bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sex differences in risk factors for dementia in the UK Biobank: An analysis of more than half a million individuals
Authors: Gong Jessica , Harris Katie , Peters Sanne , Woodward Mark 
Published: December 2020

AbstractBackgroundWhile some risk factors (RF) are known to be associated with dementia, understanding of sex differences in these risk factors is scant. This study investigated the sex differences in the strength of association across a range of RFs with dementia in the UK Biobank.MethodsUK Biobank is a prospective population‐based cohort of 502,628 participants. We examined the sex differences in a number of RFs associated with incident dementia: blood pressure (BP), diabetes mellitus, smoking status, socioeconomic status (SES), stroke, cholesterol, and body anthropometries. Cox proportional hazard regression models were used to estimate the hazard ratios (HR) for dementia for each RF, with interaction terms between sex and each RF. The shape of the relationships between BP and the risk of dementia were further explored using restricted cubic splines.ResultsOver a median of 9.0 years follow‐up, 2,502 individuals (44.5% women) developed dementia. Diabetes, smoking, lower SES, prior stroke and all body anthropometries with the exception of body mass index (BMI) were associated with elevated risk of dementia in women and men. Higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) were both associated with higher risk of dementia in women, but lower risk in men (women‐to‐men Ratio of Hazard Ratios (RHR), 1.14, 95% confidence intervals (CI) [1.05 – 1.24], for both SBP and DBP). Multiple‐adjusted splines unveiled differential relationships between SBP and the risk of dementia for men and women, indicating lower SBP increased the risk of dementia in men, but the effect attenuated for women; while higher SBP posed greater risk of dementia for women than men. Compared with high SES, women with low SES had a higher risk of dementia compared with men with low SES (RHR, 1.19 [1.01 to 1.41]).ConclusionThe relationship between BP and the risk of dementia are manifested differently for men and women, which has important clinical implications when designing targeted interventions and setting BP goals to optimise treatment outcomes. Moreover, low SES conferred greater risk of dementia in women than men when compared to high SES, warrants further investigations in potential social and medical inequities between the sexes.
Using anxiety as a sex‐specific neuropsychiatric biomarker of Alzheimer’s disease
Authors: Hunsberger Holly C. , Lee Seonjoo , Cha Jiook , Denny Christine Ann 
Published: December 2020

AbstractBackgroundNeuropsychiatric disturbances, such as depression and anxiety, are observed in 90% of Alzheimer’s disease (AD) patients and are frequent in those at risk for AD. However, until recently, much of the research narrowly targeted co‐morbid depression. Clinical reports have provided evidence that anxiety symptoms predict the conversion to AD, over and beyond the effects of depression, memory loss, and even atrophy. Similarly, epidemiological studies show that neurodegeneration and clinical symptoms occur more rapidly in females once diagnosed. Although most AD studies have been performed using male mice, females represent two‐thirds of the AD population and are more susceptible to depression and anxiety.MethodTo study how anatomical sex and anxiety impact AD progression, I used our activity‐dependent tagging system mice. These mice allow for brain‐wide indelible labeling of neurons activated during learning, which then can be compared with secondary neuronal ensembles activated during memory retrieval. The neurons activated at both time points represent a memory trace or engram. Here, we aimed to identify the neural ensembles linking anxiety and memory loss following AD progression by utilizing behavioral studies, calcium imaging and whole‐brain microscopy, in female and male mice.ResultWe found: 1) Female AD mice exhibited anxiety‐like behavior at an earlier age compared to controls and male mice, 2) AD female mice displayed memory deficits as early as 2 months of age, 3) Anxiety‐like behavior correlated with memory impairment only in AD female mice, and 4) Unlike their male counterparts, female AD mice showed a decline in memory traces in the CA3 of the hippocampus.ConclusionWe now believe that anxiety could represent a neuropsychiatric predictor of AD and are currently working to translate these findings to humans using the Alzheimer’s disease neuroimaging initiative (ADNI) dataset. We have found that in humans, anxiety predicts transition to dementia and that anxiety has a sex‐specific effect on brain atrophy.
Depletion of the AD risk gene SORL1 selectively impairs neuronal endosomal traffic independent of amyloidogenic APP processing
Authors: Knupp Allison , Mishra Swati , Martinez Refugio A. , Braggin Jacquelyn E. , Small Scott A. , Jayadev Suman , Young Jessica E. 
Published: December 2020

