Variants in the MS4A cluster interact with soluble TREM2 expression on biomarkers of neuropathology

Recent evidence suggests that Alzheimer's disease (AD) genetic risk variants (rs1582763 and rs6591561) of the MS4A locus are genome-wide significant regulators of soluble TREM2 levels such that the minor allele of the protective variant (rs1582763) is associated with higher sTREM2 and lower AD risk while the minor allele of (rs6591561) relates to lower sTREM2 and higher AD risk. Our group previously found that higher sTREM2 relates to higher Aβ40, worse blood-brain barrier (BBB) integrity (measured with the CSF/plasma albumin ratio), and higher CSF tau, suggesting strong associations with amyloid abundance and both BBB and neurodegeneration complicate interpretation. We expand on this work by leveraging these common variants as genetic tools to tune the interpretation of high CSF sTREM2, and by exploring the potential modifying role of these variants on the well-established associations between CSF sTREM2 as well as TREM2 transcript levels in the brain with AD neuropathology. Biomarker analyses leveraged data from the Vanderbilt Memory & Aging Project (n = 127, age = 72 ± 6.43) and were replicated in the Alzheimer's Disease Neuroimaging Initiative (n = 399, age = 73 ± 7.39). Autopsy analyses were performed leveraging data from the Religious Orders Study and Rush Memory and Aging Project (n = 577, age = 89 ± 6.46). We found that the protective variant rs1582763 attenuated the association between CSF sTREM2 and Aβ40 (β = -0.44, p-value = 0.017) and replicated this interaction in ADNI (β = -0.27, p = 0.017). We did not observe this same interaction effect between TREM2 mRNA levels and Aβ peptides in brain (Aβ total β = -0.14, p = 0.629; Aβ1-38, β = 0.11, p = 0.200). In contrast to the effects on Aβ, the minor allele of this same variant seemed to enhance the association with blood-brain barrier dysfunction (β = 7.0e-4, p = 0.009), suggesting that elevated sTREM2 may carry a much different interpretation in carriers vs. non-carriers of this allele. When evaluating the risk variant (rs6591561) across datasets, we did not observe a statistically significant interaction against any outcome in VMAP and observed opposing directions of associations in ADNI and ROS/MAP on Aβ levels. Together, our results suggest that the protective effect of rs1582763 may act by decoupling the associations between sTREM2 and amyloid abundance, providing important mechanistic insight into sTREM2 changes and highlighting the need to incorporate genetic context into the analysis of sTREM2 levels, particularly if leveraged as a clinical biomarker of disease in the future.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

Perivascular space burden interacts with APOE-ε4 status on cognition in older adults

Enlarged perivascular spaces (ePVS) may adversely affect cognition. Little is known about how basal ganglia ePVS interact with apolipoprotein (APOE)-ε4 status. Vanderbilt Memory and Aging Project participants (n = 326, 73 ± 7, 59% male) underwent 3 T brain MRI at baseline to assess ePVS and longitudinal neuropsychological assessments. The interaction between ePVS volume and APOE-ε4 carrier status was related to baseline outcomes using ordinary least squares regressions and longitudinal cognition using linear mixed-effects regressions. ePVS volume interacted with APOE-ε4 status on cross-sectional naming performance (β = -0.002, p = 0.002), and executive function excluding outliers (β = 0.001, p = 0.009). There were no significant longitudinal interactions (p-values>0.10) except for Coding excluding outliers (β = 0.002, p = 0.05). While cross-sectional models stratified by APOE-ε4 status indicated greater ePVS related to worse cognition mostly in APOE-ε4 carriers, longitudinal models stratified by APOE-ε4 status showed greater ePVS volume related to worse cognition among APOE-ε4 non-carriers only. Results indicated that greater ePVS volume interacts with APOE-ε4 status on cognition cross-sectionally. Longitudinally, the association of greater ePVS volume and worse cognition appears stronger in APOE-ε4 non-carriers, possibly due to the deleterious effects of APOE-ε4 on cognition across the lifespan.

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Purpose

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.

Approach

As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γ ^ s f * , the ComBat-estimated site-shift; and (iii) δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.

Results

ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.

