers for early AD diagnosis. In PD, platelets have been studied for their involvement in oxidative stress and mitochondrial dysfunction, which are key factors in the disease's pathogenesis. Emerging research shows that platelets, which release glutamate upon activation, also play a role in these disorders. Decreased glutamate uptake in platelets has been observed in Alzheimer's and Parkinson's patients, pointing to a systemic dysfunction in glutamate handling. This paper aims to elucidate the critical role that glutamate receptors play in the pathophysiology of both AD and PD. Utilizing data from clinical trials, animal models, and cellular studies, we reviewed how glutamate receptors dysfunction contributes to neurodegenerative (ND) processes such as excitotoxicity, synaptic loss, and cognitive impairment. The paper also reviews all current medications including glutamate receptor antagonists for AD and PD, highlighting their mode of action and limitations. A deeper understanding of glutamate receptor involvement including its systemic regulation by platelets could open new avenues for more effective treatments, potentially slowing disease progression and improving patient outcomes.

Individualized assessment of brain Aβ deposition with fMRI using deep learning.

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-β deposition derived from PET. We designed a Graph Convolutional Networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-β PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aβ-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aβ-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.

Ultra-sensitive SERS detection of Aβ 1–42 for Alzheimer's disease using graphene oxide/gold nanohybrids

Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder and the most common cause of dementia with significant social and economic impact. Despite the severity and prevalence of AD, early diagnosis of AD remains quite challenging. One of the important markers of neuropathology in AD patients is the aggregation of Aβ1–42 monomers into oligomers, which then become fibrils and eventually form beta-amyloid plaques. Herein, a highly sensitive surface-enhanced Raman spectroscopy (SERS) method based on graphene oxide/gold nanoparticles (GO/Au NPs) for the detection of Aβ 1–42 is proposed. Specifically, Au NPs were deposited on the GO surface by in situ reduction to form high-density hot spots for SERS. The limits of detection are 0.0232 ng mL-1 and 0.0192 ng mL-1 for Aβ1–42 monomer and fibrils. In addition, support vector machine (SVM) and one-dimensional convolutional neural network (1DCNN) algorithm were used to identify samples with different fibrils degrees. SERS is expected to be used for label-free diagnosis and early detection of AD, which has exciting potential for biomedical detection.

Recent Nanoscale Carriers for Therapy of Alzheimer's Disease: Current Strategies and Perspectives.

This review covers nanotherapeutic strategies for solving the global problems associated with Alzheimer's disease (AD). The most dramatic factor contributing humanistic, social and economic urgency of the situation is the incurability of the disease, with the drug intervention addressing only AD symptoms and retarding their progress. Key sources behind these challenges are the inability of the early diagnosis of AD, the lack of comprehensive information on the molecular mechanism of the pathogenesis, the bloodbrain barrier obstacles, and the insufficient effectiveness of currently available drugs and therapeutic strategies. The application of nanocarriers allows part of these problems to be solved, together with the improvement of drug bioavailability, prolonged circulation, and overcoming/bypassing the biological barriers. To this date, numerous types and subtypes of nanocarriers are developed and reviewed, the majority of which can be adapted for the treatment of various diseases. Therefore, herein, nanotherapy strategies are specifically categorized in term of the administration routes of AD medicines, with the noninvasive, i.e., transdermal, oral, and intranasal routes emphasized. Further, benefits/ limitations of various nanocarriers are discussed, and perspectives of their application are highlighted.

The Diagnosis of Alzheimer's Disease using EEG Signals

Alzheimer is a common and significant neurological disorder worldwide, typically associated with age-related dementia. Alzheimer's patients exhibit slower brain activities compared to healthy individuals, and the most prominent symptom of the disease is the impairment of cognitive functions. Early diagnosis of Alzheimer's is crucial to prevent the rapid progression of the disease. In this study, the feasibility of using electroencephalography (EEG) signals, a non-invasive, cost-effective, and objective method, to facilitate the diagnosis of Alzheimer's Disease (AD) was investigated.
 The study utilized EEG signals from both Alzheimer's patients and healthy individuals, which were made publicly available by Florida State University. Preprocessing was applied to the EEG signals to eliminate existing noise. Subsequently, a total of 34 various features in the time and frequency domains, such as entropy, Hjorth parameters, etc., were extracted from the EEG signals for the purpose of Alzheimer's diagnosis. Machine learning techniques, including decision trees (DT), support vector machines (SVM), and artificial neural networks (ANN), were applied to classify the data, and success rates for Alzheimer's detection were achieved.

