Faculty Bibliography

Subjective cognitive decline (SCD) refers to cognitive concerns that may occur with or without objective impairment on standardized testing. Studies suggest that SCD may be an early clinical marker of Alzheimer's disease and related dementias, and that it can predict future objective cognitive decline and progression to dementia. However, the research that supports these conclusions is primarily based on samples with homogeneous socioeconomic and cultural backgrounds. The limited studies in samples with more diverse representations of racial, ethnic, gender, and sexual orientation characteristics show varying SCD prevalence, correlates, and outcomes. Here, we review this literature, focusing on how to apply the findings to advance our understanding of SCD. We further evaluate how our findings inform a roadmap for future research. Overall, we conclude that broader investigations across more diverse populations enhance our understanding of the factors that may differentially contribute to SCD and shape its utility as a clinical metric.

BackgroundSubjective cognitive decline (SCD) is increasingly recognized in some cases as an early clinical stage in the Alzheimer's disease continuum, yet the factors that predict which individuals will progress to objective impairment remain poorly understood.ObjectiveWe evaluated risk factor differences and cognitive domain markers associated with progression in participants with subjective cognitive decline (SCD) at baseline from the NYU Alzheimer's Disease Research Center.MethodsWe included SCD non-decliners (n = 27), who remained stable, and decliners (n = 24), who progressed to mild cognitive impairment or worse, between the second to sixth yearly follow-up visits. Adjusted mixed-effects models examined group differences and associations between demographic, APOE status, psychometric test performance and comorbidities with longitudinal-decline.ResultsOverall, mean (SD) age was 67.4 (9.2) and total follow-up time was 5.1 (1.8) years. Lower education (14.9 (3.2) versus 17.3 (2.1)), Hispanic ethnicity (50.0% versus 11.0%), and hypercholesterolemia (adjusted odds ratio: 6.67) were risk factors for progression in SCD, p ≤ 0.05, whereas APOE status was not. Notably, SCD decliners were at increased risk for both amnestic and non-amnestic cognitive-decline with psychometric changes in memory, executive, and language domains (p < 0.001 for all).ConclusionsThese findings inform further work on SCD outcomes and related biomarkers, as well as preventive studies that target modifiable risk factors for SCD progression.

INTRODUCTION/BACKGROUND:We assessed plasma biomarkers for the diagnosis of early Alzheimer's disease (AD). METHODS: = 45). Plasma assays for amyloid beta (Aβ) 40, Aβ42, neurofilament light chain protein, glial fibrillary acidic protein, and phosphorylated tau181 levels were measured using single molecule array (Simoa) technology. Neuroinflammation and blood-brain barrier (BBB) biomarkers were measured using the Corplex cytokine 10-Plex kit and the angiogenesis 6-Plex kit, respectively. RESULTS:Biomarker levels were regressed by cognitive group, age, sex, race, and apolipoprotein E apoE ε4 status, yielded significant positive associations between age and numerous AD, neuroinflammation, cytokine, and BBB plasma markers. DISCUSSION/CONCLUSIONS:Linear regression analysis, adjusted for age, sex, race, and ApoE status, revealed significant differences between cognitive groups in levels of several plasma biomarkers and associations with age and sex. Neuroinflammation and BBB dysfunction showed significant positive associations with age across different stages of AD.

BackgroundPeople with subjective cognitive decline (SCD) are at increased risk of Alzheimer's disease. Prior research has suggested that amygdala volume may be more sensitive than hippocampal volume in detecting differences between SCD and age-matched controls, as well as in predicting SCD conversion to mild cognitive impairment. However, the microstructure underlying this volumetric change remains unclear.ObjectiveThis study evaluated amygdala microstructural differences in SCD versus control participants as well as associations between amygdala microstructure and volume, age, memory performance, anxiety, and depression using diffusion imaging.MethodsDiffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging were fit to diffusion MRI images. Group differences and correlations between diffusion metrics, age, memory, anxiety, and depression were assessed in 123 SCD versus 194 age-matched controls over age 55.ResultsIn the left amygdala, SCD participants had significantly lower fractional anisotropy (FA), lower kurtosis fractional anisotropy (KFA), and higher free water fraction (FWF). KFA was lower in the right amygdala in SCD. There were no significant group differences in the amygdala volume. Bilateral KFA negatively correlated with FWF, a marker of neuroinflammation and neurodegeneration. Left FA negatively correlated with orientation dispersion index, a marker of neuritic branching. Right KFA correlated with age only in SCD, while left FA correlated with anxiety only in controls.ConclusionsKFA is sensitive to group differences between SCD and controls in bilateral amygdala. KFA may reflect neuroinflammation and increased sensitivity to aging in the amygdala in SCD.

Head computed tomography (CT) imaging is a widely used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull and cerebrovascular system. It is commonly used as the first-line imaging in neurologic emergencies given its rapidity of image acquisition, safety, cost and ubiquity. Deep learning models may facilitate detection of a wide range of diseases. However, the scarcity of high-quality labels and annotations, particularly among less common conditions, substantially hinders the development of powerful models. To address this challenge, we introduce FM-HCT, a Foundation Model for Head CT for generalizable disease detection, trained using self-supervised learning. Our approach pretrains a deep learning model on a large, diverse dataset of 361,663 non-contrast 3D head CT scans without the need for manual annotations, enabling the model to learn robust, generalizable features. Our results demonstrate that the self-supervised foundation model substantially improves performance on downstream diagnostic tasks compared to models trained from scratch and previous 3D CT foundation models trained on scarce annotated datasets.

