Alzheimer's Disease Neuroimaging Initiative Research Articles

Cholinesterase inhibitor use in amyloid PET-negative mild cognitive impairment and cognitive changes

BackgroundCholinesterase inhibitors (ChEIs) are prescribed for Alzheimer’s disease (AD) and sometimes for mild cognitive impairment (MCI) without knowing underlying pathologies and its effect on cognition. We investigated the frequency of ChEI prescriptions in amyloid-negative MCI and their association with cognitive changes in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort.MethodsWe included participants with amyloid positron emission tomography (PET)-negative MCI from the ADNI. We analyzed the associations of ChEI use with cognitive changes, brain volume, and cerebrospinal fluid (CSF) total tau (t-tau), hyperphosphorylated tau181 (p-tau181), and p-tau181/t-tau ratio.ResultsChEIs were prescribed in 27.4% of amyloid PET-negative MCI and were associated with faster cognitive decline, reduced baseline hippocampal volume and entorhinal cortical thickness, and a longitudinal decrease in the frontal lobe cortical thickness.ConclusionsThe association between ChEI use and accelerated cognitive decline may stem from underlying pathologies involving reduced hippocampal volume, entorhinal cortical thickness and faster frontal lobe atrophy. We suggest that ChEI use in amyloid PET-negative MCI patients might need further consideration, and studies investigating the causality between ChEI use and cognitive decline are warranted in the future.

The Alzheimer's Disease Neuroimaging Initiative Neuropathology Core: An update.

Biomarkers for Alzheimer's disease neuropathologic change (ADNC) have been instrumental in developing effective disease-modifying therapeutics. However, to prevent/treat dementia effectively, we require biomarkers for non-AD neuropathologies; for this, neuropathologic examinations and annotated tissue samples are essential. We conducted clinicopathologic correlation for the first 100 Alzheimer's Disease Neuroimaging Initiative (ADNI) Neuropathology Core (NPC) cases. Clinical syndromes in this cohort showed 95% sensitivity and 79% specificity for predicting high/intermediate ADNC, a 21% false positive rate, and a ∼44% false negative rate. In addition, 60% with high/intermediate ADNC harbored additional potentially dementing co-pathologies. These results suggest that clinical presentation imperfectly predicts ADNC and that accurate prediction of high/intermediate ADNC does not exclude co-pathology that may modify presentation, biomarkers, and therapeutic responses. Therefore, new biomarkers are needed for non-AD neuropathologies. The ADNI NPC supports this mission with well-characterized tissue samples (available through ADNI and the National Institute on Aging) and "gold-standard" diagnostic information (soon to include digital histology). The Alzheimer's Disease Neuroimaging Initiative (ADNI) Neuropathology Core (NPC) brain donation cohort now exceeds 200 cases. ADNI NPC data in National Alzheimer's Coordinating Center format are available through the Laboratory of Neuro Imaging. Digitized slide files from the ADNI NPC will be available in 2025. Requests for ADNI brain tissue samples can be submitted online for ADNI/National Institute on Aging evaluation. Clinical diagnoses of Alzheimer's disease (AD)/AD and related dementias (ADRD) do not always predict post mortem neuropathology. Neuropathology is essential for the development of novel AD/ADRD biomarkers.

A multimodal learning machine framework for Alzheimer’s disease diagnosis based on neuropsychological and neuroimaging data

Alzheimer’s disease (AD) is the most prevalent form of dementia, with no current cure. Early screening and intervention are vital. In multimodal AD data, besides neuroimaging dimensions, neuropsychological tests based on cognitive domains also provide the clinical information for diagnosing AD. However, previous multimodal methods often fuse these neuropsychological test scores with other data, losing the rich clinical details inherent in each test, including videos, speech, images, and text. To address this, we propose a novel framework with an entropy-based polynomial dimension expansion function that restores this critical information by accurately calculating the optimal polynomial degree. Additionally, the proposed framework offers a series of cognitive-based Extreme Learning Machine (ELM) models to better utilize the detailed clinical insights from neuropsychological tests, reducing diagnostic redundancy and noise. Finally, we design a boosting ensemble strategy that combines diagnostic models from various dimensions and cognitive domains, automatically optimizing weights to enhance diagnostic accuracy. Tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, our approach achieves over 98% accuracy and F1 scores, with no observed bias between mild cognitive impairment (MCI) and AD groups. Therefore, our framework can offer clinicians more logical recommendations for diagnosing and managing the disease.

