bluuebunny/crossref_metadata_2025_split huggingface dataset:
Posterior hippocampal redundancy: A marker of non‐pathological brain aging
Authors: Blujus Jenna K , Oh Hwamee 
Published: December 2022

AbstractBackgroundSeveral neural mechanisms underlying brain resilience to Alzheimer’s disease (AD) have been proposed, including redundant neural connections between the posterior hippocampi and all other brain regions (pHipp‐redundancy), and global functional connectivity of the left frontal cortex (gLFC). It remains unclear whether these two mechanisms confer resilience to AD pathologies through similar or distinctive mechanisms. We examined if redundancy, measured through graph theory, underlies the neuroprotective effects of both pHipp‐redundancy and gLFC connectivity in the early stage of AD pathologies.MethodFrom the ADNI database, 370 CN and MCI participants were utilized. Aβ‐positivity (Aβ+) was defined as PET AV45 SUVR normalized to cerebellum greater than 1.11. Resting state data were preprocessed using fMRIprep. Network graphs were constructed with 263 functional ROIs as nodes and z‐transformed correlations between each node as edges. Redundancy was calculated by summing the direct and indirect paths (path lengths=1‐4) between each hippocampal region (i.e., anterior and posterior hippocampal regions, bilaterally and LFC) and its 262 connections. Group differences (Aβ‐status, diagnosis) were computed in R using non‐parametric ANCOVA models with 10,000 permutations. Regression analyses were conducted to examine if Aβ‐status and diagnosis moderated the relationship between SUVR and redundancy. Models covaried for age, sex, education, and APOE4.ResultAcross CN and MCI, Aβ‐ had higher bilateral pHipp‐redundancy than Aβ+ (p’s<.034). Diagnosis significantly moderated the relationship between SUVR and left pHipp‐redundancy (B=16143.0, p=.042). CN exhibited a stronger negative relationship between SUVR and left pHipp‐redundancy than MCI (B=‐20161.59, t=3.08, p<.001). Finally, in CN, Aβ‐status significantly moderated the relationship between SUVR and right aHipp‐redundancy (B=59345.0, p=.012), showing that in Aβ‐ CN, redundancy decreased with higher SUVR (B=‐60532.07, t=2.82, p=.01), while in Aβ+ CN, redundancy remained the same as SUVRs increased. No relationships between Aβ‐status and redundancy, and interactions between SUVR and Aβ‐status or diagnosis, were significant in LFC.ConclusionTogether, these findings suggest that bilateral pHipp‐redundancy may reflect normal brain aging without Aβ, rather than resilience in pathological aging, and a distinctive mechanism of brain aging from that of gLFC connectivity in the progression of AD.
Associations between brain iron deposition and structural Alzheimer’s disease signature in cognitively unimpaired adults
Authors: Stankeviciute Laura , Falcon Carles , Operto Grégory , Rojas Santiago , Grau‐Rivera Oriol , Garcia Marina , Shekari Mahnaz , Niñerola‐Baizán Aida , Perissinotti Andrés , Minguillón Carolina , Fauria Karine , Molinuevo Jose Luis , Zetterberg Henrik , Blennow Kaj , Suárez‐Calvet Marc , Cacciaglia Raffaele , Gispert Juan Domingo 
Published: December 2022

