bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cerebral blood flow dysregulation in dementia, Alzheimer’s disease and COVID19
Authors: Iadecola Costantino 
Published: December 2023

AbstractBackgroundCerebral blood vessels are essential for optimal brain health by maintaining the metabolic, thermal, biochemical and cellular homeostasis of the brain’s internal milieu. Alterations of the neurovasculature disrupt such vital homeostatic functions and are a frequent correlate of cognitive impairment in a wide variety of neurovascular, neurodegenerative, and neuroinflammatory pathologies.ResultsNeurovascular pathologies (e.g., vascular cognitive impairment), neurodegenerative conditions (e.g., Alzheimer’s disease), and systemic infections associated with immune hyperactivation (e.g., COVID19), result in neurovascular dysfunction which may contribute to short‐ and long‐term impairment of cognition. The mechanisms by which neurovascular dysfunction lead to cognitive impairment are diverse and include: (a) alterations in the factors regulating the delivery of blood to the brain (coupling between neural activity and blood flow, autoregulation, endothelial vasoactivity), (b) alterations of the blood‐brain barrier, (c) vascular trophic factor deficiency resulting in increased vulnerability of neurons and glia to injury, (d) dysregulation of the brain’s immune surveillance, (e) reduced waste disposal through perivascular, paravascular (glymphatics), and transvascular clearance pathways. In addition, in COVID19 hypoxia and endothelial inflammation caused by cytokines and hemostatic abnormalities lead to endothelial dysfunction, microvascular occlusions, and increased the risk of stroke and hypoxic brain injury. Longer term consequences (long COVID) are likely to be linked to non‐resolving inflammation driven by persistence of the virus spike protein in the skull bone marrow, meninges, and brain.ConclusionNeurovascular alterations play a central role in the mechanisms of neurovascular, neurodegenerative and neuroimmune diseases resulting in cognitive impairment. Neurovascular dysfunction impairs cognition by promoting white matter damage, accumulation of pathogenic proteins (Abeta, tau, etc.) and neuroinflammation, and by reducing the resilience of the brain to injury. Interventions to improve neurovascular function may be beneficial in the management of these devastating conditions and should be considered as an added preventative or therapeutic approach.
A novel 3D imaging method to establish baseline amyloid‐beta plaque load and analyze compound efficacy in pre‐clinical Alzheimer’s disease animal models
Authors: Ferron Gianna , Linehan Stefan T , Ragan Tim , Elk Kevin J , Garceau Dylan , Sasner Michael 
Published: December 2023

AbstractBackgroundAmyloid‐beta (Aß) plaque deposition in the brain represents a significant hallmark of Alzheimer’s Disease (AD). Standard laboratory approaches assessing Aß lack the ability to pair high‐throughput whole‐organ imaging with region‐specific quantitation, and often require tissue homogenization. To analyze changes in Aß spatiotemporally, we developed a novel Serial Two‐Photon Plus (STP2) pipeline to quantify plaque progression and depression as a function of brain region, resulting in indexed brain sections for secondary analysis using MALDI HiPLEX‐IHC with imaging mass spectrometry (IMS).MethodPlaques in the well characterized 5XFAD mouse model of AD were labeled with Methoxy‐X04, an Aß plaque‐specific compound, prior to transcardial perfusion and whole‐brain excision. The STP2 platform uses a TissueCyte system to produce high‐resolution multi‐channel datasets, yielding 3D models of each brain. The Allen Mouse Brain Common Coordinate Framework (CCFv3) was registered to each brain, and our automated pipeline quantified plaque load and density per brain region. Select resulting sections were analyzed using AmberGen MALDI HiPLEX‐IHC and Bruker Daltonics IMS for Aß42, pTau, GFAP, GLUT1, MBP, NeuN, NF‐L, PVALB, SNCA and SYN‐I.ResultQuantifying baseload plaque distribution in the 5XFAD model at 2, 3, 4, and 6‐months revealed significant changes in density of Aß plaques both spatially and temporally. STP2 imaging, combined with CCFv3 mapping and secondary proteomic analysis allows for targeted evaluation of compound treatment on the Aß plaques.ConclusionThe STP2 pipeline produces translational high‐throughput pre‐clinical AD data with enhanced sensitivity and precision, with resulting sections remaining in‐tact for further secondary analysis of spatial proteomics and pharmacodynamics in the brain.