Potential Retinal Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) represents a major diagnostic challenge, as early detection is crucial for effective intervention. This review examines the diagnostic challenges facing current AD evaluations and explores the emerging field of retinal alterations as early indicators. Recognizing the potential of the retina as a noninvasive window to the brain, we emphasize the importance of identifying retinal biomarkers in the early stages of AD. However, the examination of AD is not without its challenges, as the similarities shared with other retinal diseases introduce complexity in the search for AD-specific markers. In this review, we address the relevance of using the retina for the early diagnosis of AD and the complex challenges associated with the search for AD-specific retinal biomarkers. We provide a comprehensive overview of the current landscape and highlight avenues for progress in AD diagnosis by retinal examination.

Metabolic biomarkers for Alzheimer's disease: A mini review

Alzheimer's disease (AD) is the most common neurodegenerative disorder and a leading cause of dementia worldwide, which characterized by progressive cognitive decline, memory impairment, and changes in behavior and personality. The prevalence of AD is expected to rise significantly in the coming decades due to the aging population, making it a major public health concern. Mounting evidence suggests that metabolic dysregulation plays a critical role in the pathogenesis of AD. In this mini review, we provide an overview of the current understanding of the interplay between metabolism and AD. Specifically, we explore the dysregulation of glucose metabolism, lipid metabolism, amino acid metabolism, and nitrogen oxide synthesis pathways in AD. These metabolic alterations have been identified as potential biomarkers for early diagnosis and prognosis of AD. Additionally, we discuss the current status of research in this field, including the advancements in metabolomics and imaging techniques that have allowed for the identification and validation of metabolic biomarkers.

Fractal Dimension Analyses to Detect Alzheimer’s and Parkinson’s Diseases Using Their Thin Brain Tissue Samples via Transmission Optical Microscopy

Biological tissues in nature are fractal due to their self-similarity and porosity properties. These properties change with the progress of some diseases, including brain tissue in leading neurological disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Thus, there is an unmet clinical need to develop a tool for accurate and early diagnosis of AD and PD conditions. Although the whole brain tissues in AD and PD have been extensively studied, their local structural alterations at the nano-to-submicron levels have not been explored. In this paper, we measure the local structural alterations in different brain regions of AD and PD patients by measuring their change in fractal dimensions via optical microscopy. Our results show an increase in the fractal dimension value of ~5–10% in the affected regions of the brain tissues relative to their respective controls. For AD cases, the structural alteration is attributed to the aberrant deposition of amyloid beta protein and neurofibrillary tangles in the brain, and for PD, the gradual loss of dopaminergic neurons and abnormal accumulation of α-synuclein in the brain. The work will enhance the further understanding of alterations in the brain structures in AD and PD and its detection.

Diagnosis of Alzheimer’s Disease Based on Accelerated Mirror Descent Optimization and a Three-Dimensional Aggregated Residual Network

Alzheimer’s disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer’s disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD’s convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic.

Blood Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disorder that affects millions of people worldwide. The characteristic pathological manifestation of AD includes the deposition of extracellular insoluble β amyloid plaques and intracellular neurofibrillary tangles formed from hyperphosphorylated tau protein. Cost effective and minimally invasive peripheral blood-based biomarkers are critical for early AD diagnosis. Currently, the plasma based two fraction of β amyloid peptide ratio (Aβ42/40) and phosphorylated tau (p-tau) are considered as blood-based biomarkers for AD diagnosis. Recent research indicates that oxidative stress (OS) occurs prior to amyloid plaque (Aβ) formation and abnormal tau phosphorylation in AD. The imbalance of the master antioxidant, glutathione (GSH), and prooxidants (iron, zinc, and copper)─plays a crucial role in AD neurodegeneration. We present peripheral blood-based OS related biomarkers that are mechanistically involved in the disease process and may serve as a novel screening tool for early detection of AD onset. This OS based approach may also provide a quick and cost efficient method to monitor the effects of disease-modifying therapies in AD clinical trials.

Multi-engineered Graphene Extended-Gate Field-Effect Transistor for Peroxynitrite Sensing in Alzheimer's Disease.

The expression of β-amyloid peptides (Aβ), a pathological indicator of Alzheimer's disease (AD), was reported to be inapparent in the early stage of AD. While peroxynitrite (ONOO-) is produced excessively and emerges earlier than Aβ plaques in the progression of AD, it is thus significant to sensitively detect ONOO- for early diagnosis of AD and its pathological research. Herein, we unveiled an integrated sensor for monitoring ONOO-, which consisted of a commercially available field-effect transistor (FET) and a high-performance multi-engineered graphene extended-gate (EG) electrode. In the configuration of the presented EG electrode, laser-induced graphene (LIG) intercalated with MnO2 nanoparticles (MnO2/LIG) can improve the electrical properties of LIG and the sensitivity of the sensor, and graphene oxide (GO)-MnO2/Hemin nanozyme with ONOO- isomerase activity can selectively trigger the isomerization of ONOO- to NO3-. With this synergistic effect, our EG-FET sensor can respond to the ONOO- with high sensitivity and selectivity. Moreover, taking advantage of our EG-FET sensor, we modularly assembled a portable sensing platform for wireless tracking ONOO- levels in the brain tissue of AD transgenic mice at earlier stages before massive Aβ plaques appeared, and we systematically explored the complex role of ONOO- in the occurrence and development of AD.

