Recent research suggests that hippocampal-cerebellar (Hp-CB) functional connectivity may be altered early in the course of Alzheimer's disease (AD), given the early accumulation of AD pathology in the hippocampi and emerging evidence of cerebellar changes in early AD. This study analyzed the role of AD genetic risk (via APOE ε4 carrier status) and cerebrospinal fluid (CSF) biomarkers of AD pathology (ratio of phosphorylated tau (p-tau181) to amyloid beta (Aβ42/Aβ40)) on the relationship between age and functional Hp-CB resting state fMRI connectivity in 161 cognitively unimpaired older adults (M age =67.3; SD =9.0; 37 % APOE ε4 +). In multiple regression analyses with Hp-CB connectivity as the outcome, there were significant interactions between age and APOE ε4 status, and between age and CSF AD biomarkers. Older age was associated with greater Hp-CB connectivity in APOE ε4 non-carriers and participants with less abnormal CSF AD biomarkers. In contrast, Hp-CB connectivity was marginally lower with older age in ε4 carriers and those with more abnormal AD biomarkers. Furthermore, greater Hp-CB connectivity was associated with better episodic memory performance across all groups. These findings suggest that age-related increases in Hp-CB connectivity among APOE ε4 non-carriers and those with low AD biomarker levels reflect age-related changes that are largely unrelated to AD, while age-related decreases in Hp-CB connectivity in APOE ε4 carriers may reflect AD-related alterations. These findings also highlight the importance of cerebellar contributions to cognitive performance among older adults and suggest that Hp-CB connectivity may be altered in preclinical AD.

Moderate- to vigorous intensities of physical activity are recommended for health promotion, including brain health. Regular physical activity is considered a modifiable factor to delay -perhaps prevent- onset of dementias such as Alzheimer's disease. Little is known about the benefits of light physical activity. We analyzed data from a 998 community-dwelling, cognitively unimpaired participants from the Maine-Syracuse Longitudinal Study (MSLS) and investigated the role of light physical activity, defined by walking pace, across two time points. Results revealed light levels of walking pace were associated with higher performance at the first timepoint and less decline by time 2 in the domains of verbal abstract reasoning and visual scanning and tracking, which includes both processing speed and executive function skills. When examining change over time (N = 583), increasing walking pace was associated with less decline at time two for the domains of visual scanning and tracking, working memory, visual spatial ability, and working memory, but not verbal abstract reasoning. These findings highlight the relevance of light physical activity and the need to investigate its contribution to cognitive function. From a public health perspective, this may encourage more adults to adopt a light level of exercise and still reap health benefits.

The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans).

Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time.

There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression.

The 4 Mountain Test (4MT) is a test of allocentric spatial working memory and has been proposed as an earlier marker of predementia Alzheimer's disease (AD) than episodic verbal memory. We here compare the 4MT to the CERAD word list memory recall in both cognitively normal (CN) and mild cognitive impairment (MCI) cases with or without cerebrospinal fluid markers (CSF) of Alzheimer's disease pathology. Linear regression was used to assess the influence of CSF determined Aβ-plaque (Aβ-/+) or neurofibrillary tau tangles (Tau-/+) on 4MT and CERAD recall performance. Analyses were performed in the full sample and the CN and MCI sub-samples. Pearson correlations were calculated to examine the relationship between 4MT and tests of psychomotor speed, verbal memory, cognitive flexibility, verbal fluency, and visuo-spatial perception. Analyses showed no significant differences in 4MT scores between Aβ-/Aβ+, nor Tau-/Tau + participants, irrespective of cognitive status. In contrast, CERAD recall scores were lower in both Aβ+ compared to Aβ- (p<.01), and Tau + compared to Tau- participants (p<.01) in the full sample analyses. There were no significant differences in CERAD recall performance between Aβ- vs. Aβ+ and Tau- vs. to Tau + in the in CN/MCI sub-samples. 4MT scores were significantly correlated with tests of psychomotor speed, cognitive flexibility, and visuo-spatial perception in the full sample analyses. In conclusion, the CERAD recall outperformed the 4MT as a cognitive marker of CSF determined AD pathology. This suggests that allocentric working memory, as measured by the 4MT, may not be used as an early marker of predementia AD.

COVID-19 induces acute and long-term neurological symptoms. Links between COVID-19 neurological disturbance and Alzheimer's disease (AD) have been hypothesized because neuroinflammation plays a significant role in both diseases. However, it is unknown if COVID-19 patients with neurological disturbance present molecular alterations related to AD pathology. A better understanding of possible molecular links between COVID-19-induced neurological disease and AD would lead to improved patient follow-up and late-onset disease prevention. Here, we analyze early AD biomarkers in a Brazilian cohort of COVID-19 patients with neurological symptoms. We compared COVID-19 patients' neuroinflammatory and AD biomarker levels to controls, amnestic mild cognitive impairment (aMCI), and AD.

We analyzed cerebrospinal (CSF) biomarkers of neuroinflammation (interleukin-6 (IL6)), amyloid-beta (Aβ) proteinopathy (Aβ42/40), phosphorylated Tau (pTau181), and the neurodegeneration-associated biomarker total Tau in controls (n = 36), COVID-19 patients presenting neurological alterations (n = 35), aMCI (n = 19), and AD patients (n = 20). Comparisons were corrected by possible sex, age, and comorbidities confounding effects. CSF biomarkers were correlated with systemic and neuro-inflammation markers.

We found that severe COVID-19 patients presented higher CSF Tau than controls, comparable to alterations observed in AD patients. However, we did not find changes in CSF Aβ42/40, pTau-181/Aβ42, or Tau/Aβ42 ratios. Severe COVID-19 patients presented higher Tau, Tau/Aβ42, and pTau181/Aβ42 than mild patients. In COVID-19 patients, CSF pro-inflammatory cytokine IL6 and AD biomarkers correlated with systemic inflammatory index (SII).

Collectively, our findings reveal that CSF tau levels are comparably elevated in COVID-19 neurological patients and AD, suggesting ongoing neurodegeneration in COVID-19 neurological disease, but no biomarker alterations related to AD pathology. Furthermore, CNS AD-related biomarker levels in COVID-19 patients change in association with disease severity and systemic inflammation. Considering that inflammation may persist post-COVID, our findings urge the assessment of possible AD-related biomarker changes in COVID-19 survivors with lingering symptoms.

