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Background: Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods: This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results: The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. Conclusions: This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset. [ABSTRACT FROM AUTHOR]

Background: Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. Methods: In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. Results: The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. Conclusions: We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences. [ABSTRACT FROM AUTHOR]

Amyloid-β (Aβ) and tau are important biomarkers to predict the progression of cognitively unimpaired (CU) to dementia due to Alzheimer's disease (AD), according to the diagnosis framework from the US National Institute on Aging and the Alzheimer's Association (NIA-AA). However, it is clinically difficult to predict those subjects who were already with Aβ positive (A +) or tau positive (T +). As a typical characteristic of neurodegeneration in the diagnosis framework, the hypometabolism of the posterior cingulate cortex (PCC) has significant clinical value in the early prediction and prevention of AD. In this paper, we proposed the glucose metabolism in the PCC as a biomarker supplement to Aβ and tau biomarkers. First, we calculated the standard uptake value ratio (SUVR) of PCC based on fluorodeoxyglucose positron emission computed tomography (FDG PET) imaging. Secondly, we performed Kaplan–Meier (KM) survival analyses to explore the predictive performance of PCC SUVR, and the hazard ratio (HR) was calculated. Finally, we performed Pearson correlation analyses to explore the physiological significance of PCC SUVR. As a result, the PCC SUVR showed a consistent downward trend along the AD continuum. KM analyses showed better predictive performance when we combined PCC SUVR with cerebro-spinal fluid (CSF) Aβ42 (from HR = 2.56 to 3.00 within 5 years; from HR = 2.76 to 4.20 within 10 years) and ptau-181 (from 2.83 to 3.91 within 5 years; from HR = 2.32 to 4.17 within 10 years). There was a slight correlation between Aβ42/Aβ40 and PCC SUVR (r = 0.14, p = 0.02). In addition, several cognition scales were also correlated to PCC SUVR (from r = –0.407 to 0.383, p < 0.05). Our results showed that glucose metabolism in PCC may be a potential biomarker supplement to the Aβ and tau biomarkers to predict the progression of CU to AD. [ABSTRACT FROM AUTHOR]

We aimed to compare longitudinal brain atrophy in patients with neuromyelitis optica spectrum disorder (NMOSD) with healthy controls (HCs). The atrophy rate in patients with anti-aquaporin-4 antibody-positive NMOSD (AQP4 + NMOSD) was compared with age-sex-matched HCs recruited from the Japanese Alzheimer's Disease Neuroimaging Initiative study and another study performed at Chiba University. Twenty-nine patients with AQP4 + NMOSD and 29 HCs were enrolled in the study. The time between magnetic resonance imaging (MRI) scans was longer in the AQP4 + NMOSD group compared with the HCs (median; 3.2 vs. 2.9 years, P = 0.009). The annualized normalized white matter volume (NWV) atrophy rate was higher in the AQP4 + NMOSD group compared with the HCs (median; 0.37 vs. − 0.14, P = 0.018). The maximum spinal cord lesion length negatively correlated with NWV at baseline MRI in patients with AQP4 + NMOSD (Spearman's rho = − 0.41, P = 0.027). The annualized NWV atrophy rate negatively correlated with the time between initiation of persistent prednisolone usage and baseline MRI in patients with AQP4 + NMOSD (Spearman's rho = − 0.43, P = 0.019). Patients with AQP4 + NMOSD had a greater annualized NWV atrophy rate than HCs. Suppressing disease activity may prevent brain atrophy in patients with AQP4 + NMOSD. [ABSTRACT FROM AUTHOR]

Background: Increasing research suggests that gait abnormalities can be a risk factor for Alzheimer's Disease (AD). Notably, there is growing evidence highlighting this risk factor in individuals with amnestic Mild Cognitive Impairment (aMCI), however further studies are needed. The aim of this study is to analyze cognitive tests results and brain-related measures over time in aMCI and examine how the presence of gait abnormalities (neurological or orthopedic) or normal gait affects these trends. Additionally, we sought to assess the significance of gait and gait-related measures as prognostic indicators for the progression from aMCI to AD dementia, comparing those who converted to AD with those who remained with a stable aMCI diagnosis during the follow-up. Methods: Four hundred two individuals with aMCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were included. Robust linear mixed-effects models were used to study the impact of gait abnormalities on a comprehensive neuropsychological battery over 36 months while controlling for relevant medical variables at baseline. The impact of gait on brain measures was also investigated. Lastly, the Cox proportional-hazards model was used to explore the prognostic relevance of abnormal gait and neuropsychological associated tests. Results: While controlling for relevant covariates, we found that gait abnormalities led to a greater decline over time in attention (DSST) and global cognition (MMSE). Intriguingly, psychomotor speed (TMT-A) and divided attention (TMT-B) declined uniquely in the abnormal gait group. Conversely, specific AD global cognition tests (ADAS-13) and auditory-verbal memory (RAVLT immediate recall) declined over time independently of gait profile. All the other cognitive tests were not significantly affected by time or by gait profile. In addition, we found that ventricles size increased faster in the abnormal gait group compared to the normal gait group. In terms of prognosis, abnormal gait (HR = 1.7), MMSE (HR = 1.09), and DSST (HR = 1.03) covariates showed a higher impact on AD dementia conversion. Conclusions: The importance of the link between gait and related cognitive functions in terms of diagnosis, prognosis, and rehabilitation in aMCI is critical. We showed that in aMCI gait abnormalities lead to executive functions/attention deterioration and conversion to AD dementia. [ABSTRACT FROM AUTHOR]

