University of Caen Normandy

This paper suggests an innovative approach for controlling single-phase shunt active power filter powered by photovoltaic arrays (PV-SAPF) amidst nonlinear loads. The proposed controller design is built upon the theoretical energy stored within the half-bridge SAPF components and aims to achieve two objectives simultaneously: (i) maximize active power generation from solar panels and regulate the PV voltage at a predefined reference value using the Incremental Conductance MPPT algorithm. (ii) compensate harmonic currents and reactive power produced by nonlinear loads using the backstepping approach and Lyapunov tools. This advanced control system is designed in two loops. An inner loop comprising a Power Factor Correction (PFC) regulator based on the theoretical energy stored in the SAPF components to reduce the Total Harmonic Distortion (THD) and to guarantee a near-unit power factor. An outer loop using a Proportional-Integrator (PI) regulator to control the PV voltage. Additionally, this system includes an adaptive observer on the AC side to estimate the unmeasurable state network variables by analyzing the estimated network current. The performances of the proposed controller are rigorously assessed using stability analysis tools, and validated through various extensive numerical simulation conducted in MATLAB/SIMULINK software. Furthermore, the effectiveness and robustness of this controller are compared to another controller developed in the study, which relies exclusively on the conventional PI filter.

Introduction In France, over 90% of people living with HIV-1 (PLWH) achieve virological suppression with effective combination of antiretroviral therapies (ART), but limited data exist on the motivation for switching ART. Objective To describe the reasons and determinants for switching ART, with a particular focus on doravirine-based regimens, in routine clinical practice in France. Design This analysis of cross-sectional baseline data is part of the DoraVIH study, a French, multicenter (15 sites), two-step observational cohort study that includes prospective follow-up for a subset of participants. Methods Eligible participants were PLWH under ART regimen, virologically suppressed for at least 6-months, doravirine-naïve and switching ART regimen. Sociodemographic and clinical data, ART history, and reasons for switching ART were assessed at baseline. Results Inclusions occurred between December 13, 2021 and September 21, 2022. Of the 291 PLWH included whose data were analyzed, 143 switched to doravirine-based regimen (DOR PLWH) and 148 to another combined regimen (non-DOR PLWH). Mean age was 51.6 years and 206 participants (70.8%) were men. At baseline, 35 (25.0%) DOR PLWH and 15 (10.6%) non-DOR PLWH had Body Mass Index (BMI) ≥30 kg/m ² (p = 0.007). The most common reasons for switching were treatment simplification, tolerability and drug-drug interactions, accounting for 79.7% of all reasons. Among the 68 participants with prior tolerability issues, 47 (69.1%) switched to doravirine-based regimen. Conclusions Primary reasons for switch were treatment simplification and tolerability. Participants with obesity were more likely to switch to doravirine, reflecting physicians’ favorable perception of doravirine potential benefits, particularly in managing weight gain.

Background Assessment of longitudinal hippocampal atrophy is a well‐studied biomarker for Alzheimer’s disease (AD). However, most state‐of‐the‐art measurements calculate changes directly from MRI images using image registration/segmentation, which may misreport head motion or MRI artifacts as neurodegeneration. We present a deep learning method Regional Deep Atrophy (RDA) that (1) estimates atrophy sensitive to progression by quantifying time‐associated changes in images, especially in preclinical AD stage (as in DeepAtrophy (Dong et al., 2021)), and (2) identifies regions where longitudinal changes significantly influence temporal inference. Method RDA was trained on longitudinal T1‐weighted MRI from 155 ADNI participants and evaluated on 326 participants (Figure 1(c)). During training, two image pairs from the same participant are fed into two instances of the RDA network in arbitrary temporal order. Within each RDA network, a U‐Net is applied to one image pair of arbitrary order to predict attention regions informative of shrinkage/expansion. Attention regions are used to mask a deformation field computed by ALOHA (Das et al., 2012), and derive a total volume change measurement for attention areas. The attention regions are optimized by the Scan Temporal Order (STO) loss for one scan pair to evaluate if volume changes align with input image order, and the Relative Interscan Interval (RISI) metric to determine if larger volume changes correspond to longer interscan intervals for the whole RDA model (Figure 1). Only one longitudinal image pair is required for testing, directly generating the total volume change as atrophy measurement. Result RDA achieves the similar ability to detect differences in atrophy between stages on the AD continuum as DeepAtrophy, especially in preclinical AD (Figure 2), while having additional explainability in the form of heatmaps that summarize expansion/shrinkage regions in the brain that contribute to the RDA change measurement (Figure 3). These heatmaps, derived in a fully data‐driven manner, largely recapitulate the areas of atrophy and expansion in the MTL reported by prior studies. Conclusion RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring and progression understanding in preclinical AD.

