bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inhibition of mitochondrial carbonic anhydrases prevents Amyloid β‐induced cell death and blood brain barrier permeability in cerebrovascular endothelial cells
Authors: Lemon Nicole L , Ilies Marc A , Fossati Silvia 
Published: December 2023

AbstractBackgroundThe Neurovascular Unit (NVU) is an important multicellular structure of the central nervous system (CNS) which has been observed to become dysfunctional in Alzheimer Disease (AD) and cerebral amyloid angiopathy (CAA). Amyloidosis disrupts the function of cerebrovascular endothelial cells (ECs) through the activation of the intrinsic apoptotic pathway, and the induction of blood brain barrier (BBB) permeability. Previous findings in our lab demonstrate that Acetazolamide (ATZ) and methazolamide (MTZ), pan‐Carbonic Anhydrase (CA) inhibitors, prevent mitochondria mediated apoptosis in cerebrovascular ECs through the reduction of Aβ induced cytochrome C release from the mitochondria, caspase‐9 activation, and mitochondrial H2O2 production. Due to the reduction of mitochondria mediated cell death via pan‐CA inhibitors we hypothesize that the mitochondrial isoforms, CA‐VA/B, are involved in cell death and other deleterious mechanisms induced by Aβ. Thus, we anticipate using selective CA‐V inhibitors will reduce cerebrovascular EC apoptosis and prevent the loss of BBB resistance.MethodHuman cerebral microvascular ECs (hCMECs) were treated with Aβ40‐Q22 Dutch mutant alone and in combination with a CA‐V inhibitor. We measured outcomes of mitochondria mediated apoptosis and BBB integrity. Apoptosis was evaluated by measuring the changes in the protein expression of caspase‐9, BAX, and BCL‐2 with western blot. We used live‐cell imaging to measure active caspase‐3. The release of cytochrome C and the mitochondrial membrane potential was detected using immunocytochemistry (ICC). Mitochondria H2O2 production was measured by isolating mitochondria and using a commercially available kit, Amplex Red (Invitrogen). We assessed the permeability of hCMECs using the ECIS ZΘ technology. The changes in protein expression of EC activation markers, VCAM‐1 and ICAM‐1, were determined with western blot.ResultInhibition of CA‐VA/B prevented Aβ40‐Q22 induced DNA fragmentation, caspase‐9 and ‐3 activation, cytochrome C release from the mitochondria, loss of mitochondria membrane potential, and mitochondrial H2O2 production in human cerebrovascular ECs. The inhibition of CA‐VA/B also reduced Aβ40‐Q22 induced BBB permeability and increase in vascular adhesion protein VCAM‐1 in hCMECs.ConclusionThe inhibition of CA‐V is protective to cerebrovascular ECs in models of amyloidosis, through the prevention of mitochondria‐mediated cell death and loss of cerebral EC barrier function.
Familial FTD in India
Authors: Alladi Suvarna , Ramakrishnan Subasree , Arshad Faheem ,   Cognitive Neurology Research Group, NIMHANS
Published: December 2023

AbstractBackgroundThe burden of dementia in India is estimated to increase by 197% by 2050, and Frontotemporal dementia (FTD) is among the leading causes of young‐onset dementia. With recent advances in molecular pathology, genetic mechanisms and emerging therapies in FTD, it is important to understand familial FTD patterns in the heterogeneous sociocultural context of India. High genetic diversity is characteristic of India, and its population bears genetic imprints of European, Asian and even African genomes. The objective of this presentation is to provide an account of FTD in India, with a focus on familial FTD and genetic mechanisms recently being identified.MethodThe prevalence of FTD in clinical dementia cohorts across different geographic regions in India, frequency of familial FTD, clinical presentations in the context of sociolinguistic and ethnic diversity, and genetic mechanisms are reported.ResultThe reported prevalence of FTD in clinical and research groups across India varies from 7% to 26%. The proportion of familial FTD ranges from 9.5% in Kolkata in the east, 15% in Trivandrum in the south, 24.6% from Hyderabad and more recently, as high as 40% in a recent study from Bangalore. Few genetic mutations were reported in early studies. In a recent large effort from Bangalore, India, 200 patients with FTD syndromes underwent three generation pedigree analysis, clinical and neuropsychological evaluation and MRI according to ADNI protocol. Nearly half underwent genetic analysis by next generation sequencing and RT‐PCR was done to analyse C9orf 11 in a subset. Novel gene mutations including SQSTM1, GRN, MAPT, PSEN2, TBK1 and C9orf were detected. Of these mutations, 11 were pathogenic in 11 and 26 were variants of unknown significance (VUS). Apart from abnormal protein accumulation due to MAPT and GRN, we also identified CSF1R, TREM2 and TYROBP, that are recognised as primary microgliopathies. A few examples of VUS include PRNP, LRRK2, GBA and PANK2 mutations.ConclusionResearch from familial FTD cohorts in India is contributing to growing evidence of a wider spectrum of genetic mechanisms for FTD. Our findings provide compelling evidence to increase harmonised global efforts to reduce risk and develop novel treatment strategies for FTD.
