Diagnosis Of Early-stage Alzheimer's Disease Research Articles

Differential diagnosis of mild cognitive impairment of Alzheimer's disease by Simoa p-tau181 measurements with matching plasma and CSF.

Alzheimer's disease (AD) is characterized by a long preclinical phase. Although late-stage AD/dementia may be robustly differentiated from cognitively normal individuals by means of a clinical evaluation, PET imaging, and established biofluid biomarkers, disease differentiation between cognitively normal and various subtypes of mild cognitive impairment (MCI) remains a challenging task. Differential biomarkers for early-stage AD diagnosis with accessible biofluid samples are urgently needed. Misfolded phosphorylated tau aggregates (p-tau) are present in multiple neurodegenerative diseases known as "tauopathies", with the most common being AD. P-tau181 is a well-established p-tau biomarker to differentiate AD dementia from non-AD pathology. However, it is unclear if p-tau181 is capable of diagnosing MCI, an early AD stage, from cognitively normal subjects, or if it can discriminate MCI subtypes amnestic MCI (aMCI) from non-amnestic MCI (naMCI). Here we evaluated the capability of p-tau181 in diagnosing MCI from cognitively normal subjects and discriminating aMCI from naMCI subtypes. We collected matching plasma and CSF samples of a clinically diagnosed cohort of 35 cognitively normal, 34 aMCI, 17 naMCI, and 31 AD dementia cases (total 117 participants) with supplemental CSF Aβ42 and total tau AD biomarker levels and performed Simoa p-tau181 assays. The diagnostic capabilities of Simoa p-tau181 assays to differentiate these cohorts were evaluated. We found (i) p-tau181 can robustly differentiate MCI or aMCI from cognitively normal cohorts with matching plasma and CSF samples, but such differentiation is weaker in diagnosing naMCI from cognitively normal groups, (ii) p-tau181 is not capable of differentiating aMCI from naMCI cohorts, and (iii) either factor of Aβ or total tau burden markedly improved differentiation power to diagnose aMCI from cognitively normal group. Plasma and CSF p-tau181 levels may serve as a promising biomarker for diagnosing aMCI from normal controls in the preclinical phase. But more robust new biomarkers are needed to differentiate naMCI from cognitively normal cases or to discriminate between MCI subtypes, aMCI from naMCI.

'Eye-Tracking' with Words for Alzheimer's Disease Detection: Time Alignment of Words Enunciation with Image Regions During Image Description Tasks.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in cognitive decline, dementia, and eventually death. Diagnosing early signs of AD can help clinicians to improve the quality of life. We developed a non-invasive approach to help neurologists and clinicians to distinguish probable AD patients and healthy controls (HC). The patients' gaze points were followed based on the words they used to describe the Cookie Theft (CT) picture description task. We hypothesized that the timing of words enunciation aligns with the participant's eye movements. The moments that each word was spoken were then aligned with specific regions of the image. We then applied machine learning algorithms to classify probable AD and HC. We randomly selected 60 participants (30 AD and 30 HC) from the Dementia Bank (Pitt Corpus). Five main classifiers were applied to different features extracted from the recorded audio and participants' transcripts (AD and HC). Support vector machine and logistic regression had the highest accuracy (up to 80% and 78.33%, respectively) in three different experiments. In conclusion, point-of-gaze can be applied as a non-invasive and less expensive approach compared to other available methods (e.g., eye tracker devices) for early-stage AD diagnosis.

Polydopamine-coated hexagonal boron nitride-based electrochemical immunosensing of T-Tau as a marker of Alzheimer's disease

