Detection Of Alzheimer's Disease Research Articles

Utility of Cognitive Abilities Screening Instrument (CASI) subscores in detecting early impairment in autosomal dominant Alzheimer’s disease (ADAD)

AbstractBackgroundPrior research among patients with autosomal dominant Alzheimer’s Disease (ADAD) has identified early cognitive changes 10‐20 years before formal dementia diagnosis. ADAD provides a model of Alzheimer’s disease (AD) with a relatively predictable onset and with fewer age‐related comorbidities, and as such, may aid in identifying presymptomatic stages of cognitive change for early AD detection. The CASI is a comprehensive screening tool for dementia with multilingual adaptations and may be useful in identifying early cognitive changes among those with or at risk of ADAD.MethodWe examined whether subscales of the Cognitive Abilities Screening Instrument (CASI) could identify early cognitive changes in ADAD as well as the effects of test language (English or Spanish), education, and acculturation on CASI subscales. 141 persons (96 mutation carriers [MC], 45 noncarriers [NC]) with or at risk for inheriting PSEN1/2 or APP mutations completed the CASI. Linear mixed‐effect models were used to examine the effects of adjusted age, mutation status, mutation type (APP or PSEN), test language, education, and acculturation on the 9 CASI subscales.ResultMCs performed significantly worse on CASI subscales than NCs (ps<.001). Adjusted age (βs = ‐.29— ‐.09), mutation status (βs = ‐2.62—‐0.09), and adjusted age x mutation status (βs = ‐.29— ‐0.09) significantly predicted Attention (R2 = 0.36), Concentration (R2 = 0.40), Orientation (R2 = 0.53), Short‐Term Memory (R2 = 0.61), Long‐Term Memory (R2 = 0.27), Language (R2 = 0.39), Visuoconstruction (R2 = 0.53), Fluency (R2 = 0.34), and Abstraction/Judgment (R2 = 0.37), all ps <.001. Acculturation significantly predicted Long‐Term Memory (β = .34, p = .03). The effects of acculturation, test language, and education were not significant for any other CASI subscales.ConclusionAdjusted age, mutation status, and an interactive effect between adjusted age and mutation status appear to be important predictors in CASI performance, such that MCs closer to their familial age of dementia diagnosis are more likely to perform worse on the CASI. No subtest emerged as the best predictor of early cognitive changes in MCs. Attention to potential cultural influences on the Long‐Term Memory subtest warrants further research.

The cerebrospinal fluid soluble TREM‐1 concentration in Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI)

AbstractBackgroundThe most prevalent form of dementia in the world is Alzheimer Disease (AD), a common neurodegenerative condition. Amyloid peptides, phosphorylated tau proteins, and neuroinflammation are the disease’s hallmarks. Finding new biomarkers that can assist in early AD detection is crucial due to the late appearance of the disease symptoms. Triggering receptor expressed on myeloid cell 1 (TREM‐1) is hypothesized that may contribute to the development of AD by causing neuroinflammation. Due of its significance in inflammatory responses, it has been regarded as a promising biomarker. Literature data indicate that TREM‐1 could be potential biomarker of AD severity. Therefore the purpose of our research was to measure the concentration of soluble TREM‐1 in cerebrospinal fluid of AD and MCI patients and non‐demented controls and compare it with classical AD biomarkers.MethodThe concentrations of TREM‐1 were measured in cerebrospinal fluid (CSF) of 20 AD patients and 15 MCI patients as well as 11 non‐demented controls using multiplexing method. Classical biomarkers, such as Aβ‐42, Aβ‐42/Aβ‐40, tau and pTau181 were assessed by immunoenzyme assays.ResultTREM‐1 concentrations were significantly higher in AD and MCI patients in comparison to non‐demented controls. Moreover, in MCI patients, the levels of this protein were significantly higher than in AD patients. Additionally, increased CSF levels of TREM‐1 correlated positively with Tau, pTau181 in the whole study group.ConclusionFindings of our research suggest that TREM‐1 could be early indicator of AD pathology. However, follow‐up studies on larger study group are needed.