Sleep and nighttime behavior disorders in older adults: associations with hypercholesterolemia and hypertriglyceridemia at baseline, and a predictive analysis of incident cases at 12 months follow-up.

Sleep disorders, particularly insomnia and obstructive sleep apnea, are associated with dyslipidemia in the general population. The study's aim was to explore the association between pathological Cholesterol and Triglyceride levels, and sleep and nighttime behavior disorders (SNBD) in older adults, whether they might predict SNBD onset, and to emphasize the role of body mass index (BMI) in this association. Alzheimer's Disease Neuroimaging Initiative (ADNI) population with complete Cholesterol, Triglyceride, SNBD, and neurocognitive data were included. Logistic regression was performed to study the association between hypercholesterolemia, hypertriglyceridemia, and SNBD at baseline and at12 months. Relevant confounders, particularly BMI, were adjusted for. Among the 2,216 included cases, 1,045 (47%) were females, and the median age was 73 years (IQR: 68, 78). At baseline, 357 (16%) had SNBD and 327 (18%) at 12 months; 187 of them were incident cases. There were more cases of baseline SNBD in the hypertriglyceridemia group than in those without (19% vs. 14%, P-value = 0.003). Similarly, more follow-up SNBD cases had hypertriglyceridemia at baseline (21% vs. 16%, P-value = 0.025). SNBD cases at baseline had significantly higher serum Triglyceride levels than those without (132 vs. 118mg/dL, P-value < 0.001). Only hypertriglyceridemia was significantly associated with baseline SNBD (crude OR = 1.43, 95%CI: 1.13,1.80, P-value = 0.003), even after adjustment for confounding factors (adj. OR = 1.36, 95%CI: 1.06,1.74, P-value = 0.016) and (BMI-adj. OR = 1.29, 95%CI: 1.00,1.66, P-value = 0.048). None of the dyslipidemia forms did predict incident cases at 12 months. Hypertriglyceridemia, but not hypercholesterolemia, was associated with higher odds of SNBD. The association was independent of BMI. None of the dyslipidemia forms did predict incident SNBD over 12 months. Sleep disorders should motivate a systematic screening of dyslipidemia in older adults and vice versa.

Self-reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort.

Hearing loss is identified as one of the largest modifiable risk factors for cognitive impairment and dementia. Studies evaluating this relationship have yielded mixed results. We investigated the longitudinal relationship between self-reported hearing loss and cognitive/functional performance in 695 cognitively normal (CN) and 941 participants with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative. Within CN participants with hearing loss, there was a significantly greater rate of cognitive decline per modified preclinical Alzheimer's cognitive composite. Within both CN and MCI participants with hearing loss, there was a significantly greater rate of functional decline per the functional activities questionnaire (FAQ). In CN and MCI participants, hearing loss did not significantly contribute to the risk of progression to a more impaired diagnosis. These results confirm previous studies demonstrating a significant longitudinal association between self-reported hearing loss and cognition/function but do not demonstrate an increased risk of conversion to a more impaired diagnosis. NCT00106899 (ADNI: Alzheimer's Disease Neuroimaging Initiative, clinicaltrials.gov), NCT01078636 (ADNI-GO: Alzheimer's Disease Neuroimaging Initiative Grand Opportunity, clinicaltrials.gov), NCT01231971 (ADNI2: Alzheimer's Disease Neuroimaging Initiative 2, clinicaltrials.gov), NCT02854033 (ADNI3: Alzheimer's Disease Neuroimaging Initiative 3, clinicaltrials.gov). Hearing loss is a potential modifiable risk factor for dementia. We assessed the effect of self-reported hearing loss on cognition and function in the ADNI cohort. Hearing loss contributes to significantly faster cognitive and functional decline. Hearing loss was not associated with conversion to a more impaired diagnosis.

Ethnic differences in the prevalence of amyloid positivity and cognitive trajectories.