Alzheimer's disease (AD) is a complex pathological process that is one of the leading causes of dementia globally. The demand for diagnostic tools that are minimally invasive, timely, and accurate is on the rise. Total tau (T-Tau) protein in blood serum is a promising biomarker for predicting early-stage AD diagnosis. In this study, the hexagonal boron nitride (HBN) based immunosensor platform was developed to detect T-Tau in artificial blood serum. After the exfoliation of HBN, its surface was coated with polydopamine (PDA) in alkaline conditions. The Anti-T-Tau was immobilized on a hydrophilic nanocomposite surface using PDA's reactive catechol and quinone groups, eliminating the need for extra crosslinkers. The working electrode surface of the screen-printed carbon electrode (SPCE) was coated with HBN-PDA nanocomposite using the drop-casting method. The biofunctional surface was created by directly immobilizing Anti-T-Tau on the HBN-PDA nanocomposite-modified SPCE. The analytical performance of the HBN-PDA/Anti-T-Tau/T-Tau immunosensor in the presence of T-Tau isoforms was determined through electrochemical measurements. The linear detection range was 1–30 pg/mL with a detection limit of 0.42 pg/mL for T-Tau, which is suitable for detecting T-Tau in the blood serum.

Brief Test of Olfactory Dysfunction Based on Diagnostic Features of Specific Odors in Early-Stage Alzheimer Disease.

BACKGROUND Olfactory impairment is an early symptom of Alzheimer disease (AD). However, it is rarely assessed in clinical practice. This study aimed to assess the identification and discrimination of specific odors in patients with early-stage AD using the Sniffin' Sticks test and determine the items that would be most valuable in the diagnosis of early-stage AD in order to create a brief test of olfactory dysfunction. MATERIAL AND METHODS Three groups of participants were enrolled, including 30 patients with mild cognitive impairment due to AD (MCI-AD group), 30 with mild dementia due to AD (MD-AD group), and 30 older participants with normal cognition (NC group). All participants underwent cognitive (Clinical Dementia Rating, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-Cognitive Subscale, and verbal fluency tests) and olfactory (Burghart Sniffin' Sticks odor identification and odor discrimination tests) assessments. RESULTS The MD-AD group scored significantly lower than the MCI-AD group and the MCI-AD group scored significantly lower than the NC group in both the odor identification (P<0.001) and discrimination (P<0.05) tasks. The shortened versions of the odor identification and discrimination tasks showed good diagnostic properties in differentiating patients with AD from the NC participants (receiver operating characteristic [ROC] area under the curve [AUC]=0.912 and 0.954, respectively) and differentiating patients with MCI-AD from the NC participants (ROC AUC=0.871 and 0.959, respectively). CONCLUSIONS The brief versions of olfactory tests, containing selected items that were found to differ the most between cognitively normal participants and early-stage AD patients, have good diagnostic qualities and can aid clinicians in screening for early-stage AD.

Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation.

Recently, rich computational methods that use deep learning or machine learning have been developed using linguistic biomarkers for the diagnosis of early-stage Alzheimer disease (AD). Moreover, some qualitative and quantitative studies have indicated that certain part-of-speech (PoS) features or tags could be good indicators of AD. However, there has not been a systematic attempt to discover the underlying relationships between PoS features and AD. Moreover, there has not been any attempt to quantify the relative importance of PoS features in detecting AD. Our goal was to disclose the underlying relationship between PoS features and AD, understand whether PoS features are useful in AD diagnosis, and explore which PoS features play a vital role in the diagnosis. The DementiaBank, containing 1049 transcripts from 208 patients with AD and 243 transcripts from 104 older control individuals, was used. A total of 27 PoS features were extracted from each record. Then, the relationship between AD and each of the PoS features was explored. A transformer-based deep learning model for AD prediction using PoS features was trained. Then, a global explainable artificial intelligence method was proposed and used to discover which PoS features were the most important in AD diagnosis using the transformer-based predictor. A global (model-level) feature importance measure was derived as a summary from the local (example-level) feature importance metric, which was obtained using the proposed causally aware counterfactual explanation method. The unique feature of this method is that it considers causal relations among PoS features and can, hence, preclude counterfactuals that are improbable and result in more reliable explanations. The deep learning-based AD predictor achieved an accuracy of 92.2% and an F1-score of 0.955 when distinguishing patients with AD from healthy controls. The proposed explanation method identified 12 PoS features as being important for distinguishing patients with AD from healthy controls. Of these 12 features, 3 (25%) have been identified by other researchers in previous works in psychology and natural language processing. The remaining 75% (9/12) of PoS features have not been previously identified. We believe that this is an interesting finding that can be used in creating tests that might aid in the diagnosis of AD. Note that although our method is focused on PoS features, it should be possible to extend it to more types of features, perhaps even those derived from other biomarkers, such as syntactic features. The high classification accuracy of the proposed deep learner indicates that PoS features are strong clues in AD diagnosis. There are 12 PoS features that are strongly tied to AD, and because language is a noninvasive and potentially cheap method for detecting AD, this work shows some promising directions in this field.

