Early Diagnosis Of Alzheimer's Disease Research Articles

Causal Effects of Plasma Haptoglobin Levels on Alzheimer's Disease: A Two-Sample Mendelian Randomization Study.

A connection between plasma levels of haptoglobin (Hp) and Alzheimer's disease (AD) has been shown in several observational studies. It is debatable, nonetheless, how the two are related causally. To establish the causal relationship between Hp and AD using a two-sample Mendelian randomization (MR) study. From the extensive genome-wide association studies and FinnGen dataset, summaries and statistics pertaining to AD were gathered. We investigated the possibility of a causal link between Hp and AD using a two-sample MR study. Inverse variance weighting was used as the primary analytical technique, and it was supported by the joint application of complementary analyses and fixed effects meta-analysis to combine results from various sources. Genetically determined Hp was causally associated with AD [odds ratio (OR), 1.05; 95% confidence interval (CI), 1.02 to 1.09; p = 8.96×10-4]; Inverse variance-weighted estimates coming from different data sources were combined in a meta-analysis with consistent findings (OR, 1.03; 95% CI, 1.01 to 1.05; p = 2.00×10-3). The outcomes of the inverse MR analysis showed that AD had no appreciable causal impact on Hp. The present MR analysis shows that higher plasma Hp leads to an increased risk of AD. Strategies for plasma Hp testing may open up new doors for the early diagnosis and prevention of AD.

Ultrasensitive electrochemical detection of amyloid-beta oligomers using double amplification strategy by MXene substrate and covalent organic framework-based probe

Most of the existing electrochemical systems failed to achieve satisfactory results in early diagnosis of Alzheimer's disease (AD) owing to a deficiency of effective signal transduction. A new method for the electrochemical detection of AD biomarkers (amyloid-beta oligomers, Aβ1-42 oligomers) was developed based on a double amplification strategy. Titanium carbide (Ti3C2) MXene decorated by gold nanoparticles (Au-MXene) as the electrode substrate not only gave rise to the electrochemical response due to its paradoxical surface area and conspicuous charge mobility, but also provided vast numbers of binding sites for aptamers (Apt) of Aβ1-42 oligomers. Meanwhile, AuNPs were incorporated into covalent organic frameworks (COFs), which were further modified by Apt and electron mediator (toluidine blue, TB). The Apt/TB-Au@COFs composite was utilized as a label because of their improvement of the electron-hole separation efficiency and optimization of the charge-carrier utilization. The proposed electrochemical assay established highly efficient platform for the detection of Aβ1-42 oligomers with a linear range from 0.01 pg mL−1 to 180 pg mL−1 and an ultralow detection limit of 4.27 fg mL−1 (S/N = 3). This biosensing platform had potential applications in molecular diagnostics of AD serum samples.

Electrochemical miRNA-34a-based biosensor for the diagnosis of Alzheimer’s disease

Alzheimer's disease (AD) is the most common dementia type and a leading cause of death and disability in the elderly. Diagnosis is expensive and invasive, urging the development of new, affordable, and less invasive diagnostic tools. The identification of changes in the expression of non-coding RNAs prompts the development of diagnostic tools to detect disease-specific blood biomarkers. Building on this idea, this work reports a novel electrochemical microRNA (miRNA) biosensor for the diagnosis of AD, based on carbon screen-printed electrodes (C-SPEs) modified with two gold nanostructures and a complementary anti-miR-34a oligonucleotide probe. This biosensor showed good target affinity, reflected on a 100 pM to 1 μM linearity range and a limit of detection (LOD) of 39 pM in buffer and 94 aM in serum. Moreover, the biosensor’s response was not affected by serum compounds, indicating selectivity for miR-34a. The biosensor also detected miR-34a in the cell culture medium of a common AD model, stimulated with a neurotoxin to increase miR-34a secretion. Overall, the proposed biosensor makes a solid case for the introduction of a novel, inexpensive, and minimally invasive tool for the early diagnosis of AD, based on the detection of a circulating miRNA overexpressed in this pathology.

Non-invasive Biomarkers for Early Detection of Alzheimer's Disease: a New-Age Perspective.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. It gradually leads to memory loss, loss of thinking ability, and an overall cognitive decline. However, exhaustive literature is available to suggest that pathological changes in the brain occur decades before the first clinical symptoms appear. This review provides insight into the non-invasive biomarkers for early detection of AD that have been successfully studied in populations across the globe. These biomarkers have been detected in the blood, saliva, breath, and urine samples. Retinal imaging techniques are also reported. In this study, PubMed and Google scholar were the databases employed using keywords "Alzheimer's disease," "neurodegeneration," "non-invasive biomarkers," "early diagnosis," "blood-based biomarkers," and "preclinical AD," among others. The evaluation of these biomarkers will provide early diagnosis of AD in the preclinical stages due to their positive correlation with brain pathology in AD. Early diagnosis with reliable and timely intervention can effectively manage this disease.