We investigated the prevalence of amyloid beta (Aβ) positivity (+) and cognitive trajectories in Koreans and non-Hispanic Whites (NHWs). We included 5121 Koreans from multiple centers across South Korea and 929 NHWs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent Aβ positron emission tomography and were categorized into cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia stages. Age, sex, education, and apolipoprotein E. genotype were adjusted using multivariable logistic regression and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances in these variables. The prevalence of Aβ+ was lower in CU Koreans than in CU NHWs (adjusted odds ratio 0.60). Aβ+ Koreans showed a faster cognitive decline than Aβ+ NHWs in the CU (B=-0.314, p=.004) and MCI stages (B=-0.385, p<.001). Ethnic characteristics of Aβ biomarkers should be considered in research and clinical application of Aβ-targeted therapies in diverse populations. Koreans have a lower prevalence of Aβ positivity compared to NHWs in the CU stage. The effects of Alzheimer's risk factors on Aβ positivity differ between Koreans and NHWs. Aβ-positive (Aβ+) Koreans show faster cognitive decline than Aβ+ NHWs in the CU and MCI stages.

Do Tau Deposition and Glucose Metabolism Dissociate in Alzheimer's Disease Trajectory?

Tau aggregation demonstrates close associations with hypometabolism in Alzheimer's disease (AD), although differing pathophysiological processes may underlie their development. To establish whether tau deposition and glucose metabolism have different trajectories in AD progression and evaluate the utility of global measures of these pathological hallmarks in predicting cognitive deficits. 279 participants with amyloid-β (Aβ) status, and T1-weighted MRI scans, were selected from the Alzheimer's Disease Neuroimaging Initiative (http://adni.loni.usc.edu). We created the standard uptake value ratio images using Statistical Parametric Mapping 12 for [18F]AV1451-PET (tau) and [18F]FDG-PET (glucose metabolism) scans. Voxel-wise group and single-subject level SPM analysis evaluated the relationship between global [18F]FDG-PET and [18F]AV1451-PET depending on the Aβ status. Linear models assessed whether tau deposition or glucose metabolism better predicted clinical progression. There was a dissociation between global cerebral glucose hypometabolism and global tau load in amyloid-positive AD and amyloid-negative mild cognitive impairment (MCI) (p > 0.05). Global hypometabolism was only associated with global cortical tau in amyloid-positive MCI. Voxel-level single subject tau load better predicted neuropsychological performance, Alzheimer's disease assessment scale-cognitive (ADAS-Cog) 13 score, and one-year change compared with regional and global hypometabolism. A dissociation between tau pathology and glucose metabolism at a global level in AD could imply that other pathological processes influence glucose metabolism. Furthermore, as tau is a better predictor of clinical progression, these processes may have independent trajectories and require independent consideration in the context of therapeutic interventions.

The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals.

MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

Plasma miRNAs across the Alzheimer's disease continuum: Relationship to central biomarkers.

MicroRNAs (miRNAs) play important roles in gene expression regulation and Alzheimer's disease (AD) pathogenesis. We investigated the association between baseline plasma miRNAs and central AD biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N=803): amyloid, tau, and neurodegeneration (A/T/N). Differentially expressed miRNAs and their targets were identified, followed by pathway enrichment analysis. Machine learning approaches were applied to investigate the role of miRNAs as blood biomarkers. We identified nine, two, and eight miRNAs significantly associated with A/T/N positivity, respectively. We identified 271 genes targeted by amyloid-related miRNAs with estrogen signaling receptor-mediated signaling among the enriched pathways. Additionally, 220 genes targeted by neurodegeneration-related miRNAs showed enrichment in pathways including the insulin growth factor 1 pathway. The classification performance of demographic information for A/T/N positivity was increased up to 9% with the inclusion of miRNAs. Plasma miRNAs were associated with central A/T/N biomarkers, highlighting their potential as blood biomarkers. We performed association analysis of microRNAs (miRNAs) with amyloid/tau/neurodegeneration (A/T/N) biomarker positivity. We identified dysregulated miRNAs for A/T/N biomarker positivity. We identified Alzheimer's disease biomarker-specific/common pathways related to miRNAs. miRNAs improved the classification for A/T/N positivity by up to 9%. Our study highlights the potential of miRNAs as blood biomarkers.

