Despite advances in biomarker assessment, molecular neuroimaging, and disease-modifying therapies for cognitive disorders, China lacks well-characterized clinical cohorts integrating comprehensive clinical assessments and multimodal biomarkers.

The Shanghai Memory Study (SMS), an ongoing hospital-based cohort, enrolled participants undergoing clinical/neuropsychological assessments, genotyping, multimodal imaging, and biospecimen collection.

From 2012 to 2024, 2001 participants were enrolled: 115 cognitively unimpaired (CU), 938 with mild cognitive impairment (MCI), and 948 with dementia. Positron emission tomography (PET) scan revealed A+/T+ positivity rates of 15.8% in CU, 51.2% in MCI, and 100% in Alzheimer's disease dementia (ADD). Plasma tau phosphorylated at threonine 181 (p-tau181) level increased gradually across the AD continuum. Blood p-tau181 and 18F-Florzolotau PET showed comparable utility for amyloid status identification. In a subcohort of 251 amnestic MCI (aMCI) patients, low Aβ42/Aβ40 and elevated p-tau181 predicted 4.7-year ADD risk.

By offering an integrated framework, SMS will facilitate the exploration of AD pathogenesis and the understanding of cognitive disorders.

The SMS is an ongoing prospective cohort study based on a memory clinic in China. The SMS has established a relatively large-scale dataset that includes clinical data, biofluid markers, MRI and PET imaging, and novel biomarkers. By offering an integrated framework, SMS aims to facilitate the exploration of AD pathogenesis and deepen our understanding of cognitive disorders.

Mild cognitive impairment (MCI) is characterized by abnormal changes in spatiotemporal neuronal specificity responses. Simultaneous electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) offers a novel approach to measure these changes. Emerging evidence suggests that acupuncture may enhance cognitive function by modulating spatial or temporal central activity in individuals with MCI. However, no studies have investigated the detailed mechanisms underlying this effect.

This randomized controlled neuroimaging trial will enroll 60 patients with MCI, who will be randomly assigned to one of two groups: a real acupuncture (RA) group or a sham acupuncture (SA) group. The trial period will last 12 weeks, during which participants will receive 24 sessions of acupuncture twice weekly. The primary outcome measure will be the improvement in the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) score from baseline to post-treatment. Secondary outcomes will include improvements in specific cognitive domains such as memory, executive function, language, and attention. Simultaneous EEG-fMRI combined with correlation analysis, regression analysis, and joint independent component analysis (jICA) will elucidate the spatiotemporal central modulatory mechanisms of acupuncture in MCI patients.

This study may reveal that real acupuncture can treat cognitive impairment by modulating the brain's spatiotemporal neuronal specificity activity. Our findings will provide scientific evidence for the efficacy of acupuncture in the treatment of MCI and further add to the understanding of the neural mechanisms.

ClinicalTrials.gov, identifier [ChiCTR2400084666].

Characterizing transitions of cognitive state, including reversion from mild cognitive impairment (MCI) to normal cognition (NC) and subsequent cognitive stability or deterioration for post-reversion, has so far remained limited.

Using a retrospective cohort of subjects with an MCI diagnosis at study entry and at least two follow-up visits between 2005 and December 2022, we developed a functional multistate model framework to estimate longitudinal patterns of transition probabilities between different cognitive states.

The probability of reversion increased from 2% at baseline to a maximum of 8% by year 10 before gradual decline thereafter. For post-reversion, the probability of progression to MCI rose from 8% to 35.71% at Year 10 and subsequently stabilized.

The instantaneous risk of MCI progressing was similar to the risk of re-progression to MCI for post-reversion. Post-reversion subjects remained at an increased risk of cognitive deterioration.

The instantaneous risk of MCI progressing to AD is similar to the risk of re-progression to MCI for post-reversion. The FMSM we developed effectively utilizes multiple longitudinal markers to reveal variable transition patterns between different cognitive states. Considering both spatiotemporal dimensions and sparse irregularities from longitudinal neuroimaging and neuropsychological scales, fMLFPCA and MVFPCA help to extract variation patterns, to capture detailed changes characterizing the multidimensional evolution patterns of MCI.

To improve assessment of neuropsychiatric symptoms (NPS) by expanding the measurement properties of the Neuropsychiatric Inventory Questionnaire (NPI-Q).

Multicenter, longitudinal observational study.

Several Alzheimer's Disease Research Centers (ADRCs).

Individuals (n = 45,274) who presented to an ADRC with a collateral and completed the NPI-Q.

The NPI-Q total severity score, four NPI-Q subscales, dementia stage, expert NPS rating, consensus rating of dementia syndrome, global cognitive screening, collateral rating of daily functioning, and self-rating of depression.

There was strong evidence of criterion validity with both dementia stage and expert NPS rating for the NPI-Q total severity index, which informed cutoffs and interpretive ranges. Furthermore, subscales had adequate classification of dementia syndromes and appropriate convergent relationships with cognition, daily functioning, and mood. There was good-to-excellent evidence of reliability for the NPI-Q total severity index over several years, and subscales had adequate-to-good reliability.