Previous studies report hippocampal volume loss can help predict conversion from normative aging to mild cognitive impairment to dementia. Additionally, a growing literature indicates that stress-related allostatic load may increase disease vulnerability. The current study examined the relationship between stress-related cytokines (ie, interleukin-6 [IL-6]), cognition as measured by Mini-Mental State Examination (MMSE) scores, and hippocampal volume. Mixed models were employed to examine both within- (across time) and between-subject effects of IL-6 and hippocampal volume on MMSE score among 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The within-subject analysis found left hippocampal volume significantly (p = .009) predicted MMSE score. Between-subject analysis found the effect of IL-6 on MMSE was moderated by right hippocampal volume (p = .001). These results replicate previous findings and also extend prior work demonstrating stress-related cytokines may play a role in Alzheimer's disease progression. [ABSTRACT FROM AUTHOR]

Background. According to recent studies, amyloid-β (Aβ) isoforms as cerebrospinal fluid (CSF) biomarkers have remarkable predictive value for cognitive decline in the early stages of Alzheimer's disease (AD). Herein, we aimed to investigate the correlations between several targeted proteomics in CSF samples with Aβ ratios and cognitive scores in patients in AD spectrum to search for potential early diagnostic utility. Methods. A total of 719 participants were found eligible for inclusion. Patients were then categorized into cognitively normal (CN), mild cognitive impairment (MCI), and AD and underwent an assessment of Aβ and proteomics. Clinical Dementia Rating (CDR), Alzheimer's Disease Assessment Scale (ADAS), and Mini Mental State Exam (MMSE) were used for further cognitive assessment. The Aβ42, Aβ42/Aβ40, and Aβ42/38 ratios were considered as means of comparison to identify those peptides corresponding significantly to these established biomarkers and cognitive scores. The diagnostic utility of the IASNTQSR, VAELEDEK, VVSSIEQK, GDSVVYGLR, EPVAGDAVPGPK, and QETLPSK was assessed. Results. All investigated peptides corresponded significantly to Aβ42 in controls. In those with MCI, VAELEDEK and EPVAGDAVPGPK were significantly correlated with Aβ42 (p value < 0.001). Additionally, IASNTQSR, VVSSIEQK, GDSVVYGLR, and QETLPSK were significantly correlated with Aβ42/Aβ40 and Aβ42/38 (p value < 0.001) in this group. This group of peptides similarly corresponded to Aβ ratios in those with AD. Eventually, IASNTQSR, VAELEDEK, and VVSSIEQK were significantly associated with CDR, ADAS-11, and ADAS-13, particularly in MCI group. Conclusion. Our research suggests potential early diagnostic and prognostic utilities for certain peptides extracted from CSF-targeted proteomics research. The ethical approval of ADNI is available at ClinicalTrials.gov with Identifier: NCT00106899. [ABSTRACT FROM AUTHOR]

Alzheimer's disease and cerebral small vessel disease are the two leading causes of cognitive decline and dementia and coexist in most memory clinic patients. White matter damage as assessed by diffusion MRI is a key feature in both Alzheimer's and cerebral small vessel disease. However, disease-specific biomarkers of white matter alterations are missing. Recent advances in diffusion MRI operating on the fixel level (fibre population within a voxel) promise to advance our understanding of disease-related white matter alterations. Fixel-based analysis allows derivation of measures of both white matter microstructure, measured by fibre density, and macrostructure, measured by fibre-bundle cross-section. Here, we evaluated the capacity of these state-of-the-art fixel metrics to disentangle the effects of cerebral small vessel disease and Alzheimer's disease on white matter integrity. We included three independent samples (total n = 387) covering genetically defined cerebral small vessel disease and age-matched controls, the full spectrum of biomarker-confirmed Alzheimer's disease including amyloid- and tau-PET negative controls and a validation sample with presumed mixed pathology. In this cross-sectional analysis, we performed group comparisons between patients and controls and assessed associations between fixel metrics within main white matter tracts and imaging hallmarks of cerebral small vessel disease (white matter hyperintensity volume, lacune and cerebral microbleed count) and Alzheimer's disease (amyloid- and tau-PET), age and a measure of neurodegeneration (brain volume). Our results showed that (i) fibre density was reduced in genetically defined cerebral small vessel disease and strongly associated with cerebral small vessel disease imaging hallmarks; (ii) fibre-bundle cross-section was mainly associated with brain volume; and (iii) both fibre density and fibre-bundle cross-section were reduced in the presence of amyloid, but not further exacerbated by abnormal tau deposition. Fixel metrics were only weakly associated with amyloid- and tau-PET. Taken together, our results in three independent samples suggest that fibre density captures the effect of cerebral small vessel disease, while fibre-bundle cross-section is largely determined by neurodegeneration. The ability of fixel-based imaging markers to capture distinct effects on white matter integrity can propel future applications in the context of precision medicine. [ABSTRACT FROM AUTHOR]

Background: Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development).Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements.Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction.Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics.Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects. [ABSTRACT FROM AUTHOR]

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5–0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project. [ABSTRACT FROM AUTHOR]