AbstractBackgroundThe SORL1 gene encodes for the protein SorLA, a sorting receptor involved in retromer‐related endosomal traffic. Many SORL1 genetic variants increase Alzheimer’s disease (AD) risk, and rare loss‐of‐function truncation mutations have been found to be causal of AD. SORL1 is expressed in neurons and glia of the central nervous system and loss of SORL1 has been reported AD tissue.MethodTo model the causal loss‐of‐function mutations, we used CRISPR/Cas9 technology to deplete SORL1 in human induced pluripotent stem cells (hiPSCs) to test the hypothesis that loss of SORL1 contributes to AD pathogenesis by leading to endosome dysfunction.ResultWe report that loss of SORL1 in hiPSC‐derived neurons leads to early endosome enlargement, a cellular phenotype that is indicative of ‘traffic jams’ and is now considered a hallmark cytopathology AD. We validate defects in neuronal endosomal traffic by showing decreased localization of amyloid‐precursor protein (APP) in the trans‐Golgi network (TGN), and increased localization of APP in early endosomes, a site of APP cleavage by the b secretase BACE1. Microglia, immune cells of the CNS, which also play a role in the AD pathology also express SORL1. We therefore tested and found no effect of SORL1 depletion on endosome size or morphology in hiPSC‐derived microglia, suggesting a selective effect on neuronal endocytic trafficking. Finally, because BACE1 dependent APP fragments can cause endosome enlargement, we treated SORL1 deficient hiPSC‐derived neurons with BACE1 inhibitors and demonstrate that endosome enlargement occurs independent of amyloidogenic APP fragments.ConclusionCollectively, these findings clarify where and how SORL1 links to AD. Moreover, our data, together with recent findings, underscores how sporadic AD pathways that regulate endosomal trafficking, and autosomal‐dominant AD pathways that regulate APP cleavage, independently converge on AD’s defining cytopathology.
Different relationship of cross‐regional correlations of progression of local cortical thickness and local SUVR 18F‐flortaucipir PET values to strength of connectivity
Authors: Soucy Jean‐Paul , Mohammadi Fatameh , Boulier Thomas , Hortelan Valentine , Rosa‐Neto Pedro , Pascoal Tharick A. , Savard Melissa , Kang Min Su , Mathotaarachchi Sulantha , Therriault Joseph , Benali Habib 
Published: December 2020

AbstractBackgroundWe have demonstrated in vivo in humans that spatial progression of tau distribution and atrophy in Alzheimer’s is largely driven by connectivity (Soucy et al, HAI2020). Nevertheless, local tau accumulation and atrophy should not progress in parallel during disease evolution. Atrophy keeps worsening, while local tau density (SUVRs estimated with PET) initially increases but could then decline because of tissue loss. Therefore, despite the impact of connectivity on progression of both local atrophy and SUVRs, it likely will not remain the same for both over time. This is what we tested in this work.MethodWe studied 35 AD subjects from ADNI with 2 PET studies (18F‐florbetapir) at times Ta/Tb (mean elapsed time = 438 days, median = 454), and a T1 MRI at Ta. Eighteen subjects were non‐progressors (NP) and 17 were progressors (P) based on change between Ta/b hippocampal SUVRs values. This criterion was considered to represent how far advanced patients were in their disease. Moran I statistic was used to measure autocorrelations between connectivity and T1 MRI‐defined cortical thickness at Ta or tau PET SUVR values at both times Ta and Tb in NP and P subjects in regions separately analyzed based the “intensity” of their connectivity as defined by a standard connectivity map (Human Connectome Project). We defined 3 groups of regions from thresholds separating poorly connected from moderately connected (0.01) and moderately connected from highly connected ones (0.021).ResultIn both NPs and Ps, regions with high connectivity showed higher Z‐score (4.06) of the Moran I value for correlations between regions’ cortical thickness than those with low (2.64)/moderate (2.17) connectivity. For SUVR values however, in both NPs and Ps and at both time points, Z‐scores were always lower for the highly connected regions than for those with moderate/low connectivity.ConclusionThe way connectivity influences correlations of atrophy across regions is indeed different from the way it affects correlations of tau deposition, likely for the reasons we hypothesized above to guide the progression of each parameter. Models of tau propagation across the brain will need to factor in ongoing atrophy but reversible accumulation of tau.
Tau immunotherapy rescues cognitive impairment by clearing extracellular tau
Authors: Iqbal Khalid , Dai Chunling , Hu Wen , Liu Fei 
Published: December 2020