Potential neural substrates underlying circadian and olfactory disruptions in preclinical Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia, with over 45 million patients worldwide, and poses significant economic and emotional burdens to both patients and caregivers, significantly raising the number of those affected. Unfortunately, much of the existing research on the disease only addresses a small subset of associated symptomologies and pathologies. In this review, we propose to target the earliest stages of the disease, when symptomology first arises. In these stages, before the onset of hallmark symptoms of AD such as cognitive impairments and memory loss, circadian and olfactory disruptions arise and are detectable. Functional similarities between circadian and olfactory systems provide a basis upon which to seek out common mechanisms in AD which may target them early on in the disease. Existing studies of interactions between these systems, while intriguing, leave open the question of the neural substrates underlying them. Potential substrates for such interactions are proposed in this review, such as indirect projections that may functionally connect the two systems and dopaminergic signaling. These substrates may have significant implications for mechanisms underlying disruptions to circadian and olfactory function in early stages of AD. In this review, we propose early detection of AD using a combination of circadian and olfactory deficits and subsequent early treatment of these deficits may provide profound benefits to both patients and caregivers. Additionally, we suggest that targeting research toward the intersection of these two systems in AD could uncover mechanisms underlying the broader set of symptoms and pathologies that currently elude researchers.

Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.

Alzheimer Disease Detection using AI with Deep Learning based Features with Development and Validation based on Data Science

Alzheimer's disease (AD), a neurological condition that worsens over time, affects millions of individuals worldwide. Because of this, effective intervention and therapy depend on early and precise detection. In recent years, encouraging findings have been obtained using data science and artificial intelligence (AI) techniques in the field of medical diagnostics, particularly AD diagnosis. This work seeks to develop an accurate algorithm for diagnosing AD by identifying AI-based traits from neuroimaging and clinical data.The three key steps of the proposed methodology are data preprocessing, feature extraction, and model development and validation. To offer neuroimaging data, such as MRI and PET scans, as well as essential clinical information, a cohort of persons made up of AD patients and healthy controls is obtained. Throughout the preparation stage, the data are normalised, standardised, and quality-checked to ensure accuracy and consistency.The critical role of feature extraction in locating critical patterns and features potentially indicative of AD is critical. Advanced AI techniques like Convolutional Neural Networks and Recurrent Neural Networks are utilised to extract discriminative features from neuroimaging data after subjecting it to feature engineering methods.The retrieved features are then utilised to build a prediction model using state-of-the-art machine learning techniques such as Support Vector Machines (SVM), Random Forests, or Deep Learning architectures. Strict validation methods, such cross-validation and test datasets, are used to evaluate the model's performance in order to ensure generalizability and minimise overfitting.The project's objective is to identify AD with high specificity, sensitivity, and accuracy to support early diagnosis and tailored treatment planning. The results of this research contribute to the body of knowledge on AI-based diagnostics for neurodegenerative diseases and have the potential to significantly impact clinical practises by facilitating early interventions and improving patient outcomes. It is important to take into account the size and heterogeneity of the dataset as well as any prospective improvements and future expansions to the usage of AI in AD detection.

Mesoporous Silica Nanoparticles: Types, Synthesis, Role in the Treatment of Alzheimer's Disease, and Other Applications.

Globally, many individuals struggle with Alzheimer's disease (AD), an unrelenting and incapacitating neurodegenerative condition. Despite notable research endeavors, effective remedies for AD remain constrained, prompting the exploration of innovative therapeutic avenues. Within this context, silica-based nanoplatforms have emerged with pronounced potential due to their unique attributes like expansive surface area, customizable pore dimensions, and compatibility with living systems. These nanoplatforms hold promise as prospective interventions for AD. This assessment provides a comprehensive overview encompassing various forms of mesoporous silica nanoparticles (MSNs), techniques for formulation, and their applications in biomedicine. A significant feature lies in their ability to precisely guide and control the transport of therapeutic agents to the brain, facilitated by the adaptability of these nanoplatforms as drug carriers. Their utility as tools for early detection and monitoring of AD is investigated. Challenges and prospects associated with harnessing MSNs are studied, underscoring the imperative of stringent safety evaluations and optimization of how they interact with the body. Additionally, the incorporation of multifunctional attributes like imaging and targeting components is emphasized to enhance their efficacy within the intricate milieu of AD. As the battle against the profound repercussions of AD persists, MSNs emerge as a promising avenue with the potential to propel the development of viable therapeutic interventions.

Prediction of Progression from Mild Cognitive Impairment to Alzheimer's disease with Longitudinal and Multimodal Data.