Morphometry of medial temporal lobe subregions using high-resolution T2-weighted MRI in ADNI3: Why, how, and what's next?

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration.

Difference in trajectories according to early amyloid accumulation in cognitively unimpaired elderly.

Amyloid β (Aβ), a major biomarker of Alzheimer's disease, leads to tau accumulation, neurodegeneration and cognitive decline. Modelling the trajectory of Aβ accumulation in cognitively unimpaired (CU) individuals is crucial, as treatments targeting Aβ are anticipated. The evolution of Aβ levels was investigated to determine whether it could lead to classification into different groups by studying longitudinal Aβ changes in older CU individuals, and differences between the groups were compared. A total of 297 CU participants were included from the Alzheimer's Disease Neuroimaging Initiative database, and these participants underwent apolipoprotein E (APOE) genotyping, neuropsychological testing, brain magnetic resonance imaging, and an average of 3.03 follow-up 18F-florbetapir positron emission tomography scans. Distinct Aβ trajectory patterns were classified using latent class growth analysis, and longitudinal cognitive performances across these patterns were assessed with a linear mixed effects model. The optimal model consisted of three classes, with a high entropy value of 0.947. The classes were designated as follows: class 1, non-accumulation group (n = 197); class 2, late accumulation group (n = 70); and class 3, early accumulation group (n = 30). The late accumulation and early accumulation groups had more APOE ε4 carriers than the non-accumulation group. The longitudinal analysis of cognitive performance revealed that the early accumulation group showed the steepest decline (modified Preclinical Alzheimer's Cognitive Composite with digit symbol substitution [mPACCdigit], p < 0.001; modified Preclinical Alzheimer's Cognitive Composite with trails B [mPACCtrailsB], p < 0.001) and the late accumulation group showed a steeper decline (mPACCdigit, p = 0.014; mPACCtrailsB, p = 0.007) compared to the non-accumulation group. Our study showed the heterogeneity of Aβ accumulation trajectories in CU older individuals. The prognoses for cognitive decline differ according to the Aβ trajectory patterns.

Fixel-based and tensor-derived white matter abnormalities in relation to memory impairment and neurocognitive disorders.

Aging-related neurocognitive disorders, including Alzheimer's disease (AD) and mild cognitive impairment (MCI), have been characterised by altered brain white matter (WM), relying widely on diffusion tensor imaging (DTI). DTI's limited accuracy in assessing crossing fibres prompted novel methods that distinguish fibres crossing through same voxel-spaces, such as fixel-based analysis (FBA), highlighting subtle macrostructural and microstructural alterations in AD and MCI. We examined the FBA and DTI's specificity in determining WM features relevant to memory in the neurocognitive aging spectrum. Diffusion-weighted images were analysed in 560 participants with various neurocognitive diagnoses from the Alzheimer's Disease Neuroimaging Initiative (F:297; mean age: 73.2 ± 8). Verbal memory was measured in 488 participants using the Rey Auditory Verbal Learning Test. FBA-derived measures of fibre density (FD), fibre-bundle cross-section (FC), and their combination (FDC), DTI fractional anisotropy (FA) and mean diffusivity (MD) were examined in relation to diagnoses and memory scores, controlling for age, sex, and intracranial volume. MCI and AD groups significantly differed from controls, with lower FD and FDC in the fornix and bilateral fibres extending to the medial temporal lobes (MTL). Memory was positively associated with FD and FDC in the fornix and MTL fibres, and FC in theanterior commissure (AC). Widespread FA reductions and MD increases were observed in AD and MCI and widely associated with memory. Fixel-wise measures highlight fibre tracts that are altered distinctly at the macroscopic and microscopic level in neurocognitive aging, and reveals structures associated with memory performance that are more specifically located than tensor-derived measures.

Associations between misfolded alpha-synuclein aggregates and Alzheimer's disease pathology in vivo.