Conclusion

Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32x10-7; T1: β=-1.331, p=6.52x10-7; FW+T1: β=-1.476, p=2.53x10-10; executive function, FW: β=-1.276, p=1.46x10-9; T1: β=-1.337, p=2.52x10-7; FW+T1: β=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62x10-11; T1: β=-0.097, p=1.40x10-8; FW+T1: β=-0.101, p=1.35x10-11; executive function, FW: β=-0.125, p=1.20x10-10; T1: β=-0.163, p=4.25x10-12; FW+T1: β=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32×10-7; T1: β=-1.331, p=6.52×10-7; FW+T1: β=-1.476, p=2.53×10-10; executive function, FW: β=-1.276, p=1.46×10-9; T1: β=-1.337, p=2.52×10-7; FW+T1: β=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62×10-11; T1: β=-0.097, p=1.40×10-8; FW+T1: β=-0.101, p=1.35×10-11; executive function, FW: β=-0.125, p=1.20×10-10; T1: β=-0.163, p=4.25×10-12; FW+T1: β=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging

Introduction

It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging.

Methods

Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed.

Results

While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging.

Conclusions

There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes.

HIGHLIGHTS

Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.

MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal

Objective

Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space.

Methods

We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment.

Conclusion

MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH.

Significance

Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging

INTRODUCTION

It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging.

METHODS

Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed.

RESULTS

While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging.

CONCLUSIONS

There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes.

HIGHLIGHTS

Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.

White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease

Introduction

White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum.

Methods

Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free-water (FW) corrected and conventional, and FW-corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status.

Results

Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished.

Discussion

White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD.

Highlights

Longitudinal ComBat successfully harmonized large-scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free-water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW-corrected models may provide complementary information.

Enlarged perivascular space burden associations with arterial stiffness and cognition

Enlarged perivascular spaces (ePVS) are difficult to quantify, and their etiologies and consequences are poorly understood. Vanderbilt Memory and Aging Project participants (n = 327, 73 ± 7 years) completed 3T brain MRI to quantify ePVS volume and count, longitudinal neuropsychological assessment, and cardiac MRI to quantify aortic stiffness. Linear regressions related (1) PWV to ePVS burden and (2) ePVS burden to cross-sectional and longitudinal neuropsychological performance adjusting for key demographic and medical factors. Higher aortic stiffness related to greater basal ganglia ePVS volume (β = 7.0×10-5, p = 0.04). Higher baseline ePVS volume was associated with worse baseline information processing (β = -974, p = 0.003), executive function (β = -81.9, p < 0.001), and visuospatial performances (β = -192, p = 0.02) and worse longitudinal language (β = -54.9, p = 0.05), information processing (β = -147, p = 0.03), executive function (β = -10.9, p = 0.03), and episodic memory performances (β = -10.6, p = 0.02). Results were similar for ePVS count. Greater arterial stiffness relates to worse basal ganglia ePVS burden, suggesting cardiovascular aging as an etiology. ePVS burden is associated with adverse cognitive trajectory, emphasizing the clinical relevance of ePVS.

Baseline grey matter volumes and white matter hyperintensities predict decline in functional activities in older adults over a 5-year follow-up period

INTRODUCTION

Functional independence is an essential predictor of quality of life in aging, yet few accessible predictors of functional decline have been identified. This study examined associations between baseline structural neuroimaging markers and longitudinal functional status.

METHODS

Linear mixed effects models with follow-up time interaction terms related baseline grey matter volume and white matter hyperintensities (WMHs) to functional trajectory, adjusting for demographic and medical covariates. Subsequent models assessed interactions with cognitive status and apolipoprotein E (APOE) ε4 status.

RESULTS

Smaller baseline grey matter volumes, particularly in regions commonly affected by Alzheimer's disease (AD), and greater baseline WMHs were associated with faster functional decline over a mean 5-year follow-up. Effects were stronger in APOE-ε4 carriers on grey matter variables. Cognitive status interacted with most MRI variables.

DISCUSSION

Greater atrophy in AD-related regions and higher WMH burden at study entry were associated with faster functional decline, particularly among participants at increased risk of AD.

Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging

Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.

Biological correlates of elevated soluble TREM2 in cerebrospinal fluid

Cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells-2 (sTREM2) is an emerging biomarker of neuroinflammation in Alzheimer's disease (AD). Yet, sTREM2 expression has not been systematically evaluated in relation to concomitant drivers of neuroinflammation. While associations between sTREM2 and tau in CSF are established, we sought to determine additional biological correlates of CSF sTREM2 during the prodromal stages of AD by evaluating CSF Aβ species (Aβ_x-40), a fluid biomarker of blood-brain barrier integrity (CSF/plasma albumin ratio), and CSF biomarkers of neurodegeneration measured in 155 participants from the Vanderbilt Memory and Aging Project. A novel association between high CSF levels of both sTREM2 and Aβ_x-40 was observed and replicated in an independent dataset. Aβ_x-40 levels, as well as the CSF/plasma albumin ratio, explained additional and unique variance in sTREM2 levels above and beyond that of CSF biomarkers of neurodegeneration. The component of sTREM2 levels correlated with Aβ_x-40 levels best predicted future cognitive performance. We highlight potential contributions of Aβ homeostasis and blood-brain barrier integrity to elevated CSF sTREM2, underscoring novel biomarker associations relevant to disease progression and clinical outcome measures.

