Women show greater pathological Tau biomarkers than men along the Alzheimer's disease (AD) continuum, particularly among apolipoprotein ε-E4 (APOE4) carriers; however, the reason for this sex difference in unknown. Sex differences often indicate an underlying role of sex hormones. We examined whether testosterone levels might influence this sex difference and the modifying role of APOE4 status. Analyses included 172 participants (25 cognitively normal, 97 mild cognitive impairment, 50 AD participants) from the Alzheimer's Disease Neuroimaging Initiative (34% female, 54% APOE4 carriers, aged 55-90). We examined the separate and interactive effects of plasma testosterone levels and APOE4 on cerebrospinal fluid phosphorylated-tau181 (p-Tau) levels in the overall sample and the sex difference in p-Tau levels before and after adjusting for testosterone. A significant APOE4-by-testosterone interaction revealed that lower testosterone levels related to higher p-Tau levels among APOE4 carriers regardless of sex. As expected, women had higher p-Tau levels than men among APOE4 carriers only, yet this difference was eliminated upon adjustment for testosterone. Results suggest that testosterone is protective against p-Tau particularly among APOE4 carriers. The lower testosterone levels that typically characterize women may predispose them to pathological Tau, particularly among female APOE4 carriers.

The APOE effect on Alzheimer Disease (AD) risk is stronger in women than in men but its mechanisms have not been established. We assessed the APOE-by-sex interaction on core CSF biomarkers, brain metabolism and structure in healthy elderly control individuals (HC).

The apolipoprotein E gene (APOE) is the most important genetic risk factor for sporadic Alzheimer disease, with the ε4 allele being associated with increased cerebral amyloid-β and tau pathologies. Although APOE has been suggested to have a stronger effect in women as compared to men, there is a lack of comprehensive assessment on how the interactive effect of APOE and sex modulates regional vulnerability to tau accumulation. We previously have shown the regional vulnerability to the interactive effect of tau and APOE, yet the sex difference was not specifically addressed. In this study, we leveraged PET imaging data from the Translational Biomarkers in Aging and Dementia cohort at McGill University Research Centre for Studies in Aging to elucidate the APOE-by-sex interactive effect on tau burden. We hypothesized sex-dependent regional vulnerability to tau deposition. PET radiopharmaceuticals [18F]AZD4694 and [18F]MK6240 were used to assess amyloid-β and tau level respectively in 277 subjects from the Translational Biomarkers in Aging and Dementia cohort. We found that the interaction between APOE and sex, rather than their independent main effects, was associated with abnormal tau accumulation in medial temporal regions. Specifically, we found that female APOEε4 carriers showed significantly higher tau burden in early tau deposition regions including the hippocampus, entorhinal and parahippocampal cortices, after accounting for age, educational attainment, clinical diagnosis and neocortical amyloid load. We replicated these findings in 221 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort, in which a different tau-PET radioligand, [18F]flortaucipir, was used to assess tau burden. In conclusion, this study provides evidence from two cohort studies that interactive rather than independent effect of APOE and sex potentiates early tau deposition in women. Our results have important implications for clinical trials and practice, which should take into consideration both APOEε4 carriage status and sex for identifying individuals with the highest probability of developing tau accumulation and clinical progression.

It was unclear whether sex hormone-binding globulin (SHBG) was a circulating biomarker of Alzheimer's disease (AD). We tested the cross-sectional relationships between plasma SHBG and cerebrospinal fluid (CSF) AD biomarkers in 707 non-demented adults. Next, the influences of plasma SHBG on dynamic changes of CSF Aβ42, hippocampus volume, brain metabolism, and cognition were explored in 448 non-demented adults from the Alzheimer's disease Neuroimaging Initiative (ADNI). Finally, the predictive and diagnostic values of plasma SHBG in AD were explored. A positive correlation was found between SHBG levels in plasma and CSF. Individuals with higher plasma SHBG levels had lower CSF Aβ42 (p < 0.005), after adjusting for age, gender, education, APOE4 allele, and cognitive scores. Though no significant difference of plasma SHBG was observed between mild AD dementia and healthy normal, plasma SHBG could contribute to accelerated rates of CSF Aβ42 decrease (p < 0.0005), decline in brain metabolism (p < 0.05), and hippocampus atrophy (p < 0.01), cognitive decline (p < 0.01), as well as higher risk of AD dementia (p < 0.05). These findings indicated plasma SHBG could be a prodromal biomarker to predict disease progression in AD.