Everyday functioning declines gradually over time in Alzheimer disease (AD), with the earliest changes potentially occurring at the preclinical stage. We investigated how changes in everyday functioning relate to (changes in) amyloid and tau in a large sample of cognitively unimpaired older adults, most of whom had elevated amyloid levels at the start of the study.

This prospective study included participants from a 240-week randomized controlled trial of an anti-amyloid drug, solanezumab, and individuals who screen-failed because of a negative amyloid PET scan. A subset (n = 434) underwent repeated tau PET scans. Participants and their study partners completed the Alzheimer's Disease Cooperative Study Activities of Daily Living Prevention Instrument multiple times during the double-blind phase of the trial. Using generalized least-squares models, fit by restricted maximum likelihood, we analyzed how changes in everyday functioning related to amyloid and tau PET. We also correlated changes in amyloid and tau PET with changes in everyday functioning.

A total of 1,707 participants (71.5 ± 4.7 years, 60% female) showed a marginal decline in everyday functioning. Among individuals with elevated amyloid, those with the highest levels of neocortical and medial temporal tau showed the largest decline in everyday functioning, as reported by the participants and their study partners. Increases in neocortical and medial temporal tau correlated moderately (correlation coefficient ranging from -0.2 to -0.5) with a decline in ADCS ADL-PI scores. Functional changes were not evident with amyloid alone. At the item level, participants and their study partners were most likely to report increased difficulty at the last visit with completing complex activities and selecting and paying for items when shopping.

Higher tau levels are associated with the fastest decline in everyday functioning in the presence of elevated amyloid, and those accumulating more tau show a faster decline in everyday functioning. These findings demonstrate the utility of including sensitive measures of everyday functioning in clinical practice and clinical trials at the stage of preclinical AD.

The A4 study, ClinicalTrials.gov ID: NCT02008357; and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration, ClinicalTrials.gov ID: NCT02488720.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. We proposed a novel latent multimodal deep learning framework to predict AD cognitive status using clinical, neuroimaging, and genetic data.

Three hundred and twenty-two patients aged between 55 and 92 from the ADNI database were included in the study. Confirmatory Factor Analysis (CFA) was applied to derive the latent scores of AD cognitive impairments as the outcome. A multimodal deep neural network with three modalities, including clinical data, imaging data, and genetic data, was constructed. Attention layers and cross attention layers were added to improve prediction; modality importance scores were calculated for interpretation. Mean Absolute Error (MAE) and Mean Squared Error (MSE) were used to evaluate the model performance.

The CFA demonstrated good fit to the data. The multimodal neural network of clinical and imaging modalities with attention layers was the best predictive model, with an MAE of 0.330 and an MSE of 0.206. Clinical data contributed the most (35%) to the prediction of AD cognitive status.

Our results demonstrated the attention multimodal model's superior performance in predicting the cognitive impairment of AD, introducing attention layers into the model enhanced the prediction performance.

Limited data provides evidence-based insights on the association between the comprehensive metrics of high-density lipoproteins (HDL) and cognitive performance, especially in midlife women for whom the benefit might be the greatest.

To assess the associations of serum HDL metrics including HDL lipid content [HDL cholesterol, phospholipid (HDL-PL), triglyceride], proteins/subclasses [apolipoprotein A-1 (apoA-1); small, medium, large, total HDL particle (HDL-P); and HDL size], and cholesterol efflux capacity with cognitive performance in midlife women.

This prospective cohort study was conducted among 503 midlife women (1234 observations) from the Study of Women's Health Across the Nation HDL ancillary study. Joint models were applied to examine associations of HDL metrics assessed at midlife (50.2 ± 2.9 years, baseline of the current study) and their changes over midlife (6.1 ± 3.9 years of duration) with subsequent cognitive performance [working memory (Digit Span Backward Test), processing speed (Symbol Digit Modalities Test), and episodic memory immediate and delayed recall (East Boston Memory Test)] assessed repeatedly (maximum 5 times) 1.5 ± 1 years later over 7.72 ± 4.10 years of follow-up.

Higher total HDL-P and smaller HDL size at midlife were associated with a better subsequent immediate recall, delayed recall, and/or processing speed. Greater increase in HDL-PL, apoA-1, medium HDL-P, and total HDL-P and less increase in HDL size over midlife were associated with a better subsequent immediate and/or delayed recall.

Enhancing specific serum HDL metrics during midlife could be promising in cognitive restoration, particularly memory, the initial and predominant symptom of Alzheimer's disease.

Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer's disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials.

The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1-42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model's effectiveness at patient level was evaluated.

The research cohort's maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort's order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment.

This study highlights the generalizability of EuroPOND's AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening.

Perioperative neurocognitive disorder (PND) is a concern following anesthesia, particularly in individuals at risk for Alzheimer's disease (AD). This study compared the cognitive and pathological effects of propofol and remimazolam in a mouse model with AD following surgery. Five-month-old male ApoE4-KI mice underwent abdominal surgery under either propofol (170 mg/kg) or remimazolam (85 mg/kg) anesthesia. Cognitive function was assessed using the Morris water maze and Y-maze, and neuronal apoptosis and amyloid-beta (Aβ) deposition in the CA3 and dentate gyrus (DG) of the hippocampus were evaluated preoperatively and at 2, 4, and 7 days postoperatively. Both groups showed similar postoperative cognitive functions, with increased relative escape latency at day 2 and decreased relative spontaneous alternation at days 4 and 7. However, the neuropathological analysis revealed that propofol-induced significantly more neuronal death in the CA3 (days 4 and 7) and DG (days 2, 4, and 7), and greater Aβ accumulation in the CA3 (days 2 and 4) and DG (days 2 and 7) compared to remimazolam (p < 0.05). Propofol was associated with more pronounced neuropathologic changes in the hippocampus compared to remimazolam. These findings suggest remimazolam may be a safer anesthetic for patients at risk for neurodegenerative disorders, as it is associated with less severe hippocampal pathology, which is characteristic of AD.