Lower cerebral oxygen utilization is associated with Alzheimer's disease-related neurodegeneration and poorer cognitive performance among apolipoprotein E ε4 carriers

Oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO₂) are markers of cerebral oxygen homeostasis and metabolism that may offer insights into abnormal changes in brain aging. The present study cross-sectionally related OEF and CMRO₂ to cognitive performance and structural neuroimaging variables among older adults (n = 246, 74 ± 7 years, 37% female) and tested whether apolipoprotein E (APOE)-ε4 status modified these associations. Main effects of OEF and CMRO₂ were null (p-values >0.06), and OEF interactions with APOE-ε4 status on cognitive and structural imaging outcomes were null (p-values >0.06). However, CMRO₂ interacted with APOE-ε4 status on language (p = 0.002), executive function (p = 0.03), visuospatial (p = 0.005), and episodic memory performances (p = 0.03), and on hippocampal (p = 0.006) and inferior lateral ventricle volumes (p = 0.02). In stratified analyses, lower oxygen metabolism related to worse language (p = 0.02) and episodic memory performance (p = 0.03) among APOE-ε4 carriers only. Associations between CMRO₂ and cognitive performance were primarily driven by APOE-ε4 carriers with existing cognitive impairment. Congruence across language and episodic memory results as well as hippocampal and inferior lateral ventricle volume findings suggest that APOE-ε4 may interact with cerebral oxygen metabolism in the pathogenesis of Alzheimer's disease and related neurodegeneration.

Axonal Injury Partially Mediates Associations Between Increased Left Ventricular Mass Index and White Matter Damage

BACKGROUND AND PURPOSE

Left ventricular (LV) mass index is a marker of subclinical LV remodeling that relates to white matter damage in aging, but molecular pathways underlying this association are unknown. This study assessed if LV mass index related to cerebrospinal fluid (CSF) biomarkers of microglial activation (sTREM2 [soluble triggering receptor expressed on myeloid cells 2]), axonal injury (NFL [neurofilament light]), neurodegeneration (total-tau), and amyloid-β, and whether these biomarkers partially accounted for associations between increased LV mass index and white matter damage. We hypothesized higher LV mass index would relate to greater CSF biomarker levels, and these pathologies would partially mediate associations with cerebral white matter microstructure.

METHODS

Vanderbilt Memory and Aging Project participants who underwent cardiac magnetic resonance, lumbar puncture, and diffusion tensor imaging (n=142, 72±6 years, 37% mild cognitive impairment [MCI], 32% APOE-ε4 positive, LV mass index 51.4±8.1 g/m², NFL 1070±588 pg/mL) were included. Linear regressions and voxel-wise analyses related LV mass index to each biomarker and diffusion tensor imaging metrics, respectively. Follow-up models assessed interactions with MCI and APOE-ε4. In models where LV mass index significantly related to a biomarker and white matter microstructure, we assessed if the biomarker mediated white matter associations.

RESULTS

Among all participants, LV mass index was unrelated to CSF biomarkers (P>0.33). LV mass index interacted with MCI (P=0.01), such that higher LV mass index related to increased NFL among MCI participants. Associations were also present among APOE-ε4 carriers (P=0.02). NFL partially mediated up to 13% of the effect of increased LV mass index on white matter damage.

CONCLUSIONS

Subclinical cardiovascular remodeling, measured as an increase in LV mass index, is associated with neuroaxonal degeneration among individuals with MCI and APOE-ε4. Neuroaxonal degeneration partially reflects associations between higher LV mass index and white matter damage. Findings highlight neuroaxonal degeneration, rather than amyloidosis or microglia, may be more relevant in pathways between structural cardiovascular remodeling and white matter damage.