Cognitive reserve and resilience of cognitive networks in ageing
Authors: Habich Annegret , Abbatantuono Chiara , Garcia‐Cabello Eloy , Taurisano Paolo , Dierks Thomas , Barroso Jose , Ferreira Daniel 
Published: December 2023

AbstractBackgroundIn the ageing process, different cognitive functions decline at different time points and at different rates. Additionally, the inter‐individual variability in cognitive decline could partially depend on cognitive reserve, which represents a resilience factor against age‐related cognitive decline. These complex relationships can be captured with advanced multivariate methods such as graph theory, that can be applied to investigate cognitive networks. The present study aimed to examine the association between cognitive reserve and cognitive networks across age.MethodA total of 334 cognitively healthy participants were stratified into early‐middle‐age (37‐50 years; n = 110), late‐middle‐age (51‐64 years; n = 106), and elderly (65‐78 years; n = 118) groups. Within each age group, individuals were further subdivided into two cognitive reserve levels (high, low) based on a crystallized intelligence proxy. For each subgroup, cognitive networks were constructed based on correlations between 47 z‐scored cognitive variables. Applying graph theory, different global and nodal network measures were compared between subgroups.ResultsGraph‐theoretical analyses revealed that individuals with high cognitive reserve were characterized by a stable cognitive network across age groups. The only consistent difference between the high cognitive reserve groups emerged in modularity with the late‐middle‐age group showing decreased modularity compared to early‐middle‐age and elderly groups. In contrast, individuals with low cognitive reserve showed a marked reconfiguration of cognitive networks across age groups with a pronounced dedifferentiation in late‐middle‐age. This was reflected as a decrease in global efficiency and increase in mean network strength and small‐worldness in the late‐middle‐age group compared to the early‐middle‐age group. The elderly group had a higher global efficiency and lower mean network strength and small‐worldness compared to the late‐middle‐age group.ConclusionA high cognitive reserve was associated with the resilience of the cognitive connectome during the ageing process, while individuals with low cognitive reserve showed prominent changes in their cognitive connectome with increasing age. Gaining further insights into these mechanisms will contribute to our understanding of age‐related cognitive decline and guide the development of strategies to preserve cognitive function in ageing.