Biomarkers are necessary tools to validate the diagnosis of Alzheimer's and Parkinson's diseases (AD and PD), especially when clinical symptoms are less apparent in the early stages of diseases. Current biomarkers used in clinical practice rely on brain imaging and cerebrospinal fluid (CSF) levels of amyloid-β (Aβ) peptides and α-synuclein (α-syn) for AD and PD, respectively. However, these diagnostic techniques are not only highly invasive and costly, but they also face limitations in diagnostic accuracy, particularly for preclinical or early stages of diseases. Thus, alternative biomarkers have been sought, preferably those in more accessible and convenient biofluids. Given that antimicrobial peptides and proteins (AMPs) may interact with the neuropathogenesis of AD and PD, AMPs have been recognized as promising candidate biomarkers for AD and PD. Therefore, this review examines the literature on how levels of certain AMPs (lactoferrin, hepcidin, defensin, dermcidin, histatin, cathelicidin L-37, prion protein, amylin or islet amyloid polypeptide, substance P or neurokinin-1, and neuropeptide Y) change in the CSF and more accessible biofluids (serum, plasma, tear, or saliva) in AD and PD patients compared to controls. Based on these findings, this review highlights the advantages, challenges, and future directions of AMP-based biomarkers for AD and PD.

Alzheimer's disease (AD) has become an increasingly pressing concern for the aging population. Current AD treatments mainly focus on cognitive and neuropsychiatric symptoms-with few FDA-approved treatments targeting disease progression itself. The amyloid cascade hypothesis describes the formation and accumulation of β-amyloid (Aβ) oligomers and plaques as a primary event in AD pathogenesis. This hypothesis has served as the foundation of disease-modifying treatment development over the last decade. Recently, glutaminyl cyclase (QC) has been identified as a potential drug target in the amyloid cascade. QC catalyzes the cyclization of Aβ to form pyroglutamated Aβ (pEAβ). pEAβ acts as the seed for the formation of Aβ plaques, thus preventing the formation of pEAβ via QC inhibition, and offers a promising therapeutic strategy against AD. Here, we offer an overview of the pathway QCI research has followed-from the initial testing of imidazole-based inhibitor scaffolds to QCI structural optimization via pharmacophore identification, Varoglutamstat entering clinical trials, and further avenues of bettering specificity and potency for future QCI development.

Alzheimer's disease (AD) represents a degenerative condition affecting the nervous system, characterized by the absence of a definitive cause and a lack of a precise therapeutic intervention. Extensive research efforts are being conducted worldwide to enhance early detection methods for AD and to develop medications capable of effectively halting the initiation and progression of the disease during its early stages. Some current detection methods for early diagnosis are expensive and require invasive procedures. More and more evidence shows that gait is related to cognition. A deeper investigation into the intricate interplay between gait and cognition is necessary to elucidate their reciprocal influences and the temporal sequence of these interactions. In the future, it is hoped that with the results of clinical manifestations, neuroimaging, and electrophysiology, simple and objective gait analysis results can be used as an alternative biomarker for cognitive decline to diagnose dementia early.

This research offers a comprehensive scoping review of the contemporary landscape of clinical gait evaluation. It delineates the pertinent concepts of gait analysis and machine learning in AD and elucidates the intricate interplay between gait patterns and cognitive status.

A comprehensive literature search was conducted within PubMed for all articles published until march 18, 2024, using a set of keywords, including "machine learning and gait "and "gait and Alzheimer." original articles that met the selection criteria were included.

A strong correlation exists between autonomous gait and cognitive attributes, necessitating further investigation into the selective interplay between gait and mental factors. Conversely, the gait information of Alzheimer's disease (AD) patients can be captured using a 3D gait analysis system. Numerous gait characteristics can be derived from this gait data, and the early identification of AD can be facilitated by applying a graph neural network-based machine learning approach.

Older adults with treatment-resistant depression are at significant risk for cognitive impairment. The relationship between treatment response and cognitive function in this population is not well-established. We examined neural correlates of executive and memory function, and their relationship with prospective treatment outcomes. In the context of a longitudinal biomarker study embedded within a multi-center randomized controlled trial for late-life treatment-resistant depression, 397 participants completed baseline neuropsychological testing, and of these 234 adults successfully completed a baseline MRI scan. Multivariate regressions were used to test for brain-cognition associations between memory and executive function and brain functional connectivity, white matter integrity, and gray matter structure. Further, we employed regularized elastic net regressions to identify biomarkers predicting depression remission (MADRS≤10) in the clinical trial. Among participants who completed neuroimaging better cognition was associated with lower connectivity between components of the default mode and the frontoparietal networks and within the frontoparietal network (multivariate r=0.37, p<0.01). Using diffusion imaging data, lower tract integrity in a distributed set of tracts was associated with poorer executive function (multivariate r=0.27, p<0.05). Additionally, gray matter structure was positively associated with cognition (multivariate r=0.38, p<0.05). Education and better structural brain maintenance but not overall health were associated with better cognition. Ongoing treatment resistance was predicted by poorer cognition and gray matter structure. We identified distinct cross-sectional associations between specific neural circuits and variation in cognitive function in people with treatment-resistant late-life depression. We also found worse cognitive function and gray matter structure predicted ongoing treatment resistance to medication offered in the clinical trial.

There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four individuals with subjective cognitive decline (SCD) were enrolled in this study. All subjects completed neuropsychological assessments and underwent 18F-florbetapir PET, structural MRI, and functional MRI. A total of 315 features were extracted from the MRI, demographics, and neuropsychological scales and selected using the least absolute shrinkage and selection operator (LASSO). The logistic regression (LR) model, based on machine learning, was trained to classify SCD as either β-amyloid (Aβ) positive or negative. A nomogram was established using a multivariate LR model to predict the risk of Aβ+. The performance of the prediction model and nomogram was assessed with area under the curve (AUC) and calibration. The final model was based on the right rostral anterior cingulate thickness, the grey matter volume of the right inferior temporal, the ReHo of the left posterior cingulate gyrus and right superior temporal gyrus, as well as MoCA-B and AVLT-R. In the training set, the model achieved a good AUC of 0.78 for predicting Aβ+, with an accuracy of 0.72. The validation of the model also yielded a favorable discriminatory ability with an AUC of 0.88 and an accuracy of 0.83. We have established and validated a model based on cognitive, sMRI, and fMRI data that exhibits adequate discrimination. This model has the potential to predict amyloid status in the SCD group and provide a noninvasive, cost-effective way that might facilitate early screening, clinical diagnosis, and drug clinical trials.

Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emerges as a crucial focus area whereas electroencephalography (EEG) provides a promising diagnostic tool. To date, several studies indicated EEG dataset-based models sporting high diagnostic power in distinguishing patients with AD from healthy controls (HC). However, exploration into which features play a crucial role in the diagnosis remains limited.

This study investigates the diagnostic capabilities of EEG for distinguishing patients with AD from HCs through random forest classification on EEG features. Band power and cross-correlation from the resting state EEG dataset of 22 HCs and 160 patients with AD were calculated using Welch's periodogram and Pearson's correlation, respectively. Welch's t-test was applied to identify features demonstrating significant differences between patients with AD and HCs. Band power and cross-correlation were analyzed using a random forest classifier (RFC) and feature-importance analysis. The importance of feature categories, defined as subsets of features grouped by frequency bands (for band power features) or brain regions (for cross-correlation features), was quantified by calculating their average occurrence across all hyperparameter configurations.

Distinct patterns between the eyes-closed and eyes-open conditions in alpha power were not observed for patients with AD (vs. HC), whereas theta power (4-8 Hz) in all regions was higher in patients with AD (vs. HC)(p<0.05). Interhemispheric cross-correlation in the temporal lobes exhibited the most distinguishable distribution for the cross-correlation dataset. An RFC, exploring 512 models with varied hyperparameters followed by feature-importance analysis based on the mean decrease in impurity, highlighted "theta relative power" and "interhemispheric cross-correlation of channel pairs including temporal channels" as the most important features for distinguishing patients with AD from HCs. RFC on theta-band filtered cross-correlation dataset informed by important features demonstrated the robustness of important features across models with different hyperparameter settings.

The models achieved over 97% accuracy and 100% recall in test sets, although the interpretation of this extraordinarily high accuracy warrants caution due to the small dataset size with high data imbalance and the absence of external validation. This methodology demonstrates the efficacy of EEG-based metrics and machine learning in improving our understanding of EEG characteristics in patients with AD, emphasizing the potential of integrating machine learning techniques into clinical practices.

Understanding the molecular, cellular, and physiological components of neurodegenerative diseases (NDs) is paramount for developing accurate diagnostics and efficacious therapies. However, the complexity of ND pathology and the limitations associated with conventional analytical methods undermine research. Fortunately, microfluidic technology can facilitate discoveries through improved biomarker quantification, brain organoid culture, and small animal model manipulation. Because this technology can increase experimental throughput and the number of metrics that can be studied in concert, it demands more sophisticated computational tools to process and analyze results. Advanced analytical algorithms and machine learning platforms can address this challenge in data generated from microfluidic systems, but they can also be used outside of devices to discern patterns in genomic, proteomic, anatomical, and cognitive data sets. We discuss these approaches and their potential to expedite research discoveries and improve clinical outcomes through ND characterization, diagnosis, and treatment platforms.

At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The objective of the study was to test whether Cognivue Clarity® can differentiate True Controls, preclinical Alzheimer's disease (pAD), mild cognitive impairment (MCI) due to Alzheimer's disease (MCI-AD), AD, and MCI and dementia due to non-AD etiologies enrolled in the Bio-Hermes Study.

A total of 887 individuals completed Cognivue Clarity, amyloid PET scan, and blood-based AD biomarkers. Three Cognivue Clarity subtests differentiated between True Controls and pAD, and between cognitive impairment due to AD versus non-AD processes. This finding was leveraged to develop an amyloid-specific marker, combining the three subtests with age using machine learning to create the 4-point Cognivue Amyloid Risk Measure (CARM).

Cognivue Clarity discriminated cognitively normal from cognitively impaired individuals (p < 0.001, Cohen's d = 0.732). The CARM differentiated between individuals with amyloid and without amyloid by PET (p < 0.001, Cohen's d = 0.618) and blood-based biomarkers (p's < 0.001). Amyloid positivity and cognitive impairment increased across four CARM thresholds (p < 0.001). Dichotomizing CARM thresholds into low (CARM1/CARM2) and high (CARM3/CARM4) likelihood provided excellent discrimination for amyloid PET positivity (OR: 3.67; 95% CI 2.76-4.89). CARM categories differentiated between True Controls, pAD, MCI-AD, AD, and cognitive impairment due to non-AD etiologies (χ2 = 137.6, p < 0.001) with the majority of True Controls and non-AD etiologies being in CARM1/CARM2, and the majority of pAD, MCI-AD, and AD being in CARM3/CARM4.

Cognivue Clarity detects individuals with cognitive impairment, and a derivation benchmarked against amyloid PET was used to develop the CARM to predict the presence of amyloid. Combining the CARM and the Cognivue Clarity overall score could help identify individuals with and without cognitive impairment due to AD or non-AD etiologies, help screen for treatment protocols with anti-amyloid therapies, enrich clinical trial recruitment, and help to identify pAD for prevention studies.

ClinicalTrials. gov identifier, NCT04733989.

A major challenge in the development of more effective therapeutic strategies for Alzheimer's disease (AD) is the identification of molecular mechanisms linked to specific pathophysiological features of the disease. Importantly AD has a two-fold higher incidence in women than men and a protracted prodromal phase characterized by amnestic mild-cognitive impairment (aMCI) suggesting that biological processes occurring early can initiate vulnerability to AD. Here, we used a sample of 125 subjects from two independent study cohorts to determine the levels in plasma (the most accessible specimen) of two essential mitochondrial markers acetyl-L-carnitine (LAC) and its derivative free-carnitine motivated by a mechanistic model in rodents in which targeting mitochondrial metabolism of LAC leads to the amelioration of cognitive function and boosts epigenetic mechanisms of gene expression. We report a sex-specific deficiency in free-carnitine levels in women with aMCI and early-AD compared to cognitively healthy controls; no change was observed in men. We also replicated the prior finding of decreased LAC levels in both women and men with AD, supporting the robustness of the study samples assayed in our new study. The magnitude of the sex-specific free-carnitine deficiency reflected the severity of cognitive dysfunction and held in two study cohorts. Furthermore, patients with the lower free-carnitine levels showed higher β-amyloid(Aβ) accumulation and t-Tau levels assayed in cerebrospinal fluid (CSF). Computational analyses showed that the mitochondrial markers assayed in plasma are at least as accurate as CSF measures to classify disease status. Together with the mechanistic platform in rodents, these translational findings lay the groundwork to create preventive individualized treatments targeting sex-specific changes in mitochondrial metabolism that may be subtle to early cognitive dysfunction of AD risk.