Elevated Aortic Pulse Wave Velocity Relates to Longitudinal Gray and White Matter Changes

OBJECTIVE

To determine whether baseline aortic stiffness, measured by aortic pulse wave velocity (PWV), relates to longitudinal cerebral gray or white matter changes among older adults. Baseline cardiac magnetic resonance imaging will be used to assess aortic PWV while brain magnetic resonance imaging will be used to assess gray matter and white matter hyperintensity (WMH) volumes at baseline, 18 months, 3 years, 5 years, and 7 years. Approach and Results: Aortic PWV (m/s) was quantified from cardiac magnetic resonance. Multimodal 3T brain magnetic resonance imaging included T₁-weighted imaging for quantifying gray matter volumes and T₂-weighted fluid-attenuated inversion recovery imaging for quantifying WMHs. Mixed-effects regression models related baseline aortic PWV to longitudinal gray matter volumes (total, frontal, parietal, temporal, occipital, hippocampal, and inferior lateral ventricle) and WMH volumes (total, frontal, parietal, temporal, and occipital) adjusting for age, sex, race/ethnicity, education, cognitive diagnosis, Framingham stroke risk profile, APOE (apolipoprotein E)-ε4 carrier status, and intracranial volume. Two hundred seventy-eight participants (73±7 years, 58% male, 87% self-identified as non-Hispanic White, 159 with normal cognition, and 119 with mild cognitive impairment) from the Vanderbilt Memory & Aging Project (n=335) were followed on average for 4.9±1.6 years with PWV measurements occurring from September 2012 to November 2014 and longitudinal brain magnetic resonance imaging measurements occurring from September 2012 to June 2021. Higher baseline aortic PWV was related to greater decrease in hippocampal (β=-3.6 [mm³/y]/[m/s]; [95% CI, -7.2 to -0.02] P=0.049) and occipital lobe (β=-34.2 [mm³/y]/[m/s]; [95% CI, -67.8 to -0.55] P=0.046) gray matter volume over time. Higher baseline aortic PWV was related to greater increase in WMH volume over time in the temporal lobe (β=17.0 [mm³/y]/[m/s]; [95% CI, 7.2-26.9] P<0.001). All associations may be driven by outliers.

CONCLUSIONS

In older adults, higher baseline aortic PWV related to greater decrease in gray matter volume and greater increase in WMHs over time. Because of unmet cerebral metabolic demands and microvascular remodeling, arterial stiffening may preferentially affect certain highly active brain regions like the temporal lobes. These same regions are affected early in the course of Alzheimer disease.

A-4 Amyloid Status Modifies the Association between Subjective Cognitive Decline and Brain MRI Metrics

Objective

Subjective cognitive decline (SCD), a potential precursor to Alzheimer’s disease (ad), has been associated with increased neurodegeneration and cerebrovascular disease longitudinally. However, the impact of amyloid status, an early pathological marker of Alzheimer’s disease (ad) on these longitudinal associations is less clear. Here, we related baseline SCD to longitudinal biomarkers of brain health in the context of amyloid status.

Method

Participants included 139 non-demented older adults (72 ± 7 years) from the Vanderbilt Memory & Aging Project who completed a SCD questionnaire and fasting lumbar-puncture to quantify amyloid status (defined using published cutoffs of amyloid-beta42 levels) at baseline. 3 T brain-MRI to measure gray and white matter hyperintensity (WMH) volumes was collected at baseline, 18-months, 3-years, and 5-years. Linear mixed effects models assessed if baseline SCD X amyloid status was associated with longitudinal total and lobar grey and white matter volumes, covarying for baseline age, sex, race/ethnicity, education, diagnosis, mood, and apolipoprotein-Ee4 status. Models were also stratified by baseline amyloid status.

Results

Baseline SCD score and amyloid status interacted with total gray (p = 0.02) and WMH volume (p &lt; 0.05). In stratified models, higher total SCD predicted increased inferior lateral ventricular volume (p &lt; 0.001) among amyloid positive individuals. Conversely, in amyloid negative, greater baseline SCD was associated with increased WMH volumes globally (p = 0.03) and in the frontal and parietal lobes (p-values &lt;0.035).

Conclusion

In the presence of amyloid, the presence of SCD is predictive of neurodegeneration in ad-specific regions. Conversely, SCD without amyloidosis may reflect a cerebrovascular disease, indicated by WMHs. Results highlight how amyloid status may help delineate etiologies of SCD.