BrainChart cortical thickness centile scores: a generalizable tool to detect age‐inappropriate cortical neurodegeneration in Alzheimer’s disease
Authors: Parker Thomas D , Bethlehem Richard A.I. , Seidlitz Jakob , White Simon R , Bernstock Joshua D , Bourke Niall , David Michael CB , de Taurines Anastasia Francoise L Gailly , Giovane Martina Del , Graham Neil SN , Kolanko Magdalena A , Zimmerman Karl A , Malhotra Paresh A , Patel Maneesh , Scott Gregory , Alexander‐Bloch Aaron , Bullmore Edward T , Sharp David J 
Published: December 2023

AbstractBackgroundIdentifying age‐inappropriate cortical neurodegeneration on structural MRI is a key component of Alzheimer’s disease (AD) assessment. However, widely available reference standards that enable objective interpretation are lacking. BrainChart (www.BrainChart.io; https://github.com/BrainChart/Lifespan) calculates age and sex adjusted centile scores for structural MRI metrics using normative data from 101,457 participants across the lifecourse and can be applied across different datasets. We applied this approach to regional cortical thickness estimates and explored its utility in identifying patients with AD associated neurodegeneration.MethodCross‐sectional BrainChart cortical thickness centile scores were calculated for 241 individuals with pathologically confirmed AD (median scan time prior to death 5.8 years), as well as 1432 cognitively normal participants (CN) from the NACC dataset. Area under the receiver operating characteristic curve (AUC) analyses were performed to identify the cortical regions that best identified patients with pathologically confirmed AD. This approach was then applied to 305 participants from the ADNI‐3 cohort, all of whom underwent clinical amyloid (florbetapir) PET and tau (flortaucipir) PET within 6 months of structural MRI (CN = 185; Mild Cognitive Impairment (MCI) = 95); AD = 26).ResultThe top ten performing regions for differentiating pathologically confirmed AD from CN in the NACC dataset were entorhinal, middle temporal, inferior parietal, isthmus cingulate, fusiform, supramarginal, inferior temporal, superior temporal, precuneus and temporal pole. AUCs ranged from 0.72 – 0.77 (figure 1 and table 1). Combining these ten regions into a single model significantly improved diagnostic separability (AUC = 0.81, figure 1). Optimal cut‐points for individual regions ranged from the 25th centile to the 35th centile, with sensitivities ranging from 57.7% to 68.9%, and specificities ranging from 72.6% to 80.9%. Using progressively lower centile scores increased specificity but decreased sensitivity (table 1). In ADNI‐3 participants, a combined model of the same ten regions derived from the NACC dataset was able to discriminate amyloid and tau pet positive AD patients from amyloid and tau negative CN (AUC = 0.98), as well as amyloid and tau positive MCI patients from amyloid and tau negative CN (AUC = 0.85) (figure 2).ConclusionBrainChart age and sex adjusted cortical thickness centile scores, derived from an extensive normative dataset, represent a generalizable method for objectively identifying cortical neurodegeneration in AD.
Lower thickness of inner retinal layers is associated with an increased risk of incident dementia and Alzheimer’s disease – an individual participant data analysis of four prospective cohort studies (69,955 participants)
Authors: van der Heide Frank , Khawaja Anthony , Berendschot Tos T.J.M. , Littlejohns Thomas J , Kuzma Elzbieta , Foster Paul J , Patel Praveen , Bertelsen Geir , von Hanno Therese , Johnsen Bente , Schirmer Henrik , Rebouças Sara Cristina Lima , Grasset Leslie , Delcourt Cécile , Helmer Catherine , Webers Carroll , Kroon Abraham A , van der Kallen Carla J.H. , Eussen Simone J.P.M. , van Dongen Martien , Schalkwijk Casper , Bosma Hans , Köhler Sebastian , Schram Miranda T. , Blokland Gabriella , Linden David , Wesselius Anke , Stehouwer Coen D.A. 
