Mild Cognitive Impairment Stage Research Articles (Page 1)

BackgroundThe development and clinical use of biomarkers has dramatically changed the framework of Alzheimer's disease (AD) management, allowing the diagnosis at the mild cognitive impairment (MCI) stage. In 2015 we compared the prevalence and prognosis of AD at the MCI stage according to different criteria available at that time, and we found that the National Institute of Aging-Alzheimer Association (NIA-AA) criteria provided higher predictive accuracy for AD dementia after 3 years. Since then, we adopted these criteria in clinical practice.ObjectiveTo evaluate the long-term predictive accuracy of the 'MCI due to AD - high likelihood' criteria by taking advantage from an extended follow-up in a memory clinic setting.MethodsPatients were diagnosed according to the 'MCI due to AD - high likelihood' criteria and followed up until conversion to dementia.ResultsOne hundred and fourteen patients with 'MCI due to AD - high likelihood' were enrolled in the study and followed-up for 3.0 ± 1.8 [0.4-8.3] years. During the follow-up 106 (93.0%) patients progressed to dementia, 2 (1.8%) had stroke, 6 (5.3%) died, and none remained in MCI or reverted to normal cognitive status. The average survival time remaining in MCI, analyzed with Kaplan-Meier curve, was 3.2 (95% CI 2.9-3.6) years. Using a multivariate Cox proportional hazards regression model, patients with higher Mini-Mental State Examination kept the MCI status longer.ConclusionsThe diagnostic criteria of NIA-AA 'MCI due to AD - high likelihood' have an excellent long-term predictive accuracy in a memory clinic setting.

Background/Objectives: Visual dysfunction emerges during the mild cognitive impairment stage of early Alzheimer’s disease (AD). While previous studies have primarily focused on retinal pathology, the early pathological progression across central nodes of the visual pathway remains inadequately characterized. This study examined regional pathological and structural alterations throughout the visual pathway at different disease stages in APP/PS1 transgenic mice aged 3, 6, and 9 months. Methods: Cognitive function was first assessed using novel object recognition and Y-maze tests to stage disease progression. Subsequently, Histological staining was employed to systematically analyze pathological features in the retina, lateral geniculate nucleus (LGN), and primary visual cortex (V1). Evaluated parameters encompassed β-amyloid (Aβ) deposition levels, microglial activation status, total neuronal counts, parvalbumin (PV)-positive neuron numbers, and tissue thickness measurements of the retina and V1. Results: At 6 months, mice exhibited an early symptomatic phenotype with selective spatial working memory deficits while long-term memory remained intact. Pathological analysis revealed concurrent Aβ deposition and microglial activation in V1, retina, and hippocampus by 6 months, whereas comparable LGN changes manifested only at 9 months, demonstrating regional heterogeneity in disease progression. V1 neuronal populations remained stable through 6 months but showed significant reduction by 9 months, though PV-positive neurons were selectively preserved. The LGN exhibited no neuronal loss even at 9 months. Gross structural thickness of both retina and V1 remained unchanged across all timepoints. Conclusions: These findings demonstrate that early visual system pathology in this AD model extends beyond the retina. The primary visual cortex exhibits early pathological changes (Aβ deposition and neuroinflammation) concurrent with hippocampal involvement, progressing to selective neuronal loss in later stages. The severity and selectivity of V1 pathology surpass those observed in other visual pathway nodes, including the LGN. Thus, V1 could represent not merely an affected region but a promising site for elucidating early cortical AD mechanisms and developing novel diagnostic biomarkers.