Background Tau pathology and neurodegeneration in the medial temporal lobe (MTL) are highly associated in Alzheimer’s Disease (AD). However, the spatial pattern of neurodegeneration, contribution of individual tau inclusion types, and influence of MTL co‐pathologies (i.e., TDP‐43) remain poorly understood. Traditional semi‐quantitative ratings or staging schemes of tau pathology capture limited variability in severity and provide no differentiation between inclusion types (i.e., tangles, threads). We correlate semi‐quantitative and quantitative measures of MTL tau pathology from postmortem tissue samples with MTL cortical thickness measures from antemortem MRI. Method Hippocampus histology slides (65.2% male, age 74.09±10.74 years) were available from 138 patients with AD continuum neuropathological diagnoses and antemortem T1‐weighted imaging within 10 years of death. Ipsilateral median cortical thickness measurements in anterior/posterior hippocampus, entorhinal cortex, Brodmann areas 35/36 (BA35/36), and parahippocampal cortex were automatically derived from MRI. We digitally annotated 7 MTL sampling regions on phosphorylated tau PHF1‐stained 6 µm tissue sections and trained a machine learning method to generate summary measures of tau tangle and thread pathology in each region (Fig.1). Quantitative measures of hippocampal tau tangles and threads were compared to simplified Braak staging (B score) and semi‐quantitative neuropathologist ratings of MTL tau. We performed one‐sided Spearman correlations between tau pathology measures and ipsilateral cortical thickness, covaried for age at death, antemortem interval, sex, and semi‐quantitative MTL TDP‐43 rating. Result Both quantitative hippocampal tau measures showed significant differences across B score levels and high correlation with semi‐quantitative ratings of MTL tau, but also great variability within each score/rating (Fig.2). Additionally, hippocampal tau tangle and thread pathology measures both showed significant negative correlations with ipsilateral cortical thickness in all MTL subregions, except for the threads measurement with BA35 thickness. Semi‐quantitative tau measures showed negative correlations with thickness in all subregions except BA35. Compared to semi‐quantitative ratings, the associations with quantitative tau measures were generally stronger and statistically more robust (significant difference for tangles/BA35) (Fig.3). Conclusion In a large antemortem‐postmortem dataset, quantitative measures of postmortem tau tangle and thread pathology each showed strong, significant association with ipsilateral antemortem cortical thickness across MTL subregions, independent of TDP‐43 pathology and stronger than the association with semi‐quantitative ratings.

Background Despite evidence that sex can modulate Alzheimer’s disease (AD) risk, whether risk factors are similarly related to AD markers in women and men remains largely unexplored. We aimed to assess how a combination of potentially modifiable risk factors are associated with cognitive and pathological markers of AD in older women and men. Method We included 135 cognitively unimpaired older adults ( = 65 years old, 83 women; Table 1) from the Age‐Well trial (NCT02977819; baseline data) with multidomain assessments of modifiable risk factors, including cardiovascular (body mass index, systolic blood pressure, LDL cholesterol), lifestyle (complex mental activity throughout life, physical activity, diet), and psychological (quality of life, depressive and anxiety symptoms, rumination, worry). AD markers included the Preclinical Alzheimer Cognitive Composite‐5 (PACC‐5), a measure of global cognition sensitive to AD‐related decline, and multimodal neuroimaging and blood sampling providing measures of hippocampal volume (MRI), brain perfusion in temporo‐parietal regions, neocortical amyloid burden (PET) and p‐tau181 (plasma). Multivariate partial least squares analyses were used to assess relationships between combinations of risk factors and AD hallmarks (cognition, neurodegeneration, amyloid and tau) in women and men separately. Result In women, a combination of lower quality of life and complex mental activity throughout life and higher levels of anxiety and worry, was associated with lower PACC‐5 scores and higher p‐tau181 levels (p = .002, 60.5% of variance explained; Fig.1). In men, two significant latent variables emerged. Lower systolic blood pressure, adherence to a Mediterranean diet and quality of life, and higher levels of anxiety and worry, were associated with higher hippocampal volume and lower brain perfusion in regions sensitive to AD (p = .022, 45.2% of the variance explained; Fig.2A); while lower levels of quality of life and complex mental activity throughout life were associated with worse PACC‐5 scores and higher neocortical amyloid deposition and p‐tau181 levels (p = .023, 35.9% of the variance explained; Fig.2B). Conclusion These preliminary findings suggest that the modifiable factors that could influence AD markers may differ by sex in cognitively unimpaired older adults. Pending replication in larger and independent cohorts, these results highlight the need to consider sex specificities in prevention strategies.

Background Locus coeruleus (LC) imaging using neuromelanin‐sensitive (NM) MRI sequences is a promising biomarker for detecting early Alzheimer’s Disease (AD). Although automatic approaches have been developed to estimate LC integrity by measuring its intensity, these techniques most often rely on a single template built in a standardized space and/or depend on a number of voxels to be accounted that is defined a priori. Thus, these algorithms make it impossible to perform direct volumetric analyses and do not properly account for inter‐individual anatomical variability. To fill this gap, our aim was to develop a new multi‐atlas fully automated segmentation method using the Automatic Segmentation of Hippocampal Subfields (ASHS) software. Method We used baseline data from 102 unimpaired older adults (mean age: 73.72 ± 3.5 years; mean education: 13.25 ± 3.1 years; 58 women, 44 men) from the Age‐Well randomized controlled trial for whom high‐resolution NM MRI (T1‐w with magnetization transfer; 0.3×0.3×0.75mm3) and standard T1‐w MRI (1×1×1mm3) were available. The LC were manually segmented in 30 randomly selected participants on NM MRI, and the standard T1‐w MRI, NM MRI and bilateral segmentations were fed into the ASHS training pipeline to generate an atlas. This new atlas was applied to the 72 remaining subjects to segment the LC and we assessed the effects of age, sex and education on both i) LC intensity (normalized by the intensity of the pons) and ii) LC volume (normalized by the total intracranial volume). Result Five‐fold cross‐validation experiments revealed high accuracy of the automatic segmentation relative to manual segmentation (Dice coefficient = 0,83 ± 0,04). LC intensity was significantly higher in women than in men (F = 13.61,p<0.001) while no associations with age (ß = ‐0.0002,p = 0.86) or education (ß = 0.002,p = 0.16) were found. In contrast, LC volume was not different between men and women (F = 0.21, p = 0.65) but tended to be negatively associated with age (ß = ‐0.15,p = 0.06) and education (ß = ‐0.19,p = 0.06). Conclusion Overall, this new method allows to automatically and accurately segment the LC, and offers the opportunity to measure its integrity both in terms of intensity and volume. This is of importance since these two metrics might provide complementary information about the integrity of the LC.