Investigating associations of age at natural menopause with neuroimaging indicators of brain health in later‐life, using a British population‐based birth cohort
Authors: Needham Louisa P , Nicholas Jennifer M , Barnes Jo , Sudre Carole H , Cash David M , Coath William , Malone Ian B , Lu Kirsty , Schott Jonathan M , Richards Marcus , James Sarah‐Naomi 
Published: December 2023

AbstractBackgroundEarlier menopause age associates with poorer cognitive performance post‐menopause, but links with dementia risk are mixed and the potential mechanisms underlying such associations are unclear. Few studies have examined the relationship between menopause age and later‐life brain health.MethodOur study included 126 women from Insight 46, the neuroscience sub‐study of the 1946 British birth cohort, which included combined amyloid PET and MRI scanning at mean age 70.7 years (standard deviation = 0.66). Age at natural menopause was self‐reported, during midlife, as years since birth at the time of period cessation; women who reported surgical menopause were excluded (n = 63). We tested for interactions of menopause age with APOE‐ε4 status and used multivariable regression analyses to test associations of menopause age with later‐life total brain volume (TBV) and hippocampal volume. A generalised linear model with log‐linked gamma distribution tested associations with white matter hyperintensity volume (WMHV). Models were cumulatively adjusted for total intracranial volume and age at scan (model 0/M0), hormone therapy (HT) use (M1:M0+HT) and for prospective early‐life and sociodemographic (M2:M1+childhood cognition, education, adult socioeconomic position), reproductive (M3:M2+menarche age, parity), and health‐related (M4:M3+smoking, BMI and blood pressure at age 36 years, APOE‐ε4 status) covariables.ResultThe association of menopause age with WMHV was modified by APOE‐ε4 status (p = 0.024); each 1‐year increase in menopause age associated with a 7.4% decrease in WMHV for ε4 carriers (n = 35) and a 2.4% WMHV increase for non‐carriers (n = 90). Menopause age did not associate with hippocampal volume, but there was a positive association with TBV (Table 1) which remained significant until we accounted for health‐related factors in model 4 (Figure 1). In post‐hoc analysis, adjustment for blood pressure at age 36 attenuated the association by 11.24%, explaining more of the attenuation than other model 4 covariables.ConclusionWe find evidence for links between age at natural menopause and later‐life neuroimaging measures. Later menopause associated with greater brain volume, partly explained by midlife blood pressure. Interactive effects of APOE‐ε4 status on associations with WMHV, an indicator of brain pathology, warrant further investigation.
Which cortical regions affect the prediction of cerebrospinal fluid amyloid beta from PET images by novel residual neural network (ArcheD)
Authors: Tagmazian Arina , Vuoksimaa Eero , Pitkänen Esa , Schwarz Claudia 
Published: December 2023

AbstractBackgroundAmyloid‐β (Aβ) aggregation, as a biological hallmark of Alzheimer’s disease (AD), is a key component of biological classification of AD in the AT(N) framework. This research aimed to develop a new deep learning model for predicting Aβ cerebrospinal fluid (CSF) measures from amyloid PET scans and explore which cortical regions are most important contributors in the model’s prediction.MethodWe developed a Residual Network model (ArcheD) to predict Aβ CSF from amyloid PET images, and trained it on 1,870 PET scans from the Alzheimer’s Disease Neuroimaging Initiative. We registered PET scans to MNI152 template and used the Neuromorphometrics atlas to derive cortical gray matter (GM) regions. We extracted brain regions’ contributions to model decision‐making by guided backpropagation, and visualized these contributions as relevance heatmaps. We used bootstrapping, Welch’s t‐test and FDR correction in testing significance of brain regions’ relevance to predictions in the whole cohort, and separately in clinical diagnostic classes of AD, mild cognitive impairment (MCI), and cognitively normal (CN) cases.ResultArcheD showed high accuracy in predicting Aβ CSF (r = 0.81; p<0.00001). According to the relevance analysis for all samples (n = 1,390) temporal (mean 0.106; 95%CI [0.105, 0.108]) and parietal (0.105; 95%CI [0.104, 0.107]) lobes were the most important GM regions for the model decision‐making. Occipital (0.098; 95%CI [0.098, 0.100]) and frontal lobes (0.091; 95%CI [0.089, 0.92]) showed noticeably fewer relevance values. Based on between‐classes comparison, these regions contribute significantly more to CSF prediction in AD and MCI individuals compared to CN (p<0.05). Analysis of 49 smaller regions showed an almost 7‐fold difference between the most and least contributing regions.ConclusionWe developed a deep residual neural network model that predicted Aβ CSF from amyloid PET scans with high accuracy, and found that temporal and parietal lobes have the highest influence on Aβ CSF predictions. In the next step, we will validate the model in independent data and aim to develop the model further for clinical use.