This is the first study to provide empirically established cutoffs, interpretive ranges, and evidence of reliability over a period longer than a month on the NPI-Q and its subscales. This will improve assessment of NPS in clinical and research contexts.

Neuropsychiatric symptoms of neurodegeneration are increasingly understood as early disease markers with tremendous functional impact later in disease, but are often missed or misdiagnosed. The most common measure of these symptoms, the Neuropsychiatric Inventory Questionnaire (NPI-Q), does not have clinically actionable guidance, which this article provided. We established cutscores for several conditions and test-retest reliability over longer periods for the total score and subscales using a multicenter database.

Diffusion-tensor image analysis along the perivascular space (ALPS) index that has the potential to reflect brain interstitial fluid (ISF) dynamics may predict the development of Alzheimer's Disease (AD). We aimed to study whether brain ISF dynamics indicated by the ALPS index relate to AD dementia diagnosis and AD-related changes.

This study included a discovery cohort (n = 180) and a validation cohort (n = 127), which were composed of cognitively normal, subjective memory concern, mild cognitive impairment, and AD dementia subjects. All participants underwent brain magnetic resonance imaging examination and neuropsychological evaluation. The diffusivities and diffusion-tensor image analysis along the perivascular space (ALPS) were calculated. The support vector machine (SVM) model for AD dementia diagnosis was built in the discovery cohort and validated in the validation cohort. Linear mixed-effects models were used to evaluate the association between the ALPS and cognitive decline. Cox regression models were used to evaluate the association between the ALPS and the risk of AD dementia.

There was a lower median ALPS index in the AD dementia group compared to other groups (all P < 0.05) for both cohorts. The SVM model for AD dementia diagnosis produced an AUC of 0.802 in the discovery cohort (P < 0.001) and 0.783 in the external validation cohort (P < 0.001). Higher ALPS levels were associated with less cognitive decline (P < 0.001). Moreover, lower baseline ALPS had a greater risk of converting to AD dementia (P = 0.014).

The SVM model based on diffusivities and ALPS was effective for AD dementia diagnosis, and higher ALPS levels are associated with a lower risk of AD-related changes. These findings suggest that ALPS may provide a useful AD progression or treatment biomarker.

Dementia is a leading cause of death and disability worldwide. Here we tested the effectiveness of blood pressure (BP) reduction on the risk of all-cause dementia among 33,995 individuals aged ≥40 years with uncontrolled hypertension in rural China. We randomly assigned 163 villages to a non-physician community healthcare provider-led intervention and 163 villages to usual care. In the intervention group, trained non-physician community healthcare providers initiated and titrated antihypertensive medications according to a simple stepped-care protocol to achieve a systolic BP goal of <130 mm Hg and a diastolic BP goal of <80 mm Hg, with supervision from primary care physicians. Over 48 months, the net reduction in systolic BP was 22.0 mm Hg (95% confidence interval (CI) 20.6 to 23.4; P < 0.0001) and that in diastolic BP was 9.3 mm Hg (95% CI 8.7 to 10.0; P < 0.0001) in the intervention group compared to usual care. The primary outcome of all-cause dementia was significantly lower in the intervention group than in the usual care group (risk ratio: 0.85; 95% CI 0.76 to 0.95; P = 0.0035). Additionally, serious adverse events occurred less frequently in the intervention group (risk ratio: 0.94; 95% CI 0.91 to 0.98; P = 0.0006). This cluster-randomized trial indicates that intensive BP reduction is effective in lowering the risk of all-cause dementia in patients with hypertension. ClinicalTrials.gov: NCT03527719 .

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) carry the risk of progression to dementia, and accurate screening methods for these conditions are urgently needed. Studies have suggested the potential ability of functional near-infrared spectroscopy (fNIRS) to identify MCI and SCD. The present fNIRS study aimed to develop an early screening method for SCD and MCI based on activated prefrontal functional connectivity (FC) during the performance of cognitive scales and subject-wise cross-validation via machine learning.

Activated prefrontal FC data measured by fNIRS were collected from 55 normal controls, 80 SCD patients, and 111 MCI patients. Differences in FC were analyzed among the groups, and FC strength and cognitive scale performance were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95 % confidence interval (CI) values.

Statistical analysis revealed a trend toward more impaired prefrontal FC with declining cognitive function. Prediction models were built by combining features of prefrontal FC and cognitive scale performance and applying machine learning models, The models showed generally satisfactory abilities to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 92.0 % for MCI vs. NC, 80.0 % for MCI vs. SCD, and 76.1 % for SCD vs. NC were achieved, and the highest AUC values were 97.0 % (95 % CI: 94.6 %-99.3 %) for MCI vs. NC, 87.0 % (95 % CI: 81.5 %-92.5 %) for MCI vs. SCD, and 79.2 % (95 % CI: 71.0 %-87.3 %) for SCD vs. NC.