Background The medial temporal lobe's (MTL) early involvement in tau pathology makes it a key focus in the development of preclinical Alzheimer’s disease (AD) biomarkers. ROI analyses in prior studies reported significant MTL structural differences in cognitively normal individuals with and without ß‐amyloid (A+/‐CN). Pointwise analysis, offering spatial information of early neurodegeneration, has potential to pinpoint “signature regions” of pathological change, but has been underexplored in the MTL. This study employs a specialized pointwise analysis pipeline to examine the spatial pattern of MTL structural change in subgroups dichotomized by both ß‐amyloid and tau status in a large cohort of CN individuals. Methods A dataset of 3036 CN (A‐/A+: 1270/1558, Table 1) individuals from ADNI, HABS, A4 and ABC were analyzed. We extracted MTL regional thickness maps from MRI using tailored pipelines, ASHS‐T1 and CRASHS. For participants with prospective longitudinal MRI (five years follow‐up), regional maps of longitudinal atrophy rate were extracted using SkelDBM. Subjects with cross‐sectional tau PET available (N=563) were further divided into A and T subgroups by tracer uptake. General linear modeling was performed on each surface point to investigate cross‐sectional and longitudinal MTL structural group differences (detailed in Figure 1) and their correlation with MTL tau burden in All/A+/A‐ CN. Age and sex were covariates and cluster‐level multiple comparison correction was performed. Results A+CN demonstrated a significantly faster atrophy rate than A‐CN across the whole MTL, primarily driven by A+T+CN individuals (Figure 1‐b). Notably, A‐T+CN showed significantly faster atrophy rate in the entorhinal cortex (ERC) and Brodmann area 35 (BA35), the earliest sites of tau pathology (Figure 1‐b, second column). Figure 2‐b displays an MTL‐wise significant correlation between atrophy rate and tau in All/A+/A‐ CN. In both analyses, cross‐sectional effects are consistently weaker than longitudinal ones, but have some significant clusters in ERC and BA35. Conclusions Pointwise analysis revealed extensive tau‐associated accelerated neurodegeneration in the MTL in preclinical AD. Furthermore, accelerated atrophy was observed in early Braak regions in A‐CN with evidence of tau pathology, potentially driven by primary age‐related tauopathy (PART). These pointwise longitudinal MTL measures provide sensitive measures that may allow for disease monitoring in preclinical AD.

Background Individuals with Down Syndrome (DS) almost invariably develop Alzheimer's Disease (AD), but detecting early clinical changes is challenging due to comorbid intellectual disability, highlighting the importance of non‐invasive biomarkers. Neuroimaging of the medial temporal lobe (MTL), a key site of tau pathology, shows promise as an early AD biomarker. Here, we aimed to characterise volumetric patterns of the MTL in DS across the AD clinical continuum, and define associations with AD cerebrospinal fluid (CSF) biomarkers. Method 253 adults with DS and 190 euploid controls from the Down Alzheimer Barcelona Neuroimaging Initiative underwent a 3T‐MRI protocol, and a comprehensive clinical assessment. T1‐weighted images were used to parse the medial temporal lobe using the Automated Segmentation of Hippocampal Subfields (ASHS) pipeline. Segmentation quality was visually inspected and W‐scores were computed for MTL subregions (anterior and posterior hippocampus, entorhinal cortex (ERC), parahippocampal cortex (PHC), and Brodmann areas Br35 and Br36) to adjust volumes for total intracranial volume, age and MRI scanner. Non‐parametric statistical tests were employed to assess volumes by AD clinical stage, age, and CSF biomarkers of AD. Result Hippocampal and Br36 volumes gradually decreased with AD clinical stage, and all subregions were decreased at the dementia stage (dDS) compared to asymptomatic (aDS) and prodromal (pDS) stages (Fig. 1). Surprisingly, significantly larger ERC, PHC and Br35 volumes were found at the asymptomatic DS stage compared to controls. All subregions had decreased volumes with age, with inflexion points around 40y for the hippocampus, ERC and Br36, 45y for Br35 and 50y for PHC (Fig. 2). Most subregions exhibited significant correlations with CSF Aß42/40 ratio, p‐tau‐181 and neurofilament light chain, and the strongest associations were found with anterior and posterior hippocampus (Fig. 3). Conclusion AD clinical stage and age are associated with progressive decreasing MTL volumes. Among all subregions, the hippocampus correlated best with CSF measures and appears particularly sensitive to detect early disease processes. These results indicate effectiveness of MTL volumes as a biomarker of early AD pathological changes in DS. Further studies are required to determine the pathological substrate of MTL atrophy and understand the increased volumes in some subregions.