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a certain time frame is crucial for appropriate therapeutic interventions. However, it is challenging to capture the dynamic changes in cognitive and functional abilities over time, resulting in limited predictive performance. Our study aimed to investigate whether incorporating longitudinal multimodal data with advanced analytical methods could improve the capability to predict the risk of progressing to AD. This study included participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a large-scale multi-center longitudinal study. Three data modalities, including demographic variables, neuropsychological tests, and neuroimaging measures were considered. A Long Short-Term Memory (LSTM) model using data collected at five-time points (baseline, 6-month, 12-month, 18-month, and 24-month) was developed to predict the risk of progression from MCI to AD within two years from the index exam (the exam at 24-month). In contrast, a random forest model was developed to predict the risk of progression just based on the data collected at the index exam. The study included 347 participants with MCI at 24-month (age: mean 75, SD 7 years; 39.8% women) from ADNI, of whom 77 converted to AD over a 2-year follow-up period. The longitudinal LSTM model showed superior prediction performance of MCI-to-AD progression (AUC 0.93±0.06) compared to the random forest model (AUC 0.90±0.09). A similar pattern was also observed across different age groups. Our study suggests that the incorporation of longitudinal data can provide better predictive performance for 2-year MCI-to-AD progression risk than relying solely on cross-sectional data. Therefore, repeated or multiple times routine health surveillance of MCI patients are essential in the early detection and intervention of AD.

Specific serum autoantibodies predict the development and progression of Alzheimer’s disease with high accuracy

Autoimmunity plays a key role in the pathogenesis of Alzheimer’s disease (AD). However, whether autoantibodies in peripheral blood can be used as biomarkers for AD has been elusive. Serum samples were obtained from 1,686 participants, including 767 with AD, 146 with mild cognitive impairment (MCI), 255 with other neurodegenerative diseases, and 518 healthy controls. Specific autoantibodies were measured using a custom-made immunoassay. Multivariate support vector machine models were employed to investigate the correlation between serum autoantibody levels and disease states. As a result, seven candidate AD-specific autoantibodies were identified, including MAPT, DNAJC8, KDM4D, SERF1A, CDKN1A, AGER, and ASXL1. A classification model with high accuracy (area under the curve (AUC) = 0.94) was established. Importantly, these autoantibodies could distinguish AD from other neurodegenerative diseases and out-performed amyloid and tau protein concentrations in cerebrospinal fluid in predicting cognitive decline (P < 0.001). This study indicated that AD onset and progression are possibly accompanied by an unappreciated serum autoantibody response. Therefore, future studies could optimize its application as a convenient biomarker for the early detection of AD.

Blood-brain barrier dysfunction and Alzheimer's disease: associations, pathogenic mechanisms, and therapeutic potential.

Alzheimer's disease (AD) is a common neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ), hyperphosphorylation of tau, and neuroinflammation in the brain. The blood-brain barrier (BBB) limits solutes from circulating blood from entering the brain, which is essential for neuronal functioning. Focusing on BBB function is important for the early detection of AD and in-depth study of AD pathogenic mechanisms. However, the mechanism of BBB alteration in AD is still unclear, which hinders further research on therapeutics that target the BBB to delay the progression of AD. The exact timing of the vascular abnormalities in AD and the complex cause-and-effect relationships remain uncertain. Thus, it is necessary to summarize and emphasize this process. First, in this review, the current evidence for BBB dysfunction in AD is summarized. Then, the interrelationships and pathogenic mechanisms between BBB dysfunction and the risk factors for AD, such as Aβ, tau, neuroinflammation, apolipoprotein E (ApoE) genotype and aging, were analyzed. Finally, we discuss the current status and future directions of therapeutic AD strategies targeting the BBB. We hope that these summaries or reviews will allow readers to better understand the relationship between the BBB and AD.

A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer’s disease

Early detection and treatment can slow the progression of Alzheimer's Disease (AD), one of the most common neurodegenerative diseases. Recent studies have demonstrated the value of multimodal fusion in early AD detection. However, most approaches to this have failed to consider data modality domains, their relationships, and variations in their relative importance. To address these challenges, we propose a Hierarchical Attention-Based Multimodal Fusion framework (HAMF) that utilizes imaging, genetic and clinical data for early AD detection. In the HAMF model, attention mechanisms are utilized to learn the appropriate weights for each modality and to understand the interaction between modalities through hierarchical attention. HAMF performs better than state-of-the-art methods, achieving an accuracy of 87.2% and an AUC of 0.913, which are superior to unimodal models. By comparing the results of different unimodal and multimodal models, we find that multimodal fusion can improve model performance more than unimodal models and clinical data is the most important modality. Our ablation experiment confirmed the effectiveness of HAMF. Finally, we used SHapley Additive exPlanations (SHAP) to improve the model's interpretability. We provide the model as a guide for future research in the field, and as a framework for generating actional advice and decision support system for clinical practitioners.