Recent Advances in the Development of Near-Infrared Fluorescent Probes for the in Vivo Brain Imaging of Amyloid-β Species in Alzheimer's Disease.

The deposition of β-amyloid (Aβ) plaques in the parenchymal and cortical regions of the brain of Alzheimer's disease (AD) patients is considered the foremost pathological hallmark of the disease. The early diagnosis of AD is paramount in order to effective management and treatment of the disease. Developing near-infrared fluorescence (NIRF) probes targeting Aβ species is a potential and attractive approach suitable for the early and timely diagnosis of AD. The advantages of the NIRF probes over other tools include real-time detection, higher sensitivity, resolution, comparatively inexpensive experimental setup, and noninvasive nature. Currently, enormous progress is being observed in the development of NIRF probes for the in vivo imaging of Aβ species. Several strategies, i.e., the classical push-pull approach, "turn-on" effect, aggregation-induced emission (AIE), and resonance energy transfer (RET), have been exploited for development. We have outlined and discussed the recently emerged NIRF probes with different design strategies targeting Aβ species for ex vivo and in vivo imaging. We believe that understanding the recent development enables the prospect of the rational design of probes and will pave the way for developing future novel probes for early diagnosis of AD.

Electrochemical magneto-immunoassay for detection of ADAM10 Alzheimer's biomarker using gold nanoparticles as label

Alzheimer's disease (AD), a neurodegenerative and progressive illness with no known cure, is the most frequent cause of dementia in older adults. Dementia in AD is usually preceded by a stage of cognitive decline known as mild cognitive impairment (MCI). MCI has gained attention as an ideal target for prevention and early interventions, considering its reversible characteristic. Here, we propose a magneto-immunoassay based on a low-cost screen-printed electrode for detecting soluble ADAM10 in plasma samples, a potential biomarker for early AD diagnosis. We present a sandwich immunoassay using magnetic beads modified with antibodies to capture ADAM10 from plasma samples and using gold nanoparticles (AuNPs) as an electrochemical label. The assay was designed to accurately detect ADAM10 in diluted plasma with a limit of detection (LoD) of 32.5 pg/mL and a dynamic linear range of 10.0–1000.0 pg/mL. Twenty-three plasma samples from the elderly, including patients with AD, MCI, and healthy subjects (negative control), were analyzed by the magneto-immunoassay and enzyme-linked immunosorbent assay (ELISA), and the ADAM10 levels correlated. This work shows the potential of this protein as a biomarker in the early diagnosis and progression of AD and provides an interesting disposable device with capabilities for applications as point-of-care (PoC) to measure ADAM10 levels.

Advanced brain imaging for the diagnosis of Alzheimer disease.

The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD). Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities. Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.

Disrupted Balance of Gray Matter Volume and Directed Functional Connectivity in Mild Cognitive Impairment and Alzheimer's Disease.

Alterations in functional connectivity have been demonstrated in Alzheimer's disease (AD), an age-progressive neurodegenerative disorder that affects cognitive function; however, directional information flow has never been analyzed. This study aimed to determine changes in resting-state directional functional connectivity measured using a novel approach, granger causality density (GCD), in patients with AD, and mild cognitive impairment (MCI) and explore novel neuroimaging biomarkers for cognitive decline detection. In this study, structural MRI, resting-state functional magnetic resonance imaging, and neuropsychological data of 48 Alzheimer's Disease Neuroimaging Initiative participants were analyzed, comprising 16 patients with AD, 16 with MCI, and 16 normal controls. Volume-based morphometry (VBM) and GCD were used to calculate the voxel-based gray matter (GM) volumes and directed functional connectivity of the brain. We made full use of voxel-based between-group comparisons of VBM and GCD values to identify specific regions with significant alterations. In addition, Pearson's correlation analysis was conducted between directed functional connectivity and several clinical variables. Furthermore, receiver operating characteristic (ROC) analysis related to classification was performed in combination with VBM and GCD. In patients with cognitive decline, abnormal VBM and GCD (involving inflow and outflow of GCD) were noted in default mode network (DMN)-related areas and the cerebellum. GCD in the DMN midline core system, hippocampus, and cerebellum was closely correlated with the Mini- Mental State Examination and Functional Activities Questionnaire scores. In the ROC analysis combining VBM with GCD, the neuroimaging biomarker in the cerebellum was optimal for the early detection of MCI, whereas the precuneus was the best in predicting cognitive decline progression and AD diagnosis. Changes in GM volume and directed functional connectivity may reflect the mechanism of cognitive decline. This discovery could improve our understanding of the pathology of AD and MCI and provide available neuroimaging markers for the early detection, progression, and diagnosis of AD and MCI.