MicroRNA super-resolution imaging in blood for Alzheimer’s disease

We propose a novel blood biomarker detection method that uses miRNA super-resolution imaging to enable the early diagnosis of Alzheimer's disease (AD). Here, we report a singlemolecule detection method for visualizing disease-specific miRNA in tissue from an AD mice model, and peripheral blood mononuclear cells (PBMCs) from AD patients. Using optimized Magnified Analysis of Proteome (MAPs), we confirmed that five miRNAs contribute to neurodegenerative disease in the brain hippocampi of 5XFAD and wild-type mice. We also assessed PBMCs isolated from the whole blood of AD patients and a healthy control group, and subsequently analyzed those samples using miRNA super-resolution imaging. We detected more miR-200a-3p expression in the cornu ammonis 1 and dentate gyrus regions of 3 month-old 5XFAD mice than in wild-type mice. Additionally, miRNA super-resolution imaging of blood provides AD diagnosis platform for studying miRNA regulation inside cells at the single molecule level. Our results present a potential liquid biopsy method that could improve the diagnosis of early stage AD and other diseases. [BMB Reports 2023; 56(3): 190-195].

Improved biomarker quantification of silicon nanowire field-effect transistor immunosensors with signal enhancement by RNA aptamer: Amyloid beta as a case study

Quantifying proteins at ultra-low levels in high ionic-strength solutions by silicon nanowire field-effect transistor (SiNWFET) immunosensors is critically hindered due to the limited Debye length, charge distributions and uncontrollable orientations of protein molecules on the NW surfaces. In this study, we propose a novel strategy to overcome this issue by employing aptamer as bio-amplifier for different types of SiNWFET immunosensors to quantify Amyloid Beta (Aβ) 1-42, a biomarker for early-stage diagnosis of Alzheimer disease (AD) with ultra-low level in human serum (HS), as a representative target. Our method, presented for the first time, exhibits an outstanding performance in combination with mSAM-SiNWFET immunosensors (designed by integrating SiNWFET as transducer, mouse antibody as bio-receptor, and mixed self-assembled monolayers (mSAMs) for biofouling resistance) to sense Aβ 1-42 down to 100 fg/mL in HS, the lowest level achieved in comparison with the most advanced sensing technologies for Aβ 1-42. The empirical data also reveal that varied concentrations of Aβ 1-42 in diverse high ionic-strength environments (150 mM BTP and HS) express good linear relationship with signal amplified by aptamer. This novel approach is therefore potentially applied for early-stage diagnosis of AD and other clinical trials of biomarkers with ultra-low content in blood.

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View.

Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

Multiplexed femtomolar detection of Alzheimer's disease biomarkers in biofluids using a reduced graphene oxide field-effect transistor

Alzheimer's disease (AD) is a neurodegenerative disease that accounts for 70% of all dementia. Early stage diagnosis of AD is essential as there is no certain treatment after the lesion has progressed in the late stage. Nevertheless, there are still limitations of early diagnosis of AD using neuroimaging and psychological memory assessments. Here, we demonstrate ultrasensitive and multiplexed detection of pivotal AD biomarkers (Aβ1-42 and t-Tau) in biofluids using a reduced graphene oxide field-effect transistor (gFET). The proposed approach provides a wide logarithmically linear range of detection from 10−1–105 pg mL−1 and a femtomolar-level limit of detection in biofluids (human plasma and artificial cerebrospinal fluid) as well as phosphate-buffered saline (PBS). Furthermore, as these core biomarkers have different surface charges in physiological conditions based on the isoelectric point (pI), we achieved a distinctive output signal for each biomarker. The gFET biosensor platform presented in this paper has great potential and can be used for early diagnosis of AD in clinical practice as well as accurate analysis based on the surface charge of the analytes.