AbstractBackgroundIron dyshomeostasis appears to be an early event in the Alzheimer’s continuum. However, little is known how incipient cerebral iron deposition affects brain structure and function in cognitively unimpaired (CU) individuals. Here, we assessed the relationship between mean intensities in subcortical nuclei, a proxy measure for iron content, and regional cortical thickness as well as brain metabolism in middle‐aged CU individuals and sought for interactions with biomarkers of Alzheimer’s pathology.MethodWe included 288 CU adults from the ALFA+ cohort (Table 1). Mean intensities were quantified from T2‐weighted magnetic resonance images in 18 a priori defined subcortical nuclei. Due to the high correlations among regional intensities, we performed a principal component analysis to obtain a single measurement of cerebral iron load (Table 2). Cortical thickness (CT) of the “Alzheimer’s disease (AD) signature” composite region of interest (ROI) was assessed using FreeSurfer v6.0. [18F]fluorodeoxyglucose (FDG) PET scans were used to calculate the standardized uptake value ratio (SUVr) within a meta ROI. We created separate general linear models where cortical thickness and PET meta‐ROIs were the dependent variables, while the first principal component (PC1), age, sex, APOE‐ε4 status, and education level were entered as predictors. In additional models, CSF levels of amyloid‐beta ratio (Aβ42/40) and phosphorylated tau (p‐tau) were entered as covariates. We also tested the interaction between PC1 and CSF Aβ42/40 and pTau status. Statistical significance was set at p<0.05.ResultPC1 explained 47.64% of the variance of mean intensities in subcortical regions with lower values denoting higher iron concentration. Lower PC1 values were negatively associated with AD‐signature thickness (p=0.004), but not with brain glucose metabolism in the meta ROI (p=0.936). (Table 3. Figure 1). These results remained unchanged once AD core CSF biomarkers were entered into the model. No significant interactions with abnormal levels of CSF Aβ or p‐Tau were revealed with AD‐signature thickness or brain metabolism.ConclusionIn CU individuals, higher content of iron deposition is associated with reduced thickness, but not glucose metabolism, in cortical areas that typically show atrophy early in the Alzheimer’s continuum. This association was not modified by the presence of altered biomarkers of AD pathology.
Relationship between [18F]flortaucipir PET visual patterns and neurodegeneration
Authors: Heeman Fiona , Moscoso Alexis , Camacho Valle , van Essen Martijn , Grothe Michel J. , Lin Li , Mainta Isminni , Ribaldi Federica , Devous Michael D. , Pontecorvo Michael J. , Frisoni Giovanni B , Garibotto Valentina , Schöll Michael 
Published: December 2022

AbstractBackgroundThe introduction of tau positron emission tomography (PET) ligands has allowed for the in vivo assessment of tau pathology in Alzheimer’s disease (AD). The recent development and FDA approval of [18F]flortaucipir (FTP) reader guidelines has enabled standardized visual assessment of FTP PET scans as showing negative, moderate or advanced AD tau patterns, thereby facilitating implementation of FTP scans in clinical practice. Although various studies have reported associations between regional FTP signal and neurodegeneration, the relationship between the standardized visual FTP AD patterns and markers of neurodegeneration has not yet been studied. This study aims to understand how FTP AD visual patterns relate to markers of neurodegeneration from structural MRI and FDG‐PET scans. Specifically, it focusses on characterizing regional neurodegeneration associated with the moderate AD pattern, both in cognitively normal (CN) and cognitively impaired participants (CI).MethodParticipants from five cohorts were included: the Alzheimer’s Disease Neuroimaging Initiative, Harvard Aging Brain Study, A4 study, the Geneva Memory Clinic cohort and AVID’s A05 study. These cohorts include CN participants, mild cognitively impaired and AD dementia patients who have available FTP, MRI and/or FDG scans, a subset also received follow‐up MRI scans. Furthermore, Centiloid values are available for all participants to quantitatively determine amyloid‐β (Aβ) positivity. All FTP scans are currently independently evaluated by three trained readers, blinded to clinical and imaging information. Majority read is used to assign scans to the negative, moderate or advanced AD tau pattern groups.ResultA total of 1948 participants underwent baseline FTP PET and MRI scans, a subset of 462 participants also underwent FDG‐PET scans. Between‐group differences in MRI‐ and FDG‐derived patterns of neurodegeneration are evaluated cross‐sectionally. In addition, the value of FTP AD patterns for predicting longitudinal neurodegeneration using MRI‐derived atrophy measures is assessed using Linear Mixed Models. All analyses are conducted separately for CN and CI participants, as well as stratified by Aβ (positive vs. negative) status. NB. Results of these analyses will be presented during the conference.ConclusionThis study will contribute to our understanding of the clinical relevance and prognostic value of the moderate and advanced FTP AD patterns in CN and CI individuals.
Greater AD prediction accuracy is obtained using hippocampal grading over hippocampal volume
Authors: Morrison Cassandra , Dadar Mahsa , Shafiee Neda , Collins D Louis 
Published: December 2022