A-4 Cerebrospinal Fluid and Plasma Neurofilament Light in Relation to Longitudinal Objective and Subjective Cognitive Decline in Older Adults

Objective

Cerebrospinal fluid (CSF) and plasma neurofilament light (NFL) concentrations were assessed in relation to longitudinal objective and subjective cognitive outcomes in older adults ranging from normal cognition to mild cognitive impairment. Interactive effects were assessed for apolipoprotein E ϵ4 (APOE4) carriership, a strong genetic risk factor for Alzheimer’s disease and molecular moderator of vascular disease.

Method

Vanderbilt Memory & Aging Project participants (CSF n = 149, 72 ± 6 years; plasma n = 333, 73 ± 7 years) underwent fasting blood draw and lumbar puncture at baseline for NFL quantification. Serial neuropsychological assessments and subjective cognitive decline (SCD) questionnaires were completed at 18-month increments. Linear mixed effects regression models adjusted for age, sex, race/ethnicity, education, APOE4 carriership (for main effect models), and depressed mood. NFL x APOE4 interaction terms were used as predictors in follow-up models.

Results

CSF NFL predicted steeper declines in an executive functioning composite score (β = −0.0001, p = 0.001) and WAIS-IV Coding (β = −0.001, p = 0.001). An APOE4 interaction was present for executive functioning (β = −0.0002, p = 0.005) such that CSF NFL associations with longitudinal decline were stronger among APOE4+ participants. Plasma NFL predicted worsening SCD (β = 0.27, p = 0.002) and objective cognitive decline across all domains (p-values &lt;0.05), with multiple APOE4 interactions (p-values &lt;0.05) suggesting stronger associations with objective cognitive decline among APOE4+ participants.

Conclusions

Both CSF and plasma NFL detect neuropathology associated with cognitive decline among non-demented older adults, especially among APOE4 carriers. Findings further support the value of SCD as reflecting neurodegenerative changes associated with accelerated cognitive aging.

A-8 Sex Modifies the Association between CSF Neurogranin and Cognitive Decline in Older Adults

Objective

Neurogranin is a postsynaptic protein associated with declining memory and executive functioning in Alzheimer’s disease (ad). While previous research suggests neurogranin concentrations in ad are higher in women, it is unclear whether sex differences exist in earlier disease stages or predict different cognitive outcomes. This study investigates cerebrospinal fluid (CSF) neurogranin in relation to longitudinal cognitive decline in older adults ranging from normal cognition to mild cognitive impairment, assessing for interactions by sex.

Method

Vanderbilt Memory & Aging Project participants completed baseline fasting lumbar puncture (n = 155, 73 ± 8 years) for neurogranin quantification and serial neuropsychological assessments at 18-month intervals. Linear mixed effect regression adjusting for age, sex (for main effect models), race/ethnicity, education, cognitive diagnosis, depressed mood, and APOE-ε4 carrier status.

Results

CSF neurogranin predicted worse cognitive decline across multiple domains (p-values &lt;0.05). Sex interactions existed for Boston Naming Test (β = −0.007, p = 0.001), WAIS-IV Coding (β = −0.01, p = 0.02), Hooper Visual Orientation Test (β = −0.005, p = 0.02), and Category (animals) Fluency (β = −0.005, p = 0.048) wherein CSF neurogranin predicted worse decline among women (p-values≤0.03) but not men (p-values≥0.36).

Conclusion

Results suggest that among nondemented older adults, CSF neurogranin predicts worse longitudinal cognitive decline in women but not in men. Further research is needed to elucidate underlying mechanisms that may account for these differences, such as possible sex hormone factors. These findings highlight the importance of pursuing individualized prevention and treatment approaches to combat accelerated cognitive aging that take into account the possibility of multiple, divergent disease pathways preceding ad and dementia among various demographic groups.

Association of Aortic Stiffness With Biomarkers of Neuroinflammation, Synaptic Dysfunction, and Neurodegeneration

OBJECTIVES

To test the hypothesis that increased aortic stiffening is associated with greater CSF evidence of core Alzheimer disease pathology (β-amyloid [Aβ], phosphorylated tau [p-tau]), neurodegeneration (total tau [t-tau]), synaptic dysfunction (neurogranin), neuroaxonal injury (neurofilament light [NFL]), and neuroinflammation (YKL-40, soluble triggering receptor expressed on myeloid cells 2 [sTREM2]), we analyzed pulse wave velocity (PWV) data and CSF data among older adults.