Background Education has been associated with reserve mechanisms and lower dementia risk, but the literature shows inconsistent results on the association between education and brain outcomes across the lifespan. Considering that both dementia risk and education are likely to differ between sexes, our study aims at understanding the association between education and brain outcomes across the lifespan and whether it differs by sex. Method In 207 healthy individuals (110women) aged 19‐84 years old (47.98±18.75), we investigated the association between years of education and multimodal neuroimaging (structural‐MRI, FDG‐PET, Florbetapir‐PET) and how this association was modulated by age and sex. Analyses were restricted to regions involved in Alzheimer’s disease and/or reserve mechanisms (hippocampal volume, temporoparietal metabolism, neocortical amyloid, anterior and posterior cingulate cortices [ACC/PCC] for each modality). Result There was no main effect of years of education on neuroimaging (ps>.09) nor interaction between education and the other variables. However, exploratory interactions conducted within young (18‐40), middle‐aged (40‐60) and older (60+yo) groups separately showed that, only in the older group, sex interacted with education on ACC volume, revealing that education was associated with greater volume only in older women (Figure; pinteraction = .006). Complementary analyses conducted in an independent and larger sample of older adults (n = 135, 83women, >65yo) in whom lifestyle was assessed retrospectively, suggest that midlife and late‐late‐life mental engagement, more than early‐life engagement (reflecting mainly education), are related to greater brain outcomes in late‐life (ACC volume, PCC amyloid burden, ACC metabolism). We assessed the moderative effect of sex on these associations and found that the association between grey matter volume and occupation (midlife engagement) was mainly driven by women (pinteraction = .02). The interaction between early‐life engagement and sex on ACC volume highlighted in the main cohort was replicated, yet at a trend‐level (pinteraction = .08). Conclusion Our study suggests that education is not strongly associated with age‐related differences in structure, metabolism or amyloid burden. Older women with higher levels of education, however, showed higher ACC volumes. These results suggest that education could promote brain reserve in late life in a sex‐dependent manner. Future studies should investigate the mechanisms behind these differences in brain reserve.

Background The heterogeneity of Alzheimer’s disease (AD) and lack of well‐validated markers of co‐pathologies present a substantial challenge for therapeutics. We previously found phenotypes defined by Tau (T) ‐ Neurodegeneration (N) discordance linked to non‐Alzheimer’s pathologies (e.g. TDP‐43, vascular disease). In this work, we aim to leverage T‐N mismatch for identifying distinct spatial‐temporal progression patterns of non‐AD pathologies. Method We performed T‐N regression on 1040 scan pairs (n = 722 individuals) from ADNI, using cortical thickness (N) and 18F‐Flortaucipir uptake (T) in 20 bilateral cortical regions of interest. As in previous work, residuals were identified as canonical (T∼N), vulnerable (N>T) and resilient (N<T). Here, we apply SuStaIn, a phenotype discovery and stage inference algorithm, to standardized T‐N residuals in canonical and vulnerable cases (n=608), expecting the latter would reflect co‐pathologies. Result Besides the "canonical" subtype (S0), SuStaIn identified three subtypes with distinct T‐N mismatch (N>T) progression profiles (Figure 1). Two exhibited different but progressively diffuse spatial patterns of T‐N mismatch — the anterior subtype (S1) starting from frontal, and the posterior subtype (S2) initiating from occipital/temporal regions. The third subtype (S3) exhibited temporal‐limbic mismatch patterns, with spreading to anterior limbic regions. The three mismatch subtypes had worse cognition, greater age and larger rate of amyloid positivity than canonical subtype (Table 1). SuStaIn‐identified stage was associated with age and worse cognition but not tau severity (Figure 2A‐C), indicating the stages do not represent AD progression. The anterior subtype had the largest white matter hyperintensity (WMH) volume, increasing with higher stages (Figure 2D), suggesting an association with vascular disease. The temporal‐limbic subtype demonstrated poorest memory performance (Figure 2E) and may be associated with TDP‐43 pathology, as suggested in our prior work. 90.1% with longitudinal scans did not change subtype or transitioned from canonical to mismatch subtypes (Figure 2F), supporting the stability of classifications. Conclusion We identified distinct T‐N mismatch progression trajectories in AD, potentially reflecting progression of co‐pathologies. It is important to better define these groups in the context of anti‐amyloid therapies to better understand their effectiveness in populations with likely co‐pathology and whether these mismatch trajectories driven by non‐AD factors would continue independent of treatment.