We examined moderation effects of sex and diagnosis on the effect of positive florbetapir positron emission tomography (PET) amyloid-β (Aβ) scan (A+) on hippocampus subfield volumes in 526 normal control (NC) and early mild cognitive impairment (eMCI) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI2; ADNI-GO). Regression moderation models showed that women- but not men- with NC designation did not show reduced subiculum volumes despite A+. At the eMCI stage, A+ was detrimental across sexes. Findings were significant while accounting for the effects of age, cognition at screening, education, and APOE4 carrier status. These findings suggest that women with A+ have early neural resistance to Alzheimer's disease-related amyloid burden.

Studies have shown that women on the Alzheimer's disease (AD) continuum have more pathological tau in the brain and cerebrospinal fluid (CSF), than men. Some studies have found that higher levels of tau biomarkers are more strongly associated with clinical AD, cognitive decline and neurodegeneration in women than in men. Despite major developments in the use of plasma tau phosphorylated at threonine 181 (p-tau181) as an AD biomarker, it is unknown whether these sex differences apply to plasma p-tau181. In 1060 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (47% women, 73.8 ± 7.6 years old), we examined sex differences in plasma p-tau181 levels and their association with other biomarkers, cognitive decline and incident AD. Linear regressions tested for an effect of sex on plasma p-tau181 levels and for plasma p-tau181 × sex interactions on CSF p-tau181, as well as entorhinal cortex tau, cortical amyloid-β (Aβ) deposition, and brain glucose metabolism, quantified using PET imaging. Linear mixed effects models tested for a sex × baseline plasma p-tau181 interaction on change in cognition over time. Finally, Cox models tested for a sex × plasma p-tau181 interaction on the risk of AD dementia in participants who were free of dementia at baseline. Despite similar plasma p-tau181 levels between sexes, women had lower brain glucose metabolism, greater brain Aβ and entorhinal cortex tau deposition, higher CSF p-tau181 and faster cognitive decline in relation to higher baseline plasma p-tau181 levels compared with men. Among Aβ positive, dementia-free participants, women had higher rates of incident AD dementia associated with increasing baseline plasma p-tau181 levels, relative to men. Our results suggest that sex may impact the clinical interpretation of plasma p-tau181 concentrations. If replicated, these findings could have important implications for the use of plasma p-tau181 as an accessible AD biomarker and screening tool for preventive and therapeutic clinical trials.

We aimed to examine the potential effect of sex on the longitudinal association of baseline serum uric acid levels with brain amyloid accumulation over time among older adults with and without abnormal amyloid. At baseline, the study sample comprised 499 older adults, including 276 men and 223 women. Linear mixed-effects regression models were fitted to estimate the individual slopes of change in brain amyloid accumulation [as measured by AV45 standardized uptake value ratio (SUVR)] over time. At baseline, we did not observe a relationship between serum uric acid levels and brain amyloid deposition in women or men regardless of amyloid status. Among amyloid negative subjects, women and men did not differ in the relationship between baseline serum uric acid and the annual change in amyloid accumulation in subjects with normal amyloid levels. In amyloid positive women, serum uric acid levels were not associated with the annual change in amyloid accumulation (unstandardized β = 0.0005, SE = 0.0006, p value = 0.4179). However, in amyloid positive men, serum uric acid levels were negatively associated with the annual change in amyloid accumulation (unstandardized β = -0.0015, SE = 0.0005, p value = 0.0048). These findings support a potential sex-specific effect on the relationship between serum uric acid levels and amyloid accumulation among amyloid positive older adults.

Alzheimer's disease (AD) impacts men and women differently, but the effect of sex on predementia stages is unclear. The objective of this study was to examine whether sex moderates the impact of florbetapir positron emission tomography (PET) amyloid positivity (A+) on verbal learning and memory performance and hippocampal volume (HV) in normal cognition (NC) and early mild cognitive impairment (eMCI).

Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.