Published: December 2023

AbstractBackgroundRetinal imaging tools may be biomarkers for the early identification of individuals at risk for dementia. Currently, it remains unclear whether retinal neurodegeneration precedes the onset of clinical dementia as few prospective data have been published. The objective To investigate whether retinal neurodegeneration, estimated from lower thickness of inner retinal layers, was associated with incident all‐cause dementia and Alzheimer’s disease.MethodWe performed an individual participant data meta‐analysis using data from four prospective cohort studies with a total of 69,955 participants (n = 1,087 cases of incident all‐cause dementia; n = 520 cases incident Alzheimer’s disease; follow‐up time median [interquartile range; IQR] 11.3 [8.8 ‐ 11.5] years). We investigated the associations of baseline macular retinal nerve fiber layer (mRNFL) thickness and ganglion cell‐inner plexiform layer (mGCIPL) thickness (expressed per standard deviation lower thickness) with incident dementia. We used Cox proportional hazards models adjusted for age, sex, educational level, and other key dementia risk factors. Missing data for covariables were imputed. We tested for interaction with age, sex, apolipoprotein E (APOE) genotype status, and educational level.ResultAfter adjustment for age, sex, educational level, and key dementia risk factors, lower baseline mRNFL thickness was significantly associated with higher incidence of all‐cause dementia and Alzheimer’s disease (per SD lower mRNFL thickness, hazard risk [95% confidence interval], 1.10 [1.02; 1.17] and 1.11 [1.001; 1.23], respectively); and lower mGCIPL thickness was not significantly associated with these outcomes (per SD lower mGCIPL thickness, 1.04 [0.99; 1.10] and 1.01 [0.94; 1.09], respectively). Age, sex, apolipoprotein E (APOE) genotype status, and educational level did not (consistently) modify these associations.ConclusionThis individual participant data meta‐analysis found that lower baseline thickness of inner retinal layers was associated with up to approximately 10% higher risk of incident all‐cause dementia and Alzheimer’s disease. These findings suggest that retinal neurodegeneration precedes the onset of clinical dementia and that retinal imaging tools may be biomarkers for the early identification of individuals at risk for dementia.
Novel genomic loci shape two neuroanatomical dimensions of AD in non‐demented populations
Authors: Wen Junhao , Yang Zhijian , Skampardoni Ioanna , De Pian Marilena , Erus Guray , Mamourian Elizabeth , Cui Yuhan , Zhao Bingxin , Nasrallah Ilya M. , Toga Arthur W. , Shou Haochang , Shen Li , Davatzikos Christos 
Published: December 2023

AbstractBackgroundArtificial intelligence has been in burgeoning demand to be applied to magnetic imaging resonance (MRI) data to parse the neuroanatomical heterogeneity of Alzheimer’s disease (AD) and/or mild cognitive impairment (MCI) (Wen et al., 2021a, 2021b; Yang et al., 2021). However, the genetic heterogeneity leading to this neuroanatomical heterogeneity, especially at the early asymptomatic stages, is not fully elucidated.MethodA deep semi‐supervised representation learning method, called Surreal‐GAN (Yang et al., 2022), was applied to T1‐weighted MRI from AD/MCI patients from ADNI to define the two neuroanatomical dimensions of AD at the symptomatic stages. The R1 dimension was characterized by widespread brain atrophy without an exclusive focus on the medial temporal lobe, whereas the R2 dimension showed focal medial temporal lobe atrophy (Wen et al., 2022). We then applied the trained model to 33,540 non‐demented individuals from UK Biobank. Imputed genotyping data were analyzed in genome‐wide association studies (GWAS). We performed multiple linear regressions controlling for confounding factors (e.g., age) via Plink (Purcell et al., 2007). We defined a genomic locus (represented by the top lead SNP) considering linkage disequilibrium. Finally, we manually queried all leading SNPs on GWAS Catalog (Buniello et al., 2019) to determine if the genomic loci were novel, provided they were not previously associated with any clinical traits.ResultGWAS discovered 24 genomic loci, of which 7 were novel for R1 and R2, respectively (Fig. 1). For R1, 8 mapped genes, based on physical positions, eQTL, and chromatin interactions, were associated with AD‐related traits in the literature. For example, the KDM2B gene from the genomic locus (rs9795600, P‐value = 1.13e‐08) was previously associated with AD (Gouveia et al., 2022). For R2, 13 mapped genes were associated with AD‐related traits. For example, the TESC gene from the genomic locus (rs77956314, P‐value = 9.99e‐29) was associated with neurofibrillary tangles measurement, which is formed by hyperphosphorylation of a microtubule‐associated protein known as tau in AD (Wang et al., 2020).ConclusionOur results confirmed genetic variants previously associated with AD and identified novel genomic loci contributing to this neuroanatomical heterogeneity. Further research is needed to understand these novel loci’s biological pathways and functional consequences.