Background: CC-chemokine ligand 2 (CCL2), the key immunomodulatory chemokine for microglial activation, has been implicated in the pathogenesis of Alzheimer's disease (AD). Whether the association of CCL2 single nucleotide polymorphisms (SNPs) and the risk of AD is still controversial.Objective: We aimed to investigate whether CCL2 rs4586 SNP is associated with the pathological changes and cognitive decline of AD.Methods: A total of 486 participants with longitudinal cerebrospinal fluid (CSF) amyloid-β (Aβ) and phospho-tau (P-tau) biomarkers, 18F-Florbetapir and 18F-flortaucipir-positron emission tomography (PET), and cognitive assessments from the Alzheimer's disease Neuroimaging Initiative were included in the study. The effects of CCL2 rs4586 SNP on the pathological changes and cognitive decline of AD were assessed with linear mixed-effects models and evaluated according to the Aβ-status so as to identify whether the effects were independent of Aβ status.Results: CCL2 rs4586-CC carriers exhibited a slower global Aβ-PET accumulation, particularly within stage I and stage II. However, they exhibited a faster accumulation of CSF P-tau and global tau-PET standard uptake value ratios, especially in Braak I and Braak III/IV and the inferior temporal gyrus. The congruent effects of CCL2 rs4586 on tau accumulation existed only in the Aβ-group, as is shown in global tau-PET and Braak I. However, CCL2 rs4586 was not associated with the cognitive decline.Conclusion: Our findings showed that the CCL2 rs4586-CC (versus TT/TC) genotype was associated with slower Aβ deposition and faster tau accumulation, and the latter of which were independent of Aβ status. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is considered a biomarker of microglial activation. The relationships between CSF sTREM2 levels and Alzheimer's disease (AD) CSF core biomarkers, cognitive status, and neurodegeneration remain unclear.Objective: To assess the association between CSF sTREM2 levels and AD progression and other AD hallmarks.Methods: Using the Alzheimer's Disease Neuroimaging Initiative database, we investigated 1,035 participants, including 310 cognitively normal controls, 527 patients with mild cognitive impairment, and 198 patients with dementia. They were grouped according to CSF pathology (A/T profile) severity. CSF sTREM2 levels were compared between the groups, and linear regression analysis was performed to evaluate the factors affecting sTREM2 levels. The predictive effectiveness of sTREM2 levels was tested, and the correlation with other indicators was explored. The increase rate was assessed using linear mixed-effects models.Results: Higher CSF sTREM2 levels were associated with older age as well as higher CSF p-tau or t-tau and amyloid-β levels (all p < 0.001), but not with cognitive status. sTREM2 levels were not correlated with the baseline or longitudinal scale and neuroimaging result changes, and could not predict clinical conversion, but were correlated with multiple non-amyloid-β and non-tau CSF cytokines related to inflammation and neurodegeneration (p < 0.0001). The increased sTREM2 expression rate did not change among groups.Conclusion: CSF sTREM2 levels were jointly determined by age, amyloid-β, and tau pathologies, leading to complex AD cognitive continuum changes. Although sTREM2 levels could not predict cognitive deterioration and neurodegeneration, they could reflect the microglial state as a non-specific biomarker. [ABSTRACT FROM AUTHOR]

Background: Reduced signal on fluorodeoxyglucose-positron emission tomography (FDG-PET) is a valid proxy for neurodegeneration in Alzheimer's disease (AD). Perivascular space (PVS) is believed to be associated with AD pathology and cognitive decline.Objective: This study aimed to investigate the associations of PVS with FDG-PET and cognitive performance based on the burden of amyloid pathology.Methods: We used magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). MRI-visible PVS in basal ganglia (BG) and centrum semi-oval (CSO) were visually classified as: none/mild, moderate or frequent/severe. The association of PVS with brain FDG-PET was explored based on the burden of amyloid pathology, where a cerebrospinal fluid (CSF) t-tau/Aβ42 with the ratio≥0.27 was defined as high amyloid pathology. Moreover, the relationships between PVS and cognitive performance variables (ADNI-MEM and ADNI-EF) were studied.Results: For participants with higher tau/Aβ42 ratio, CSO-PVS severity was independently associated with lower FDG-PET. There were significant interaction effects between moderate or frequent/severe CSO-PVS and time on FDG decline in people with high amyloid pathology. The interaction between CSO-PVS and time (follow-up) was consistently associated with ADNI-MEM and ADNI-EF decline in individuals with high amyloid pathology.Conclusion: The study established the differential utility of PVS in BG and CSO for predicting brain metabolism. These findings suggest that CSO-PVS serves as a contributing factor to brain metabolism and cognitive decline associated with amyloid pathology. [ABSTRACT FROM AUTHOR]

Background: Accurate, cheap, and easy to promote methods for dementia prediction and early diagnosis are urgently needed in low- and middle-income countries. Integrating various cognitive tests using machine learning provides promising solutions. However, most effective machine learning models are black-box models that are hard to understand for doctors and could hide potential biases and risks.Objective: To apply cognitive-test-based machine learning models in practical dementia prediction and diagnosis by ensuring both interpretability and accuracy.Methods: We design a framework adopting Rule-based Representation Learner (RRL) to build interpretable diagnostic rules based on the cognitive tests selected by doctors. According to the visualization and test results, doctors can easily select the final rules after analysis and trade-off. Our framework is verified on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 606) and Peking Union Medical College Hospital (PUMCH) dataset (n = 375).Results: The predictive or diagnostic rules learned by RRL offer a better trade-off between accuracy and model interpretability than other representative machine learning models. For mild cognitive impairment (MCI) conversion prediction, the cognitive-test-based rules achieve an average area under the curve (AUC) of 0.904 on ADNI. For dementia diagnosis on subjects with a normal Mini-Mental State Exam (MMSE) score, the learned rules achieve an AUC of 0.863 on PUMCH. The visualization analyses also verify the good interpretability of the learned rules.Conclusion: With the help of doctors and RRL, we can obtain predictive and diagnostic rules for dementia with high accuracy and good interpretability even if only cognitive tests are used. [ABSTRACT FROM AUTHOR]