Amyloid (Aβ) pathology is the earliest detectable pathophysiological event along the Alzheimer's continuum, which can be measured both in the cerebrospinal fluid (CSF) and by Positron Emission Tomography (PET). Yet, these biomarkers identify two distinct Aβ pools, reflecting the clearance of soluble Aβ as opposed to the presence of Aβ fibrils in the brain. An open question is whether risk factors known to increase Alzheimer's' disease (AD) prevalence may promote an imbalance between soluble and deposited Aβ. Unveiling such interactions shall aid our understanding of the biological pathways underlying Aβ deposition and foster the design of effective prevention strategies. We assessed the impact of three major AD risk factors, such as age, APOE-ε4 and female sex, on the association between CSF and PET Aβ, in two independent samples of non-demented individuals (ALFA: n = 320, ADNI: n = 682). We tested our hypotheses both in candidate regions of interest and in the whole brain using voxel-wise non-parametric permutations. All of the assessed risk factors induced a higher Aβ deposition for any given level of CSF Aβ42/40, although in distinct cerebral topologies. While age and sex mapped onto neocortical areas, the effect of APOE-ε4 was prominent in the medial temporal lobe, which represents a target of early tau deposition. Further, we found that the effects of age and APOE-ε4 was stronger in women than in men. Our data indicate that specific AD risk factors affect the spatial patterns of cerebral Aβ aggregation, with APOE-ε4 possibly facilitating a co-localization between Aβ and tau along the disease continuum.

We introduced a hypometabolic convergence index (HCI) to characterize in a single measurement the extent to which a person's fluorodeoxyglucose positron emission tomogram (FDG PET) corresponds to that in Alzheimer's disease (AD). Apolipoprotein E ε4 (APOE ε4) gene dose is associated with three levels of risk for late-onset AD. We explored the association between gene dose and HCI in cognitively normal ε4 homozygotes, heterozygotes, and non-carriers.

We investigated differences due to sex in brain structural volume and cortical thickness in older cognitively normal (N=742), cognitively impaired (MCI; N=540) and Alzheimer's Dementia (AD; N=402) individuals from the ADNI and AIBL datasets (861 Males and 823 Females). General linear models were used to control the effect of relevant covariates including age, intracranial volume, magnetic resonance imaging (MRI) scanner field strength and scanner types. Significant volumetric differences due to sex were observed within different cortical and subcortical regions of the cognitively normal group. The number of significantly different regions was reduced in the MCI group, and no region remained different in the AD group. Cortical thickness was overall thinner in males than females in the cognitively normal group, and likewise, the differences due to sex were reduced in the MCI and AD groups. These findings were sustained after including cerebrospinal fluid (CSF) Tau and phosphorylated tau (pTau) as additional covariates.

Alzheimer's disease (AD) prevalence varies by sex, suggesting that sex chromosomes, sex hormones and/or their signaling could potentially modulate AD risk and progression. Low testosterone levels are reported in men with AD. Further, variation in the androgen receptor (AR) gene has been associated with AD risk and cognitive impairment. We assessed measures of plasma testosterone levels as a biomarker of AD in male participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Baseline testosterone levels were significantly different between clinical diagnosis groups [cognitively normal controls, mild cognitive impairment (MCI), or AD], with the lowest testosterone levels in men with AD. Lower baseline testosterone levels were associated with higher baseline clinical severity. Change in testosterone levels between baseline and 1-year follow-up varied by diagnosis; MCI had the greatest decreases in testosterone levels between baseline and 1-year follow-up. Despite differences by clinical diagnosis, there was no association between plasma testosterone and CSF biomarkers of AD pathology. We also tested single nucleotide polymorphisms (SNPs) in AR for association with AD risk in a separate cohort from ADNI and found 26 SNPs associated with risk for AD. The top associated SNP is predicted to be an expression quantitative trait locus for AR in multiple tissues, including brain, with the AD-associated risk allele predicted to confer lower AR expression. Our findings suggest a link between the androgen pathway and AD through Aβ/tau independent pathways. These effects may be most pronounced during conversion from MCI to dementia.

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

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

APOEɛ4 allele carriers present with an increased risk for late-onset Alzheimer's disease (AD), show cognitive symptoms at an earlier age, and are more likely to transition from mild cognitive impairment (MCI) to dementia but despite this, it remains unclear whether or not the ɛ4 allele controls the rate of disease progression.

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.

In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer's disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning-based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype 18F-FDG PET, age, and sex.

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Alzheimer's disease (AD) is highly heterogenous, with significant variations in both clinical presentation and neurobiology. To explore this, we used normative modelling on multimodal neuroimaging data to index spatial patterns of neuroanatomical and neuropathological variability in AD participants. Furthermore, we quantify group differences in between-participant dissimilarity for each modality. Finally, we proposed a disease severity index based on outlier deviations, assessed the relationships between the severity index and cognitive function and examined whether the disease index was predictive of disease progression.