METHODS

Participants free of stroke and dementia from the Vanderbilt Memory and Aging Project, an observational community-based study, underwent cardiac magnetic resonance to assess aortic PWV (meters per second) and lumbar puncture to obtain CSF. Linear regressions related aortic PWV to CSF Aβ, p-tau, t-tau, neurogranin, NFL, YKL-40, and sTREM2 concentrations after adjustment for age, race/ethnicity, education, apolipoprotein (APOE) ε4 status, Framingham Stroke Risk Profile, and cognitive diagnosis. Models were repeated testing PWV interactions with age, diagnosis, APOE ε4, and hypertension on each biomarker.

RESULTS

One hundred forty-six participants were examined (age 72 ± 6 years). Aortic PWV interacted with age on p-tau (β = 0.31, p = 0.04), t-tau, (β = 2.67, p = 0.05), neurogranin (β = 0.94, p = 0.04), and sTREM2 (β = 20.4, p = 0.05). Among participants >73 years of age, higher aortic PWV related to higher p-tau (β = 2.4, p = 0.03), t-tau (β = 19.3, p = 0.05), neurogranin (β = 8.4, p = 0.01), and YKL-40 concentrations (β = 7,880, p = 0.005). Aortic PWV had modest interactions with diagnosis on neurogranin (β = -10.76, p = 0.03) and hypertension status on YKL-40 (β = 18,020, p < 0.001).

CONCLUSIONS

Among our oldest participants, ≥74 years of age, greater aortic stiffening is associated with in vivo biomarker evidence of neuroinflammation, tau phosphorylation, synaptic dysfunction, and neurodegeneration, but not amyloidosis. Central arterial stiffening may lead to cumulative cerebral microcirculatory damage and reduced blood flow delivery to tissue, resulting in neuroinflammation and neurodegeneration in more advanced age.

Lower Left Ventricular Ejection Fraction Relates to Cerebrospinal Fluid Biomarker Evidence of Neurodegeneration in Older Adults

BACKGROUND

Subclinical cardiac dysfunction is associated with decreased cerebral blood flow, placing the aging brain at risk for Alzheimer's disease (AD) pathology and neurodegeneration.

OBJECTIVE

This study investigates the association between subclinical cardiac dysfunction, measured by left ventricular ejection fraction (LVEF), and cerebrospinal fluid (CSF) biomarkers of AD and neurodegeneration.

METHODS

Vanderbilt Memory & Aging Project participants free of dementia, stroke, and heart failure (n = 152, 72±6 years, 68% male) underwent echocardiogram to quantify LVEF and lumbar puncture to measure CSF levels of amyloid-β42 (Aβ42), phosphorylated tau (p-tau), and total tau (t-tau). Linear regressions related LVEF to CSF biomarkers, adjusting for age, sex, race/ethnicity, education, Framingham Stroke Risk Profile, cognitive diagnosis, and apolipoprotein E ɛ4 status. Secondary models tested an LVEF x cognitive diagnosis interaction and then stratified by diagnosis (normal cognition (NC), mild cognitive impairment (MCI)).

RESULTS

Higher LVEF related to decreased CSF Aβ42 levels (β= -6.50, p = 0.04) reflecting greater cerebral amyloid accumulation, but this counterintuitive result was attenuated after excluding participants with cardiovascular disease and atrial fibrillation (p = 0.07). We observed an interaction between LVEF and cognitive diagnosis on CSF t-tau (p = 0.004) and p-tau levels (p = 0.002), whereas lower LVEF was associated with increased CSF t-tau (β= -9.74, p = 0.01) and p-tau in the NC (β= -1.41, p = 0.003) but not MCI participants (p-values>0.13).

CONCLUSIONS

Among cognitively normal older adults, subclinically lower LVEF relates to greater molecular evidence of tau phosphorylation and neurodegeneration. Modest age-related changes in cardiovascular function may have implications for pathophysiological changes in the brain later in life.

Lower Cardiac Output Relates to Longitudinal Cognitive Decline in Aging Adults

Background

Subclinical reductions in cardiac output correspond to lower cerebral blood flow (CBF), placing the brain at risk for functional changes.

Objectives

This study aims to establish the consequences of reduced cardiac output on longitudinal cognitive outcomes in aging adults.

Methods

Vanderbilt Memory and Aging Project participants free of clinical dementia and heart failure (n = 306, 73 ± 7, 58% male) underwent baseline echocardiography to assess cardiac output (L/min) and longitudinal neuropsychological assessment at baseline, 18 months, 3 and 5 years. Linear mixed-effects regressions related cardiac output to trajectory for each longitudinal neuropsychological outcome, adjusting for age, sex, race/ethnicity, education, body surface area, Framingham Stroke Risk Profile score, apolipoprotein E (APOE) ε4 status and follow-up time. Models were repeated, testing interactions with cognitive diagnosis and APOE-ε4 status.