Cognitive impairment is required to diagnose mild cognitive impairment with Lewy bodies (MCI-LB). However, associations of impairments across cognitive domains remain unclear. In this cross-sectional study, we investigated these associations by assessing the cognitive connectome of MCI-LB patients compared with healthy controls (HC), mild cognitive impairment due to Alzheimer’s disease (MCI-AD), and dementia with Lewy bodies (DLB). Using the National Alzheimer’s Coordinating Center database, we built cognitive connectomes for MCI-LB (n = 88), HC (n = 3703), MCI-AD (n = 1789), and DLB (n = 104) by correlating 24 cognitive measures. We compared global and nodal network measures of centrality (importance of cognitive measure), integration (communication across cognitive measures), and segregation (specialisation of cognitive measures) between groups. For global measures, MCI-LB showed lower segregation than HC, with no significant differences from MCI-AD, and lower integration) and higher segregation than DLB. For nodal measures, MCI-LB compared with HC and MCI-AD showed differences in executive and memory measures, respectively. MCI-LB showed several nodal differences compared with DLB, involving executive, processing speed/attention, and language measures. Our findings suggest that MCI-LB involves early changes in the cognitive connectome, particularly reduced segregation that becomes more pronounced at the DLB stage and shows overlap with MCI-AD, offering insights into cognitive impairment in MCI-LB.

Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. Although advancements in deep learning (DL) and machine learning (ML) models improve diagnostic precision, the lack of large datasets limits further enhancements, necessitating the use of complementary data. Existing convolutional neural networks (CNNs) effectively process visual features but struggle to fuse multimodal data effectively for AD diagnosis. To address these challenges, we propose NeuroNet-AD, a novel multimodal CNN framework designed to enhance AD classifcation accuracy. NeuroNet-AD integrates Magnetic Resonance Imaging (MRI) images with clinical text-based metadata, including psychological test scores, demographic information, and genetic biomarkers. In NeuroNet-AD, we incorporate Convolutional Block Attention Modules (CBAMs) within the ResNet-18 backbone, enabling the model to focus on the most informative spatial and channel-wise features. We introduce an attention computation and multimodal fusion module, named Meta Guided Cross Attention (MGCA), which facilitates effective cross-modal alignment between images and meta-features through a multi-head attention mechanism. Additionally, we employ an ensemble-based feature selection strategy to identify the most discriminative features from the textual data, improving model generalization and performance. We evaluate NeuroNet-AD on the Alzheimer’s Disease Neuroimaging Initiative (ADNI1) dataset using subject-level 5-fold cross-validation and a held-out test set to ensure robustness. NeuroNet-AD achieved 98.68% accuracy in multiclass classification of normal control (NC), MCI, and AD and 99.13% accuracy in the binary setting (NC vs. AD) on the ADNI dataset, outperforming state-of-the-art models. External validation on the OASIS-3 dataset further confirmed the model’s generalization ability, achieving 94.10% accuracy in the multiclass setting and 98.67% accuracy in the binary setting, despite variations in demographics and acquisition protocols. Further extensive evaluation studies demonstrate the effectiveness of each component of NeuroNet-AD in improving the performance.