The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease.

Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.

Synthesis and Bioevaluation of 2-Styrylquinoxaline Derivatives as Tau-PET Tracers.

This study focused on designing and evaluating Tau-PET tracers for noninvasive positron emission computed tomography (PET) imaging of neurofibrillary tangles (NFTs), a hallmark pathology of Alzheimer's disease (AD). The tracers were synthesized with a 2-styrylquinoxaline scaffold and varying lengths of FPEG chains. The compound [18F]15, which had two ethoxy units, showed high affinity for recombinant K18-Tau aggregates (Ki = 41.48 nM) and the highest selectivity versus Aβ1-42 aggregates (8.83-fold). In vitro autoradiography and fluorescent staining profiles further validated the binding of [18F]15 or 15 toward NFTs in brain sections from AD patients and Tau-transgenic mice. In normal ICR mice, [18F]15 exhibited an ideal initial brain uptake (11.21% ID/g at 2 min) and moderate washout ratio (2.29), and micro-PET studies in rats confirmed its ability to penetrate the blood-brain barrier with the peak SUV value of 1.94 in the cortex. These results suggest that [18F]15 has the potential to be developed into a useful Tau-PET tracer for early AD diagnosis and evaluation of anti-Tau therapeutics.

Unveiling the Molecular Footprint: Proteome-Based Biomarkers for Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by progressive cognitive decline and memory loss. Early and accurate diagnosis of AD is crucial for implementing timely interventions and developing effective therapeutic strategies. Proteome-based biomarkers have emerged as promising tools for AD diagnosis and prognosis due to their ability to reflect disease-specific molecular alterations. There is of great significance for biomarkers in AD diagnosis and management. It emphasizes the limitations of existing diagnostic approaches and the need for reliable and accessible biomarkers. Proteomics, a field that comprehensively analyzes the entire protein complement of cells, tissues, or bio fluids, is presented as a powerful tool for identifying AD biomarkers. There is a diverse range of proteomic approaches employed in AD research, including mass spectrometry, two-dimensional gel electrophoresis, and protein microarrays. The challenges associated with identifying reliable biomarkers, such as sample heterogeneity and the dynamic nature of the disease. There are well-known proteins implicated in AD pathogenesis, such as amyloid-beta peptides, tau protein, Apo lipoprotein E, and clusterin, as well as inflammatory markers and complement proteins. Validation and clinical utility of proteome-based biomarkers are addressing the challenges involved in validation studies and the diagnostic accuracy of these biomarkers. There is great potential in monitoring disease progression and response to treatment, thereby aiding in personalized medicine approaches for AD patients. There is a great role for bioinformatics and data analysis in proteomics for AD biomarker research and the importance of data preprocessing, statistical analysis, pathway analysis, and integration of multi-omics data for a comprehensive understanding of AD pathophysiology. In conclusion, proteome-based biomarkers hold great promise in the field of AD research. They provide valuable insights into disease mechanisms, aid in early diagnosis, and facilitate personalized treatment strategies. However, further research and validation studies are necessary to harness the full potential of proteome-based biomarkers in clinical practice.

Advances in targeted tracking and detection of soluble amyloid-β aggregates as a biomarker of Alzheimer's disease

Misfolding and aggregation of amyloid-β (Aβ) peptides are key hallmarks of Alzheimer's disease (AD). With accumulating evidence suggesting that different Aβ species have varied neurotoxicity and implications in AD development, the discovery of affinity ligands and analytical approaches to selective distinguish, detect, and monitor Aβ becomes an active research area. Remarkable advances have been achieved, which not only promote our understanding of the biophysical chemistry of the protein aggregation during neurodegeneration, but also provide promising tools for early detection of the disease. In view of this, we summarize the recent progress in selective and sensitive approaches for tracking and detection of Aβ species. Specific attentions are given to soluble Aβ oligomers, due to their crucial roles in AD development and occurrence at early stages. The design principle, performance of targeting units, and their cooperative effects with signal reporters for Aβ analysis are discussed. The applications of the novel targeting probes and sensing systems for dynamic monitoring oligomerization, measuring Aβ in biosamples and in vivo imaging in brain are summarized. Finally, the perspective and challenges are discussed regarding the future development of Aβ-targeting analytical tools to explore the unknown field to contribute to the early diagnosis and treatment of AD.