Results

Lower baseline cardiac output related to faster declines in language (β = 0.11, p = 0.01), information processing speed (β = 0.31, p = 0.006), visuospatial skills (β = 0.09, p = 0.03), and episodic memory (β = 0.02, p = 0.001). No cardiac output x cognitive diagnosis interactions were observed (p > 0.26). APOE-ε4 status modified the association between cardiac output and longitudinal episodic memory (β = 0.03, p = 0.047) and information processing speed outcomes (β = 0.55, p = 0.02) with associations stronger in APOE-ε4 carriers.

Conclusion

The present study provides evidence that even subtle reductions in cardiac output may be associated with more adverse longitudinal cognitive health, including worse language, information processing speed, visuospatial skills, and episodic memory performances. Preservation of healthy cardiac functioning is important for maintaining optimal brain aging among older adults.

Lower cardiac output is associated with neurodegeneration among older adults with normal cognition but not mild cognitive impairment

Subclinical cardiac dysfunction is associated with smaller total brain volume on magnetic resonance imaging (MRI). To study whether cardiac output relates to regional measurements of grey and white matter structure, older adults (n = 326) underwent echocardiogram to quantify cardiac output (L/min) and brain MRI. Linear regressions related cardiac output to grey matter volumes measured on T₁ and white matter hyperintensities assessed on T₂-FLAIR. Voxelwise analyses related cardiac output to diffusion tensor imaging adjusting for demographic, genetic, and vascular risk factors. Follow-up models assessed a cardiac output x diagnosis interaction with stratification (normal cognition, mild cognitive impairment). Cardiac output interacted with diagnosis, such that lower cardiac output related to smaller total grey matter (p = 0.01), frontal lobe (p = 0.01), and occipital lobe volumes (p = 0.01) among participants with normal cognition. When excluding participants with cardiovascular disease and atrial fibrillation, associations emerged with smaller parietal lobe (p = 0.005) and hippocampal volume (p = 0.05). Subtle age-related cardiac changes may disrupt neuronal homeostasis and impact grey matter integrity prior to cognitive impairment.

Adverse Vascular Risk Relates to Cerebrospinal Fluid Biomarker Evidence of Axonal Injury in the Presence of Alzheimer's Disease Pathology

BACKGROUND

Vascular risk factors promote cerebral small vessel disease and neuropathological changes, particularly in white matter where large-caliber axons are located. How Alzheimer's disease pathology influences the brain's vulnerability in this regard is not well understood.

OBJECTIVE

Systemic vascular risk was assessed in relation to cerebrospinal fluid concentrations of neurofilament light, a biomarker of large-caliber axonal injury, evaluating for interactions by clinical and protein markers of Alzheimer's disease.

METHODS

Among Alzheimer's Disease Neuroimaging Initiative participants with normal cognition (n = 117), mild cognitive impairment (n = 190), and Alzheimer's disease (n = 95), linear regression related vascular risk (as measured by the modified Framingham Stroke Risk Profile) to neurofilament light, adjusting for age, sex, education, and cognitive diagnosis. Interactions were assessed by cognitive diagnosis, and by cerebrospinal fluid markers of Aβ42, hyperphosphorylated tau, and total tau.

RESULTS

Vascular risk and neurofilament light were not related in the main effect model (p = 0.08). However, interactions emerged for total tau (p = 0.01) and hyperphosphorylated tau (p = 0.002) reflecting vascular risk becoming more associated with cerebrospinal fluid neurofilament light in the context of greater concentrations of tau biomarkers. An interaction also emerged for the Alzheimer's disease biomarker profiles (p = 0.046) where in comparison to the referent 'normal' biomarker group, individuals with abnormal levels of both Aβ42 and total tau showed stronger associations between vascular risk and neurofilament light.

CONCLUSION

Older adults may be more vulnerable to axonal injury in response to higher vascular risk burdens in the context of concomitant Alzheimer's disease pathology.

Mild Cognitive Impairment Staging Yields Genetic Susceptibility, Biomarker, and Neuroimaging Differences

Introduction

While Alzheimer’s disease (AD) is divided into severity stages, mild cognitive impairment (MCI) remains a solitary construct despite clinical and prognostic heterogeneity. This study aimed to characterize differences in genetic, cerebrospinal fluid (CSF), neuroimaging, and neuropsychological markers across clinician-derived MCI stages.