Background The medial temporal lobe (MTL) has distinct cortical subregions that are differentially vulnerable to pathology and neurodegeneration in diseases such as Alzheimer’s disease. However, previous protocols for segmentation of MTL cortical subregions on magnetic resonance imaging (MRI) vary substantially across research groups, and have been informed by different cytoarchitectonic definitions, precluding consistent interpretations. The Hippocampal Subfields Group aims to create a harmonized, histology‐based protocol for segmentation of MTL cortical subregions that can reliably be applied to T2‐weighted MRI with high in‐plane resolution. Method Nissl‐stained sections from the temporal lobes of three human specimens (66‐90 years old; 2 female) were annotated by four expert neuroanatomists for the following MTL subregions: entorhinal cortex (ERC), Brodmann’s Area 35 (BA35; largely corresponding to “transentorhinal” cortex), Brodmann’s Area 36 (BA36), and parahippocampal cortex (PHC). On each histology section, the number of annotations and the spatial overlap of annotations were analyzed to determine the consensus of the anterior to posterior range of each structure. Gross anatomical landmarks, detectable on MRI and reliably corresponding with each range, were then selected to create an MRI ranging protocol. Feasibility of this MRI protocol was tested by two independent raters across four MRI scans (two healthy adults, two older adults), and agreement in range selection was assessed using Cohen’s kappa statistic. Result The proposed MTL ranging protocol is shown in Fig. 1, and corresponding histology data substantiating the protocol is shown in Fig. 2. MRI‐visible gross anatomical landmarks that reliably corresponded with the anterior or posterior range of each subregion on histology included the anterior‐most appearance of the collateral sulcus (Fig. 3A), hippocampal head (Fig. 3B), hippocampal body, and anterior calcarine fissure (Fig. 3C). This protocol demonstrated high feasibility when applied to MRI, with average kappa values of 0.75 ± 0.07, representing a “substantial” level of agreement of range selection. Conclusion Future directions include obtaining consensus on this protocol from the larger research community through a Delphi procedure, and expansion of the protocol to include slice‐by‐slice segmentation guidelines for full delineation. This harmonized, histology‐based protocol will facilitate critical research on MTL subregion vulnerability and their contributions to memory deficits in Alzheimer’s disease.

Background The medial temporal lobe's (MTL) early involvement in tau pathology makes it a key focus in the development of preclinical Alzheimer’s disease (AD) biomarkers. ROI analyses in prior studies reported significant MTL structural differences in cognitively normal individuals with and without β‐amyloid (A+/‐CN). Pointwise analysis, offering spatial information of early neurodegeneration, has potential to pinpoint “signature regions” of pathological change, but has been underexplored in the MTL. This study employs a specialized pointwise analysis pipeline to examine the spatial pattern of MTL structural change in subgroups dichotomized by both β‐amyloid and tau status in a large cohort of CN individuals. Methods A dataset of 3036 CN (A‐/A+: 1270/1558, Table 1) individuals from ADNI, HABS, A4 and ABC were analyzed. We extracted MTL regional thickness maps from MRI using tailored pipelines, ASHS‐T1 and CRASHS. For participants with prospective longitudinal MRI (five years follow‐up), regional maps of longitudinal atrophy rate were extracted using SkelDBM. Subjects with cross‐sectional tau PET available (N=563) were further divided into A and T subgroups by tracer uptake. General linear modeling was performed on each surface point to investigate cross‐sectional and longitudinal MTL structural group differences (detailed in Figure 1) and their correlation with MTL tau burden in All/A+/A‐ CN. Age and sex were covariates and cluster‐level multiple comparison correction was performed. Results A+CN demonstrated a significantly faster atrophy rate than A‐CN across the whole MTL, primarily driven by A+T+CN individuals (Figure 1‐b). Notably, A‐T+CN showed significantly faster atrophy rate in the entorhinal cortex (ERC) and Brodmann area 35 (BA35), the earliest sites of tau pathology (Figure 1‐b, second column). Figure 2‐b displays an MTL‐wise significant correlation between atrophy rate and tau in All/A+/A‐ CN. In both analyses, cross‐sectional effects are consistently weaker than longitudinal ones, but have some significant clusters in ERC and BA35. Conclusions Pointwise analysis revealed extensive tau‐associated accelerated neurodegeneration in the MTL in preclinical AD. Furthermore, accelerated atrophy was observed in early Braak regions in A‐CN with evidence of tau pathology, potentially driven by primary age‐related tauopathy (PART). These pointwise longitudinal MTL measures provide sensitive measures that may allow for disease monitoring in preclinical AD.

Background Individuals with Down Syndrome (DS) almost invariably develop Alzheimer's Disease (AD), but detecting early clinical changes is challenging due to comorbid intellectual disability, highlighting the importance of non‐invasive biomarkers. Neuroimaging of the medial temporal lobe (MTL), a key site of tau pathology, shows promise as an early AD biomarker. Here, we aimed to characterise volumetric patterns of the MTL in DS across the AD clinical continuum, and define associations with AD cerebrospinal fluid (CSF) biomarkers. Method 253 adults with DS and 190 euploid controls from the Down Alzheimer Barcelona Neuroimaging Initiative underwent a 3T‐MRI protocol, and a comprehensive clinical assessment. T1‐weighted images were used to parse the medial temporal lobe using the Automated Segmentation of Hippocampal Subfields (ASHS) pipeline. Segmentation quality was visually inspected and W‐scores were computed for MTL subregions (anterior and posterior hippocampus, entorhinal cortex (ERC), parahippocampal cortex (PHC), and Brodmann areas Br35 and Br36) to adjust volumes for total intracranial volume, age and MRI scanner. Non‐parametric statistical tests were employed to assess volumes by AD clinical stage, age, and CSF biomarkers of AD. Result Hippocampal and Br36 volumes gradually decreased with AD clinical stage, and all subregions were decreased at the dementia stage (dDS) compared to asymptomatic (aDS) and prodromal (pDS) stages (Figure 1). Surprisingly, significantly larger ERC, PHC and Br35 volumes were found at the asymptomatic DS stage compared to controls. All subregions had decreased volumes with age, with inflexion points around 40y for the hippocampus, ERC and Br36, 45y for Br35 and 50y for PHC (Figure 2). Most subregions exhibited significant correlations with CSF Aβ42/40 ratio, p‐tau‐181 and neurofilament light chain, and the strongest associations were found with anterior and posterior hippocampus (Figure 3). Conclusion AD clinical stage and age are associated with progressive decreasing MTL volumes. Among all subregions, the hippocampus correlated best with CSF measures and appears particularly sensitive to detect early disease processes. These results indicate effectiveness of MTL volumes as a biomarker of early AD pathological changes in DS. Further studies are required to determine the pathological substrate of MTL atrophy and understand the increased volumes in some subregions.