We examined the relations of misfolded alpha synuclein (α-synuclein) with Alzheimer's disease (AD) biomarkers in two large independent cohorts. We included Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably Two (BioFINDER-2) and Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (n=2315, cognitively unimpaired, mild cognitive impairment, AD dementia) who had cross-sectional cerebrospinal fluid (CSF) α-synuclein measurement from seed-amplification assay as well as cross-sectional and longitudinal amyloid beta (Aβ) and tau levels (measured in CSF and/or by positron emission tomography). All analyses were adjusted for age, sex, and cognitive status. Across cohorts, the main biomarker associated with α-synuclein positivity at baseline was higher levels of Aβ pathology (all p values≤0.02), but not tau. Looking at longitudinal measures of AD biomarkers, α-synuclein -positive participants had a statistically significant faster increase of Aβ load, although of modest magnitude (1.11 Centiloid/year, p=0.02), compared to α-synuclein -negative participants in BioFINDER-2 but not in ADNI. We showed associations between concurrent misfolded α-synuclein and Aβ levels, providing in vivo evidence of links between these two molecular disease pathways in humans. Amyloid beta (Aβ), but not tau, was associated with alpha-synuclein (α-synuclein) positivity. Such association was consistent across two cohorts, beyond the effect of age, sex, and cognitive status. α-synuclein-positive participants had a small, statistically significant faster increase in Aβ positron emission tomography levels in one of the two cohorts.

Overview of ADNI MRI.

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

A comprehensive investigation of the associations between tensor-based morphometry indices and executive functions, memory, language, and visuospatial abilities in patients in the Alzheimer's disease continuum

ObjectivesBased on the literature, tensor-based morphometry (TBM) parameters were related to neurocognitive functions such as memory, learning, language ability, and executive functions. The present study aims to evaluate the associations between TBM indices with executive functions, memory, language, and visuospatial abilities and the value of TBM in the clinical diagnosis of Alzheimer’s disease (AD) among individuals with Alzheimer's disease continuum and mild cognitive impairment (MCI) from Alzheimer's Disease Neuroimaging Initiative (ADNI). MethodsThe authors used ADNI-memory (ADNI-MEM), ADNI-executive functions (ADNI-EF), ADNI-language (ADNI-LAN), and ADNI-visuospatial (ADNI-VS) composite scores. TBM parameters, including measure 1, which represents average within a statistically defined region-of-interest inside the temporal lobes and measure 2 which indicates average within an anatomically defined region-of-interest including bilateral temporal lobes were utilized in the current study. Statistical analysis was performed using IBM SPSS Statistics version 26, and Pearson’s correlation, Bonferroni’s correction, and multiple linear regression were utilized for data analysis. P <0.05 was considered statistically significant. ResultsAfter screening 800 participants, 270 (151 men, 119 women) were selected for a study with TBM scores and cognition-related assessments at 6, 12, and 24 months. Groups included healthy controls (n=53), MCI (n=158), and Alzheimer’s Disease (AD) (n=59). TBM indices correlated with cognitive scores in MCI and AD groups but not healthy controls. Changes in TBM indices and cognitive scores were significantly correlated in MCI and AD groups over 24 months. TBM indices were weak predictors of cognitive decline at all time points. ConclusionsTBM can help physicians diagnose MCI and AD early. However, TBM could not strongly predict cognitive functions decline at all time points.

Predicting CDR status over 36 months with a recall-based digital cognitive biomarker.

Word-list recall tests are routinely used for cognitive assessment, and process scoring may improve their accuracy. We examined whether Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) derived, process-based digital cognitive biomarkers (DCBs) at baseline predicted Clinical Dementia Rating (CDR) longitudinally and compared them to standard metrics. Analyses were performed with Alzheimer's Disease Neuroimaging Initiative (ADNI) data from 330 participants (mean age=71.4±7.2). We conducted regression analyses predicting CDR at 36 months, controlling for demographics and genetic risk, with ADAS-Cog traditional scores and DCBs as predictors. The best predictor of CDR at 36 months was M, a DCB reflecting recall ability (area under the curve =0.84), outperforming traditional scores. Diagnostic results suggest that M may be particularly useful to identify individuals who are unlikely to decline. These results suggest that M outperforms ADAS-Cog traditional metrics and supports process scoring for word-list recall tests. More research is needed to determine further applicability with other tests and populations. Process scoring and latent modeling were more effective than traditional scoring. Latent recall ability (M) was the best predictor of Clinical Dementia Rating decline at 36 months. The top digital cognitive biomarker model had odds ≈ 90 times greater than the top Alzheimer's Disease Assessment Scale-Cognitive subscale model. Particularly high negative predictive value supports literature on cognitive testing as a useful screen. Consideration of both cognitive and pathological outcomes is needed.