AbstractBackgroundTo develop new Alzheimer’s disease (AD) treatments, diagnostic tools must be applied before too much irreversible neurodegeneration occurs. One of the key features used to identify AD neurodegeneration is hippocampal (HC) volume. However, prediction accuracy using HC volume is not high enough to be implemented in clinical and research settings. The goal of this study was to compare prediction accuracy of HC volume and HC grading, a method that measures local morphological similarity of the HC to a library of NC and AD subjects.MethodsParticipants were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and were included if they had a baseline diagnosis of either healthy older adult (cognitively normal, CN) or AD and baseline MRI scans. A total of 841 older adults met the inclusion criteria (513 CN and 328 AD). A second analysis examining the 388 amyloid positive participants was also completed (179 CN and 209 AD). A previously validated MRI analysis method (SNIPE) was used for HC grading (Coupe et al., 2012) and a similar non‐local patch‐based method was used to segment the HC to obtain volumes (Coupe et al. 2010). HC volumes provided by ADNI using Freesurfer were also analyzed for comparison. Age, sex, and either SNIPE grading, SNIPE volume, or Freesurfer volumes were used as features in three support vector machines to classify participants as either CN or AD, using a 10‐fold cross validation.ResultsUsing SNIPE grading, 88% accuracy was obtained when classifying CN from AD. SNIPE volume, generated 76% accuracy, while Freesurfer HC volume resulted in 67% accuracy when distinguishing CN from AD. The second analysis of only the amyloid positive participants yielded accuracies of 87% (SNIPE Grading), 77% (SNIPE volume), and 52% (Freesurfer volume).ConclusionHC grading, as measured by SNIPE, provides much higher accuracy at classifying CN from AD than both patch‐based HC segmentation volumes or Freesurfer HC volumes. These findings suggest that HC grading may be a useful tool at successfully predicting AD. The ability to correctly differentiate between groups who are amyloid positive is important for improving participant selection in clinical trials.
Modules of genotypic variance reflect heterogeneity across TDP‐43 proteinopathies
Authors: Spencer Barbara E , Irwin David J. , Van Deerlin Vivianna M , Lee Eddie B , Elman Lauren , Quinn Colin , Grossman Murray , Wolk David A. , McMillan Corey T 
Published: December 2022

AbstractBackgroundTAR DNA‐binding protein (TDP‐43) proteinopathies yield a variety of neurodegenerative conditions including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), and hippocampal sclerosis of aging (HS‐Aging). However, the contribution of both shared and disparate single nucleotide polymorphisms (SNPs) to phenotypic presentations is unclear. We hypothesize that shared genetic variation may drive susceptibility across diseases while disparate genetic variation may contribute to our understanding of disease‐specific drivers.MethodWe compared the relative associations of GWAS summary statistics for ALS, FTLD‐TDP, and HS‐Aging using weighted correlation analysis to identify data‐driven modules of highly correlated SNPs. A gene ontology enrichment analysis for biological processes was performed separately for each identified module. Enrichment was determined by overrepresentation test. Module SNPs were used to construct polygenic risk scores (PRS). PRS were tested for association with neuropathological diagnosis in an independent sample of autopsy‐confirmed individuals.ResultWe identified 493,591 SNPs commonly genotyped across ALS, FTLD‐TDP, and HS‐Aging GWAS summary statistics, and the top 1% of these were selected for analysis. We identified five modules: three modules associated with a single GWAS phenotype, ALS (M1, containing 1,907 SNPs), FTLD‐TDP (M2, containing 1,727 SNPs), and HS‐Aging (M3, containing 1,031 SNPs), as well as two modules associated with the phenotypic pairs, ALS‐FTLD (M4, 140 SNPs) and FTLD‐HS (M5, containing 129 SNPs). The top 10 overrepresented pathways by fold enrichment that met FDR p<.05 include processes related to nervous system development, including neuron projection and axon guidance, and the regulation of synapse organization and assembly in M1‐ALS and M2‐FTLD‐TDP. Notably, M1‐ALS uniquely includes pathways involved in skeletal muscle satellite cell activation as well as metabolic processes, which have known dysfunction in ALS. Pathways related to neurogenesis were uniquely observed in M3‐HS‐Aging. A PRS was calculated for M1‐ALS and we observed a significantly higher dosage of risk in a neuropathologically‐defined sample of primary ALS (n=131) than primary FTLD‐TDP cases (n=110; d=.27, p=.04).ConclusionExamining genotypes revealed modules of both shared and disparate correlated SNPs across phenotypes associated with TDP‐43 proteinopathies which have biologically plausible pathway and neuropathological associations. We suggest that genetic variation across TDP‐43 proteinopathies may contribute to individual‐level presentations of these syndromes.
Cross‐Platform Synaptic Network Analysis of Alzheimer’s Disease Contextualizes Proteomic Targets for Functional Validation
Authors: Herskowitz Jeremy H , Walker Courtney K , Seyfried Nicholas T. 
Published: December 2022