Background The heterogeneity of Alzheimer’s disease (AD) and lack of well‐validated markers of co‐pathologies present a substantial challenge for therapeutics. We previously found phenotypes defined by Tau (T) ‐ Neurodegeneration (N) discordance linked to non‐Alzheimer’s pathologies (e.g. TDP‐43, vascular disease). In this work, we aim to leverage T‐N mismatch for identifying distinct spatial‐temporal progression patterns of non‐AD pathologies. Method We performed T‐N regression on 1040 scan pairs (n=722 individuals) from ADNI, using cortical thickness (N) and ¹⁸F‐Flortaucipir uptake (T) in 20 bilateral cortical regions of interest. As in previous work, residuals were identified as canonical (T∼N), vulnerable (N>T) and resilient (N<T). Here, we apply SuStaIn, a phenotype discovery and stage inference algorithm, to standardized T‐N residuals in canonical and vulnerable cases (n=608), expecting the latter would reflect co‐pathologies. Result Besides the "canonical" subtype (S0), SuStaIn identified three subtypes with distinct T‐N mismatch (N>T) progression profiles (Figure 1). Two exhibited different but progressively diffuse spatial patterns of T‐N mismatch — the anterior subtype (S1) starting from frontal, and the posterior subtype (S2) initiating from occipital/temporal regions. The third subtype (S3) exhibited temporal‐limbic mismatch patterns, with spreading to anterior limbic regions. The three mismatch subtypes had worse cognition, greater age and larger rate of amyloid positivity than canonical subtype (Table 1). SuStaIn‐identified stage was associated with age and worse cognition but not tau severity (Figure 2A‐C), indicating the stages do not represent AD progression. The anterior subtype had the largest white matter hyperintensity (WMH) volume, increasing with higher stages (Figure 2D), suggesting an association with vascular disease. The temporal‐limbic subtype demonstrated poorest memory performance (Figure 2E) and may be associated with TDP‐43 pathology, as suggested in our prior work. 90.1% with longitudinal scans did not change subtype or transitioned from canonical to mismatch subtypes (Figure 2F), supporting the stability of classifications. Conclusion We identified distinct T‐N mismatch progression trajectories in AD, potentially reflecting progression of co‐pathologies. It is important to better define these groups in the context of anti‐amyloid therapies to better understand their effectiveness in populations with likely co‐pathology and whether these mismatch trajectories driven by non‐AD factors would continue independent of treatment.

Background Despite evidence that sex can modulate Alzheimer’s disease (AD) risk, whether risk factors are similarly related to AD markers in women and men remains largely unexplored. We aimed to assess how a combination of potentially modifiable risk factors are associated with cognitive and pathological markers of AD in older women and men. Method We included 135 cognitively unimpaired older adults (≥65 years old, 83 women; Table 1) from the Age‐Well trial (NCT02977819; baseline data) with multidomain assessments of modifiable risk factors, including cardiovascular (body mass index, systolic blood pressure, LDL cholesterol), lifestyle (complex mental activity throughout life, physical activity, diet), and psychological (quality of life, depressive and anxiety symptoms, rumination, worry). AD markers included the Preclinical Alzheimer Cognitive Composite‐5 (PACC‐5), a measure of global cognition sensitive to AD‐related decline, and multimodal neuroimaging and blood sampling providing measures of hippocampal volume (MRI), brain perfusion in temporo‐parietal regions, neocortical amyloid burden (PET) and p‐tau181 (plasma). Multivariate partial least squares analyses were used to assess relationships between combinations of risk factors and AD hallmarks (cognition, neurodegeneration, amyloid and tau) in women and men separately. Result In women, a combination of lower quality of life and complex mental activity throughout life and higher levels of anxiety and worry, was associated with lower PACC‐5 scores and higher p‐tau181 levels (p = .002, 60.5% of variance explained; Fig. 1). In men, two significant latent variables emerged. Lower systolic blood pressure, adherence to a Mediterranean diet and quality of life, and higher levels of anxiety and worry, were associated with higher hippocampal volume and lower brain perfusion in regions sensitive to AD (p = .022, 45.2% of the variance explained; Fig. 2A); while lower levels of quality of life and complex mental activity throughout life were associated with worse PACC‐5 scores and higher neocortical amyloid deposition and p‐tau181 levels (p = .023, 35.9% of the variance explained; Fig. 2B). Conclusion These preliminary findings suggest that the modifiable factors that could influence AD markers may differ by sex in cognitively unimpaired older adults. Pending replication in larger and independent cohorts, these results highlight the need to consider sex specificities in prevention strategies.