4 Evaluating Plasma GFAP for the Detection of Alzheimer’s Disease Dementia

Objective:Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.Participants and Methods:This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.Results:The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p&lt;0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p&lt;0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps&lt;0.001) as well as higher CDR Sum of Boxes (p&lt;0.001).Conclusions:Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.

Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection.

Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually causes brain cells to die. This neurological condition progressively hampers cognitive and memory functions, along with the ability to carry out fundamental tasks over time. From the symptoms it is very difficult to detect during its early stage. It has become necessary to develop a computer assisted diagnostic models for the early AD detection. This survey work, discussed about a review of 110 published AD detection methods and techniques from the year 2011 to till-date. This study lies in its comprehensive exploration of AD detection methods using a range of artificial intelligence (AI) techniques and neuroimaging modalities. By collecting and analysing 50 papers related to AD diagnosis datasets, the study provides a comprehensive understanding of the diversity of input types, subjects, and classes used in AD research. Summarizing 60 papers on methodologies gives researchers a succinct overview of various approaches that contribute to enhancing detection accuracy. From the review, data are acquired and pre-processed form multiple modalities of neuroimaging. This paper mainly focused on review of different datasets used, various feature extraction methods, parameters used in neuro images. To diagnosis the Alzheimer's disease, the existing methods utilized three most common artificial intelligence techniques such as machine learning, deep learning, and transfer learning. We conclude this survey work by providing future perspectives for AD diagnosis at early stage.

59 Investigating the Relationship Between Neuropsychological Test Performance and Electrophysiological Measures of Semantic Functioning in Alzheimer's Disease.

Objective:Improving the timeline for intervention in Alzheimer's disease (AD) has considerable potential to delay and mitigate disability and suffering. Neuropsychological assessment is useful for distinguishing AD from normal aging and other dementias but is less useful in preclinical detection due to its limited sensitivity. The N400 (N4), a language-based EEG event-related potential (ERP) related to semantic functioning, is a promising candidate marker of AD with potential to improve early detection and monitoring of AD. For example, studies have shown that individuals with AD show a reduced N4 "effect"—a smaller difference in the size of the N4 to semantically congruent vs. incongruent word-pairs. The goal of this study is to assess the presence of the N4 effect in healthy seniors, and those with amnestic mild cognitive impairment (MCI) or mild AD, and to evaluate associations between performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the N4 across these samples.Participants and Methods:Fifty older adults (intact=27, combined MCI/mild AD group=23; "impaired") completed neuropsychological testing, including the RBANS, as part of a larger study. Participants were re-contacted and returned for EEG assessment between several weeks to one year later. During EEG recording, participants completed a word-pair judgement paradigm, which involved distinguishing between semantically congruent and incongruent word-pairs. Data was collected and analyzed according to customized N4 analysis scripts provided as part of ERPCORE, an online resource for acquiring and analyzing common ERP components (Kappenman et al., 2021; https://osf.io/thsqg/). The change in N4 amplitude between congruent and incongruent trials (the N4 effect) was used as an index of participants' semantic functioning. Participants' N4 effect was quantified using the mean amplitude from 300-550 milliseconds poststimulus at electrode Cz.Results:Repeated measures ANOVAs indicated a significant effect of trial type on the N400 amplitude in the intact individuals (F(1, 26)=77.66, p&lt;.001), which remained significant in the sample as a whole (F(1, 48)=65.18, p&lt;.001). Although intact participants numerically showed a larger N4 effect (intact: M=-4.02, SD=2.37; impaired: M=-2.60, SD=3.40), the expected group-by-trial interaction was not significant (F(1, 48)=3.01, p=.089). Correlational analyses revealed no significant associations between the N4 effect and the RBANS Total Scale scores (r=-.14, p=.32), nor for the Immediate Memory (r=-.002, p=.99), Visuospatial/Constructional (r=-.069, p=.63), Language (r=-.15, p=.30) Attention (r=-.21, p=.14), or Delayed Memory (r=-.18 p=.58) indexes.Conclusions:Results confirmed the presence of the N4 effect in intact participants and in the sample as a whole. Although the N4 effect was numerically smaller in the impaired group as expected, this difference was not significant in the present sample. Likewise, we observed no evidence for associations between the size of N4 effect and performance on RBANS indexes. Overall, the present study provides mixed evidence for the utility of the N4 as a biomarker in mild AD. Factors that may have contributed to the lack of associations between the N4 effect and the RBANS include the limited sample size and variable lengths of time between participants' initial cognitive assessments and EEG testing.