The developed screening method based on fNIRS and machine learning has the potential to predict early-stage cognitive impairment based on prefrontal FC data collected during cognitive scale-induced activation.

This study aims to evaluate the Functional Activities Questionnaire (FAQ; 10 items) for assessing the quality of daily living activities among older adults in Iran.

A total of 680 participants completed the Persian version of FAQ. We used the Rasch partial credit model, exploratory factor analysis, confirmatory factor analysis, and receiver operating characteristic analysis to evaluate the psychometric properties of the FAQ among Iranian older adults.

The findings from exploratory factor analysis, confirmatory factor analysis, and item response theory analysis supported the usefulness of the Persian version of the FAQ to be used in Iran. The one-factor model of the FAQ exhibited strong internal consistency, as evidenced by McDonald's omega (≥0.75), Factor Determinacy Index (≥0.8), and Overall Reliability of Fully-Informative prior Oblique Nonequivalent Anchor Parameter scores index (≥0.8). In addition, we observed measurement invariance and consistent response patterns for all items in a logical sequence, indicating the good internal consistency of the FAQ relevance.

Persian version of FAQ-10 items is a valid and reliable instrument for measuring daily living activities among Iranian older adults. Significance/Implications: The FAQ will be a practical tool for measuring activities of daily living in community-based settings as part of comprehensive geriatric assessment in older adults.

The formation and retrieval of episodic memory is dependent on the coordinated activity of multiple brain regions and neural networks, with the Papez circuit playing a critical role in this process. Recently, the role of the perivascular space (PVS) in cognitive function has garnered increasing attention. However, the role of PVS function between neural circuits and cognitive function in amnestic mild cognitive impairment (aMCI) patients remains unknown. Therefore, this study aims to (1) investigate alterations in the effective connectivity of the Papez circuit and PVS function in patients with aMCI and (2) explore the role of PVS function between the effective connectivity of the Papez circuit and episodic memory.

Sixty participants, all of whom underwent multimodal MRI (fMRI, dMRI, and sMRI) and neuropsychological testing, were recruited for this case‒control study. General linear models were used to compare the effective connectivity within the Papez circuit and PVS function between aMCI patients and healthy controls (HCs) and further explore the role of PVS function between the effective connectivity within the Papez circuit and episodic memory.

The effective connectivity between multiple critical regions within the Papez circuit, notably in the hippocampus, anterior cingulate cortex, and parahippocampal gyrus, was significantly weakened in aMCI patients. Moreover, a significant reduction in the along the perivascular space (ALPS) index was observed among aMCI patients, accompanied by a marked increase in PVS volume, indicating significant PVS dysfunction. Further moderation analysis revealed that PVS function moderated the relationship between effective connectivity within the Papez circuit and episodic memory.

The effective connectivity within the Papez circuit and PVS function are closely related to cognitive function, particularly episodic memory, and enhancing PVS function may serve as a novel therapeutic target for slowing cognitive decline.

Sarcopenia, with its complex diagnostic process, is a likely independent predictor of poor prognosis in patients with Alzheimer's disease (AD). However, research on the clinical characteristics and biomarkers of AD patients with sarcopenia (ADSA) is limited.

This study included 180 ADSA and 188 AD patients without sarcopenia (ADNSA), and evaluated demographics, cognitive function, motor capacity, emotional state, and daily living abilities.

ADSA patients were older, with worse motor and cognitive functions, more severe depression, poorer social functioning, and lower daily living abilities compared to ADNSA patients. Multivariate regression identified age, low Frailty Rating Scale (FRS) scores, low serum albumin level, and low creatinine/cystatin C ratio (CCR) as risk factors for sarcopenia. A nomogram model based on these indicators demonstrated high discriminative power and clinical utility.

Sarcopenia significantly affects AD patients' various functions. The nomogram model aids in the early detection of and personalized interventions for sarcopenia in AD.

Sarcopenia is a risk factor for Alzheimer's disease (AD), and the coexistence of sarcopenia affects various functions and quality of life in patients with AD. Serum albumin and Frailty Rating Scale (FRS) scores are significantly associated with both sarcopenia and cognitive assessment indicators in AD patients with sarcopenia (ADSA). The combined sarcopenia nomogram model with indexes of age at diagnosis, creatinine/cystatin C ratio (CCR), FRS score, and albumin levels can aid in effectively identifying and personalizing interventions for sarcopenia in the AD population.

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease. Differentiating early MCI (EMCI) from late MCI (LMCI) is crucial for early diagnosis and intervention. This study used free-water diffusion tensor imaging (fw-DTI) to investigate white matter differences and voxel-based correlations with Mini-Mental State Examination (MMSE) scores. Data from the Alzheimer's Disease Neuroimaging Initiative included 476 healthy controls (CN), 137 EMCI participants, and 62 LMCI participants. Significant MMSE differences were found between the CN and MCI groups, but not between EMCI and LMCI. However, distinct white matter changes were observed: LMCI showed a higher f-index and lower fw-fractional anisotropy (fw-FA) compared to EMCI in several white matter regions. These findings indicate specific white matter tracts involved in MCI progression. Voxel-based correlations between fw-DTI metrics and MMSE scores further supported these results. In conclusion, this study provides crucial insights into white matter changes associated with EMCI and LMCI, offering significant implications for future research and clinical practice.