Background Limbic‐predominant age‐related TDP‐43 encephalopathy (LATE) is a neurodegenerative disease that is often comorbid with Alzheimer's disease (AD) and for which there are no reliable specific chemical or PET biomarkers available. Recent progress in disease‐modifying treatments for AD elevates the need for reliable in vivo detection of LATE and other comorbid neurodegenerative diseases. The promise of postmortem and antemortem MRI studies in LATE is that they will lead to the discovery of patterns of neurodegeneration associated with TDP‐43 pathology that could be reliably detected in vivo and used as a biomarker of LATE. Method Several groups, including ours, have imaged brain tissue (e.g., whole hemisphere, or intact temporal lobe) from autopsies conducted in older adults with and without cognitive impairment. Specimens were imaged using high‐field, high‐resolution magnetic resonance imaging (MRI) and quantitative maps of cortical thickness were derived. Statistical associations between these structural measures and either computationally derived quantitative or expert‐assigned semi‐quantitative markers of TDP‐43 and tau pathology are computed as statistical parametric maps and statistically thresholded to identify clusters of significant associations. Alternatively, such associations can be derived from antemortem imaging studies, where MRI is performed in living individuals and linked to postmortem pathology measures. Result Both TDP‐43 and tau pathology are linked to cortical thinning in the medial temporal lobe, as revealed by postmortem and antemortem studies. Patterns of thinning associated with each pathology are distinct yet overlapping, and sensitive to cohort selection, choice of pathology measure, and other factors. Conclusion Reliable in vivo detection of TDP‐43 based on in vivo MRI signatures is likely possible but requires effort from the community to pool scarce ex vivo imaging and pathology datasets, standardization of quantitative pathology and MRI measures, and extensive validation.

Background Tau pathology and neurodegeneration in the medial temporal lobe (MTL) are highly associated in Alzheimer’s Disease (AD). However, the spatial pattern of neurodegeneration, contribution of individual tau inclusion types, and influence of MTL co‐pathologies (i.e., TDP‐43) remain poorly understood. Traditional semi‐quantitative ratings or staging schemes of tau pathology capture limited variability in severity and provide no differentiation between inclusion types (i.e., tangles, threads). We correlate semi‐quantitative and quantitative measures of MTL tau pathology from postmortem tissue samples with MTL cortical thickness measures from antemortem MRI. Method Hippocampus histology slides (65.2% male, age 74.09±10.74 years) were available from 138 patients with AD continuum neuropathological diagnoses and antemortem T1‐weighted imaging within 10 years of death. Ipsilateral median cortical thickness measurements in anterior/posterior hippocampus, entorhinal cortex, Brodmann areas 35/36 (BA35/36), and parahippocampal cortex were automatically derived from MRI. We digitally annotated 7 MTL sampling regions on phosphorylated tau PHF1‐stained 6 μm tissue sections and trained a machine learning method to generate summary measures of tau tangle and thread pathology in each region (Figure 1). Quantitative measures of hippocampal tau tangles and threads were compared to simplified Braak staging (B score) and semi‐quantitative neuropathologist ratings of MTL tau. We performed one‐sided Spearman correlations between tau pathology measures and ipsilateral cortical thickness, covaried for age at death, antemortem interval, sex, and semi‐quantitative MTL TDP‐43 rating. Result Both quantitative hippocampal tau measures showed significant differences across B score levels and high correlation with semi‐quantitative ratings of MTL tau, but also great variability within each score/rating (Figure 2). Additionally, hippocampal tau tangle and thread pathology measures both showed significant negative correlations with ipsilateral cortical thickness in all MTL subregions, except for the threads measurement with BA35 thickness. Semi‐quantitative tau measures showed negative correlations with thickness in all subregions except BA35. Compared to semi‐quantitative ratings, the associations with quantitative tau measures were generally stronger and statistically more robust (significant difference for tangles/BA35) (Figure 3). Conclusion In a large antemortem‐postmortem dataset, quantitative measures of postmortem tau tangle and thread pathology each showed strong, significant association with ipsilateral antemortem cortical thickness across MTL subregions, independent of TDP‐43 pathology and stronger than the association with semi‐quantitative ratings.