BackgroundSmaller midbrain volumes predict Alzheimer’s Disease (AD) progression and faster conversion from Mild Cognitive Impairment (MCI) to dementia. Along with this, various midbrain-target areas are characterized by neuroinflammation since the MCI stage. The concomitance of neuroinflammation, Αβ and tau appears to be a strong predictor for conversion from MCI to dementia.Yet, how midbrain degeneration could cause disease progression, and what mechanisms are involved in triggering neuroinflammation in midbrain-target areas such as the hippocampus remain unexplored.MethodsUsing adult C57BL/6N mice we generated a new mouse model carrying lesions in three midbrain nuclei, the dopaminergic Ventral Tegmental Area (VTA) and Substantia Nigra pars compacta (SNpc) and the serotonergic Interpeduncular Nucleus (IPN), to evaluate the consequences of dopamine and serotonin deprivation in midbrain-target areas. We characterized this model by performing stereological cell counts, analysis of monoaminergic fibers, monoamine levels, electrophysiology and behavioral tests. We then assessed hippocampal neuroinflammation by analyzing glia cell count, changes in morphology, NLRP3 inflammasome activation and cytokine levels, and microglia transcriptional profiling. In a separate set of experiments, we induced experimental midbrain lesion in Tg2576 transgenic mice overexpressing the Swedish mutant amyloid precursor protein, to evaluate the effect of monoamine deprivation on the hippocampus in concomitance with amyloid-β (Aβ) accumulation. The lesion performed in Tg2576 mice, as opposed to that in C57BL/6N mice, provides valuable insights into how neuroinflammation is influenced by Aβ accumulation versus the exclusive impact of impaired monoaminergic signaling.ResultsThe concomitant depletion of dopaminergic and serotonergic inputs within the hippocampus of C57BL/6N mice provokes a pronounced activation of microglia via the NLRP3-inflammasome pathway, accompanied by increased IL-1β expression. Pharmacological intervention with either dopaminergic (L-DOPA or A68930) or serotonergic (fluoxetine) agents abrogates this neuroinflammatory response. In the Tg2576 transgenic mouse model of amyloid pathology, which exhibits progressive Aβ deposition, superimposed midbrain degeneration markedly amplifies AD-like neuropathology. This includes exacerbation of microglial reactivity, robust astrocyte response, precocious Aβ plaque burden, and induction of pathological tau hyperphosphorylation. Notably, administration of L-DOPA or fluoxetine significantly attenuates both the astrocyte reactivity and tau hyperphosphorylation in the lesioned Tg2576 cohort.ConclusionsThese results highlight the pivotal role of midbrain damage for the amplification of neuroinflammatory cascades and AD pathology. Moreover, they offer mechanistic insight into the faster progression to dementia in patients with midbrain deficits. By translating these findings into clinical practice, we can advance towards a precision medicine approach in disease management.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13024-025-00893-2.

Mild cognitive impairment (MCI) is a prodromal stage of dementia, and its early detection is critical for improving clinical outcomes. However, current diagnostic tools such as brain magnetic resonance imaging (MRI) and neuropsychological testing have limited accessibility and scalability. Using machine-learning models, we aimed to evaluate whether multimodal physical and behavioral measures, specifically gait characteristics, body mass composition, and sleep parameters, could serve as digital biomarkers for estimating MCI severity. We recruited 80 patients diagnosed with MCI and classified them into early- and late-stage groups based on their Mini-Mental State Examination scores. Participants underwent clinical assessments, including the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet Korean Version, gait analysis using GAITRite, body composition evaluation via dual-energy X-ray absorptiometry, and polysomnography-based sleep assessment. Brain MRI was also performed to obtain structural imaging data. We evaluated the classification performance across various models, including support vector machines, random forest, multilayer perceptron, and convolutional neural network, using unimodal and multimodal datasets. Machine learning models trained on physical and behavioral data alone achieved a high classification accuracy (AUC up to 94%), comparable to that of MRI-based models, in differentiating early- and late-stage MCI. Combining physical and behavioral and MRI features yielded marginal improvements in the prediction performance. Gait velocity, lean body mass, and sleep efficiency were among the top predictors of cognitive function. Multimodal digital biomarkers or multimodal physical and behavioral signals can effectively estimate MCI severity and may offer a scalable, low-cost approach for early detection and monitoring of cognitive decline in real-world settings.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19364-1.

Neuropsychiatric symptom trajectories across cognitive spectrums of Alzheimer's disease: Joint probabilities with cognitive and functional decline.

Electroencephalography functional network for screening amyloid positivity in mild cognitive impairment: a cross-sectional study.