Functional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on resting-state prefrontal FC and neuropsychological tests via machine learning.

Functional connectivity data measured by fNIRS were collected from 55 normal controls (NCs), 80 SCD individuals, and 111 MCI individuals. Differences in FC were analyzed among the groups. FC strength and neuropsychological test scores were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95% confidence interval (CI) values.

Statistical analysis revealed a trend toward compensatory enhanced prefrontal FC in SCD and MCI individuals. The models showed a satisfactory ability to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 94.9% for MCI vs. NC, 79.4% for MCI vs. SCD, and 77.0% for SCD vs. NC were achieved, and the highest AUC values were 97.5% (95% CI: 95.0%-100.0%) for MCI vs. NC, 83.7% (95% CI: 77.5%-89.8%) for MCI vs. SCD, and 80.6% (95% CI: 72.7%-88.4%) for SCD vs. NC.

The developed screening method based on resting-state prefrontal FC measured by fNIRS and machine learning may help predict early-stage cognitive impairment.

Cognitive deficits contribute to disability in Parkinson's disease (PD). Cognitive intra-individual variability (IIV) is associated with cognitive decline in age-related disorders, but IIV has not been related to functional ability in PD. We examined IIV in predicting functional ability in participants with PD.

De-identified National Alzheimer's Coordinating Center data (N = 1,228) from baseline and follow-up visits included participants with PD propensity score matched to control participants at baseline on age (M = 72), education (M = 15), and gender (28% female). PD symptom duration averaged 6 years. Outcome measures included the Functional Ability Questionnaire (FAQ), overall test battery mean (OTBM) of ten cognitive variables, IIV calculated as the standard deviation of cognitive data for each participant, Geriatric Depression Scale (GDS), and Unified PD Rating Scale gait and posture items. Baseline FAQ status in the PD group was predicted using logistic regression with age, education, cognition, GDS, and motor function as predictors. We compared baseline characteristics of PD participants with and without functional impairment at follow up.

PD participants showed lower OTBM and greater IIV, GDS, and motor dysfunction than controls (p < .0001). Education, OTBM, IIV, GDS, and gait predicted functional status (77% overall classification; AUC = .84). PD participants with functional impairment at follow up showed significantly lower OTBM and greater IIV, GDS, and motor dysfunction at baseline (p < .001).

IIV independently predicts functional status in participants with PD while controlling for other variables. PD participants with functional impairment at follow up showed greater IIV than those without functional impairment at follow up.

Prodromal dementia is largely underdiagnosed in primary care.

To develop a clinical model for detecting prodromal dementia within the operative boundaries of primary care practice.

The study employed the Functional Activities Questionnaire (FAQ) and Montreal Cognitive Assessment (MoCA) to evaluate a "functional-cognitive" step-down screening model, in which the MoCA is administered subsequent to reported symptoms on the FAQ. It classified participants from the Alzheimer's Disease Imaging Initiative to three diagnostic categories: (1) healthy cognition (n = 396), (2) mild cognitive impairment without conversion (n = 430), and (3) prodromal dementia assessed 24 months before diagnosis (n = 164).

Analyses indicated that the step-down model (Model 1) performed significantly better than an alternative model that applied the FAQ as a single measure (Model 2) and compared well with another model that administered both screening measures to all participants (Model 3). Gradient Boosting Trees classifications yielded the following estimations for Model 1/Model 2/ Model 3, respectively: Sensitivity = 0.87/0.77/0.89, Specificity = 0.68/0.47/0.70, PPV = 0.73/0.40/0.75, NVP = 0.84/0.81/0.87, F1 Score = 0.79/0.52/0.81, AUC = 0.78/0.67/0.79.

These analyses support the proposed model. The study offers algorithms for validated measures, which were developed from a well characterized clinical sample. Their accuracy will likely improve further with new data from diverse clinical settings. These results can serve primary care in a timely manner in light of the recent advances in pharmacological treatment of dementia and the expected increase in demand for screening.

Assessing treatments for Alzheimer's disease (AD) relies on reliable tools for measuring AD progression. In this analysis, we evaluate the sensitivity of clinical progression measures in AD within randomized controlled trials (RCTs) with confirmed positive amyloid (Aβ+) status prior to trial enrollment.

Excluding trials targeting non-cognitive symptoms, we conducted meta-analyses on progression measures from 25 selected RCTs using R version 4.2.0, along with the metafor and emmeans libraries.

The Functional Activities Questionnaire (FAQ) demonstrated the greatest sensitivity over 12 weeks. Other cognitive measures demonstrated lower sensitivity. The integrated Alzheimer's Disease Rating Scale (iADRS) and Clinical Dementia Rating-Sum of Boxes (CDR-SB) seemed more effective than their individual cognitive components. Neuropsychiatric measures were the least sensitive in measuring progression.