Plasma phosphorylated tau (p-tau)217 is an early Alzheimer's disease (AD) biomarker, but the timing of pathological changes and cognitive decline varies substantially. Digital cognitive assessments can detect subtle cognitive changes, suggesting they may complement p-tau217 for early detection. Here, we evaluate whether combining these tools improves the detection of individuals at risk for future decline.

We analyzed 954 amyloid-positive cognitively unimpaired individuals who completed a digital cognitive assessment and a blood test for p-tau217, assessing their ability to predict future decline on the Preclinical Alzheimer Cognitive Composite (PACC) and Mini-Mental State Examination (MMSE).

Combining performance on a digital cognitive assessment with p-tau217 improved identification of individuals who declined on the PACC and MMSE in the next 5 years. The predictive value was stronger in apolipoprotein E ε4 noncarriers but did not differ by sex.

This approach offers a sensitive method for identifying individuals at high risk for AD-related cognitive decline.

Combining plasma phosphorylated tau 217 with baseline digital cognitive assessment improved the prediction of cognitive decline on gold-standard neuropsychological tests over the next 5 years, achieving greater accuracy than either measure alone. This combination also predicted a decline in a global cognitive screening test. Pairing a blood test with a digital cognitive assessment offers a scalable and feasible approach for Alzheimer's disease screening.

Although previous studies have shown that specific platelet indices had correlations with cognitive impairment, the associations between platelet indices and cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathology remain unclear.

Our study included 1,047 cognitively normal participants from the Chinese Alzheimer's Biomarker and LifestylE (CABLE) study. The total participants had an average age of 58.33 years, a female proportion of 41.5% and average educational attainment of 9.58 years. Multiple linear regression models were used to analyze the associations of five platelet indices (plateletcrit [PCT], platelet count [PLT], mean platelet volume [MPV], platelet distribution width [PDW], and platelet large cell ratio [PLCR]) with CSF AD biomarkers after adjusting for age, gender, education and APOE ε4 allele status. Furthermore, the interactive, stratified and sensitivity analyses were further conducted to verify their relationships.

In the total participants, we found higher PCT levels were significantly correlated with higher CSF P-tau/Aβ42 (β = 0.102, P = 0.008) and T-tau/Aβ42 (β = 0.102, P = 0.008), as well as lower CSF Aβ42 (β = -0.089, P = 0.018) and Aβ42/Aβ40 (β = -0.093, P = 0.018). Moreover, other four platelet indices (PLT, MPV, PDW, PLCR) demonstrated moderate correlations with CSF AD biomarkers. The interaction analyses revealed that age affected the correlations between PCT and PLT with CSF Aβ42. Importantly, the associations between PCT and the aforementioned CSF AD biomarkers became more significant in the late-life group, but turned non-significant in the mid-life group. Besides, sensitivity analyses confirmed the robustness of our findings.

Our study provided preliminary evidence suggesting potential associations between platelet indices (especially PCT) and CSF AD biomarkers in cognitively intact adults. Nonetheless, more studies are needed to further validate these findings.

Technological advances and artificial intelligence now make it feasible to administer cognitive assessments on touch-screen devices. The aim of this study is to develop a Uyghur version of the NUCOG cognitive screening application and evaluate its reliability, validity, and optimal cutoff scores among Uyghur people with cognitive impairment.

The English version of the NUCOG app was translated and adapted into the Uyghur version (NUCOG-U). A total of 250 Uyghur people aged 55-80, including 90 normal controls, 91 patients with mild cognitive impairment (MCI), and 69 dementia patients, were randomly selected and administered with NUCOG, MoCA-U, Mini-Mental State Examination (MMSE), and other neuropsychological batteries. ROC curves were generated to determine the optimal cutoff values.

NUCOG-U version showed high internal consistency (Cronbach's α = 0. 826), inter-rater reliability (ICC = 0.999), and test - retest reliability (r = 0.998, p < 0.001). NUCOG scores were significantly correlated with those of MoCA-U (r = 0.896, p < 0.001) and MMSE(r = 0.899, p < 0.001). NUCOG scores were significantly different among the three groups (p < 0.001). The optimal cutoff value for MCI was 80.5, with a sensitivity of 100% and specificity of 73%, and 70 for dementia, with a sensitivity of 94.1% and specificity of 100%.

The NUCOG-U shows high reliability and validity and is suitable for screening cognitive function in the elderly Uyghur population. The optimal cutoff scores to detect mild cognitive impairment and dementia in the Uyghur people are 80.5 and 70, respectively.

Assessment of cognitive decline in the earliest stages of Alzheimer disease (AD) is important but challenging. AD is a neurodegenerative disease characterized by gradual cognitive decline. Disease stages range from preclinical AD, in which individuals are cognitively unimpaired, to mild cognitive impairment (MCI) and dementia. Digital technologies promise to enable detection of early, subtle cognitive changes. Although the field of digital cognitive biomarkers is rapidly evolving, a comprehensive overview of the reporting of psychometric properties (ie, validity, reliability, responsiveness, and clinical meaningfulness) is missing. Insight into the extent to which these properties are evaluated is needed to identify the validation steps toward implementation.

This scoping review aimed to identify the reporting on quality characteristics of smartphone- and tablet-based cognitive tools with potential for remote administration in individuals with preclinical AD or MCI. We focused on both psychometric properties and practical tool characteristics.

This scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. In total, 4 databases (PubMed, Embase, Web of Science, and PsycINFO) were systematically searched from January 1, 2008, to January 5, 2023. Studies were included that assessed the psychometric properties of cognitive smartphone- or tablet-based tools with potential for remote administration in individuals with preclinical AD or MCI. In total, 2 reviewers independently screened titles and abstracts in ASReview, a screening tool that combines manual and automatic screening using an active learning algorithm. Thereafter, we manually screened full texts in the web application Rayyan. For each included study, 2 reviewers independently explored the reported information on practical and psychometric properties. For each psychometric property, examples were provided narratively.

In total, 11,300 deduplicated studies were identified in the search. After screening, 50 studies describing 37 different digital tools were included in this review. Average administration time was 13.8 (SD 10.1; range 1-32) minutes, but for 38% (14/37) of the tools, this was not described. Most tools (31/37, 84%) were examined in 1 language. The investigated populations were mainly individuals with MCI (34/37, 92%), and fewer tools were examined in individuals with preclinical AD (8/37, 22%). For almost all tools (36/37, 97%), construct validity was assessed through evaluation of clinical or biological associations or relevant group differences. For a small number of tools, information on structural validity (3/37, 8%), test-retest reliability (12/37, 32%), responsiveness (6/37, 16%), or clinical meaningfulness (0%) was reported.

Numerous smartphone- and tablet-based tools to assess cognition in early AD are being developed, whereas studies concerning their psychometric properties are limited. Often, initial validation steps have been taken, yet further validation and careful selection of psychometrically valid outcome scores are required to demonstrate clinical usefulness with regard to the context of use, which is essential for implementation.

While clinical disease stages remained largely unchanged in the 2024 update of the Alzheimer disease (AD) criteria, tau-positron emission tomography (PET) was introduced as a core biomarker and its spatial extent was incorporated into the revised biological stages of the disease. It is important to consider both the clinical and the biological stages and understand their discrepancies.

To compare individuals who have discrepant biological and clinical stages with those who have congruent stages in terms of copathologies, comorbidities, and demographics.

Participants were from the Swedish BioFINDER-2 (inclusion from 2017 through 2023) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (inclusion from 2015 through 2024). BioFINDER-2 included a prospective population-based (cognitively normal [CN] older adults) and memory clinic-based cohort (participants with subjective cognitive impairment [SCD], mild cognitive impairment [MCI], and dementia). ADNI included a volunteer-based sample. All participants who were amyloid-β positive and had undergone tau-PET were included. In BioFINDER-2, 838 participants of a total of 1979 were included, and of 927 with tau-PET in ADNI, 380 were included.

The clinical (CN to dementia) and biological (based on PET; initial [amyloid-β-positive only] to advanced [amyloid-β-positive, elevated, and widespread tau]) stages from the revised AD criteria.

Cross-sectional measures of neurodegeneration (cortical thickness, TAR DNA-binding protein 43 [TDP-43] imaging signature, neurofilament light [NfL]), α-synuclein cerebrospinal fluid status, plasma glial fibrillary acidic protein, white matter lesions, infarcts, microbleeds, comorbidities, and demographics.

There were 838 BioFINDER-2 participants (mean age, 73.9 [SD, 7.3] years; 431 women [51%]; 407 men [49%]) and 380 ADNI participants (average age, 72.9 [SD, 7.0] years; 194 women [51%]; 186 mean [49%]) included. In BioFINDER-2, 37.7% of the sample had congruent biological and clinical stages (reference group), 51.3% had more advanced clinical impairment compared with their clinical stage (clinical > biological) and 11.0% had the opposite (biological > clinical). The main differences were between the reference group and the clinical > biological group: the latter participants were more often positive for α-synuclein pathology, had higher NfL levels, greater TDP-43-like atrophy, and higher burden of cerebral small vessel disease lesions (all false discovery rate P < .05). The only difference between the biological > clinical and the reference group was that the former had less neurodegeneration (thicker cortex; all false discovery rate P < .001). The main results were replicated in the independent ADNI cohort, where congruent 56.1% of participants had biological and clinical stages; 36.1% were in the category clinical > biological, and 7.9% in biological > clinical.

Copathologies play an important role in symptom severity in individuals who harbor less tau-tangle pathology than expected for their clinical impairment. These results highlight the importance of measuring non-AD biomarkers in patients with AD with worse cognitive impairment than expected based on their biological stage, which could impact the clinical diagnosis and prognosis.

Two thirds of Alzheimer's disease (AD) patients are female. Genetic and chronic health risk factors for AD affect females more negatively compared to males.

This multimodal neuroimaging study aimed to examine sex differences in cognitively unimpaired older adults on: (1) amyloid-β via 18F-AV-45 Florbetapir PET imaging, (2) neurodegeneration via T1 weighted MRI volumetrics, (3) cerebral blood flow via ASL-MRI. We identified AD risk factors including genetic (APOE genotype status) and health markers (fasting glucose, mean arterial pressure, waist-to-hip ratio, and android and gynoid body fat) associated with neuroimaging outcomes for which we observed sex differences.

Participants were sedentary, amyloid-β positive older adults (N = 112, ages 65-87 years) without evidence of cognitive impairment (CDR = 0).

Multivariate analysis of covariance models adjusted for intracranial volume, age, and years of education demonstrated lower volume [F (7, 102) = 2.67, p = 0.014] and higher blood flow F (6, 102) = 4.25, p ≤ 0.001) among females compared to males in regions of interest connected to AD pathology and the estrogen receptor network. We did not observe sex differences in amyloid-β levels. Higher than optimal waist to hip ratio was most strongly associated with lower volume among female participants.

Findings suggest genetic and chronic health risk factors are associated with sex-specific AD neuroimaging biomarkers. Underlying sex-specific biological pathways may explain these findings. Our results highlight the importance of considering sex differences in neuroimaging studies and when developing effective interventions for AD prevention and risk reduction.

Depression is often cited as a major modifiable risk factor for dementia, though the relative contributions of a true causal relationship, reverse causality and confounding factors remain unclear. This study applied a subset of the Bradford Hill criteria for causation to depression and dementia including strength of effect, specificity, temporality, biological gradient and coherence.