Hypertension and cerebral blood flow in the development of Alzheimer's disease.

We investigated the interactive associations between amyloid and hypertension on the entorhinal cortex (EC) tau and atrophy and the role of cerebral blood flow (CBF) as a shared mechanism by which amyloid and hypertension contribute to EC tau and regional white matter hyperintensities (WMHs). We analyzed data from older adults without dementia participating in the Add-Tau study (NCT02958670, n=138) or Alzheimer's Disease Neuroimaging Initiative (ADNI) (n=523) who had available amyloid-positron emission tomography (PET), tau-PET, fluid-attenuated inversion recovery (FLAIR), and T1-weighted magnetic resonance imaging (MRI). A subsample in both cohorts had available arterial spin labeling (ASL) MRI (Add-Tau: n=78; ADNI: n=89). The detrimental effects of hypertension on AD pathology and EC thickness were more pronounced in the Add-Tau cohort. Increased amyloid burden was associated with decreased occipital gray matter CBF in the ADNI cohort. In both cohorts, lower regional gray matter CBF was associated with higher EC tau and posterior WMH burden. Reduced cerebral perfusion may be one common mechanism through which hypertension and amyloid are related to increased EC tau and WMH volume. Hypertension is associated with increased entorhinal cortex (EC) tau, particularly in the presence of amyloid. Decreased cortical cerebral blood flow (CBF) is associated with higher regional white matter hyperintensity volume. Increasing amyloid burden is associated with decreasing CBF in the occipital lobe. MTL CBF and amyloid are synergistically associated with EC tau.

Influence of Different Diagnostic Criteria on Alzheimer Disease Clinical Research.

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria. A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only β-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115). Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.

Amygdala atrophies in specific subnuclei in preclinical Alzheimer's disease.

Magnetic resonance imaging (MRI) segmentation algorithms make it possible to study detailed medial temporal lobe (MTL) substructures as hippocampal subfields and amygdala subnuclei, offering opportunities to develop biomarkers for preclinical Alzheimer's disease (AD). We identified the MTL substructures significantly associated with tau-positron emission tomography (PET) signal in 581 non-demented individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI-3). We confirmed our results in our UCLouvain cohort including 110 non-demented individuals by comparing volumes between individuals with different visual Braak's stages and clinical diagnosis. Four amygdala subnuclei (cortical, central, medial, and accessory basal) were associated with tau in amyloid beta-positive (Aβ+) clinically normal (CN) individuals, while the global amygdala and hippocampal volumes were not. Using UCLouvain data, we observed that both Braak I-II and Aβ+ CN individuals had smaller volumes in these subnuclei, while no significant difference was observed in the global structure volumes or other subfields. Measuring specific amygdala subnuclei, early atrophy may serve as a marker of temporal tauopathy in preclinical AD, identifying individuals at risk of progression. Amygdala atrophy is not homogeneous in preclinical Alzheimer's disease (AD). Tau pathology is associated with atrophy of specific amygdala subnuclei, specifically, the central, medial, cortical, and accessory basal subnuclei. Hippocampal and amygdala volume is not associated with tau in preclinical AD. Hippocampus and CA1-3 volume is reduced in preclinical AD, regardless of tau.

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.

Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clinical practice. In biomedical studies with long-term follow up, participants are often monitored through periodic clinical visits with repeat measurements until an occurrence of the event of interest (e.g. disease onset) or the study end. Acknowledging the dynamic nature of disease risk and clinical information contained in the longitudinal markers, we propose an innovative concordance-assisted learning algorithm to derive a real-time risk stratification score. The proposed approach bypasses the need to fit regression models, such as joint models of the longitudinal markers and time-to-event outcome, and hence enjoys the desirable property of model robustness. Simulation studies confirmed that the proposed method has satisfactory performance in dynamically monitoring the risk of developing disease and differentiating high-risk and low-risk population over time. We apply the proposed method to the Alzheimer's Disease Neuroimaging Initiative data and develop a dynamic risk score of Alzheimer's Disease for patients with mild cognitive impairment using multiple longitudinal markers and baseline prognostic factors.