AbstractBackgroundAlzheimer’s disease (AD) is histopathologically characterized by loss of neuronal connectivity associated with extensive tau and Abeta pathologies. Tau immunization can inhibit the seeding and spread of tau pathology and reduce not only tau but also Abeta pathology and rescue cognitive impairment in transgenic tau overexpression mouse models of AD pathology. The specific objective of this study was to determine if tau immunization in wild type aged mice, which like AD patients do not overexpress either APP or tau, can rescue cognitive impairment.MethodWe investigated the effect of immunization with monoclonal antibody 77E9 against tau 184‐195 in 18month‐old male C57 BL/6 wild type (WT) mice, 15 microgram/200 microliter intravenous weekly injection for 6 weeks or once every four weeks injection for 24 weeks; mice injected with mouse IgG were used as controls.ResultWe found (1) that tau immunization did not significantly alter the level of brain tau as tested by Western blots developed with 77E9 or with rabbit polyclonal tau antibodies 92e or 134d to total tau, (2) that aged mice were impaired as compared with the young animals as determined by novel object recognition test and Morris water maze test, and showed dendritic and synaptic deficits and (3) that tau immunization rescued these deficits and cognitive impairment.ConclusionThe therapeutic beneficial effect of immunization with tau antibody is probably through the restoration of neuronal connectivity due to clearance of primarily extracellular tau. Tau immunotherapy is thus likely to be effective only in those conditions where a reduction of extracellular tau will benefit.
Elucidating the pathogenic mechanisms of AD brain‐derived, tau‐containing extracellular vesicles: Highly transmissible and preferential propagation to GABAergic neurons
Authors: Ikezu Tsuneya , Ruan Zhi , Pathak Dhruba , Muraoka Satoshi , DeLeo Annina M. , Kayed Rakez , Gendelman Howard E. , Ikezu Seiko 
Published: December 2020

AbstractBackgroundsTau‐containing extracellular vesicles (EVs) are transmissible and induce tau phosphorylation and conformational change in the recipient neurons. However, whether human brain‐derived EVs induce tau pathology has not yet been characterized in the mouse brain. Here, we assess the mechanisms of disease spread after intrahippocampal injection of human brain‐derived EVs into the aged mouse model.MethodsEV‐enriched fractions were isolated from unfixed frozen human brain samples from AD, prodromal AD (pAD), control (CTRL) cases, and tau knockout (TKO) mouse brains. Isolated EVs containing 300 pg of human total tau were sterotaxically injected into the right outer molecular layer of the dentate gyrus of 18 months‐old C57BL/6 female mice. 4.5 months after the injection, hippocampal slices were prepared for whole‐cell patch clamp recordings of CA1 pyramidal neurons were undertakent. Hippocampi were analyzed with immunohistochemistry using phosphorylated‐tau (p‐tau) epitopes including AT8. EVs were examined for protein composition by protein mass‐spectroscopy, the neuronal uptake in vitro, and structural analysis by the atomic force microscopy (AFM).ResultsSemiquantitative brain‐wide immunohistochemistry of p‐tau revealed that inoculation of AD or pAD‐EVs induced tau propagation throughout the hippocampus, including the dentate gyrus, CA3 and CA1 subregions. AT8 was localized primarily in GAD67+ GABAergic neurons in pAD and AD EVs groups, accompanied with reduced amplitude of inhibitory postsynaptic currents and Excitatory‐Inhibitory ratio in amplitube of postsynaptic currents in CA1 pyramidal neurons in pAD EVs. AFM analysis showed higher density of tau oligomers in both AD and pAD EVs while only AD EVs showed significantly higher neuronal uptake compared to CTRL EVs. Finally, proteomic analysis showed that AD EVs are enriched in disease and glia‐related molecules compared to CTRL EVs, which may contribute to their enhanced neuronal uptake.ConclusionsIntracranial injection of AD or pAD EVs induced p‐tau accumulation primarily in GABAergic neurons throughout the hippocampus, resulted in higher uptake by neurons, and tau oligomer conformation, indicating of their pathogenic potency as seeding factors. GABAergic neuronal dysfunction in the hippocampal neuronal circuitry reported in early AD brains could be attributed to specific EV mediated tau propagation in this cell type, a phenomenon meriting further investigation and validation.
Plasma extracellular vesicles of neuronal and astrocytic origins: Biomarker carriers and pathogenic effectors in Alzheimer’s disease
Authors: Kapogiannis Dimitrios , Eren Erden , Hunt Jack F.V. , Shardell Michelle , Vogt Nicholas M. , Johnson Sterling C. , Bendlin Barbara B. , Nogueras‐Ortiz Carlos , Delgado‐Peraza Francheska , Eitan Erez 
Published: December 2020