Advent of Proteomic Tools for Diagnostic Biomarker Analysis in Alzheimer's Disease.

Locating remedies for Alzheimer's disease (AD) has been majorly restricted by the inefficiency to establish a definitive detection model for early-stage diagnosis of pathological events. This current lapse in AD diagnosis also limits the therapeutic efficiency of the drugs, which might have been effective if given at the earlier stages of the disease. The indicated situation directs towards the burgeoned need for an effective biomarker technique that will help in early detection of AD and would be imminently useful to facilitate improved diagnosis and stimulate therapeutic trials. Till date, the major biomarkers, specifically associated with AD detection, may help in determining the early-stage AD diagnosis and identifying alterations in the cellular proteome, offering deeper insight into disease etiology. Currently existing multidisciplinary clinical diagnosis of AD is a very tedious, expensive procedure and requires highly trained and skilled professionals who are rarely available outside the specialty clinics. Mutations in amyloid precursor protein (APP) or Presenilin 1 and 2 (PSEN1 and PSEN2) are some biomarkers acting as critical checkpoints for AD diagnosis. However, the presence of some associated biomarkers in cerebrospinal fluid (CSF) such as total-Tau (tTau), phosphorylated- Tau (pTau) 181 and Amyloid-β (Aβ) 1-42 using structural or functional imaging techniques is considered for confirmatory diagnosis of AD. Furthermore, the molecular diagnosis of AD incorporates various sophisticated techniques including immuno-sensing, machine learning, nano conjugation-based detections, etc. In the current review description, we have summarized the various diagnostic approaches and their relevance in mitigating the long-standing urgency of targeted diagnostic tools for detection of AD.

CSF sAPPα and sAPPβ levels in Alzheimer's Disease and Multiple Other Neurodegenerative Diseases: A Network Meta-Analysis.

The soluble amyloid protein procurer α (sAPPα) and β (sAPPβ) have been postulated as promising new cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease (AD) and multiple other neurodegenerative diseases, but have failed to meet expectations with their often discordant and even contradictory findings to date. The aim of the study was to systematically explore this issue. Cochrane Library, PubMed, and CNKI were systematically searched without language or date restrictions. This network meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and also adhered to the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. Twenty studies, comprising ten groups, were eligible and included. Overall, 19 eligible studies with 1634 patients contributed to the analysis of CSF sAPPα levels and 16 eligible studies with 1684 patients contributed to the analysis of CSF sAPPβ levels. CSF sAPPβ levels are significantly higher in AD than in corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP); higher in Control than in Depression, CBS and PSP; higher in Parkinson's disease dementia (PDD) than in CBS and PSP; higher in mild cognitive impairment progressed to AD dementia during the follow-up period (pMCI) than in Depression and PSP; higher in stable mild cognitive impairment (sMCI) than in Depression. With regard to CSF sAPPα levels, there were no significant difference among groups. However, surprisingly, the resultant rankings graphically showed that pMCI populations have the highest levels of CSF sAPPα and sAPPβ. Furthermore, it seemed there was a positive correlation between CSF sAPPα and sAPPβ levels. The measurement of CSF sAPPα and sAPPβ levels may provide an alternative method for the diagnosis of early-stage AD, pMCI, which is conducive to preventive therapy.

A simple approach to quantitative determination of soluble amyloid-β peptides using a ratiometric fluorescence probe

Alzheimer's disease (AD) is a progressive neurodegenerative illness that affects the elderly population worldwide. The definite diagnosis of AD still depends on post-mortem pathological examination of amyloid plaques consisting of amyliod-β peptides (Aβ) fibrils in the brain so far. However, these fibrils are not closely linked to the development of the disease. Alternatively, soluble Aβ are believed to be more reliable biomarkers for early diagnosis of AD. Here, we report a simple approach to quantitative detection of soluble Aβ species using N-(6-(benzothiazol-2-yl)pyridin-3-yl)-5-(dimethylamino)naphthalene-1-sulfonamide (BPNS) as a ratiometric fluorescence Zn2+ probe. This ratiometric fluorescence assay is based on the competition of soluble Aβ with BPNS for Zn2+, that is, soluble Aβ species with higher chelation affinity can capture Zn2+ from BPNS‒Zn2+ adduct, thereby reactivating the ratiometric fluorescence response of BPNS. BPNS exhibited perfect linear relationship (R2 = 0.998) in accordance with the concentration of soluble Aβ in the presence of Zn2+. The assay possesses strong anti-interference capacity against exogenous agent or the other proteins, thanks to the high selectivity for soluble Aβ species. Importantly, this assay can quantitatively detect soluble Aβ species from different types of biological fluids, such as artificial cerebrospinal fluid (ACSF), serum, and plasma in half an hour. This assay provides a low-cost, fast, sensitive, and simple approach for quantitative detection of soluble Aβ species and may serve as a potential tool for early-stage AD diagnosis.