Methods

Vanderbilt Memory & Aging Project participants with MCI were categorized into 3 severity subtypes at screening based on neuropsychological assessment, functional assessment, and Clinical Dementia Rating interview, including mild (n = 18, 75 ± 8 years), moderate (n = 89 72 ± 7 years), and severe subtypes (n = 18, 78 ± 8 years). At enrollment, participants underwent neuropsychological testing, 3T brain magnetic resonance imaging (MRI), and optional fasting lumbar puncture to obtain CSF. Neuropsychological testing and MRI were repeated at 18-months, 3-years, and 5-years with a mean follow-up time of 3.3 years. Ordinary least square regressions examined cross-sectional associations between MCI severity and apolipoprotein E (APOE)-ε4 status, CSF biomarkers of amyloid beta (Aβ), phosphorylated tau, total tau, and synaptic dysfunction (neurogranin), baseline neuroimaging biomarkers, and baseline neuropsychological performance. Longitudinal associations between baseline MCI severity and neuroimaging and neuropsychological trajectory were assessed using linear mixed effects models with random intercepts and slopes and a follow-up time interaction. Analyses adjusted for baseline age, sex, race/ethnicity, education, and intracranial volume for MRI models.

Results

Stages differed at baseline on APOE-ε4 status (early < middle = late; p-values < 0.03) and CSF Aβ (early > middle = late), phosphorylated and total tau (early = middle < late; p-values < 0.05), and neurogranin concentrations (early = middle < late; p-values < 0.05). MCI stage related to greater longitudinal cognitive decline, hippocampal atrophy, and inferior lateral ventricle dilation (early < late; p-values < 0.03).

Discussion

Clinician staging of MCI severity yielded longitudinal cognitive trajectory and structural neuroimaging differences in regions susceptible to AD neuropathology and neurodegeneration. As expected, participants with more severe MCI symptoms at study entry had greater cognitive decline and gray matter atrophy over time. Differences are likely attributable to baseline differences in amyloidosis, tau, and synaptic dysfunction. MCI staging may provide insight into underlying pathology, prognosis, and therapeutic targets.

Free-water metrics in medial temporal lobe white matter tract projections relate to longitudinal cognitive decline

Although hippocampal volume has served as a long-standing predictor of cognitive decline, diffusion magnetic resonance imaging studies of white matter have shown similar relationships. Still, it remains unclear if gray matter and white matter interact to predict cognitive impairment and longitudinal decline. Here, we investigate whether free-water (FW) and FW-corrected fractional anisotropy (FA_T) within medial temporal lobe white matter tracts provides meaningful contribution to cognition and cognitive decline beyond hippocampal volume. Using data from the Vanderbilt Memory & Aging Project (n = 319), we found that FW was associated with baseline memory and executive function beyond that of hippocampal volume and other comorbidities. Longitudinal analyses demonstrated significant interactions of hippocampal volume and inferior longitudinal fasciculus (p = 0.043) and cingulum bundle (p = 0.025) FA_T on memory decline and with fornix FA_T (p = 0.025) on decline in executive function. Results suggest that FW metrics of white matter have a unique role in cognitive decline and should be included in theoretical models of aging, cerebrovascular disease, and Alzheimer's disease.

Cerebrospinal fluid biomarkers of neurodegeneration, synaptic dysfunction, and axonal injury relate to atrophy in structural brain regions specific to Alzheimer's disease

INTRODUCTION

Patterns of atrophy can distinguish normal cognition from Alzheimer's disease (AD), but neuropathological drivers of this pattern are unknown. This study examined associations between cerebrospinal fluid biomarkers of AD pathology, synaptic dysfunction, and neuroaxonal injury with two AD imaging signatures.

METHODS

Signatures were calculated using published guidelines. Linear regressions related each biomarker to both signatures, adjusting for demographic factors. Bootstrapped analyses tested if associations were stronger with one signature versus the other.

RESULTS

Increased phosphorylated tau (p-tau), total tau, and neurofilament light (P-values <.045) related to smaller signatures (indicating greater atrophy). Diagnosis and sex modified associations between p-tau and neurogranin (P-values<.05) and signatures, such that associations were stronger among participants with mild cognitive impairment and female participants. The strength of associations did not differ between signatures.

DISCUSSION

Increased evidence of neurodegeneration, axonopathy, and tau phosphorylation relate to greater AD-related atrophy. Tau phosphorylation and synaptic dysfunction may be more prominent in AD-affected regions in females.