Hippocampal injections of soluble amyloid-beta oligomers alter electroencephalographic activity during wake and slow-wave sleep in rats

BackgroundSoluble amyloid-beta oligomers (Aβo) begin to accumulate in the human brain one to two decades before a clinical diagnosis of Alzheimer’s disease (AD). The literature supports that soluble Aβo are implicated in synapse and neuronal losses in the brain regions such as the hippocampus. This region importantly contributes to explicit memory, the first type of memory affected in AD. During AD preclinical and prodromal stages, people are also experiencing wake/sleep alterations such as insomnia (e.g., difficulty initiating sleep, decreased sleep duration), excessive daytime sleepiness, and sleep schedule modifications. In addition, changes in electroencephalographic (EEG) activity during wake and sleep have been reported in AD patients and animal models. However, the specific contribution of Aβo to wake/sleep alterations is poorly understood and was investigated in the present study.MethodsChronic hippocampal injections of soluble Aβo were conducted in male rats and combined with EEG recording to determine the progressive impact of Aβ pathology specifically on wake/sleep architecture and EEG activity. Bilateral injections were conducted for 6 consecutive days, and EEG acquisition was done before, during, and after Aβo injections. Immunohistochemistry was used to assess neuron numbers in the hippocampal dentate gyrus (DG).ResultsAβo injections did not affect the time spent in wakefulness, slow wave sleep (SWS), and paradoxical sleep but altered EEG activity during wake and SWS. More precisely, Aβo increased slow-wave activity (SWA; 0.5–5 Hz) and low-beta activity (16–20 Hz) during wake and decreased theta (5–9 Hz) and alpha (9–12 Hz) activities during SWS. Moreover, the theta activity/SWA ratio during wake and SWS was decreased by Aβo. These effects were significant only after 6 days of Aβo injections and were found with alterations in neuron counts in the DG.ConclusionsWe found multiple modifications of the wake and SWS EEG following Aβo delivery to the hippocampus. These findings expose a specific EEG signature of Aβ pathology and can serve the development of non-invasive and cost-effective markers for the early diagnosis of AD or other amyloid-related diseases.

Neural Oscillation Disorder in the Hippocampal CA1 Region of Different Alzheimer's Disease Mice.

Alzheimer's disease (AD) is a well-known neurodegenerative disease that gradually induces neural network dysfunction and progressive memory deficits. Neural network activity is represented by rhythmic oscillations that influence local field potentials (LFPs). However, changes in hippocampal neural rhythmic oscillations in the early stage of AD remain largely unexplored. This study investigated neural rhythmic oscillations in 3-month-old APP/PS1 and 5x- FAD mice to assess early neural connectivity in AD. LFPs were recorded from the hippocampal CA1 region with implanted microelectrode arrays while the mice were in the awake resting stage. Welch fast Fourier transforms, continuous wavelet transforms, and phase-amplitude coupling analyses were used to compute the power density of different frequency bands and phase-amplitude modulation indices in the LFPs. Our results showed impaired theta, low gamma, and high gamma frequency band power in APP/PS1 and 5xFAD mice during the awake resting stage. AD mice also showed decreased delta, alpha, and beta frequency band power. Impaired theta-low gamma and theta-high gamma phaseamplitude coupling were observed in 5xFAD mice. This study revealed neural network activity differences in oscillation power and cross-frequency coupling in the early stage of AD, providing a new perspective for developing biomarkers for early AD diagnosis.

Identification of Copper Metabolism Related Biomarkers, Polygenic Prediction Model, and Potential Therapeutic Agents in Alzheimer's Disease.

The underlying pathogenic genes and effective therapeutic agents of Alzheimer's disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models. To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy. The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored. A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD. The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.

Alterations of Audiovisual Integration in Alzheimer's Disease.

Audiovisual integration is a vital information process involved in cognition and is closely correlated with aging and Alzheimer's disease (AD). In this review, we evaluated the altered audiovisual integrative behavioral symptoms in AD. We further analyzed the relationships between AD pathologies and audiovisual integration alterations bidirectionally and suggested the possible mechanisms of audiovisual integration alterations underlying AD, including the imbalance between energy demand and supply, activity-dependent degeneration, disrupted brain networks, and cognitive resource overloading. Then, based on the clinical characteristics including electrophysiological and imaging data related to audiovisual integration, we emphasized the value of audiovisual integration alterations as potential biomarkers for the early diagnosis and progression of AD. We also highlighted that treatments targeted audiovisual integration contributed to widespread pathological improvements in AD animal models and cognitive improvements in AD patients. Moreover, investigation into audiovisual integration alterations in AD also provided new insights and comprehension about sensory information processes.

Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.

Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.