For optimal design of anti-amyloid-β (Aβ) and anti-tau clinical trials, we need to better understand the pathophysiological cascade of Aβ- and tau-related processes. Therefore, we set out to investigate how Aβ and soluble phosphorylated tau (p-tau) relate to the accumulation of tau aggregates assessed with PET and subsequent cognitive decline across the Alzheimer's disease (AD) continuum. Using human cross-sectional and longitudinal neuroimaging and cognitive assessment data, we show that in early stages of AD, increased concentration of soluble CSF p-tau is strongly associated with accumulation of insoluble tau aggregates across the brain, and CSF p-tau levels mediate the effect of Aβ on tau aggregation. Further, higher soluble p-tau concentrations are mainly related to faster accumulation of tau aggregates in the regions with strong functional connectivity to individual tau epicenters. In this early stage, higher soluble p-tau concentrations is associated with cognitive decline, which is mediated by faster increase of tau aggregates. In contrast, in AD dementia, when Aβ fibrils and soluble p-tau levels have plateaued, cognitive decline is related to the accumulation rate of insoluble tau aggregates. Our data suggest that therapeutic approaches reducing soluble p-tau levels might be most favorable in early AD, before widespread insoluble tau aggregates. The interplay between amyloid and tau pathology in Alzheimer's disease is still not well understood. Here, the authors show that amyloid-related increased in soluble p-tau is related to subsequent accumulation of tau aggregates and cognitive decline in early stage of the disease. [ABSTRACT FROM AUTHOR]

Alzheimer's disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are two commonly used cognitive screening and diagnostic tools.Objective: Our goal was to assess their efficacy for monitoring cognitive changes, as well as the correlation between the two tests.Methods: At baseline, participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were divided into four groups based on their cognitive diagnoses: healthy control (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). MMSE or MoCA scores were compared among the four groups using an analysis of variance (ANOVA) model with repeated measures with post-hoc Bonferroni correction. For those participants who had both MMSE and MoCA assessments done, a Pearson correlation analysis was performed between the two assessments for each visit.Results: The MMSE scores were significantly different among the four groups at baseline, which was true for each of the three annual follow-up visits. By contrast, the MoCA scores were not significantly different between HC and EMCI groups at either baseline or any of the follow-up visits. For participants with a diagnosis of LMCI, the cognitive performance deteriorated in a linear manner 12 months after the baseline, which was independent of MMSE or MoCA. At last, the MMSE scores were moderately related to MoCA scores, which got stronger along with the time of follow-up.Conclusion: MMSE and MoCA are comparable as cognitive assessment tools to monitor cognitive changes. In addition, the measurements of MMSE and MoCA are moderately correlated for the follow-up visits. [ABSTRACT FROM AUTHOR]

Background: We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects.Objective: We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD.Methods: MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior.Results: The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01).Conclusion: These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD. [ABSTRACT FROM AUTHOR]

Background: The population aging increased the prevalence of brain diseases, like Alzheimer's disease (AD). Early identification of individuals with higher odds of cognitive decline is essential to maintain quality of life. Imaging evaluation of individuals at risk of cognitive decline includes biomarkers extracted from brain positron emission tomography (PET) and structural magnetic resonance imaging (MRI).Objective: We propose investigating ensemble models to classify groups in the aging cognitive decline spectrum by combining features extracted from single imaging modalities and combinations of imaging modalities (FDG+AMY+MRI, and a PET ensemble).Methods: We group imaging data of 131 individuals into four classes related to the individuals' cognitive assessment in baseline and follow-up: stable cognitive non-impaired; individuals converting to mild cognitive impairment (MCI) syndrome; stable MCI; and Alzheimer's clinical syndrome. We assess the performance of four algorithms using leave-one-out cross-validation: decision tree classifier, random forest (RF), light gradient boosting machine (LGBM), and categorical boosting (CAT). The performance analysis of models is evaluated using balanced accuracy before and after using Shapley Additive exPlanations with recursive feature elimination (SHAP-RFECV) method.Results: Our results show that feature selection with CAT or RF algorithms have the best overall performance in discriminating early cognitive decline spectrum mainly using MRI imaging features.Conclusion: Use of CAT or RF algorithms with SHAP-RFECV shows good discrimination of early stages of aging cognitive decline, mainly using MRI image features. Further work is required to analyze the impact of selected brain regions and their correlation with cognitive decline spectrum. [ABSTRACT FROM AUTHOR]

Background: With the rapid development of neurobiology and neuroimaging technologies, mounting evidence shows that Alzheimer's disease (AD) is caused by the build-up of two abnormal proteins, amyloid-β plaques (A) and neurofibrillary tangles (T). Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD.Objective: Although tremendous efforts have been made to link biological indicators to the progression of AD, limited attention has been paid to investigate the multi-factorial role of socioeconomic status (SES) in the prevalence or incidence of AD. There is high demand to explore the synergetic effect of sex and SES factors in moderating the neurodegeneration process caused by the accumulation of A and T biomarkers.Methods: We carry out a meta-data analysis on the longitudinal neuroimaging data, clinical outcomes, genotypes, and demographic data in Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu).Results: Our major findings include 1) education and occupation show resilience effects at the angular gyrus, superior parietal lobule, lateral occipital-temporal sulcus, and posterior transverse collateral sulcus where we found significant slowdown of neurodegeneration due to higher education level or more advanced occupation rank; 2) A and T biomarkers manifest different spatial patterns of brain resilience; 3) BDNF (brain-derived neurotrophic factor) single nucleotide polymorphism (SNP) rs10835211 shows strong association to the identified resilience effect; 4) the identified resilience effect is associated with the clinical manifestation in memory, learning, and organization performance.Conclusion: Several brain regions manifest resilience from SES to A and T biomarkers. BDNF SNPs have a potential association with the resilience effect from SES. In addition, cognitive measures of learning and memory demonstrate the resilience effect. [ABSTRACT FROM AUTHOR]

Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD.Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI.Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI.Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient's Mini-Mental State Examination scores.Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory. [ABSTRACT FROM AUTHOR]

Background: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection.Objective: The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia.Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia.Results: The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques.Conclusion: These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers. [ABSTRACT FROM AUTHOR]

Background: Anosognosia, or unawareness of memory deficits, is a common manifestation of Alzheimer's disease (AD), but greatly variable in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) subjects. Self-referential network (SRN) is responsible for self-referential processing and considered to be related to AD progression.Objective: Our aim is to explore connectivity changes of SRN and its interaction with memory-related network and primary sensorimotor network (SMN) in the AD spectrum.Methods: About 444 Alzheimer's Disease Neuroimaging Initiative subjects (86 cognitively normal [CN]; 156 SCD; 146 aMCI; 56 AD) were enrolled in our study. The independent component analysis (ICA) method was used to extract the SRN, SMN, and memory-related network from all subjects. The alteration of functional connectivity (FC) within SRN and its connectivity with memory-related network/SMN were compared among four groups and further correlation analysis between altered FC and memory awareness index as well as episodic memory score were performed.Results: Compared with CN group, individuals with SCD exhibited hyperconnectivity within SRN, while aMCI and AD patients showed hypoconnectivity. Furthermore, aMCI patients and AD patients both showed the interruption of the FC between the SRN and memory-related network compared to CN group. Pearson correlation analysis showed that disruptive FC within SRN and its interaction with memory-related network were related to memory awareness index and episodic memory scores.Conclusion: In conclusion, impaired memory awareness and episodic memory in the AD spectrum are correlated to the disconnection within SRN and its interaction with memory-related network. [ABSTRACT FROM AUTHOR]

Background: Histopathologic studies have demonstrated differential amyloid-β (Aβ) burden between cortical sulci and gyri in Alzheimer's disease (AD), with sulci having a greater Aβ burden.Objective: To characterize Aβ deposition in the sulci and gyri of the cerebral cortex in vivo among subjects with normal cognition (NC), mild cognitive impairment (MCI), and AD, and to evaluate if these differences could improve discrimination between diagnostic groups.Methods: T1-weighted 3T MR and florbetapir (amyloid) positron emission tomography (PET) data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). T1 images were segmented and the cortex was separated into sulci/gyri based on pial surface curvature measurements. T1 images were registered to PET images and regional standardized uptake value ratios (SUVr) were calculated. A linear mixed effects model was used to analyze the relationship between clinical variables and amyloid PET SUVr measurements in the sulci/gyri. Receiver operating characteristic (ROC) analysis was performed to define amyloid positivity. Logistic models were used to evaluate predictive performance of clinical diagnosis using amyloid PET SUVr measurements in sulci/gyri.Results: 719 subjects were included: 272 NC, 315 MCI, and 132 AD. Gyral and sulcal Aβ increased with worsening cognition, however there was a greater increase in gyral Aβ. Females had a greater gyral and sulcal Aβ burden. Focusing on sulcal and gyral Aβ did not improve predictive power for diagnostic groups.Conclusion: While there were significant differences in Aβ deposition in cerebral sulci and gyri across the AD spectrum, these differences did not translate into improved prediction of diagnosis. Females were found to have greater gyral and sulcal Aβ burden. [ABSTRACT FROM AUTHOR]

Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD. [ABSTRACT FROM AUTHOR]

Background: Individuals with mild cognitive impairment (MCI) are at high risk of progression to Alzheimer's disease (AD) dementia, but some remain stable. There is a need to identify those at higher risk of progression to improve patient management and outcomes.Objective: To evaluate the trajectory of plasma neurofilament light chain (pNFL) prior to progression from MCI to AD dementia, the performance of pNFL, in combination with the Mini-Mental State Examination (MMSE), as a predictor of progression from MCI to AD dementia and to inform clinicians on the use of pNFL as a predictive biomarker.Methods: Participants (n = 440) with MCI and longitudinal follow-up (mean = 4.2 years) from the AD Neuroimaging Initiative dataset were included. pNFL as a marker for neurodegeneration and the MMSE as a cognitive measure were investigated as simple/practical predictors of progression. The risk of progressing from MCI to AD dementia associated with pNFL and MMSE scores was assessed using Cox and logistic regression models.Results: The current risk of progression to AD dementia was 37%higher in individuals with high pNFL (> 56 ng/L) compared to those with average pNFL (≤40 ng/L). A combination of baseline pNFL and MMSE could differentiate those who progressed within 5 years (AUC = 0.75) from stable individuals. Including change in MMSE over 6-12 months further improved the model (AUC = 0.84).Conclusion: Our findings reveal that combining pNFL with a simple dementia screener (MMSE) can reliably predict whether a person with MCI is likely to progress to AD dementia within 5 years. [ABSTRACT FROM AUTHOR]