Early-Stage Identification and Pathological Development of Alzheimer's Disease Using Multimodal MRI.

Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD.

Towards non-invasive diagnostic imaging of early-stage Alzheimer's disease.

One way to image the molecular pathology in Alzheimer’s disease (AD) is by positron emission tomography using probes that target amyloid fibrils. However, these fibrils are not closely linked to the development of the disease. It is now thought that early stage biomarkers that instigate memory loss comprise of Aβ oligomers (AβOs). Here we report a sensitive molecular magnetic resonance imaging (MRI) contrast probe that is specific for AβOs. We attach oligomer-specific antibodies onto magnetic nanostructures and show the complex is stable and it binds to AβOs on cells and brain tissues to give a MRI signal. When intranasally administered to an AD mouse model, the probe readily reached hippocampal AβOs. In isolated samples of human brain tissue, we observed an MRI signal that distinguished AD from controls. Such nanostructures that target neurotoxic AβOs are potentially useful for evaluating the efficacy of new drugs and ultimately for early-stage AD diagnosis and disease management.

Biomarkers of Alzheimer’s disease in body fluids

Various innovative diagnostic methods for Alzheimer's disease (AD) have been developed in view of the increasing prevalence and consequences of later-life dementia. Biomarkers in cerebrospinal fluid (CSF) and blood for AD are primarily based on the detection of components derived from amyloid plaques and neurofibrillary tangles (NFTs). Published reports on CSF and blood biomarkers in AD indicate that although biomarkers in body fluids may be utilized in the clinical diagnosis of AD, there are no specific markers that permit accurate and reliable diagnosis of early-stage AD or the monitoring of disease progression.

Fluorodeoxyglucose-Positron Emission Tomography in the differential diagnosis of early-onset dementia: a prospective, community-based study

BackgroundThe aim of this study was to evaluate the diagnostic accuracy of positron emission tomography (PET) using F18 fluorodeoxyglucose (FDG) in the differential diagnosis of early-onset Alzheimer's disease (AD) and other dementias in a community-dwelling population.MethodsA prospective sample of 102 individuals presenting consecutively to a primary care centre for examination of suspected early-onset dementing diseases. The mean age of symptom onset of dementia in our patients was 60.06 ± 4.28 years (mean ± 1SD, 95% lower confidence intervals (CI) 54.75, upper 63.37). Patients were evaluated using standard clinical criteria for the diagnosis of dementia. Functional neuroimaging data was obtained and nuclear medicine physicians blind to the clinical diagnosis generated FDG-PET diagnoses. Final clinical diagnoses based on all available data were then established and compared against PET diagnoses.ResultsForty-nine patients received a final clinical diagnosis of early-stage AD (MMSE score 20.97 ± 5.10). There were 29 non-AD demented patients, 11 depressed patients and a miscellaneous group of 13 patients. Among patients with AD, the sensitivity and specificity of FDG-PET was 78% (95% CI: 66–90%) and 81% (95% CI: 68–86%), respectively. The positive likelihood ratio (PLR) for a FDG-PET scan positive for the diagnosis of AD was 4.11 (95% CI: 2.29–7.32) and negative likelihood ratio (NLR) for a negative FDG-PET scan in the absence of AD was 0.27 (95% CI: 0.16–0.46). The pre-test probability was 48% and post-test probability was 79.02%. The specificity of FDG-PET in the differential diagnosis of other dementias, including frontotemporal dementia, was greater than 95%.Recruitment methods in this study provide a sample that may be more representative of patients in the general population and indicate that FDG-PET imaging can contribute to the diagnosis of AD in younger adults with major increases in the positive likelihood rates and post-test probability.ConclusionThe high specificity of FDG-PET suggests this technique might help in the diagnosis of frontotemporal dementia and other forms of early-onset dementia.