Precision medicine in neurodegeneration: the IHI-PROMINENT project.

Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Recent advances in diagnostics and approval for new pharmaceutical treatments for Alzheimer's disease (AD), the most common etiology of dementia, heralds the beginning of precision medicine in this field. However, their implementation will challenge an already over-burdened healthcare systems. There is a need for innovative digital solutions that can drive the related clinical pathways and optimize and personalize care delivery. Public-private partnerships are ideal vehicles to tackle these challenges. Here we describe the Innovative Health Initiative (IHI) public-private partnership project PROMINENT that has been initiated by connecting leading dementia researchers, medical professionals, dementia patients and their care partners with the latest innovative health technologies using a precision medicine based digital platform. The project builds upon the knowledge and already implemented digital tools from several collaborative initiatives that address new models for early detection, diagnosis, and monitoring of AD and other neurodegenerative disorders. The project aims to provide support to improvement efforts to each aspect of the care pathway including diagnosis, prognosis, treatment, and data collection for real world evidence and cost effectiveness studies. Ultimately the PROMINENT project is expected to lead to cost-effective care and improved health outcomes.

How Helpful are Blood and Cerebrospinal Fluid Biomarkers for the Early Diagnosis of Alzheimer's Disease?

Early diagnosis of Alzheimer's disease (AD) relies on imaging and fluid biomarkers to objectively detect and quantitatively evaluate brain AD pathologies. Amyloid- and tau-PET scans recently enabled clinicians to make definitive diagnoses based on abnormal amyloid/tau deposition in the human brain. Since PET scans and blood-based biomarkers are mutually complementary, the identification of suitable blood-based biomarkers is essential for screening brain AD pathologies. According to the National Institute on Aging and Alzheimer's Association (NIA-AA) Research Framework published in 2018, AD is defined by its underlying pathologic processes, which can be diagnosed in vivo using suitable biomarkers. These biomarkers are categorized under the ATN classification system, including β amyloid deposition (A), pathologic tau (T), and neurodegeneration (N). Originally, all the ATN fluid biomarkers were assessed using cerebrospinal fluid, but currently these biomarkers can be quantified using blood samples. Among those blood-based ATN biomarkers, the plasma levels of Aβ42/40 ratio, p-tau, and neurofilament light chain (NfL) representing "A," "T," and "N" biomarkers, respectively, are useful in the early diagnosis of AD. However, further studies and additional data are required before utilizing these blood-based biomarkers as stand-alone diagnostic markers for AD.

SOBA (soluble oligomer binding assay): A breakthrough or the early diagnosis of Alzheimer's disease.

SOBA (soluble oligomer binding assay): A breakthrough or the early diagnosis of Alzheimer's disease.

Topological properties of individual gray matter morphological networks in identifying the preclinical stages of Alzheimer's disease: a preliminary study.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are preclinical stages of Alzheimer's disease (AD). Individual biomarkers are essential for evaluating altered neurological outcomes at both SCD and MCI stages for early diagnosis and intervention of AD. In this study, we aimed to investigate the relationships between topological properties of the individual brain morphological network and clinical cognitive performances among healthy controls (HCs) and patients with SCD or MCI. The topological measurements of individual morphological networks were analyzed using graph theory, and inter-group differences of standard graph topology were correlated and regressed to scores of clinical cognitive functions. Compared with HCs, the topology of the individual morphological networks in SCD and MCI patients was significantly altered. At the global level, altered topology was characterized by lower global efficiency, shorter characteristics path length, and normalized characteristics path length [all P<0.05, false discovery rate (FDR) corrected]. In addition, at the regional level, SCD and MCI patients exhibited abnormal degree centrality in the caudate nucleus and nodal efficiency in the caudate nucleus, right insula, lenticular nucleus, and putamen (all P<0.05, FDR corrected). The topological features of individual gray matter morphological networks may serve as biomarkers to improve disease prognosis and intervention in the early stages of AD, namely SCD and MCI. Moreover, these findings may further elucidate the relationships between brain morphological alterations and cognitive dysfunctions in SCD and MCI.

Bis-iodine-labeled Curcumin as a Potential CT Imaging Agent for β-amyloid Plaques in the Brain.