Functional measures generally outperformed other measure categories. Purely cognitive domain-based measures were suboptimal for tracking early AD progression. Ideally, future measures should incorporate both cognitive and functional components to enhance sensitivity.

Concerns remain regarding the limitations of current outcome measures used in AD clinical trials, particularly their sensitivity in the early and preclinical stages of the disease, which hampers their reliability as indicators of AD progression. The Functional Activities Questionnaire (FAQ) demonstrated the most substantial weighted mean change over 12 weeks, followed by the Mini-Mental State Examination (MMSE). Functional measures outperformed other measure categories. Composite scores of integrated Alzheimer's Disease Rating Scale and Clinical Dementia Rating-Sum of Boxes are more sensitive to change than their individual cognitive components, possibly driven by the functional components of the score. Neuropsychiatric measures analyzed in this study appeared to be the least sensitive in measuring progression.

Instrumental activities of daily living (iADLs) increasingly involve technology (e.g., making payments online, texting). The current study examined the applicability and diagnostic accuracy of technology-based iADLs in those evaluated for Alzheimer's disease and related dementias (ADRD).

A total of 264 care partners of persons undergoing comprehensive interdisciplinary evaluations completed the Functional Activities Questionnaire and 11 technology-based iADL items.

Technology-based iADLs applied to more than 80% of patients. Average dependence on technology-based items was overall less than for traditional iADLs. The addition of technology-based items to traditional iADL items slightly improved the ability to identify individuals with dementia. When considered separately, technology-based iADL items demonstrated comparable ability to distinguish between diagnostic stages.

Technology use is common in older adults with ADRD for a range of daily activities. Accounting for technology use increases the content validity of existing iADL measures for the modern context and yields comparable diagnostic accuracy.

Technology use is often integral to daily activity performance for individuals with Alzheimer's disease and related dementias (ADRD).Daily technologies, such as smartphones, were used frequently by those with ADRD.Many individuals were less dependent on technology activities than traditional activities.Adding technology questions slightly increased diagnostic accuracy for detecting dementia.

Executive dysfunction is characteristic of behavioral variant frontotemporal dementia (bvFTD) but can be challenging to detect. Dispersion-based intraindividual variability (IIV-d) is hypothesized to reflect a sensitive index of executive dysfunction and has demonstrated relevance to functional decline but has not been evaluated in bvFTD.

We report on 477 demographically matched participants (159 cognitively healthy [CH], 159 clinical Alzheimer's disease [AD], 159 clinical bvFTD/prodromal bvFTD) who completed the Uniform Data Set 3.0 Neuropsychological Battery. IIV-d was measured using the coefficient of variance (CoV; raw and demographically adjusted) across 12 Uniform Data Set 3.0 Neuropsychological Battery indicators and the informant-rated Functional Activities Questionnaire assessed daily functioning.

Analysis of covariance showed that participants in the bvFTD/prodromal bvFTD group exhibited higher raw and demographically adjusted CoV compared to CH participants, at a very large effect size (d = 1.28-1.47). Demographically adjusted (but not raw) CoV was lower in the bvFTD/prodromal bvFTD group than the AD group, though the effect size was small (d = .38). Both CoV metrics accurately differentiated the bvFTD/prodromal bvFTD and CH groups (areas under the curve = .84), but not bvFTD/prodromal bvFTD and AD groups (areas under the curve = .59). Regression analyses in the bvFTD/prodromal bvFTD group indicated that higher IIV-d on both metrics was associated with greater daily functioning impairment, over and above covariates.

Compared to healthy adults, individuals with bvFTD/prodromal bvFTD show greater levels of performance variability across a battery of neuropsychological measures, which interferes with everyday functioning. These data demonstrate the clinical utility and ecological validity of IIV-d in bvFTD/prodromal bvFTD, though these findings should be replicated in more diverse samples. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The most prevalent cause of dementia is Alzheimer's disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer's disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer's disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer's, prior to individuals developing Alzheimer's disease dementia. Regrettably, the identification of early-stage Alzheimer's disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer's disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance in-depth knowledge of the current status of Alzheimer's disease's diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed.

To evaluate the extent to which demographic factors-and their intersections-influence the applicability of items assessing activities of daily living (ADLs) in a sample of older adults.

Participants' (n = 44,713) Functional Activities Questionnaire (FAQ) scores from a multicenter database were evaluated to see how participant and collateral demographics, contextual, and clinical characteristics impacted ADL nonapplicability (NA). Collateral, contextual, and clinical characteristics were matched in those with and without NA. The effect of participant demographics and their interactions on NA responses were modeled with logistic regression.