Objective: Neuropsychiatric symptoms (NPS) are often present in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia. NPS are associated with structural and functional changes in the brain such as atrophy, regional hypometabolism, and hypoperfusion, considered proxies of neurodegeneration. Our objective was to evaluate the association between NPS and regional cerebral tau burden, a more direct representation of neurodegeneration, in cognitively normal (CN), MCI, and AD dementia individuals.Methods: Cross-sectional NPS were assessed using the Neuropsychiatric Inventory (NPI) in 410 CN, 199 MCI, and 61 AD dementia participants who underwent flortaucipir tau positron emission tomography as part of the AD Neuroimaging Initiative (ADNI). Total NPI score and two factors of NPS (affective and hyperactive) were used in analyses. Linear regression models with backward elimination were employed with NPI as dependent variable and regional tau or tau-amyloid interaction as predictor of interest. Covariates included education, age, sex, Rey Auditory Verbal Learning Test Total Learning, and Trail Making Test B.Results: There were significant associations (p < 0.05) between the NPI variables (total score, Affective factor) and entorhinal and precuneus tau across all participants. These associations were also significant for the tau-amyloid interaction. These effects were significant in cognitively symptomatic participants (MCI and AD dementia), but not in CN participants.Conclusions: Increased tau burden in the entorhinal and precuneus cortices was modestly associated with greater NPS in MCI and AD dementia. Further evaluation of NPS and their effect on early-stage AD could aid in finding new interventions and slowing disease progression. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid t-tau (CSF t-tau) is a measure of neurodegeneration in Alzheimer's disease (AD) and has been increasingly demonstrated to be a non-specific biomarker within the AD continuum.Objective: We sought to test whether t-tau influences the longitudinal effects of amyloid-β (Aβ) and phospho-tau (p-tau) on memory and executive function (EF) in mild cognitive impairment (MCI).Methods: 319 MCI individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with baseline and 2-year CSF Aβ, p-tau, t-tau, and neuropsychological assessments were studied. Mediation and moderation analyses evaluated the role of t-tau in the effects of Aβ and p-tau on memory and EF over 2 years.Results: We found that high baseline p-tau but not Aβ was associated with higher t-tau and lower memory scores at 2 years follow-up. The association between p-tau and memory impairment was partially mediated by t-tau, whereby higher p-tau was indirectly associated with lower memory via higher t-tau. t-tau also moderated the association between p-tau and memory. When t-tau level was relatively lower, higher p-tau was associated with lower memory scores at 2 years. When t-tau level was higher, the memory scores were low regardless of the p-tau level.Conclusion: Tau-induced neurodegeneration is one key pathway by which AD pathology (p-tau) affects memory impairment. Furthermore, in individuals with lower levels of tau-induced neurodegeneration, higher levels of p-tau were required for memory impairment. Our findings suggest that t-tau plays a significant role in how early AD pathology affects cognitive outcomes. [ABSTRACT FROM AUTHOR]

Objective: Background: Plasma neurofilament light chain (NFL) is a recognized biomarker for Alzheimer's disease (AD) and inflammation. Intrinsically organized default mode network core subsystem and frontoparietal network (FPN) and their interactions support complex cognitive function. The present study investigated the inflammatory effect on cognitive integrity via plasma NFL coupling internetwork interactions in AD.Objective: Objective: This study investigates the hypothesis that inflammation-related plasma NFL could affect the interactions of the core subsystem and FPN, which leads to the aggravation of the clinical symptoms of AD-spectrum patients.Objective: Methods: A total of 112 AD-spectrum participants underwent complete resting-state fMRI, neuropsychological tests, and plasma NFL at baseline (n = 112) and after approximately 17 months of follow-up (n = 112). The specific intersystem changes in the core subsystem and FPN were calculated and compared across groups. Then, the classifications of different AD-spectrum groups were analyzed using the association of plasma NFL and the changed intersystem interacting regions. Finally, mediation analysis was applied to investigate the significance of plasma NFL coupling networks on cognitive impairments in these subjects.Objective: Results: Discrimination of disease-related interactions of the core subsystem and FPN was found in AD-spectrum patients, which was the neural circuit fundamental to plasma NFL disrupting cognitive integrity. Furthermore, the clinical significance of plasma NFL coupling networks on AD identification and monitoring cognitive impairments were revealed in these subjects.Conclusion: The characteristic change in inflammation-related plasma NFL coupled with brain internetwork interactions could be used as a potential observation indicator in the progression of AD patients. [ABSTRACT FROM AUTHOR]

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between Alzheimer's disease (AD) and normal cognitive state because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored.Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI.Methods: Based on the "triple network" model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan.Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient's brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient's clinical Mini-Mental State Examination assessment scale sores.Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders. [ABSTRACT FROM AUTHOR]

Background: The links between cerebral small vessel disease (CSVD) burden and neuropsychiatric symptoms (NPS) have not been fully studied.Objective: We aimed to explore the associations of the CSVD burden with Neuropsychiatric Inventory (NPI) total scores and its subsyndromes in the elderly without dementia.Methods: We investigated 630 non-demented participants from the Alzheimer's Disease Neuroimaging Initiative. All of them had NPI assessments and 3 Tesla MRI scans at baseline and 616 had longitudinal NPI assessments during the follow-up. Linear mixed-effects models were used to investigate the cross-sectional and longitudinal associations of CSVD burden with NPI total scores and its subsyndromes.Results: Higher CSVD burden longitudinally predicted more serious neuropsychiatric symptoms, including NPS (p &lt; 0.0001), hyperactivity (p = 0.0006), affective symptoms (p = 0.0091), and apathy (p &lt; 0.0001) in the total participants. Lacunar infarcts (LIs), white matter hyperactivities (WMHs), and cerebral microbleeds (CMBs) might play important roles in the occurrence of NPS, since they were longitudinally associated with specific neuropsychiatric subsyndromes. LIs contributed to hyperactivity (p = 0.0092), psychosis (p = 0.0402), affective symptoms (p = 0.0156), and apathy (p &lt; 0.0001). WMHs were associated with hyperactivity (p = 0.0377) and apathy (p = 0.0343). However, CMBs were only related to apathy (p = 0.0141).Conclusion: CSVD burden was associated with multiple neuropsychiatric symptoms, suggesting the importance of monitoring and controlling vascular risk factors. Different markers of CSVD were associated with specific subsyndromes of NPS, suggesting that different markers tended to occur in different encephalic regions. [ABSTRACT FROM AUTHOR]