A total of 491 557 participants in UK Biobank aged between 40 and 69 at enrolment and followed up for a mean duration of 12.4 years were studied. Diagnoses of depression and dementia were ascertained from linked health records, self-reports and death certificate registration. Depressive symptoms were measured at enrolment using a combination of questions based on the Patient Health Questionnaire-9 depression screening questionnaire. Regional grey matter volumes were measured using T1-weighted MRI in 41 929 participants.

Depression was a strong risk factor for incident dementia with an OR of 1.76 (95% CI 1.63 to 1.90), a relationship which was found to be specific to depression rather than commonly proposed confounders. Depressive symptoms increased rapidly in the 10 years prior to dementia diagnosis. The severity of depressive symptoms showed a dose-response relationship with dementia risk. Depression at older ages correlated with reduced grey matter volume in an Alzheimer's pattern whereas younger onset depression was associated with reduced grey matter volume in the frontal lobes and cerebellum.

This study provides evidence that the link between depression and dementia is due to reverse causation with a smaller component of causation with clear evidence of both mechanisms driving the association.

Although the Alzheimer's Association (AA) biomarker-only Alzheimer disease (AD) criteria and the International Working Group clinical-pathologic AD criteria differ, both approaches appreciate the need for early detection efforts. Within the AA approach, clinical stage 2 recognizes that someone can be cognitively unimpaired but still experience "subtle cognitive decline" (SCD) measured by subjective report or objective decline using neuropsychological measures. While significant attention has been given to subjective cognitive decline methods, there are no systematic examinations of the operational definition of SCD using objective neuropsychological measures. Therefore, the primary aim of this review was to identify and describe the approaches used to classify objective SCD.

A systematic literature search was performed using PubMed/MEDLINE, Web of Science, and PsycInfo databases for articles with dates ranging from the start of the database through November 1, 2023. Included studies were peer-reviewed, described a discrete objective SCD category, included participants aged 50+ without mild cognitive impairment (MCI) or dementia, and focused on aging or AD. A modified Newcastle-Ottawa Scale was used to assess the quality of included studies. Data were extracted by the 2 authors who then categorized and described the classification approaches.

Of the 1,361 publications initially identified, 70 case-control studies met criteria for inclusion. SCD definitions generally fell into 6 categories based on using similar methodology: (1) SCD based on a specified cutoff on a single cognitive test (n = 6); (2) SCD based on a cutoff (e.g., 10th percentile) on a cognitive composite score (n = 9); (3) objectively defined SCD (Obj-SCD) using cutoffs (e.g., -1 SD) on multiple individual neuropsychological measures (n = 24); (4) "Pre-MCI" criteria defined using a Clinical Dementia Rating of 0.5 but normal performance on neuropsychological testing (n = 12); (5) cutoff based on longitudinal rate of cognitive decline (e.g., over 1 year) (n = 13); and (6) data-driven/clustering approach to classification (n = 8). Two studies used multiple classification approaches.

Six promising classification approaches were identified in the existing literature, with the Obj-SCD and Pre-MCI approaches being the most commonly applied. Additional work is needed to compare SCD approaches head-to-head to identify the most prognostically useful, particularly within racially/ethnically diverse older adults.

BackgroundImaging biomarkers are essential in Alzheimer's disease (AD) diagnosis, particularly since the introduction of the ATN criteria by the NIA-AA. These criteria include amyloid-β plaques (amyloid PET), fibrillar tau (tau PET), and neurodegeneration (FDG PET or MRI). Early-phase amyloid PET imaging has shown a strong correlation with FDG PET at the group level.ObjectiveThis study evaluates the comparability of early-phase FBB PET (eFBB) perfusion imaging and FDG PET metabolic imaging at both group and individual levels.MethodsA retrospective study included 103 patients with mild cognitive impairment (MCI) or mild dementia suspected of AD who underwent FDG PET and dual-phase 18F-Florbetaben PET (including a 5-min eFBB scan) between 2019 and 2023, along with 33 healthy controls. Imaging analyses included qualitative, semi-quantitative, and voxel-wise techniques to compare eFBB and FDG PET scans.ResultseFBB and FDG PET SUVR values showed a strong correlation across all brain regions (rho = 0.879). Visual assessments of eFBB and FDG PET by two raters achieved intra-observer agreement rates of 87.5% and 86.4%, respectively. Voxel-wise analysis revealed moderate to good overlap, as indicated by Dice-Sørensen coefficients, in the MCI and mild dementia groups. Discriminative performance between eFBB and FDG PET was comparable, with no significant differences as eFBB reliably reflected brain metabolic patterns observed on FDG PET, supporting its diagnostic utility.ConclusionseFBB PET could serve as a surrogate biomarker for FDG PET in the diagnostic evaluation of neurodegenerative dementias. Dual-phase amyloid PET enables simultaneous assessment of neurodegeneration and amyloid burden, offering a streamlined and resource-efficient approach for clinical practice.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting in diverse forms such as memory loss and movement disorders, pose significant challenges due to their complex molecular mechanisms and heterogeneous patient presentations. Diagnosis often relies heavily on clinical assessments and neuroimaging, with definitive confirmation frequently requiring post-mortem autopsy. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative potential. These technologies can enable the development of non-invasive tools for early diagnosis, biomarker identification, personalized treatment strategies, patient subtyping and stratification, and disease risk prediction. This review aims to provide a starting point for researchers, both with and without clinical backgrounds, who are interested in applying ML to NDs. We will discuss available data resources for key diseases like Alzheimer's and Parkinson's, explore how ML can revolutionize neurodegenerative care, and emphasize the importance of integrating multiple high-dimensional data sources to gain deeper insights and inform effective therapeutic strategies.