At least one FAQ item (most commonly bill payment, taxes, playing games, and meal preparation) was rated as NA in up to one third of participants across ethnoracial groups. Dementia staging had the largest impact on NA, followed by participant demographics. In a matched sample, logistic models revealed that participant demographics, in particular sex, best predicted NA. However, meaningful interactions with ethnoracial group were noted for bill payment, taxes, meal preparation, and game engagement, suggesting that demographic intersections (e.g., younger vs. older Latinxs) meaningfully predict whether a given ADL was applicable to an individual participant.

Neuropsychology is predicated on accurate assessments of both cognition and daily functioning and, in an increasingly diverse aging population, there should be careful consideration of demographic factors, their interactions, and historical contexts that drive day-to-day demands. This study establishes limitations of existing measures and paths forward for creating fair measures of functioning in older adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neurodegenerative diseases (NDDs) form a heterogeneous, widespread group of disorders, generally characterized by progressive cognitive decline and neuropsychiatric disturbances. One of the abilities that seems particularly vulnerable to the impairments in neurodegenerative diseases is the capability to manage one's personal finances. Indeed, people living with neurodegenerative diseases were shown to consistently present with more problems on performance-based financial tasks than healthy individuals. While objective, performance-based tasks provide insight into the financial competence of people living with neurodegenerative diseases in a controlled, standardized setting; relatively little can be said, based on these tasks, about their degree of success in dealing with the financial demands, issues, or questions of everyday life (i.e., financial performance). The aim of this systematic review is to provide an overview of the literature examining self and informant reports of financial performance in people living with neurodegenerative diseases. In total, 22 studies were included that compared the financial performance of people living with mild cognitive impairment (MCI), Alzheimer's disease (AD), Parkinson's disease, or multiple sclerosis to a (cognitively) normal control group. Overall, the results indicate that people living with neurodegenerative diseases are more vulnerable to impairments in financial performance than cognitively normal individuals and that the degree of reported problems seems to be related to the severity of cognitive decline. As the majority of studies however focused on MCI or AD and made use of limited assessment methods, future research should aim to develop and adopt more comprehensive assessments to study strengths and weaknesses in financial performance of people living with different neurodegenerative diseases.

Objective: To evaluate the latent structure, internal consistency, convergent and discriminant validity, diagnostic accuracy, and criterion validity of the Montreal Cognitive Assessment's auditory items (MoCA-22), which has previously been evaluated in small samples if at all. Methods: 11,284 participants completed the MoCA over 1-2 visits to an Alzheimer Disease Research Center (Mage = 69.2, Meducation = 15.9, 57.6% women, 92.4% non-Hispanic white). MoCA-22 items were probed with alpha, omega, confirmatory factor analysis, and test-retest correlations. Scores were related to measures of neurocognition, daily functioning, behavioral-psychological symptoms (BPS), and vision performance for convergent-discriminant and criterion validity. Dementia stage was used to calculate area under the receiver operating characteristic (AUC-ROC) curves and cutoffs for mild cognitive impairment (MCI) and dementia. Results: A single-factor had good fit (CFI = .961; TLI = .945; RMSEA = .061; SRMR = .031), with good internal consistency (Omega total = .83) and test-retest consistency (ICC = .92 at 2.7 years). The strongest convergent correlations were with general cognition and executive functioning, while discriminant validity was demonstrated with its weakest and negative correlations being with BPS. There was strong classification accuracy in distinguishing MCI from normal cognition (AUC = .79; optimal cutoff point < 18), and mild-to-moderate dementia from MCI (AUC = .85; optimal cutoff point < 13). Furthermore, the MoCA-22 had negligible-to-small differences among those with and without vision limitations. Conclusions: These findings add to the evidence of the MoCA-22's utility and it serves as a useful cognitive screening tool with sound reliability and validity.

In healthy people, the "fight-or-flight" sympathetic system is counterbalanced by the "rest-and-digest" parasympathetic system. As we grow older, the parasympathetic system declines as the sympathetic system becomes hyperactive. In our prior heart rate variability biofeedback and emotion regulation (HRV-ER) clinical trial, we found that increasing parasympathetic activity through daily practice of slow-paced breathing significantly decreased plasma amyloid-β (Aβ) in healthy younger and older adults. In healthy adults, higher plasma Aβ is associated with greater risk of Alzheimer's disease (AD). Our primary goal of this trial is to reproduce and extend our initial findings regarding effects of slow-paced breathing on Aβ. Our secondary objectives are to examine the effects of daily slow-paced breathing on brain structure and the rate of learning.

Adults aged 50-70 have been randomized to practice one of two breathing protocols twice daily for 9 weeks: (1) "slow-paced breathing condition" involving daily cognitive training followed by slow-paced breathing designed to maximize heart rate oscillations or (2) "random-paced breathing condition" involving daily cognitive training followed by random-paced breathing to avoid increasing heart rate oscillations. The primary outcomes are plasma Aβ40 and Aβ42 levels and plasma Aβ42/40 ratio. The secondary outcomes are brain perivascular space volume, hippocampal volume, and learning rates measured by cognitive training performance. Other pre-registered outcomes include plasma pTau-181/tTau ratio and urine Aβ42. Recruitment began in January 2023. Interventions are ongoing and will be completed by the end of 2023.