INTRODUCTION/BACKGROUND:Measuring plasma biomarkers effectively assesses early-stage Alzheimer's disease. METHODS:Subjects were categorized as cognitively unimpaired (CU) (n = 66), CU with subjective cognitive decline (SCD) (n = 100), and mild cognitive impairment (MCI) (n = 25). Plasma biomarkers measured were amyloid beta (Aβ) 40, Aβ42, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), tau phosphorylated at threonine 181 (pTau181), neuroinflammatory biomarkers, and blood-brain barrier biomarkers. Amyloid and tau positron emission tomography (PET) imaging was performed in 186 and 144 subjects, respectively. RESULTS:Comparing those having MCI, both CU and SCD participants had significantly lower amyloid PET standardized uptake value ratio (SUVR) (p < 0.001; p = 0.005). Higher amyloid PET SUVR was significantly associated with higher pTau181 (p = 0.001) and a higher pTau181/Aβ42 ratio (p < 0.001). Higher tau PET SUVR was associated with lower plasma Aβ42 (p = 0.020), older age (p = 0.005), higher GFAP (p = 0.020), and lower interleukin-8 levels (p < 0.001). DISCUSSION/CONCLUSIONS:Our study supports plasma biomarker monitoring of at-risk patients at various stages of pre-dementia.

Reliable structural brain measurements are essential for studying neurodegeneration and for designing adequately powered aging and Alzheimer's disease (AD) research. We evaluated the test-retest reliability of FreeSurfer 7.1 morphometric measures in 100 older adults (mean age 73.5 years) ranging from cognitively unimpaired to dementia. Each participant underwent two T1-weighted 3T MRI scans on the same scanner within a short interval (mean 5.5 weeks), minimizing biological change. Segmentation was performed in both standard cross-sectional and longitudinal FreeSurfer modes, focusing on AD-relevant volumes of entorhinal cortex, hippocampus, lateral ventricles, choroid plexus, and the AD cortical thickness signature. Reliability was quantified using absolute and root-mean-square test-retest differences, standard deviation of differences, and intraclass correlation coefficients. Longitudinal processing improved precision by 15-50% across most measures compared with cross-sectional processing, with the largest gain observed for entorhinal thickness. Larger, anatomically well-defined regions (e.g., hippocampus, AD signature) demonstrated higher reliability than small structures or those with complex geometry (e.g., entorhinal cortex, choroid plexus). Image quality, indexed by the Euler characteristic, was the only factor significantly associated with measurement variability; reliability was unrelated to age, sex, cognitive status, inter-scan interval, or amyloid/tau PET burden. Power analyses indicated that detecting a 1% within-individual change requires sample sizes ranging from 36 (AD signature) to >300 (entorhinal cortex). We observed low reliability of choroid plexus volumetry by FreeSurfer 7. These results provide practical benchmarks for expected FreeSurfer measurement variability in older adults. They highlight the advantages of longitudinal processing and rigorous quality control for research on brain aging and AD.

INTRODUCTION/BACKGROUND:Though brain fog is common in Long-coronavirus disease 2019 (Long-COVID), the incidence of mild cognitive impairment (MCI) is unknown. METHODS:In an observational cohort study, recovered COVID-positive, Long-COVID, and COVID-negative subjects underwent blinded evaluation using National Alzheimer's Coordinating Center (NACC) and National Institute on Aging (NIA) -Alzheimer's Association diagnostic criteria for dementia and MCI. The cumulative incidence of MCI was calculated for each group, and the hazard of MCI was compared between groups. RESULTS:Among 260 subjects, the cumulative incidence of MCI over 4.4 years was higher with Long-COVID (27%) versus recovered-COVID (5%) or COVID-negative status (1%). There was a higher hazard of MCI for patients with Long-COVID compared to those without (hazard ratio [HR] 3.93, 95% confidence interval [CI] 1.86-8.31, p < 0.001), and specifically for the Alzheimer's disease (AD) -related MCI subtype (HR 3.20, 95% confidence interval [CI] 1.14-9.00, p = 0.027). DISCUSSION/CONCLUSIONS:The cumulative incidence and adjusted hazard of MCI (and specifically AD-related MCI) at 4.4 years was significantly higher among Long-COVID patients compared to recovered-COVID and COVID-negative controls.

Neurodegeneration, along with amyloid and tau, define the AT(N) framework of Alzheimer's disease that has shaped the development of diagnostics and therapeutics. Yet, biomarker development for neurodegeneration has lagged behind that for amyloid and tau, with limited definition of its heterogeneous microstructural aspects that may each serve as critical measures. In this issue of the JCI, Gong et al. leveraged diffusion MRI to derive a unique measure of axonal injury or axonal density index (ADI). Through cross-sectional and longitudinal analyses, they demonstrated that the ADI has superior performance in detecting, tracking, and predicting clinical impairment compared with prior diffusion MRI methods to evaluate axonal health and standard biomarkers of amyloid and tau. As such, the ADI measure may serve as an important expansion of the neurodegeneration biomarker repertoire.