Background: Studies have demonstrated that both tau and cardiovascular risk are associated with cognitive decline, but the possible synergistic effects of these pathologic markers remain unclear.Objective: To explore the interaction of AD biomarkers with a specific vascular risk marker (pulse pressure) on longitudinal cognition.Methods: Participants included 139 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Biomarkers of tau, amyloid-β (Aβ), and vascular risk (pulse pressure) were assessed. Neuropsychological assessment provided memory, language, and executive function domain composite scores at baseline and 1-year follow-up. Multiple linear regression examined interactive effects of pulse pressure with tau PET independent of Aβ PET and Aβ PET independent of tau PET on baseline and 1-year cognitive outcomes.Results: The interaction between pulse pressure and tau PET significantly predicted 1-year memory performance such that the combined effect of high pulse pressure and high tau PET levels was associated with lower memory at follow-up but not at baseline. In contrast, Aβ PET did not significantly interact with pulse pressure to predict baseline or 1-year outcomes in any cognitive domain. Main effects revealed a significant effect of tau PET on memory, and no significant effects of Aβ PET or pulse pressure on any cognitive domain.Conclusion: Results indicate that tau and an indirect marker of arterial stiffening (pulse pressure) may synergistically contribute to memory decline, whereas Aβ may have a lesser role in predicting cognitive progression. Tau and vascular pathology (particularly in combination) may represent valuable targets for interventions intended to slow cognitive decline. [ABSTRACT FROM AUTHOR]

With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, and previous studies have shown its association with accelerated aging. In this study, we hypothesized that single nucleotide polymorphisms (SNPs) that contributed to aging acceleration are also associated with the progression from mild cognitive impairment (MCI) to AD. By applying genetic correlation analysis and single-locus survival analysis, we investigated the associations between intrinsic- and extrinsic-epigenetic-age-acceleration (IEAA and EEAA) related SNPs and the progression time from MCI to AD dementia using the data of 767 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and 1 373 MCI patients from the National Alzheimer's Coordinating Center (NACC) study. Genetic correlations were found between IEAA/EEAA and AD (positive for IEAA-AD and negative for EEAA-AD). We revealed that 70 IEAA and 81 EEAA SNPs had associations with the progression time from MCI to AD with Bayesian false-discovery probability ≤ 0.8 in the ADNI study, with 22 IEAA SNPs and 16 EEAA SNPs being replicated in the NACC study (p < .05). Polygenic risk score (PRS) analysis showed that EEAA PRS but not IEAA PRS was associated with AD progression and the trend of decreasing fusiform gyrus volume in 2 data sets. Risk models incorporating both EAA PRSs did not show any significant improvement in predictive accuracy. Our results revealed multiple genetic variants with pleiotropic effects on both EAA and AD, which suggested shared genetic architecture between epigenetic age acceleration and AD progression. [ABSTRACT FROM AUTHOR]

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research. The Polygenic Risk Score Knowledge Base (PRSKB) is a web-based interface that stores data from >2,300 distinct genome-wide association studies, and can estimate polygenic risk scores for general use. [ABSTRACT FROM AUTHOR]

Introduction: The Alzheimer's Disease Composite Score (ADCOMS) is a tool developed to detect clinical progression and measure treatment effect in patients in early stages of Alzheimer's disease (AD). The psychometric properties of the ADCOMS have been established; however, the threshold for clinical meaningfulness has yet to be identified. Methods: Anchor-based, distribution-based, and ROC curve analyses were used to estimate clinically meaningful thresholds for change in ADCOMS for patients with mild cognitive impairment (MCI) and AD dementia. This study included data from three sources: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the National Alzheimer's Coordinating Center (NACC), and a legacy dataset that included data from four sources: the placebo group from three MCI trials and an earlier data cut from ADNI. Results were stratified by disease severity (MCI vs. dementia) and APOE ε4 carrier status. Results: A total of 5355 participants were included in the analysis. The ADCOMS was able to detect change for MCI and dementia patients who experienced a meaningful decline in cognition (as defined by the Clinical Dementia Rating Scale Sum of Boxes [CDR-SOB]) between baseline and month 12. The following ADCOMS cut-offs were proposed: 0.05 for MCI and 0.10 for dementia. Conclusions: The ADCOMS was previously established as a valid and reliable tool for use in clinical trials for MCI due to AD and dementia populations. By defining thresholds for clinically meaningful change of ADCOMS, this work is an important step in interpreting clinical findings and estimates of treatment effects in early stage AD trials. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process. [ABSTRACT FROM AUTHOR]