INTRODUCTIONA definitive diagnosis of Alzheimer's disease (AD) requires the identification of pathological changes. Plasma miRNAs have emerged as potential AD diagnostic biomarkers.METHODSA matched case‐control study was conducted using convenience sampling to evaluate the ability of candidate miRNAs in differentiating probable AD patients with mild cognitive impairment (MCI) or mild dementia (MD) stages. The initial sample included 29 patients and 58 controls. Plasma levels of miRNAs were measured by real‐time polymerase chain reaction (RT‐PCR) and their associations with scores from a comprehensive neuropsychological battery of cognitive tests analyzed by Spearman's correlation.RESULTSA miR‐181a, miR‐181c, and miR‐495 signature showed area‐under‐curve values indicative of strong diagnostic capacity and biomarker‐based staging. Higher levels of these miRNAs were associated with worse scores in the different assessed cognitive tests.DISCUSSIONThis study reports for the first‐time alterations in plasma miR‐495 levels in both MCI and MD patients. Future studies with larger cohorts are essential to validate the findings.HighlightsAlterations in plasma miR‐495 levels are reported for the first time in AD patients.miR‐181a, miR‐181c, and miR‐495 levels were higher in AD patients compared to controls.Higher levels of these miRNAs were related to worse cognitive test scores.miRNA signature was able to distinguish AD stages.

BackgroundEarly detection of dementia requires highly accurate and efficient screening tests that minimize patient burden.ObjectiveTo develop a machine learning model predicting dementia conversion within 3–5 years using Cube Copying Test (CCT) drawings at baseline.MethodsThis retrospective study analyzed CCT drawing data from 767 patients at the Center for Comprehensive Care and Research on Memory Disorders (2011–2020). Of the 2303 patients who met the inclusion criteria, 534 were excluded due to mild cognitive impairment (MCI) persistence, pending diagnoses, or new neurovascular diseases, while 1002 were lost to follow-up. Eligibility criteria included a baseline Mini-Mental State Examination (MMSE) score ≥24, absence of dementia diagnosis or anti-dementia medication intake, and completion of a 3–5-year follow-up without meeting exclusion criteria.ResultsOf 767 patients, 457 converted to dementia (318 with Alzheimer's disease, 116 with dementia with Lewy bodies, and 23 with frontotemporal dementia) within 3–5 years, while 310 did not. The model achieved an area under the curve of 0.85 for predicting dementia conversion. Shapley Additive exPlanations analysis identified PatchCore-derived features as the strongest predictors, distinguishing drawing patterns of converters and non-converters.ConclusionsIn patients who convert to Alzheimer's disease, dementia with Lewy bodies, or frontotemporal dementia, the very early stages of constructional apraxia-like symptoms already exist at the preclinical stage or MCI stage. Applying deep learning-based anomaly-detection models can detect these early drawing distortions that differ from normal aging and contribute to improving the performance of dementia-conversion prediction.

BackgroundGlobal cognitive performance is influenced by change in cerebral structure and educational background. However, little is known about how education moderates the impact of cerebral structural changes on different cognitive domains and other non-cognitive dysfunction across the clinical stages of Alzheimer's disease (AD).ObjectiveTo explore the moderating effect of education on relationship between cerebral structure and various clinical manifestations across the AD continuum, ranging from mild cognitive impairment (MCI) to AD.MethodsThis cross-sectional study included data of 570 patients diagnosed with MCI or AD. The total years of education were used as a moderating variable, and AD-related cerebral structure changes were assessed through visual ratings on magnetic resonance imaging (MRI). Multiple linear regressions were performed to examine whether education moderated the association between cerebral structure and clinical characteristics at different diagnostic stages of AD.ResultsPatients with higher levels of education demonstrated better cognitive ability, enhanced activities of daily living, and milder neuropsychiatric symptoms. The moderating effect of education was most prominent during the MCI or early AD stages, showing cognitive domain-specific effects. In these stage, education alleviated the negative impacts of neurostructural changes on immediate learning but exacerbated the detrimental effects of cerebral structural changes on speed/executive function, language, and episodic memory.ConclusionsEducation serve as a moderator in relationship between cerebral structure and various clinical characteristics. The moderating effect of education is domain-specific and most noticeable in the early stage of AD.