AbstractBackgroundCognitive decline in Alzheimer’s disease (AD) correlates well with the extent of tau pathology and synapse or dendritic spine loss. Thus, studying synaptic processes may aid in the identification of new therapeutic targets that limit cognitive decline. The entorhinal cortex (EC) is an initial site of tau tangle formation, from which tau can propagate to other brain regions through synaptic connections. We developed a pipeline whereby dendritic spine density and morphology as well as synaptic tau measurements from the EC were incorporated into a synaptic proteomic network to identify mechanisms of synaptic pathology in AD patients.MethodGolgi‐stained dendrites from postmortem human BA28 EC were imaged using high‐resolution brightfield microscopy and digitally reconstructed in 3D for morphometric analysis. Synaptosome fractions were isolated from the same BA28 samples. Levels of synaptosome and insoluble phosphorylated tau were measured by ELISA. Additionally, liquid chromatography coupled with tandem mass spectrometry‐based proteomics was performed on synaptosomes. Weighted Gene Co‐Expression Network Analysis (WGCNA) was used to generate a synaptic network of protein co‐expression modules. Module expression was correlated with dendritic spine measurements and phospho‐tau levels to guide selection of proteins for validation in model systems. Validation experiments were performed in neurons and Neuro‐2a cells, using a CRISPR activation system for target gene upregulation.ResultSpine density in the EC is reduced in AD, but not in cognitively normal individuals with AD pathology (CAD) cases. AD cases exhibit more synaptosomal pS396 tau than CAD cases. Module expression was correlated with dendritic spine density and morphology. We identified TWF2 as a hub protein of Module 16, which is positively correlated with thin spine length. Twf2 upregulation increased thin spine length in cultured neurons. Module expression was then correlated with phospho‐tau levels. We identified PPP1R7 as a hub protein of Module 24, which is inversely correlated with synaptosomal pS396 tau in human EC.ConclusionWe demonstrate that incorporating orthogonal cellular and molecular measurements into a proteomic network enables unbiased identification of proteins functionally involved in regulating those traits. Beyond elucidating synaptic processes in AD, this pipeline offers a blueprint to better contextualize omics‐based targets with relevant biological mechanisms.
Hippocampal subregional thinning related to tau pathology in early stages of Alzheimer’s disease
Authors: Berron David , Baumeister Hannah , Diers Kersten , Reuter Martin , Xie Long , Olsson Emil , Andersson Felix , Wisse Laura EM , Strandberg Olof , Smith Ruben , Stomrud Erik , Hansson Oskar 
Published: December 2022

AbstractBackgroundSubregions in the medial temporal lobe (MTL) are affected early by Alzheimer’s disease (AD) pathology and subject to grey matter atrophy. Measuring the earliest AD‐related atrophy in the hippocampus is challenging as region‐of‐interest (ROI) analyses of hippocampal subregional volumes collapse across voxels within anatomical subregions. PET imaging studies, however, report accumulation of tau pathology between anatomical subregions in the earliest disease stages (Berron et al., 2021) fitting reports from the neuropathological literature (Lace et al., 2019; Ravikumar et al., 2021). Thus, sensitive in vivo methods of point‐wise structural measures are needed in order to detect the earliest hippocampal thinning in AD along the anterior‐posterior as well as the medial‐lateral hippocampal axis.MethodHere we analyzed data from 76 amyloid‐beta negative (Ab‐) cognitively normal (CN), 46 Ab+ CN individuals and 25 Ab+ patients with mild cognitive impairment (MCI) from the BioFINDER‐2 study, who underwent 7 Tesla T2‐weighted structural magnetic resonance imaging, tau positron emission tomography imaging (using 18F‐RO‐948) and cognitive assessments. First, we segmented hippocampal subfields and extrahippocampal subregions. Second, we calculated point‐wise hippocampal thickness estimates (Diers et al.) of hippocampal subfields subiculum, cornu ammonis (CA)1, CA2 and CA3 on the level of the hippocampal body. Thirdly, we extracted local tau‐PET SUVR from Area 35 (A35), entorhinal cortex and amygdala. Finally, we assessed relationships between hippocampal local thickness and tau accumulation as well as cognitive performance.ResultOur analyses revealed earliest hippocampal thinning associated with tau accumulation in an area spanning the boundary of subiculum and CA1 at the level of the anterior hippocampal body. Ab+ MCI patients showed more posterior thinning in comparison to Ab‐ CU participants. Median thickness in an ROI comprising vertices with A35 tau‐related thinning (A35‐TauThinning‐ROI) was significantly lower in MCI Ab+ and tended to be lower in CU Ab+ compared to CU Ab‐. Higher median thickness in the hippocampal A35‐TauThinning‐ROI, but not whole CA1 nor subiculum thickness, was associated with better 10‐Word‐Delayed‐Recall and higher PACC scores.ConclusionOur results suggest that tau‐related thinning of hippocampal subregions can be observed already in early disease stages. Tau‐related point‐wise thickness measures were more sensitive compared to volumetric measures of anatomical subregions.
The Alzheimer’s Cell Atlas (TACA): A single‐cell type transcriptomics and network pathobiology map for target identification and drug repurposing in Alzheimer’s disease and related dementia
Authors: Zhou Yadi , Xu Jielin , Hou Yuan , Bekris Lynn M. , Pieper Andrew A. , Cummings Jeffrey , Leverenz James B. , Cheng Feixiong 
Published: December 2022