Background Increased stress, a proposed risk factor for Alzheimer’s disease (AD), is associated with increased brain and cognitive vulnerabilities in older populations, which may be different in women and men. Objective To examine cross‐sectional associations between circulating stress hormones (epinephrine, norepinephrine, cortisol, dehydroepiandrosterone sulfate (DHEAS), and DHEAS/cortisol ratio) and multimodal measures of brain health and cognition sensitive to AD. Method 132 cognitively unimpaired older participants without clinical depression (age = 74.0 ± 4 years, females: n = 80) were included from the Age‐Well baseline dataset. Stress hormones were measured in overnight fasting blood serum (cortisol, DHEAS) and plasma (epinephrine, norepinephrine). The association of stress hormone levels with glucose metabolism and perfusion in AD sensitive brain regions, including the anterior and posterior cingulate cortex (ACC, PCC), insula and, precuneus, along with neocortical amyloid deposition and cognitive markers, including memory and Preclinical Alzheimer's Cognitive Composite‐5 (PACC5), was assessed. Linear regression models with and without stratification by sex adjusting for covariates of age, sex, education, subclinical anxiety, and depression were conducted. Result In the total cohort, higher epinephrine was associated with lower glucose metabolism (Figure 1) in the ACC (adj.‐β = ‐0.26, p = .027), PCC (adj.‐β = ‐0.32, p = .006), and precuneus (adj.‐β = ‐0.27, p = .021) and lower perfusion in the PCC (adj.‐β = ‐0.23, p = .013). Sex‐stratified analyses showed interactions (all p’s < .1): In males (but not in females), higher cortisol was associated with lower episodic memory (adj.‐β = ‐0.33, p = .02), short‐term memory (adj.‐β = ‐0.32, p = .014) and PACC5 scores (adj.‐β = ‐0.28, p =.04), suggesting a stress‐related vulnerability in the cognitive system of men. Stress biomarkers were not associated with neocortical amyloid deposition (all p’s ≥ .1). Conclusion Our results demonstrate the involvement of stress hormones, particularly epinephrine and cortisol, in increased vulnerability of the brain and cognition in older adults and the manifestation of sex specificities in this context. The role of stress on brain and cognitive health and related sex differences needs to be considered in intervention programs.

Background Locus coeruleus (LC) imaging using neuromelanin‐sensitive (NM) MRI sequences is a promising biomarker for detecting early Alzheimer’s Disease (AD). Although automatic approaches have been developed to estimate LC integrity by measuring its intensity, these techniques most often rely on a single template built in a standardized space and/or depend on a number of voxels to be accounted that is defined a priori. Thus, these algorithms make it impossible to perform direct volumetric analyses and do not properly account for inter‐individual anatomical variability. To fill this gap, our aim was to develop a new multi‐atlas fully automated segmentation method using the Automatic Segmentation of Hippocampal Subfields (ASHS) software. Method We used baseline data from 102 unimpaired older adults (mean age: 73.72 ± 3.5 years; mean education: 13.25 ± 3.1 years; 58 women, 44 men) from the Age‐Well randomized controlled trial for whom high‐resolution NM MRI (T1‐w with magnetization transfer; 0.3x0.3x0.75mm³) and standard T1‐w MRI (1x1x1mm³) were available. The LC were manually segmented in 30 randomly selected participants on NM MRI, and the standard T1‐w MRI, NM MRI and bilateral segmentations were fed into the ASHS training pipeline to generate an atlas. This new atlas was applied to the 72 remaining subjects to segment the LC and we assessed the effects of age, sex and education on both i) LC intensity (normalized by the intensity of the pons) and ii) LC volume (normalized by the total intracranial volume). Result Five‐fold cross‐validation experiments revealed high accuracy of the automatic segmentation relative to manual segmentation (Dice coefficient = 0,83 ± 0,04). LC intensity was significantly higher in women than in men (F=13.61, p<0.001) while no associations with age (β=‐0.0002, p=0.86) or education (β=0.002, p=0.16) were found. In contrast, LC volume was not different between men and women (F=0.21, p=0.65) but tended to be negatively associated with age (β=‐0.15, p=0.06) and education (β=‐0.19, p=0.06). Conclusion Overall, this new method allows to automatically and accurately segment the LC, and offers the opportunity to measure its integrity both in terms of intensity and volume. This is of importance since these two metrics might provide complementary information about the integrity of the LC.

Background Education has been associated with reserve mechanisms and lower dementia risk, but the literature shows inconsistent results on the association between education and brain outcomes across the lifespan. Considering that both dementia risk and education are likely to differ between sexes, our study aims at understanding the association between education and brain outcomes across the lifespan and whether it differs by sex. Method In 207 healthy individuals (110women) aged 19‐84 years old (47.98±18.75), we investigated the association between years of education and multimodal neuroimaging (structural‐MRI, FDG‐PET, Florbetapir‐PET) and how this association was modulated by age and sex. Analyses were restricted to regions involved in Alzheimer’s disease and/or reserve mechanisms (hippocampal volume, temporoparietal metabolism, neocortical amyloid, anterior and posterior cingulate cortices [ACC/PCC] for each modality). Result There was no main effect of years of education on neuroimaging (ps>.09) nor interaction between education and the other variables. However, exploratory interactions conducted within young (18‐40), middle‐aged (40‐60) and older (60+yo) groups separately showed that, only in the older group, sex interacted with education on ACC volume, revealing that education was associated with greater volume only in older women (Figure; pinteraction=.006). Complementary analyses conducted in an independent and larger sample of older adults (n=135, 83women, >65yo) in whom lifestyle was assessed retrospectively, suggest that midlife and late‐late‐life mental engagement, more than early‐life engagement (reflecting mainly education), are related to greater brain outcomes in late‐life (ACC volume, PCC amyloid burden, ACC metabolism). We assessed the moderative effect of sex on these associations and found that the association between grey matter volume and occupation (midlife engagement) was mainly driven by women (pinteraction=.02). The interaction between early‐life engagement and sex on ACC volume highlighted in the main cohort was replicated, yet at a trend‐level (pinteraction=.08). Conclusion Our study suggests that education is not strongly associated with age‐related differences in structure, metabolism or amyloid burden. Older women with higher levels of education, however, showed higher ACC volumes. These results suggest that education could promote brain reserve in late life in a sex‐dependent manner. Future studies should investigate the mechanisms behind these differences in brain reserve.