1 Detection of Cognitive Subtypes Within Cognitively Normal Older Adults Using Hierarchical Community Detection

Objective:Identifying individuals at the earliest stages of Alzheimer’s Disease (AD) would enable development and study of interventions prior to onset of symptoms. However, differentiating age-related cognitive changes from subtle pathological changes remains a challenge in the field. Methods that would enable earlier detection of AD in elders with no subjective or objective cognitive concerns (i.e., individuals in the preclinical stage) would be of great interest. Community detection, a metric founded in graph theory, offers an alternative approach for characterizing subtle heterogeneity within aging samples and has the potential to inform cognitive variability in aging.Participants and Methods:Using a hierarchical community detection, we examined whether cognitive subtypes could be identified in 226 cognitively normal older adults (from the Alzheimer’s Disease Neuroimaging Initiative [ADNI] study). Cognitive profiles of each community were characterized first using MANOVAs to examine the relationship between community membership and 12 age-, gender-, and education-corrected neuropsychological variables. Pair-wise comparisons were examined for significant main effects. We then examined whether these subtypes were related to biomarkers (cortical volumes, fluorodeoxyglucose (FDG)-positron emission tomography (PET) hypometabolism) or clinical progression. All p values were corrected for multiple comparisons.Results:Three communities (i.e., cognitive subtypes) were identified within the healthy aging sample. The first and largest community identified (N = 106) was characterized by a relative weakness on a single measure visuospatial executive function. Both the second (N = 76) and third community (N = 44) scored significantly lower on immediate, delayed, and recognition memory relative to the first community. The third community was characterized by a relative weakness in category fluency and speeded visual sequencing as well (p &lt; .000). The three communities did not differ on age, gender, education, race, or ethnicity. Community membership was associated with entorhinal volume (with the second and third communities having significantly smaller entorhinal volumes than the first community), though community membership was not significantly associated with other biomarkers examined. Conversion rate reached trend level significance at 12 month follow up (more converters in the third community).Conclusions:Hierarchical community detection is an alternative method for characterizing neuropsychological variation and it appears sensitive to relatively small differences that may be observed in a normal aging sample. While the sample size was relatively small, this approach shows promise for potentially leading to earlier detection of cognitive decline among individuals classified to be aging normally (e.g., community 3).

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.

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.&#x0D; 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.

Early Detection of Alzheimer's Disease in Postmenopausal Women Using Thalamic Subnuclear Volumetry.

Alzheimer's disease (AD) and aging are intrinsically interconnected with each other and are mediated by molecular, cellular, and biological systems. In particular, a specific pattern of brain volume atrophy is the most profound risk factor for cognitive impairment, including AD, that is directly linked to aging. Thus, this study aimed to investigate knowledge on the early detection of AD in postmenopausal women, focusing on the volume changes of the subcortical regions, including the thalamic subnuclei, in women with AD vs. postmenopausal women. Twenty-one women with AD and twenty-one postmenopausal women without AD underwent magnetic resonance imaging (MRI). Women with AD showed significantly reduced volumes in the hippocampus, thalamus, and amygdala compared with postmenopausal women (p < 0.05, FWE-corrected). After adjustments for age, the right hippocampal volume was found to be significantly lower in the women with AD, but the volumes of the thalamus and amygdala were relatively unaffected. The women with AD exhibited significantly reduced volume in the right laterodorsal nucleus of the thalamus compared with the postmenopausal women (p < 0.05, Bonferroni-corrected). Our findings suggest that the reduced volume of both the right laterodorsal thalamic nucleus and right hippocampus may serve as a potential biomarker for the early detection of AD in postmenopausal women.