BackgroundOlfactory identification decline is a known early marker of Alzheimer's disease and is already present at the mild cognitive impairment (MCI) stage. While being linked with episodic memory, its predictive value for cognitive performance and distinguishing between clinical stages remains unclear.ObjectiveThis study examined (1) the predictive value of olfactory identification for episodic memory performance and (2) its utility for discriminating individuals with MCI from those with subjective cognitive decline (SCD).MethodsParticipants included 45 individuals with MCI (mean age = 80.08, SD = 5.86) and 48 with SCD (mean age = 75.82, SD = 5.64) from the Consortium for the Early Identification of Alzheimer's Disease-Quebec cohort. We evaluated olfactory identification with the University of Pennsylvania Smell Identification Test (UPSIT), and episodic memory with the Rey Auditory Verbal Learning Test (RAVLT). LASSO regression models were used to predict RAVLT total and delayed recall scores, using 80% of data for training and 20% for testing.ResultsUPSIT significantly predicted both RAVLT total (β = 0.45, p = 0.03) and delayed recall (β = 0.18, p = 0.02), independent of diagnostic group. Including UPSIT in the models increased explained variance from 9% to 19% for total recall, and from 8% to 20% for delayed recall. The MCI group had significantly lower UPSIT performance than the SCD group (p = 0.01). Linear discriminant analysis yielded 69% classification accuracy, with higher specificity (79%) than sensitivity (58%).ConclusionsOlfactory identification enhances prediction of episodic memory performance and may be used as a cost-effective, non-invasive early screening tool for MCI.

Background/Objective: Mild cognitive impairment (MCI) often represents the prodromal stage of neurodegenerative dementia. Identification of Alzheimer disease (AD) and other dementias in the MCI stage is essential for early intervention. Transcranial magnetic stimulation (TMS) has gained interest as a non-invasive method to evaluate cortical excitability and neurotransmitter function. This systematic review aims to evaluate the diagnostic utility of TMS-derived indices, such as short-latency afferent inhibition (SAI), short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), and long-interval intracortical inhibition (LICI) in MCI populations. Methods: Following PRISMA guidelines, 14 studies were selected, encompassing 476 MCI patients. Reported outcomes related to TMS measures (SAI, SICI, ICF, LICI) were reviewed across various MCI phenotypes. Results: Most studies report reduced SAI, a marker of cholinergic dysfunction, in amnestic MCI and MCI due to AD. Alterations in SICI and ICF, markers of GABAergic and glutamatergic dysfunction, were more variable, mainly observed in MCI of non-AD type. LICI showed no consistent changes. One study demonstrated increased clinicians' diagnostic confidence when TMS data were incorporated. Conclusions: TMS measures hold promise as a non-invasive tool for early and differential diagnosis of MCI. Further standardized and longitudinal research is needed to confirm its clinical applicability.

Astrocyte reactivation can be assessed using positron emission tomography (PET) ligands targeting monoamine oxidase B (MAO-B). 11C-SL25.1188 binds reversibly to MAO-B, allowing precise density measurements, but requires invasive arterial sampling. This study aimed to develop a simplified, noninvasive method to quantify MAO-B with 11C-SL25.1188 PET in Alzheimer's disease (AD). Six patients with mild cognitive impairment (MCI), five patients with AD, and six healthy controls (HCs) underwent 11C-SL25.1188 PET scans. The distribution volume ratios (DVRs) were calculated and compared using two methods: the original multilinear reference tissue model (MRTMO) and the Logan plot. Changes in MAO-B densities, plasma glial fibrillary acidic protein (GFAP) levels, and abnormal protein aggregation were examined among subjects. A strong agreement was observed between the DVRs estimated using MRTMO and those obtained with the Logan plot (r2 = 0.89), with the cerebellar cortex used as the reference region. This region was selected based on its similar total distribution volume values and comparable MAO-B levels between patients with AD and HCs. Patients with MCI showed higher DVRs in the parietal cortex compared to those with moderate AD. Moreover, patients with moderate AD had higher plasma GFAP levels than HCs but similar levels to patients with MCI. MAO-B density in patients with MCI/AD can be accurately estimated by calculating DVRs using a simplified quantification method that does not require arterial blood sampling. The estimated MAO-B density shows an increase that peaks at the MCI stage, suggesting early astrocyte reactivation in the progression of AD pathology.