Our HRV-ER trial was groundbreaking in demonstrating that a behavioral intervention can reduce plasma Aβ levels relative to a randomized control group. We aim to reproduce these findings while testing effects on brain clearance pathways and cognition.

ClinicalTrials.gov NCT05602220. Registered on January 12, 2023.

Digital technologies permit new ways of performing instrumental activities of daily living (iADLs) for older adults, but these approaches are not usually considered in existing iADL measures. The current study investigated how a sample of older adults report using digital versus analog approaches for iADLs.

248 older adults completed the Digital and Analog Daily Activities Survey, a newly developed measure of how an individual performs financial, navigation, medication, and other iADLs.

The majority of participants reported regularly using digital methods for some iADLs, such as paying bills (67.7%) and using GPS (67.7%). Low digital adopters were older than high adopters (F(2, 245) = 12.24, p < .001), but otherwise the groups did not differ in terms of gender, years of education, or history of neurological disorders. Participants who used digital methods relatively more than analog methods reported greater levels of satisfaction with their approach and fewer daily errors.

Many older adults have adopted digital technologies for supporting daily tasks, which suggests limitations to the validity of current iADL assessments. By capitalizing on existing habits and enriching environments with new technologies, there are opportunities to promote technological reserve in older adults in a manner that sustains daily functioning.

In Alzheimer's disease (AD) research, subjective reports of cognitive and functional decline from participant-study partner dyads is an efficient method of assessing cognitive impairment and clinical progression.

Demographics and subjective cognitive/functional decline (Everyday Cognition Scale [ECog]) scores from dyads enrolled in the Brain Health Registry (BHR) Study Partner Portal were analyzed. Associations between dyad characteristics and both ECog scores and study engagement were investigated.

A total of 10,494 BHR participants (mean age = 66.9 ± 12.16 standard deviations, 67.4% female) have enrolled study partners (mean age = 64.3 ± 14.3 standard deviations, 49.3% female), including 8987 dyads with a participant 55 years of age or older. Older and more educated study partners were more likely to complete tasks and return for follow-up. Twenty-five percent to 27% of older adult participants had self and study partner-report ECog scores indicating a possible cognitive impairment.

The BHR Study Partner Portal is a unique digital tool for capturing dyadic data, with high impact applications in the clinical neuroscience and AD fields. Highlights The Brain Health Registry (BHR) Study Partner Portal is a novel, digital platform of >10,000 dyads. Collection of dyadic online subjective cognitive and functional data is feasible. The portal has good usability as evidenced by positive study partner feedback. The portal is a potential scalable strategy for cognitive impairment screening in older adults.

The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.

Disease-modifying treatments for Alzheimer's disease highlight the need for early detection of cognitive decline. However, at present, most primary care providers do not perform routine cognitive testing, in part due to a lack of access to practical cognitive assessments, as well as time and resources to administer and interpret the tests. Brief and sensitive digital cognitive assessments, such as the Digital Clock and Recall (DCR™), have the potential to address this need. Here, we examine the advantages of DCR over the Mini-Mental State Examination (MMSE) in detecting mild cognitive impairment (MCI) and mild dementia.

We studied 706 participants from the multisite Bio-Hermes study (age mean ± SD = 71.5 ± 6.7; 58.9% female; years of education mean ± SD = 15.4 ± 2.7; primary language English), classified as cognitively unimpaired (CU; n = 360), mild cognitive impairment (MCI; n = 234), or probable mild Alzheimer's dementia (pAD; n = 111) based on a review of medical history with selected cognitive and imaging tests. We evaluated cognitive classifications (MCI and early dementia) based on the DCR and the MMSE against cohorts based on the results of the Rey Auditory Verbal Learning Test (RAVLT), the Trail Making Test-Part B (TMT-B), and the Functional Activities Questionnaire (FAQ). We also compared the influence of demographic variables such as race (White vs. Non-White), ethnicity (Hispanic vs. Non-Hispanic), and level of education (≥ 15 years vs. < 15 years) on the DCR and MMSE scores.

The DCR was superior on average to the MMSE in classifying mild cognitive impairment and early dementia, AUC = 0.70 for the DCR vs. 0.63 for the MMSE. DCR administration was also significantly faster (completed in less than 3 min regardless of cognitive status and age). Among 104 individuals who were labeled as "cognitively unimpaired" by the MMSE (score ≥ 28) but actually had verbal memory impairment as confirmed by the RAVLT, the DCR identified 84 (80.7%) as impaired. Moreover, the DCR score was significantly less biased by ethnicity than the MMSE, with no significant difference in the DCR score between Hispanic and non-Hispanic individuals.

DCR outperforms the MMSE in detecting and classifying cognitive impairment-in a fraction of the time-while being not influenced by a patient's ethnicity. The results support the utility of DCR as a sensitive and efficient cognitive assessment in primary care settings.

ClinicalTrials.gov identifier NCT04733989.