AbstractBackgroundCirculating neuronal and astrocytic Extracellular Vesicles (NEVs, AEVs) provide biomarkers for Alzheimer’s disease (AD) (including total and p‐Tau in NEVs, complement in AEVs). In a large case‐control study leveraging Baltimore Longitudinal Study of Aging samples, Tau‐related NEV biomarkers predicted future AD diagnosis with high accuracy, sensitivity and specificity. To validate NEV Tau biomarkers for predicting cognitive decline in late middle‐age we leveraged Wisconsin Registry for Alzheimer’s prevention (WRAP) study samples. To examine convergent validity we used transgenic AD mice. Finally, we examined functional effects of circulating EVs.MethodsWe isolated NEVs and AEVs from plasma through immunocapture against L1CAM and GLAST respectively. Total and p‐Tau were measured in 292 serum samples from 146 cognitively unimpaired WRAP participants, divided into longitudinal cognitive decliner and non‐decliner groups. We also measured Tau biomarkers in circulating NEVs and brains of 28 mice (4 2xTg, 15 3xTg, 9 WT). To study functional effects of circulating EVs, we co‐cultured AEVs and NEVs (from 8 AD patients, 8 controls) with rat cortical and human iPSC‐derived neurons and performed cell viability and neurodegeneration assays.ResultsIn WRAP, cognitive status was associated with NEV Tau biomarkers depending on age, with cognitively declining compared to stable individuals showing higher pTau231 (p < 0.01), total Tau (p <0.001) and pTau181 (p < 0.05) with older age. Combining longitudinal NEV biomarkers achieved up to 94% area‐under‐curve (AUC), 86.0% sensitivity and 86.7% specificity for predicting cognitive decline. In mice, total Tau and pTau181 levels in circulating NEVs showed strong correlations with levels in cortex and hippocampus (p < 0.001; Figure: Fraction1 acquired ante‐mortem, Fraction 2 acquired during euthanasia). Functionally, AEVs (and, less effectively, NEVs) of AD patients induced complement Membrane Attack Complex deposition, membrane disruption, neurodegeneration, and necroptosis on recipient neurons. Neurodegenerative effects were not produced by total circulating EVs from AD patients or AEVs/NEVs from controls and were suppressed by complement inhibitors.ConclusionsNEV Tau biomarkers predict cognitive decline in late middle‐age humans. Tau in circulating NEVs reflect brain levels in mice. Functionally, AD patients’ circulating AEV/NEVs are neurotoxic. Inhibition of EV‐associated complement components may be therapeutically beneficial in AD.
Data‐driven approach reveals heterogeneity and region‐specific association of white matter hyperintensities with the APOE genotype
Authors: Habes Mohamad , Sotiras Aristeidis , Erus Guray , Toledo Jon B. , Janowitz Deborah , Wolk David A. , Shou Haochang , Bryan Nick , Doshi Jimit , Voelzke Henry , Schminke Ulf , Hoffmann Wolfgang , Resnick Susan M. , Grabe Hans J. , Davatzikos Christos 
Published: December 2020

AbstractBackgroundWe used a transparent machine learning algorithm to identify subtypes of white matter damage in aging and to characterize their clinical representations. White matter hyperintensities (WMH), a common finding on brain magnetic resonance imaging (MRI), are known to be associated with cognitive impairment and dementia. Previous studies have shown spatial heterogeneity of WMH appearance, but the contribution of Alzheimer disease (AD) dementia and related genetic risk factors to this heterogeneity remains unclear. We sought to investigate the heterogeneity of WMH, in the general population, leveraging state of the art pattern analysis methods and exploring whether the APOE genotype is contributing to this heterogeneity.MethodA sample of N=1836 people was obtained from the Study of Health in Pomerania (SHIP), Germany, covering a wide age range (22–84 years, median age 52.2±13.16). WMH were automatically segmented on multi‐modal MRI scans and decomposed into four spatially distinctive components using Non‐Negative Matrix Factorization (NNMF), a machine‐learning based structural covariance method. Associations of these components with the APOE genotype (available for n=303) was investigated in subjects older than 60 years of age, using a multivariable linear regression model for each component, with WMH component loadings as the outcome variable and APOE genotype (encoded as shown in Table 1) as the independent variable. Age, sex and ship sub‐cohort were included as covariates. All reported P‐values were Bonferroni corrected for multiple comparisons.ResultData‐driven approach identified four WMH components, consistent with clinical categorization in deep and periventricular lesions, while further dividing periventricular WMH into posterior, frontal and dorsal components (Figure 1). APOE genotype was significantly associated with the dorsal periventricular WMH component (P=0.035, Bonferroni corrected), but not with the other three WMH components (Table 1).ConclusionWe obtained a high consistency of data‐driven WMH components and established clinical categorizations. Our data demonstrated that the appearance of WMH follows heterogeneous regional distribution patterns. Additionally, the APOE genotype, the strongest genetic risk factor for sporadic AD, is specifically associated with one of the components indicating spatial specificity and a potential link to early MRI changes in the predementia phase.
Visualization of deep learning relevance maps for AD detection
Authors: Budding Celine , EitelAlbrecht FabianJan‐Philipp , Ritter Kerstin 
Published: December 2020