This review examines the application of olfactory testing in the early stages of mild cognitive impairment (MCI) associated with Alzheimer's disease (AD), highlighting its potential and challenges in early screening and intervention. Olfactory function is typically divided into three domains: odor identification, odor discrimination, and odor threshold. Among these, odor identification and discrimination are closely linked to higher cognitive processes and exhibit significant impairment in patients with AD and MCI. Moreover, the anatomical and functional characteristics of the olfactory system make it a promising target for the early detection of neurodegenerative disorders. This review also outlines various olfactory assessment tools and evaluates their clinical utility. Future research should aim to enhance the accuracy and cultural adaptability of olfactory tests and integrate them with multimodal diagnostic approaches to advance early detection and intervention strategies for AD.

The neurovascular unit is critical for brain health, and its dysfunction has been linked to Alzheimer’s disease (AD). However, a cell-type-resolved understanding of how diverse vascular cells become dysfunctional and contribute to disease has been missing. Here, we applied Vessel Isolation and Nuclei Extraction for Sequencing (VINE-seq) to build a comprehensive transcriptomic atlas from 101 individuals along AD progression. Our analysis of over 842,646 parenchymal and vascular nuclei reveals that vascular dysfunction in AD is driven by transcriptional changes rather than shifts in cell proportions, with brain endothelial cells (BECs) and smooth muscle cells (SMCs) most affected. Strikingly, these molecular signatures emerge early at the mild cognitive impairment (MCI) stage, implicating vascular dysfunction early in AD pathogenesis. Stratifying by pathology reveals distinct vascular responses to β-amyloid and tau: β-amyloid burden primarily perturbs BECs and SMCs, while tau pathology predominantly impacts glial cells. We identify dysregulated angiopoietin signaling across multiple vascular cell types as a key axis, with antagonistic ANGPT2 in vascular cells and ANGPT1 in astrocytes becoming progressively dysregulated with AD. Together, this work provides a foundational resource that reveals early and pathology-specific pathways of vascular dysfunction in AD.

BackgroundDementia is a complex, multifactorial syndrome characterised by cognitive decline and impaired daily functioning, representing a major risk factor for long-term care dependency. As the prevalence of dementia will increase due to demographic change, healthcare systems face growing challenges, including timely diagnosis, equitable access to care, and managing the rising demand for health services. Speech-language therapy (SLT) and occupational therapy (OT) can help maintain cognitive function and quality of life, particularly in the early stages of dementia or mild cognitive impairment (MCI). However, their utilisation in Germany remains poorly understood.ObjectiveThis study evaluates the utilisation and prescription patterns of SLT and OT among people with dementia or MCI and explores factors associated with therapy use, based on German claims data.MethodsA retrospective cohort study was conducted using routinely collected data from the research database of the Institute for Applied Health Research Berlin (InGef), including anonymised health records of 5 million individuals in Germany. The dataset covered the period from 2017 to 2022. Incident cases of dementia or MCI were tracked for two years following diagnosis to assess therapy use and prescription patterns. Different facets associated with therapy use were analysed using multivariable logistic regression.ResultsA total of 63,496 individuals (58% female, 42% male) were included (81.8% with dementia, 18.2% with MCI). Of these, 4.2% received SLT and 10.3% received OT (at least one prescription within the two-year follow-up period).Male sex (Odds Ratio [OR] 1.24, 95% confidence interval [CI] 1.09–1.40) and certain dementia types were significantly associated with higher odds of receiving SLT compared to individuals with Alzheimer’s disease (AD): dementia classified elsewhere (OR 3.34, 95% CI 2.46–4.53), vascular dementia (OR 1.71, 95% CI: 1.36–2.15), and MCI (OR 1.61, 95% CI: 1.28–2.03). In contrast, these dementia types were associated with lower odds of receiving OT. Older age was negatively associated with SLT use, whereas no consistent age-related pattern was observed for OT utilisation.ConclusionOur findings reveal low utilisation of SLT and OT, highlighting significant gaps in allied health service provision for people with dementia or MCI. These results underscore the need for improved referral pathways and targeted strategies to better integrate allied health professionals into routine dementia care.Trial registrationThe study was not registered.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12913-025-13149-y.