Interest is growing in the potential role of manganese (Mn) in Alzheimer's Disease (ADRD). This nested pilot study of a ferroalloy workers cohort was aimed to investigate the effects of long-term occupational Mn exposure on cognitive function through β-amyloid (Aβ) modification and brain deposition, as well as metabolomic, lipidomic and proteomic profiling. We examined 6 male exposed workers (median age 63, exposure duration 31 yrs), and 5 historical controls (median age 60) who had undergone brain PET scan imaging showing higher Aβ deposition among the exposed compared to the controls (p < 0.05). The average annual cumulative respirable Mn of the ferroalloy workers was 329.23 ± 516.39 μg/m3 (geometric mean 118.59). Average Mn level in plasma of the exposed subjects (0.704 ± 0.2 ng/mL) was significantly higher than the controls (0.397 ± 0.18). Pathway analyses using LC-MS/MS results revealed impacted metabolomic pathways such as olfactory signaling, mitochondrial fatty acid beta-oxidation, biogenic amine synthesis, SLC-mediated transmembrane transport, and glycerophospholipid and choline metabolism in the Mn exposed group. Single molecule arrays (Simoa) analysis revealed notable modifications of AD-related plasma biomarkers; protein microarray (chip) showed significant changes (p < 0.05) in the levels of some plasma antibodies targeting autoimmune and neuronal associated proteins such as Aβ (25-35), GFAP, Serotonin, Human NOVA1, and Human Siglec-1/CD169 among the Mn exposed individuals. This data provides evidence on Mn-induced alterations of pathways and biomarkers associated with cognitive neurodegenerative diseases.

Background18F-fluoro-2-deoxy-2-D-glucose positron emission tomography (18F-FDG PET) is a biomarker of neuronal injury, according to the revised National Institute on Aging-Alzheimer's Association criteria.ObjectiveThis multicenter prospective cohort study aimed to evaluate the value of 18F-FDG PET for differential diagnosis of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) in comparison with phosphorylated tau protein (p-tau181) in cerebrospinal fluid (CSF).MethodsIn total, 138 patients (AD, 119; FTLD, 19) from 11 participating institutions underwent clinical and neuropsychological examinations, magnetic resonance imaging (MRI), CSF examination, and 18F-FDG PET at baseline. The cases were visually classified into predefined dementia patterns using 18F-FDG PET by three experts. A region-of-interest (ROI)-based automated analysis of 18F-FDG PET was also performed. The participants were followed up for 12 months, and the clinical diagnosis of dementia was re-evaluated.ResultsThe sensitivity, specificity, and accuracy of the visual reading of18 F-FDG PET for differentiating AD from FTLD were 94%, 78%, and 92%, respectively. In contrast, those of p-tau181 in CSF were 62%, 79%, and 65%, respectively. The sensitivity, the primary endpoint, was 32% higher for 18F-FDG PET than for p-tau181 in CSF. Additionally, the accuracy, the secondary endpoint, was 27% higher for 18F-FDG PET than for p-tau181 in CSF. In addition to the visual reading of 18F-FDG PET, the ROI-based automated analysis showed sensitivity, specificity, and accuracy of 81%, 79%, and 81%, respectively.ConclusionsThis study showed that the diagnostic performance of 18F-FDG PET in differential diagnosis of AD and FTLD was higher than that of p-tau181 in CSF.Trial registrationUMIN-CTR (UMIN 000016427, https://www.umin.ac.jp/ctr/) and Japan Registry of Clinical Trials (jRCTs041180098, https://jrct.mhlw.go.jp/).

Brain atrophy may precede symptoms in Alzheimer's disease (AD), but it remains unclear whether atrophy in this preclinical stage falls within a distinct brain network or is associated with transitional cognitive changes. We investigated cortical thickness in cognitively unimpaired older adults with varying amyloid-β accumulation and estimated the connectivity of each individual's atrophy pattern using a large normative connectome (n = 1000). A distinct network was connected to atrophy patterns in amyloid-β-positive (n = 1242) versus negative (n = 536) participants. This preclinical AD network was similar to a previously published atrophy network associated with AD dementia (r = 0.8284, p = 0.016). In leave-one-out cross-validation, atrophy patterns connected to the preclinical AD network were associated with lower cognitive performance (p = 0.0018), greater subjective cognitive decline (p < 0.001), and amyloid-β levels (p < 0.001). Atrophy in preclinical AD maps to a network similar to AD dementia that is associated with amyloid-β and cognition, demonstrating an atrophy-related network across the continuum of AD.

Dementia prevention has been recognized as a top priority by public health authorities due to the lack of a reversible cure. In this regard, digital dementia-preventive lifestyle services (DDLS) emerge as potentially pivotal services, aiming to address modifiable risk factors on a large scale. This study aims to identify the top-funded companies offering DDLS and evaluate their clinical evidence to gain insights into the international service landscape.

A systematic screening of two financial databases (Pitchbook and Crunchbase) was conducted. Corresponding published clinical evidence was collected through a systematic literature review and analyzed regarding study purpose, results, quality of results, and level of clinical evidence.

The ten top-funded companies offering DDLS received a total funding of EUR 128.52 million, of which three companies collected more than 75%. Clinical evidence was limited due to only nine eligible publications, small clinical subject groups, the absence of longitudinal study designs, and no direct evidence of dementia prevention.

Our study shows that the level of funding received by companies does not reflect the clinical effectiveness of DDLS. The study serves as an initial step toward understanding how DDLS are currently evaluated in today's market and highlights the need for a more rigorous evaluation of DDLS effectiveness.

This study investigated the prevalence and clinical characteristics of suspected non-Alzheimer disease pathophysiology (SNAP) across varying cognitive statuses and cerebral small vessel disease (CSVD) burden.

We included 1992 participants with cognitive status categorized as cognitively unimpaired, mild cognitive impairment, or dementia. β-amyloid (Aβ, A) positivity was assessed by Aβ PET, and neurodegeneration (N) positivity was determined through hippocampal volume. Participants were further divided by the presence or absence of severe CSVD. The clinical and imaging characteristics of A-N+ (SNAP) group were compared with those of the A-N- and A+N+ groups.

SNAP participants were older and had more vascular risk factors compared with A-N- and A+N+ in the CSVD(-) cohort. SNAP and A+N+ showed similar cortical thinning. At the dementia stage, SNAP had a cognitive trajectory similar to A+N+ in the CSVD(-) cohort. However, SNAP exhibited less cognitive decline than A+N+ in the CSVD(+) cohort.

SNAP is characterized by distinct clinical and imaging characteristics; however, it does not necessarily indicate a benign prognosis, particularly at the dementia stage. These findings highlight the need to assess SNAP in relation to the cognitive stage and CSVD presence to better understand its progression and guide interventions.