AbstractBackgroundDeep learning methods, in particular convolutional neural networks (CNNs), have been very successfully applied to the diagnosis of AD and MCI based on structural MRI data. However, since the training of those models needs the adaptation of many parameters, they are often criticized for being a “black‐box”. Here, we compute heatmaps using layer‐wise relevance propagation (LRP) to (1) validate CNN models and (2) explain neural network decision in single patients. Furthermore, we investigated whether the resulting heatmaps help in identifying subgroups of AD and MCI.MethodWe included 2964 T1‐weighted MR scans of 732 participants (185 AD, 116 MCI‐converters, 211 MCI‐non converters and 210 Cognitive Normal [CN]) from the ADNI data base. The AD/CN cohort were split into a training/validation (80 %) and a test cohort (20 %) based on participant ID. A CNN model consisting of four 3D convolutional layers was then trained to separate patients with AD and CN. The model was tested on the AD/CN test set and applied to MCI‐converters / MCI‐non‐converters. Using LRP, we produced for each MRI scan a heatmap in the input space, indicating the relevance of each voxel for the final classification decision. The heatmaps were clustered using spectral relevance analysis.ResultThe CNN model resulted in a balanced accuracy of 85.12 % on the AD/CN test set and 67.37 % on the MCI‐converter / MCI‐non‐converter set. We additionally evaluated the CNN models with respect to different subgroups, including gender (88.11 % for females, 82.10 % for males) and age (92.57 % for 60‐73 years, 89.06 % for 74‐78 years, 74.17 % for 79‐90 years). Among regions with the highest LRP evidence for AD and MCI are hippocampus and parahippocampal gyrus. Spectral clustering revealed a higher variability within the AD and MCI subgroups than for CN.ConclusionIn this study, we show the potential of a transparent deep learning framework in order to understand the underlying computing principles and their effect on clinical outcomes such as clinical diagnosis (AD vs. MCI vs. CN), gender and age. Future studies are necessary to address the robustness of heatmaps methods and their applicability in clinical routine.
Deep learning models for generating diagnostic explanations
Authors: Dyrba Martin , Pallath Arjun H. , Marzban Eman N. , Teipel Stefan J. 
Published: December 2020

AbstractBackgroundAlthough machine learning approaches achieve high diagnostic accuracy when detecting Alzheimer’s disease (AD) based on MRI scans, they are rarely applied in clinical studies due to a lack of suitable methods for model comprehensibility and interpretability. Recent advances in convolutional neural networks (CNN) visualization algorithms and methods for generating textual explanations may fill this gap. Here, we will introduce these novel approaches, present first prototypical software using these techniques, and outline the potential of such tools for clinical studies.MethodWe implemented a CNN model trained on 662 T1‐weighted MRI scans obtained from ADNI. Various CNN visualization algorithms were compared, which generate relevance maps indicating the contribution of individual image areas for detecting AD. Further, we created an interactive web application to display them and interactively change visualization parameters. For evaluation, we compared relevance scores in the hippocampus region with hippocampal volume.ResultThe CNN model reached a cross‐validated area under the curve (AUC) of 0.93±0.06 for AD vs. controls and 0.74±0.09. The layer‐wise relevance propagation (LRP) algorithm was found to provide most informative and robust results (Fig. 1). The web app is shown in Fig. 2. Hippocampal volume and relevance scores of the hippocampus region‐of‐interest correlated with r= ‐0.79 (Fig. 3). Additionally, relevance maps highlighted various other image regions with local cortical and subcortical atrophy.ConclusionCNN relevance maps may support clinicians in their MRI examinations. CNN models for generating textual explanations may offer additional information, but are not yet available due to the vast amount of labeled training samples required for training.