Investigating the association between white matter fiber density in the superior longitudinal fasciculus and inhibitory control in groups at‐risk of Alzheimer’s disease
Authors: Tahara‐Eckl Lenore T , Roberts Reece P , Morgan Catherine A , Dell'Acqua Flavio , Kirk Ian J , Melzer Tracy R , Dalrymple‐Alford John C , Anderson Tim J , Cutfield Nick J , Tippett Lynette J 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is associated with poorer memory and reduced grey matter volume. Non‐amnestic cognition and white matter (WM) changes are an alternative line of enquiry and may reveal earlier changes. We investigated how inhibitory control relates to the superior longitudinal fasciculus (SLF) in groups at‐risk of AD.MethodParticipants (n = 229; aged > 55 years) from the Dementia Prevention Research Clinic, New Zealand were classified as control (C; n = 35), subjective cognitive decline (SCD; n = 60), single‐domain amnestic MCI (aMCI; n = 55), multiple‐domain MCI (mMCI; n = 52), and AD (n = 27). Using diffusion MRI, we measured WM fiber density (FD), fiber cross‐section (FC), and fiber density cross‐section (FDC) in the SLF (SLF1, SLF2, SLF3 in each hemisphere, Figure 1). We also measured inhibition (Trail‐Making B, Stroop Inhibition, Switching, Hayling Incongruency), generativity (Letter Fluency, Category Fluency), and processing speed (Trail‐Making A, Stroop Color Naming, Word Reading).ResultMultivariate analyses showed impaired cognition across groups, with stronger impairments in inhibition (F(4, 206) = 5.41, p < .001; Wilk’s Λ = 0.55, η2p = 0.12) than processing speed (F(4, 217) = 2.45, p = .001; Wilk’s Λ = 0.84, η2p = 0.04). Generativity was also impaired across groups (p < .01). Significant variation in FD was found across groups in all SLF tracts, except for left SLF1 (Figure 2a). Between‐group differences (Figure 2c) included greater FD in C in the right SLF2 than mMCI (p = .04, d = 0.58), in the right SLF3 than SCD (p = .04, d = 0.57) and mMCI (p = .003, d = 0.77), and in the left SLF3 than SCD (p = .04, d = 0.65), mMCI (p = .01, d = 0.69), and AD (p = .02, d = 0.67). Two separate partial least‐squares analyses showed that poorer inhibition, but not processing speed, was associated with lower FD in the SLF (Figure 3).ConclusionOverall, we found that reduced FD in the SLF was associated with poorer inhibitory control in risk groups of AD. Therefore, WM alterations in the SLF may provide an early marker in AD.
Vascular risk factors and fMRI activation in a prodromal dementia population
Authors: Kallevag Oystein 
Published: December 2023

AbstractBackgroundVascular risk factors (VRF) are associated with an increased risk of developing dementia (1). Functional magnetic resonance imaging (fMRI) demonstrates the ability to detect early changes in neuronal activation before cognitive changes are clinically measurable (2). We investigate whether the number of VRF in cognitively healthy persons with a minimum of two VRF present was associated with a change in neuronal activation in the brain measured with task‐based fMRI.Method56 cognitively healthy, defined by Mini‐Mental State Exam (MMSE) and clinical dementia rating (CDR), elderly persons completed an fMRI examination with task‐based fMRI and N‐Back paradigm to access Working Memory (WM). Vascular risk factors included in the study were stable coronary artery disease or cerebrovascular disease according to MRI criteria, hypercholesterolemia, hypertension, diabetes mellitus and overweight (3). We analyzed the fMRI data at a single time point using the FMRIB Software Library (FSL v6.0; Analysis Group, Oxford, UK).ResultThe mean age was 68.4 (± 5.4) years. The number of VRF present were VRF2 (n = 21), VRF3 (n = 25) and VRF4 (n = 10). The median MMSE were: VRF2: 29 (IQR 29 ‐ 30), VRF3: 30 (IQR 28.8 ‐ 30) and VRF4: 29.5 (IQR 28.3 ‐ 30). A low number of VRF (VRF2) revealed higher neuronal activation than the groups with a higher number of VRF (VRF3 and VRF4). The regional activated clusters in the VRF2 group showed activation in the superior parietal lobule, visual cortex, premotor cortex, Broca’s area and anterior intra‐parietal sulcus.ConclusionOur results show that a lower neuronal activation measured with fMRI is associated with a higher number of VRF. We suggest further studies to assess whether altered neuronal activation measured by fMRI in cognitively healthy elderly persons with VRFs can be associated with a higher risk of developing cognitive decline and dementia. • O'Brien JT, Thomas A. Vascular dementia. The Lancet. 2015;386(10004):1698‐706. • Chen J, Glover G. Functional Magnetic Resonance Imaging Methods. Neuropsychology Review. 2015;25(3):289‐313. • Khalifa K, Bergland AK, Soennesyn H, Oppedal K, Oesterhus R, Dalen I, et al. Effects of Purified Anthocyanins in People at Risk for Dementia: Study Protocol for a Phase II Randomized Controlled Trial. Front Neurol. 2020;11:916.