AbstractBackgroundRecent advances in massive single‐cell/nucleus (sc/sn) transcriptomic data have great potential for identification of cell‐type specific Alzheimer’s disease (AD) pathobiology, as well as target discovery for drug repurposing.MethodWe designed and implemented a multimodal omics analytic framework for the processing of sc/sn transcriptomic datasets, analyses of gene expression, pathobiology, cell‐cell communications with results presentation, interpretation, and translation. We performed an exhaustive search of cell type‐specific gene/protein network modules and cell‐cell interaction networks using our recently compiled ligand‐receptor and the human protein‐protein interactome networks. Furthermore, we developed a web portal with comprehensive visualization tools, such as cell and gene expression viewers, volcano plot and protein‐protein interaction network for differentially expressed genes (DEGs), ligand‐receptor interaction network for cell‐cell interactions, and heatmap for drug perturbation profiles cell type‐specific DEGs.ResultWe created The Alzheimer’s Cell Atlas (TACA, available at https://taca.lerner.ccf.org). In this endeavor, we compiled an AD brain cell atlas consisting of more than one million cells/nuclei from 20 datasets, covering major brain regions (cortex, hippocampus, cerebellum, etc.) and cell types (astrocytes, microglia, neurons, oligodendrocytes, etc.). We also conducted over 1,000 DE comparisons of these datasets to reveal cell‐type specific gene expression alterations. Major comparison types are (i) case vs healthy control; (ii) sex‐specific differential expression, (iii) genotype‐driven DE (i.e., APOE4/4 vs. APOE3/3; TREM2 R47H vs. common variants) analysis; and (iv) others. In addition, each comparison was followed by human protein‐protein interactome network module analysis, pathway enrichment analysis, and gene‐set enrichment analysis. For drug screening, we conducted gene set enrichment analysis for all comparisons with over 700,000 drug perturbation profiles connecting more than 12,000 human genes and 13,000 drugs/compounds. A total of over 300 analyses of cell‐cell interactions against 6,000 experimentally validated ligand‐receptor interactions were also conducted. We then generated summaries for the genes (for target identification) and drugs (for drug repurposing) from all analyses in sex‐specific and cell type‐specific manners.ConclusionWe envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for therapeutic discovery.
Histopathologic Brain Age Estimation via Context‐aware Attention‐based Deep Multiple Instance Learning
Authors: Marx Gabriel A. 
Published: December 2022

AbstractBackgroundWhile a large list of age‐related changes have been described in the human brain, the extent to which they represent the result of common pathological reactions versus signatures of underlying brain aging remains unclear. One approach to sorting out the relative importance of the myriad of changes is deep learning, which has emerged as a powerful tool for image analysis and computer vision. However, large digitized whole slide image datasets from human aging brains have not been previously available.MethodIn this study, we leveraged a large novel collection of uniformly processed digitized human post‐mortem brain tissue sections to create a histological brain age estimation model. We further investigated the effect of cognitive impairment and exogenous stress on the model. This was accomplished by developing a context‐aware attention‐based deep multiple instance learning model on 702 human brain tissues sections (age range 50‐110 yr) from the hippocampus stained with Luxol Fast Blue counterstained with hematoxylin and eosin (LH&E) on a brain age estimation task.ResultOur model estimated brain age within a mean absolute error of 6.2 years. Learned attention weights corresponded to neuroanatomical regions known to be vulnerable to age‐related change. We found that deviations from our estimated histopathologic brain age significantly correlated with the clinical marker of cognitive status (p=0.042). In addition, we found evidence of significantly accelerated age (p=1.12x10‐23) in a cohort of subjects with a neuropathological diagnosis of chronic traumatic encephalopathy (CTE), a neurodegenerative disease caused by mild yet repetitive traumatic brain injury, that displays features that overlap with aging.ConclusionThese data indicate that estimated histopathologic age can be used as a reliable pathologic correlate to identify factors that contribute to accelerated or decelerated brain aging.