Cognitive fluctuations are a core clinical feature of dementia with Lewy bodies (DLB), but their contribution to the everyday functioning difficulties evident DLB are not well understood. The current study evaluated whether intraindividual variability across a battery of neurocognitive tests (intraindividual variability-dispersion) and daily cognitive fluctuations as measured by informant report are associated with worse daily functioning in DLB.

The study sample included 97 participants with consensus-defined DLB from the National Alzheimer's Coordinating Center (NACC). Intraindividual variability-dispersion was measured using the coefficient of variation, which divides the standard deviation of an individual's performance scores across 12 normed neurocognitive indices from the NACC neuropsychological battery by that individual's performance mean. Informants reported on daily cognitive fluctuations using the Mayo Fluctuations Scale (MFS) and on daily functioning using the functional activities questionnaire (FAQ).

Logistic regression identified a large univariate association of intraindividual variability-dispersion and presence of daily cognitive fluctuations on the MFS (Odds Ratio = 73.27, 95% Confidence Interval = 1.38, 3,895.05). Multiple linear regression demonstrated that higher intraindividual variability-dispersion and presence of daily cognitive fluctuations as assessed by the MFS were significantly and independently related to worse daily functioning (FAQ scores).

Among those with DLB, informant-rated daily cognitive fluctuations and cognitive fluctuations measured in the clinic (as indexed by intraindividual variability-dispersion across a battery of tests) were independently associated with poorer everyday functioning. These data demonstrate ecological validity in measures of cognitive fluctuations in DLB.

Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time.

It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data.

The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model.

We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients.

Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.

Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is the most severe complication of carbon monoxide poisoning, which seriously endangers patients' quality of life. This study aims to investigate the efficacy of hyperbaric oxygen (HBO2) on improving dementia symptoms in patients with DEACMP.

A retrospective analysis was performed on DEACMP patients, who visited Xiangya Hospital, Central South University from June 2014 to June 2020. Among them, patients who received conventional drug treatment combined with HBO2 treatment were included in an HBO2 group, while those who only received conventional drug treatment were included in a control group. HBO2 was administered once daily. Patients in the HBO2 group received 6 courses of treatment, with each course consisting of 10 sessions. The Hasegawa Dementia Scale (HDS) was used to diagnose dementia, and the Clinical Dementia Rating (CDR) was used to grade the severity of dementia for DEACMP. The Alzheimer's Disease Assessment Scale-Cognitive Section (ADAS-Cog), the Functional Activities Questionnaire (FAQ), the Neuropsychiatric Inventory (NPI), and the Clinician's Interview-Based Impression of Change-Plus Caregiver Input (CIBIC-Plus) were performed to assess cognitive function, ability to perform activities of daily living (ADL), behavioral and psychological symptoms, and overall function. The study further analyzed the results of objective examinations related to patients' dementia symptoms, including magnetic resonance imaging detection of white matter lesions and abnormal electroencephalogram (EEG). The changes of the above indicators before and after treatment, as well as the differences between the 2 groups after treatment were compared.

There was no significant difference in the HDS score and CDR grading between the 2 groups before treatment (both P>0.05). After treatment, the score of ADAS-Cog, FAQ, NPI, and CIBIC Plus grading of the 2 groups were significantly improved, and the improvement of the above indicators in the HBO2 group was greater than that in the control group (all P<0.05). The effective rate of the HBO2 group in treating DEACMP was significantly higher than that of the control group (89.47% vs 65.87%, P<0.05). The objective examination results (white matter lesions and abnormal EEG) showed that the recovery of patients in the HBO2 group was better than that in the control group.

Hyperbaric oxygen can significantly relieve the symptoms of dementia in patients with DEACMP.

Assessing one's functional capacity-in addition to neuropsychological performance-is essential for determining neurocognitive status, and functional assessment is often provided via informant report. Although informant characteristics have been shown to influence reports of participant functioning, the degree to which they moderate relationships between reported functioning and participant performance on neuropsychological testing is unclear. Moreover, associations among informant characteristics, reported functioning, and neuropsychological performance have not been adequately examined with non-Hispanic Black (NHB) samples, despite this population's disproportionately high risk of Alzheimer's disease and related dementias.

In this cross-sectional observational study, we examined the influence of informant characteristics on informant reports of participant functioning (assessed via the Functional Activities Questionnaire [FAQ]) and associations between reported functioning and participant performance on neuropsychological testing, among NHB adult participants in the National Alzheimer's Coordinating Center cohort (n = 1024).

Informants who were younger, female, more educated, knew participants longer, or lived with participants reported poorer participant functioning (p < .001). However, younger (vs. older) informants provided reports of functioning that were more predictive of visuoconstructional ability and visual memory, and male (vs. female) informants provided reports of functioning that were more predictive of verbal memory, visuoconstructional ability and visual memory, and language (ps < .001).

Within the context of neurocognitive evaluations of NHB participants, informant characteristics may influence subjective reports of participants' functioning and the extent to which reported functioning corroborates objective participant performance on neuropsychological testing.