Alzheimer's disease (AD) is one of the most common causes of dementia, affecting many old people. By designing and synthesizing intracerebral imaging probes, we tried to provide a new solution for the early diagnosis of AD. We designed and synthesized bis-iodine-labeled curcumin, and verified its performance through in vivo and in vitro experiments. In this study, bis-iodine-labeled curcumin (7, BICUR) was synthesized. In the in vitro mass spectrum binding assay, Kd values of BICUR with Aβ1-40 and Aβ1-42 aggregates were 46.29 nM and 64.29 nM, respectively. Aβ plaques in AD brain adjacent sections were positively stained by BICUR, which was similar to some other curcumin derivatives. The Log P value of BICUR was 1.45. In the biodistribution experiment, BICUR showed the highest initial brain uptake (5.87% compared to the blood concentration) two minutes after the tail vein injection and rapid clearance from the mouse brain. In the acute toxicity experiment, BICUR showed low toxicity, and the LD50 was >100 mg/kg. Moreover, BICUR showed a high stability in vitro (86.68% unchanged BICUR after incubation for 120min in mouse brain homogenate). Besides, BICUR produced an enhanced CT imaging effect that could be sensitively detected in vitro, but it also showed an obvious differentiation from surrounding tissues after intracerebral injection. All results suggested that BICUR could probably act as a targeted CT imaging agent for Aβ plaques in the brain.

Gut microbiome characteristics in subjective cognitive decline, mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis.

The gut microbiome has been reported to be closely related to Alzheimer's disease (AD) progression. Here, a comprehensive meta-analysis of gut microbial characteristics in AD, mild cognitive impairment (MCI) and subjective cognitive decline (SCD) was performed to compare gut microbial alterations at each stage. A total of 10 databases (CNKI, WanFang, VIP, SinoMed, WOS, PubMed, Embase, Cochrane Library, PsycINFO and Void) were searched and 34 case-control studies were included. α and β diversity and the relative abundance of gut microbiota were analysed as outcome indices. Data analysis was performed using Review Manager (5.4.1) and R. Chao1 and Shannon index levels in AD were significantly lower compared with healthy controls (HCs), and the Chao1 index was significantly lower in MCI compared with HCs. There was a significant difference in β diversity of gut microbiomes in patients (SCD, MCI, AD) compared with HCs. The relative abundance of Firmicutes at the phylum level was significantly lower in patients with AD and MCI than HCs. However, the relative abundance of Bacteroidetes at the phylum level was significantly higher in patients with MCI than HCs. There was an increasing trend for Enterobacteriaceae and a decreasing trend for Ruminococcaceae, Lachnospiraceae and Lactobacillus during AD; Lactobacillus showed a decreasing trend early in SCD. Our results indicated that there were gut microbiological abnormalities in AD, even as early as the SCD stage. The dynamic, consistent changes in gut microbes with the disease process showed that they might serve as potential biomarkers for early identification and diagnosis of AD.

Systemic alterations of tricarboxylic acid cycle enzymes in Alzheimer's disease.

Mitochondrial dysfunction, especially tricarboxylic acid (TCA) cycle arrest, is strongly associated with Alzheimer's disease (AD), however, its systemic alterations in the central and peripheral of AD patients are not well defined. Here, we performed an integrated analysis of AD brain and peripheral blood cells transcriptomics to reveal the expression levels of nine TCA cycle enzymes involving 35 genes. The results showed that TCA cycle related genes were consistently down-regulated in the AD brain, whereas 11 genes were increased and 16 genes were decreased in the peripheral system. Pearson analysis of the TCA cycle genes with Aβ, Tau and mini-mental state examination (MMSE) revealed several significant correlated genes, including pyruvate dehydrogenase complex subunit (PDHB), isocitrate dehydrogenase subunits (IDH3B, IDH3G), 2-oxoglutarate dehydrogenase complex subunit (DLD), succinyl-CoA synthetase subunit (SUCLA2), malate dehydrogenase subunit (MDH1). In addition, SUCLA2, MDH1, and PDHB were also uniformly down-regulated in peripheral blood cells, suggesting that they may be candidate biomarkers for the early diagnosis of AD. Taken together, TCA cycle enzymes were systemically altered in AD progression, PDHB, SUCLA2, and MDH1 may be potential diagnostic and therapeutic targets.

A Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease

Alzheimer’s disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild cognitive impairment (MCI). Moreover, this study presents a unique approach to decomposing AD into stages using machine-learning architectures with the help of tensor-based morphometric image analysis. The proposed model, which uses a neural network built on the Xception architecture, was thoroughly assessed by comparing it with the most recent convolutional neural network (CNN) models described in the literature. The proposed method outperformed the other models in terms of performance, achieving an impressive average classification accuracy of 95.81% using the dataset. It also had very high sensitivity, specificity, accuracy, and F1 scores, with average scores of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. In addition, it showed a superior classification ability compared to alternative methods, especially for MCI estimation, as evidenced by a mean area under the ROC curve (AUC) of 0.97. Our study demonstrated the effectiveness of deep-learning-based morphometric analysis using brain images for early AD diagnosis.

The Sigma Receptors in Alzheimer's Disease: New Potential Targets for Diagnosis and Therapy.

Sigma (σ) receptors are a class of unique proteins with two subtypes: the sigma-1 (σ1) receptor which is situated at the mitochondria-associated endoplasmic reticulum (ER) membrane (MAM), and the sigma-2 (σ2) receptor, located in the ER-resident membrane. Increasing evidence indicates the involvement of both σ1 and σ2 receptors in the pathogenesis of Alzheimer's disease (AD), and thus these receptors represent two potentially effective biomarkers for emerging AD therapies. The availability of optimal radioligands for positron emission tomography (PET) neuroimaging of the σ1 and σ2 receptors in humans will provide tools to monitor AD progression and treatment outcomes. In this review, we first summarize the significance of both receptors in the pathophysiology of AD and highlight AD therapeutic strategies related to the σ1 and σ2 receptors. We then survey the potential PET radioligands, with an emphasis on the requirements of optimal radioligands for imaging the σ1 or σ2 receptors in humans. Finally, we discuss current challenges in the development of PET radioligands for the σ1 or σ2 receptors, and the opportunities for neuroimaging to elucidate the σ1 and σ2 receptors as novel biomarkers for early AD diagnosis, and for monitoring of disease progression and AD drug efficacy.

Vertical Graphene-Based Printed Electrochemical Biosensor for Simultaneous Detection of Four Alzheimer's Disease Blood Biomarkers.

Early detection and timely intervention play a vital role in the effective management of Alzheimer's disease. Currently, the diagnostic accuracy for Alzheimer's disease based on a single blood biomarker is relatively low, and the combined use of multiple blood biomarkers can greatly improve diagnostic accuracy. Herein, we report a printed electrochemical biosensor based on vertical graphene (VG) modified with gold nanoparticles (VG@nanoAu) for the simultaneous detection of four Alzheimer's disease blood biomarkers. The printed electrochemical electrode array was constructed by laser etching and inkjet printing. Then gold nanoparticles were modified onto the working electrode surface via electrodeposition to further improve the sensitivity of the sensor. In addition, the entire printed electrochemical sensing system incorporates an electrochemical micro-workstation and a smartphone. The customized electrochemical micro-workstation incorporates four electro-chemical control chips, enabling the sensor to simultaneously analyze four biomarkers. Consequently, the printed electrochemical sensing system exhibits excellent analytical performance due to the large surface area, biocompatibility, and good conductivity of VG@nanoAu. The detection limit of the sensing system for Aβ40, Aβ42, T-tau, and P-tau181 was 0.072, 0.089, 0.071, and 0.051 pg/mL, respectively, which meets the detection requirements of Alzheimer's disease blood biomarkers. The printed electrochemical sensing system also exhibits good specificity and stability. This work has great value and promising prospects for early Alzheimer's disease diagnosis using blood biomarkers.

Electrochemical Immunosensors Developed for Amyloid-Beta and Tau Proteins, Leading Biomarkers of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ(1-40), Aβ(1-42), t-tau, cleaved-tau (c-tau), p-tau181, p-tau231, p-tau381, and p-tau441). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).

Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. But if AD is detected early, it can greatly reduce the severity of the disease. Functional connection networks (FCNs) can be used for the early diagnosis of AD, but they are undirected graphs and lack the description of causal information. Moreover, most of FCNs take brain regions as nodes, and few studies have been carried out focusing on the connections of the brain network. Although effective connection networks (ECNs) are digraphs, they do not reflect the causal relationships between brain connections. Therefore, we innovatively propose an edge-centric ECN (EECN) to explore the causality of the co-fluctuating connection in brain networks. Firstly, the traditional conditional Granger causality (GC) method is improved for constructing ECNs based on the suppression relationship between structural connection network (SCN) and FCN. Then based on the improved GC method, edge time series and EECNs are constructed. Finally, we perform dichotomous tasks on four stages of AD to verify the accuracy of our proposed method. The results show that this method achieves good results in six classification tasks. Finally, we present some brain connections that may be essential for early AD classification tasks. This study may have a positive impact on the application of brain networks.