Halloysite nanotubes-loaded conductive polymer as substrate and label material for sensitive detection of amyloid-β protein by electrochemical immunosensor

Amyloid-beta protein (Aβ) is a unique biomarker for Alzheimer's disease (AD). The sandwich-type electrochemical immunosensor, one of the key tools for detecting biomarkers, relies on a high-performance signal amplification approach to enhance its sensitivity. Ni/PdH nanodendrites (Ni/PdH NDs) have increased catalytic activity due to their unique interaction with palladium hydride and their nickel-rich surface, tunable shape and high specific surface area. Modified halloysite nanotubes (mHNT)-loaded with polypyrrole (PPy@mHNT) possess excellent dispersion and a large surface area. This enables the formation of a conductive network to prevent the accumulation of Ni/PdH NDs. Additionally, it exposes more electrocatalytic active centers, effectively amplifying electrical signals. By utilizing Ni/PdH@PPy@mHNT as the labeling material, it shows a consistent and remarkable electrocatalytic activity in H2O2 reduction, leading to signal amplification. The acid-etched HNT coated with polyaniline (PANI@eHNT) exhibits an exceptionally low background signal and outstanding conductivity. This not only accelerates electron transfer on the electrode surface, but also ensures the stable incubation of biomolecules post-amino grafting. Utilizing NH2-PANI@eHNT as a substrate material can guarantee stable biomolecule incubation, offer a stable sensing platform and enhance immunosensor performance. The signal can be amplified and the immunosensor's sensitivity can be raised through the efficient cooperation of the aforementioned nanomaterials. Under optimum circumstances, the electrochemical immunosensor had the lowest detection limit of 5.53 fg mL−1 and a linear range of 50 fg mL−1 to 100 ng mL−1. Based on the outstanding performance previously mentioned, this immunosensor is anticipated to aid in the early detection of AD.

Reinforcement-Learning-Based Localization of Hippocampus for Alzheimer's Disease Detection.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily impacting memory and cognitive functions. The hippocampus serves as a key biomarker associated with AD. In this study, we present an end-to-end automated approach for AD detection by introducing a reinforcement-learning-based technique to localize the hippocampus within structural MRI images. Subsequently, this localized hippocampus serves as input for a deep convolutional neural network for AD classification. We model the agent-environment interaction using a Deep Q-Network (DQN), encompassing both a convolutional Target Net and Policy Net. Furthermore, we introduce an integrated loss function that combines cross-entropy and contrastive loss to effectively train the classifier model. Our approach leverages a single optimal slice extracted from each subject's 3D sMRI, thereby reducing computational complexity while maintaining performance comparable to volumetric data analysis methods. To evaluate the effectiveness of our proposed localization and classification framework, we compare its performance to the results achieved by supervised models directly trained on ground truth hippocampal regions as input. The proposed approach demonstrates promising performance in terms of classification accuracy, F1-score, precision, and recall. It achieves an F1-score within an error margin of 3.7% and 1.1% and an accuracy within an error margin of 6.6% and 1.6% when compared to the supervised models trained directly on ground truth masks, all while achieving the highest recall score.

Time-series visual explainability for Alzheimer’s disease progression detection for smart healthcare

Artificial intelligence (AI)-based diagnostic systems provide less error-prone and safer support to clinicians, enhancing the medical decision-making process. This study presents a smart and reliable healthcare framework for detecting Alzheimer's disease (AD) progression. Early detection of AD before the onset of clinical symptoms is the most crucial step in starting timely treatment. To predict the conversion of cognitively normal patients to those with AD, three-dimensional (3D) magnetic resonance imaging (MRI) whole-brain neuroimaging methods have been extensively studied. However, depending on the 3D volume, this method is computationally expensive. To solve this problem, we used an approximate rank pooling method originally designed for video action recognition with a 3D MRI volume to obtain a compressed representation of multiple two-dimensional (2D) MRI slices. This study proposes a hybrid multimodal CNN-Bi-LSTM deep model for AD progression detection, in which the resulting dynamic 2D images are fused with cognitive features. Moreover, a novel explainable AI approach is proposed to provide visual explanations using the resulting longitudinal 2D dynamic images. Temporal explanations were provided by visualizing the affected brain regions captured using longitudinal 2D MRIs. By utilizing a sample of 1,692 subjects with multimodal data from the Alzheimer’s Disease Neuroimaging Initiative dataset, our method was assessed using a 10-fold cross-validation process. The model achieved an area under the receiver operating characteristics curve (AUC) of 94% using longitudinal 2D three-time-step dynamic image data. The fusion of 2D dynamic images with cognitive features enhanced the performance by 2% in terms of the AUC. Patients who gradually develop AD, show changes in various brain regions. For such patients, our system highlights the critical role of the hippocampus, medial amygdala, caudal hippocampus, and lateral amygdala at the initial time steps. In the late stages of AD, the system detects abnormalities in extra brain regions such as the medial temporal gyrus, superior temporal gyrus, fusiform gyrus, and caudal hippocampus; indicating that patients have completely progressed to AD.