Sleep apnea‐related hypoxemia, not sleep fragmentation, is associated with white matter hyperintensities in older adults
Authors: Berisha Destiny E , Rizvi Batool , Chappel‐Farley Miranda G. , Taylor Lisa M. , Sattari Negin , Dave Abhishek , Chen Ivy Y. , Neikrug Ariel , Benca Ruth M , Yassa Michael A. , Mander Bryce A 
Published: December 2023

AbstractBackgroundSleep apnea is a type of sleep‐disordered breathing that increases risk for Alzheimer’s disease‐related pathology potentially through cerebrovascular pathology, likely exacerbated by two features of sleep apnea: intermittent hypoxia and sleep fragmentation. White matter hyperintensities (WMH), a marker of small vessel cerebrovascular disease, increase in aging and in AD. Here, we tested the hypothesis that apnea‐related hypoxemia severity, and not sleep fragmentation, are associated with WMH volume in older adults.MethodThirty‐two older adults (73.4±5.3 years, 19 female, AHI = 15.2±18.9) were evaluated with overnight polysomnography (PSG). Apnea severity was quantified using the Apnea‐Hypopnea Index (AHI), Respiratory Disturbance Index (RDI), and blood oxygen desaturation (Oxygen Desaturation Index (ODI), duration, frequency of = 4% desaturations, and desaturation nadir) stratified by sleep stages. T1‐MPRAGE and T2‐FLAIR scans were acquired using a 3T Siemens scanner. Total WMH volumes were derived using a semi‐automated algorithm. Overnight memory retention was assessed using the change in the lure discrimination index (LDI) for the emotional version of the Mnemonic Discrimination Task.ResultTraditional measures of apnea severity and sleep fragmentation were not significantly associated with global WMH volume. However, both max duration of oxygen desaturation events (r = 0.44, p = 0.011) and max oxygen desaturation percentage (in rapid‐eye movement (REM) and in total sleep: r = 0.48, p = 0.005, and r = 0.44, p = 0.01), were significantly associated with total WMH volume. Similar relationships were found with time spent below 90% and 80% oxygen saturation (Kendall’s tau = 0.33, p = 0.02 and tau = 0.275, p = 0.03). These relationships survived adjustment for sex, age, and hyperlipidemia and/or hypertension medication at time of PSG. Global WMH volume was also negatively associated with overnight memory retention (r = ‐0.38, p = 0.03).ConclusionThese findings suggest that hypoxemia severity, compared to sleep fragmentation, are more strongly associated with global WMH volume. As WMH volume was associated with overnight memory retention, this may suggest a pathway in which OSA increases WMH, which is subsequently associated with tau burden, worsening sleep‐dependent memory.
Predicting cognitive decline in a low‐dimensional representation of brain morphology
Authors: Lamontagne‐Caron Rémi , Desrosiers Patrick , Potvin Olivier , Duchesne Simon , Doyon Nicolas 
Published: December 2023

AbstractBackgroundIdentifying early signs of neurodegeneration due to Alzheimer’s disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, cortical decline in aging and high inter‐individual variability complicates the determination of AD‐related neurodegeneration on trajectories. Further, the high‐dimensional nature of these trajectories necessitate the use of a transformation into lower‐dimensional spaces in order to perform comparative statistical analyses, model decline, and identify criteria differentiating pathological from normal trajectories.MethodIn this project, we computed trajectories in a 2D representation of a 62‐dimensional manifold of individual cortical thickness measures. To compute this representation, we used a nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We first created a UMAP embedding on measurements from 6,237 cognitively healthy participants (3,556 women) aged 18 to 100 years old from the NOMIS database. We then projected longitudinal data from 537 mild cognitively impaired (MCI) subjects and 340 AD subjects from ADNI into this embedding. Each participant had multiple visits (n = 2), one year apart. Finally, differences in trajectories were analysed by clustering the reduced data and comparing the positional variations through time between MCI and AD subjects. The validity of the approach was verified through cross‐validation.ResultFirst, the embedding was shown to be positively associated (r = 0.65) with participants' age (see figure 1) in the NOMIS data. When projected in this space, differences between ADNI’s MCI and AD distributions were found (see figure 2). Average trajectories between clusters were shown to be significantly different between MCI and AD subjects (see figure 3). Moreover, we showed that some clusters and trajectories between clusters were more prone to host AD subjects. Finally, cross‐validation showed an accuracy of up to 72% at predicting cognitive decline in MCI participants over 2,000 bootstrap iterations.ConclusionA 2D low‐dimensional cortical thickness representation embeds sufficient discriminatory information as to predict decline to AD in MCI participants with strong accuracy.