BackgroundAccurate and simple detection of cognitive decline is important for the prediction of dementia and identification of drug indications. The Cogstate Brief Battery (CBB) is useful in assessing cognitive function during the preclinical and mild cognitive impairment stages. However, whether it is beneficial for assessing brain function in older Japanese adults remains unclear.ObjectiveThis study aimed to assess the association of the CBB score with those of traditional cognitive tests and brain imaging in assessing cognitive function in older Japanese adults with normal cognitive function and those with mild cognitive impairment.MethodsCommunity-dwelling older adults in Usuki city underwent CBB, traditional cognitive tests, magnetic resonance imaging, and amyloid positron emission tomography. The association of the CBB score with the Japanese version of the Montreal Cognitive Assessment (MoCA-J), Japanese version of Mini-Mental State Examination (MMSE), hippocampal atrophy on magnetic resonance imaging, and brain amyloid deposition on 11C-Pittsburgh compound-B positron emission tomography was examined.ResultsIn total, 170 participants were included in this study. Among them, 59 were positive for the C-Pittsburgh compound-B. The CBB score was significantly associated with the MoCA-J and MMSE score in all patients. Further, it was significantly associated with hippocampal atrophy in the amyloid-positive group.ConclusionsThe CBB is associated with the MoCA-J and MMSE scores and may thus be a useful tool for the assessing cognitive decline in Japanese older adults.

The effect of biperiden on episodic memory: Testing the serial position effect.

Beta-synuclein is an emerging blood biomarker for detecting synaptic damage in Alzheimer's disease (AD) but its role in early AD as well as in other dementias is unclear. We measured with immunoprecipitation mass-spectrometry serum beta-synuclein levels in an exploratory cohort of 80 patients recruited at the University of Perugia (Perugia, Italy) (n=56 AD; n=24 controls) and in a validation cohort of 269 patients recruited at the University of Barcelona (Barcelona, Spain) (n=108 AD; n=53 frontotemporal lobar degeneration (FTLD); n=73 dementia with Lewy bodies and mild cognitive impairment (MCI) with Lewy bodies, together Lewy body disease (LBD); n=27 controls). We tested associations with diagnostic groups, cognitive decline and other cerebrospinal fluid (CSF) and blood markers (phosphorylated tau protein in position 181 (pTau181), neurofilament light chain protein (NfL), glial fibrillar acidic protein (GFAP)). Serum beta-synuclein level was progressively increased in the AD continuum across the preclinical, MCI and dementia stages compared with controls and was correlated with serum pTau181 (r=0.710), NfL (r=0.494) and GFAP concentrations (r=0.621, p<0.001 for all). The biomarker showed high accuracy for the discrimination of AD vs controls (area under the curve (AUC): 0.87) and AD-MCI vs non-AD MCI (AUC: 0.96). High serum beta-synuclein level was correlated with lower Mini-Mental State Examination (MMSE) points at baseline (r=-0.461, p<0.001) and associated with MMSE change at follow-up after accounting for age, sex and the time from baseline to last follow-up visit (p=0.006). Serum beta-synuclein level was similar between FTLD and controls, whereas, in LBD, it was higher with AD copathology as evidenced by CSF analysis (p<0.001). High serum beta-synuclein level is a promising biomarker for AD-related synaptic damage.