Background: The question that whether the presence of osteoarthritis (OA) can modify the effects of apolipoprotein E4 (APOE4) genotype on longitudinal change in cognitive performance among non-demented older people remains unclear.Objective: To examine whether the association of APOE4 genotype with change in verbal episodic memory over time is modified by the presence of OA among non-demented older people.Methods: Longitudinal data from 1,400 non-demented older people were obtained from the Alzheimer's Disease Neuroimaging Initiative database. The sample included 466 healthy individuals and 934 mild cognitive impairment. The effects of the OA×APOE4 genotype interaction term on longitudinal change in cognitive performance were examined using linear mixed-effects regression models. Global cognition was assessed by the Mini-Mental State Examination score and Clinical Dementia Rating-Sum of Boxes. Verbal episodic memory was evaluated by the Rey Auditory Verbal Learning Test (RAVLT) immediate recall and delayed recall score.Results: We found that OA interacted with APOE4 genotype to influence longitudinal change in verbal episodic memory (as assessed by RAVLT immediate recall score) but not global cognition. Specifically, the OA-/APOE4+ group had a steeper decline in RAVLT immediate recall score compared with the OA+/APOE4+ group. However, there was no difference in RAVLT immediate recall score between OA-/APOE4-and OA+/APOE4-individuals.Conclusion: Our study suggested that the association of APOE4 genotype with change in RAVLT immediate recall score over time is modified by the presence of OA at earliest stages of the disease. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration.Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy.Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer's Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant.Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results.Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy. [ABSTRACT FROM AUTHOR]

Background: Hallucinations in Alzheimer's disease (AD) have been linked to more severe cognitive and functional decline. However, research on visual hallucinations (VH), the most common type of hallucinations in AD, is limited.Objective: To investigate the cognitive and cerebral macrostructural and metabolic features associated with VH in AD.Methods: Twenty-four AD patients with VH, 24 with no VH (NVH), and 24 cognitively normal (CN) matched controls were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Differences in regional gray matter (GM) volumes and cognitive performance were investigated with whole brain voxel-based morphometry analyses of MRI structural brain scans, and analyses of neuropsychological tests. Glucose metabolic changes were explored in a sub-sample of patients who had FDG-PET scans available.Results: More severe visuoconstructive and attentional deficits were found in AD VH compared with NVH. GM atrophy and hypometabolism were detected in occipital and temporal areas in VH patients in comparison with CN. On the other hand, NVH patients had atrophy and hypometabolism mainly in temporal areas. No differences in GM volume and glucose metabolism were found in the direct comparison between AD VH and NVH.Conclusion: In addition to the pattern of brain abnormalities typical of AD, occipital alterations were observed in patients with VH compared with CN. More severe visuoconstructive and attentional deficits were found in AD VH when directly compared with NVH, and might contribute to the emergence of VH in AD. [ABSTRACT FROM AUTHOR]

Background: Brain accumulation of amyloid-β is a hallmark event in Alzheimer's disease (AD) whose underlying mechanisms are incompletely understood. Case-control genome-wide association studies have implicated numerous genetic variants in risk of clinically diagnosed AD dementia.Objective: To test for associations between case-control AD risk variants and amyloid PET burden in older adults, and to assess whether a polygenic measure encompassing these factors would account for a large proportion of the unexplained variance in amyloid PET levels in the wider population.Methods: We analyzed data from the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Global cortical amyloid PET burden was the primary outcome. The 38 gene variants from Wightman et al. (2021) were analyzed as predictors, with PRSice-2 used to assess the collective phenotypic variance explained.Results: Known AD risk variants in APOE, PICALM, CR1, and CLU were associated with amyloid PET levels. In aggregate, the AD risk variants were strongly associated with amyloid PET levels in the MCSA (p = 1.51×10-50) and ADNI (p = 3.21×10-64). However, in both cohorts the non-APOE variants uniquely contributed only modestly (MCSA = 2.1%, ADNI = 4.4%) to explaining variation in amyloid PET levels.Conclusion: Additional case-control AD risk variants added only modestly to APOE in accounting for individual variation in amyloid PET burden, results which were consistent across independent cohorts with distinct recruitment strategies and subject characteristics. Our findings suggest that advancing precision medicine for dementia may require integration of strategies complementing case-control approaches, including biomarker-specific genetic associations, gene-by-environment interactions, and markers of disease progression and heterogeneity. [ABSTRACT FROM AUTHOR]

Background: Depression is a late-life risk factor for cognitive decline. Evidence suggests an association between Alzheimer's disease (AD) associated pathologic changes and depressive symptoms.Objective: To investigate the influence of AT(N) biomarker profile (amyloid-β [A], p-tau [T], and neurodegeneration [N]) and gender on cross-sectional associations between subclinical depressive symptoms and cognitive function among older adults without dementia.Methods: Participants included 868 individuals without dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Depressive symptoms were measured using the Geriatric Depression Scale (GDS). ADNI neuropsychological composite scores assessed memory and executive function (EF). PET, cerebrospinal fluid, and MRI modalities classified the study sample into biomarker profiles: normal biomarkers (A-T-N-), AD continuum (A+T±N±), and suspect non-AD pathology (SNAP; A-T±N-or A-T-N±). Multivariate regression models were used to investigate associations between GDS and cognitive domains.Results: GDS was negatively associated with memory (β= -0.156, p < 0.001) and EF (β= -0.147, p < 0.001) in the whole sample. When classified by biomarker profile, GDS was negatively associated with memory and EF in AD continuum (memory: β= -0.174, p < 0.001; EF: β= -0.129 p = 0.003) and SNAP (memory: β= -0.172, p = 0.005; EF: β= -0.197, p = 0.001) subgroups. When stratified by sex, GDS was negatively associated with memory (β= -0.227, p < 0.001) and EF (β= -0.205, p < 0.001) in men only.Conclusion: The association between subclinical depressive symptoms and cognitive function is highly influenced by the AT(N) biomarker profile. [ABSTRACT FROM AUTHOR]

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity. Different types of atrophy in Alzheimer's disease may reflect different disease stages or biologically distinct subtypes. Here the authors use longitudinal neuroimaging data to demonstrate five distinct patterns of atrophy with different demographical and cognitive characteristics. [ABSTRACT FROM AUTHOR]