Treatment and Management of the Elderly Patients with Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia in older people. Cerebral deposition of amyloid plaques containing amyloid beta-peptide (Aβ) has traditionally been considered the central feature of AD. γ-Secretase is the pivotal enzyme in generating the C terminus of Aβ, that determines its aggregability and propensity for deposition. Drugs that regulate the production of Aβ by inhibiting gamma-secretase activity could provide an effective therapeutics for AD, however we can't use these drugs clinically now. Anti-amyloid immunotherapy for AD has received considerable attention following reports that amyloid pathology was reduced in an amyloid precursor protein (APP) transgenic mouse model on vaccination with aggregated Aβ42. Clinical trials were terminated after four early reports of autoimmune meningoencephalitis, but a post-mortem study in one patient showed evidence of plaque reduction. Currently, no effective pharmacologic interventions have been researched enough to support their use in prevention of AD. Many epidemiological studies suggest that apolipoprotein E type 4 allele, diabetes, obesity, hypertension, and hyperlipidemia were associated the risk factor for late onset AD. Two key treatment approaches for AD have been driven by retrospective epidemiology: non-steroidal anti-inflammatory drugs and cholesterol-lowering statins. In both cases, the exact target in the disease cascade remains to be elucidated. In constant, only one acetylcholinesterase inhibitor, donepezil has been proven effective and used for patients with mild or moderate AD in Japan. Donepezil have demonstrated the effectiveness in stabilizing cognitive function and delaying behavioral symptoms. Given the likelihood that agents will become available that reliably delay onset and/or slow progression of AD, it will be important to detect preclinical AD as early as possible for maximal treatment effect. Cognitive function is generally examined by Mini-mental State Examination (MMSE), revised version of Hasegawa's Dementia Rating Scale (HDS-R) and Wechsler adult intelligence scale-revised (WAIS-R). The positron emission tomography or the single photon emission computed tomography were used for diagnosis of early stage AD. Caregivers should analyze behaviors and environmental factors which precede harmful abnormal conduct and try to avoid them.

Memory Rehabilitation Techniques for People with Early-Stage Dementia

Summary: Errorless learning (EL) can be supported through a variety of specific learning methods, which may vary along other parameters including for example the degree of effortful processing required. In memory rehabilitation for people with early-stage Alzheimer's disease (AD), achieving the optimal balance between elimination of errors and active, effortful processing during learning is likely to be critical. This paper describes a comparison of four different errorless methods in a single case experimental design. The participant, who had a diagnosis of early-stage AD, attempted to relearn forgotten face-name associations using one of four methods: Spaced retrieval, mnemonic elaboration, cueing with increasing assistance, and cueing with decreasing assistance. Best results were achieved in the mnemonic and cueing with increasing assistance conditions, while cueing with decreasing assistance produced the least learning. It is suggested that, within an errorless learning paradigm, strategies requiring more effortful processing may produce superior gains compared to strategies relying on implicit or passive processing. This has important implications for clinical rehabilitation interventions for people with early-stage dementia.

Functional disability in Alzheimer's disease.

Functional impairment is a core symptom of Alzheimer's disease. The most accurate indicator of functional impairment is the decline in performance of Activities of Daily Living (ADL). Several scales have been used to measure ADL in geriatric and Alzheimer's disease (AD) populations including the Disability Assessment for Dementia (DAD). The key to making a correct differential diagnosis between benign cognitive impairment and dementia is the detection of a decline in functioning. The instrumental ADL (IADL) relevant items of the DAD could provide crucial guidance in the diagnosis of early stage AD for primary care physicians. It is anticipated that the role of primary care physicians, already essential for the management of the AD patient, will expand to include the diagnosis of earlier cases and the prescription of cholinergic drugs as treatment. ADL assessment tools such as the DAD may help their tasks.