Understanding the role of biological aging on cognitive vulnerability among individuals at risk of Alzheimer’s disease
Authors: Rodríguez‐Fernández Blanca , Sánchez‐Benavides Gonzalo , Minguillon Carolina , Fauria Karine , De Vivo Immaculata , Navarro Arcadi , Gispert Juan Domingo , Molinuevo Jose Luis , Sala‐Vila Aleix , Vilor‐Tejedor Natalia , Crous‐Bou Marta ,   for the ALFA study
Published: December 2023

AbstractBackgroundLeukocyte telomere length (LTL) is an objective biomarker of biological aging and has been proposed to play a crucial role in AD progression by impairing cognitive resilience. However, a limited number of studies have evaluated the role of LTL in cognition, showing inconsistent results, particularly in the context of AD pathology. The main objective of this study was to evaluate the association between LTL and cognitive performance in middle‐aged cognitively unimpaired individuals at increased risk of AD.MethodThe study included 1,532 participants from the ALFA cohort. LTL was determined by qPCR in peripheral blood leukocytes. LTL values were log‐transformed, normalized by computing z‐scores and residualized (rLTL) against age and sex using a linear regression model. A cognitive battery was administered to assess verbal memory, psychomotor speed, visual processing, and executive function. Episodic memory (EM), executive function, and global cognitive composites were calculated by averaging normalized raw scores of all subtests in each domain. Generalized linear models were implemented to assess the cross‐sectional association between rLTL and cognitive performance. Association analyses were performed separately in the accelerated aging group (rLTL < percentile 50th) and decelerated aging group (rLTL > percentile 50th). Stratified analyses by sex were conducted.ResultIndividuals at accelerated aging had higher systolic blood pressure and a different distribution of familiar history of AD than those at decelerated aging [Table 1]. Longer rLTL was associated with better performance in the EM domain only among individuals within the accelerated aging group. Remarkably, only free recall measurements remained significant after correction for multiple comparisons. Conversely, longer rLTL was associated with worse psychomotor speed in the whole sample and among individuals in the decelerated group [Table 2]. Women mainly drove these results. Among men, longer rLTL had a beneficial effect on semantic fluency but a detrimental effect on cued recall among those at decelerated aging [Table 3].ConclusionrLTL has a differential effect on cognitive domains among cognitively unimpaired individuals at accelerated/decelerated biological aging. Moreover, results differed depending on sex and cognitive domain. Our results suggest a complex heterogeneity in biological aging mechanisms that appear to change in individuals at increased risk of AD.
The association between cognitive reserve and age‐related neuroimaging features in homozygous APOE ε4 carriers
Authors: Lee Wei‐Ju , Chen Hung‐Chieh , Yang Yu‐Hsiu 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is the most common neurodegenerative dementia syndrome, which is characterized by irreversibly progressive cognitive decline, neuropsychiatric symptoms, and impaired function of independent daily living. The majority of AD occurs in individuals aged over 65 years and is termed late‐onset AD. The apolipoprotein E (APOE) ε4 allele is the highest individual risk factor. Despite lifelong influences of APOE ε4, not all APOE ε4 carriers develop cognitive decline, some APOE ε4 carriers appear to be better than others in maintaining structural and functional properties of their brains while aging. Although previous studies showed that the AD patients with higher levels of cognitive reserve had more advanced Alzheimer’s type pathology in the comparable clinical severity, the underlying mechanism is unclear. Thus, the association between the individual’s cognitive reserve and the aging‐related brain pathologies need further investigation.MethodThis study recruited dementia free homozygous APOE ε4 allele carriers and all participants’ cognitive statuses were evaluated by neuropsychological tests. Cognitive reserve was evaluated by Cognitive Reserve Index questionnaire (CRIq). In neuroimage study, we collected brain MRI images and analyzed the severity of hippocampal atrophy and white matter hyperintensities (WMH). The relationship between different kinds of cognitive reserve and age‐related neuroimaging findings were analyzed.ResultIn total, 39 participants carrying two APOE ε4 alleles were analyzed, with an average age of 61.5 years, an education level of 13.1 years, and an average MMSE of 26.4 points. Correlation analysis showed significant associations between CRI and neuropsychological tests, especially in short‐term memory, visuospatial function, and language function tests. In neuroimaging analysis, CRI‐education was associated with hippocampal atrophy, which suggested more education might help to prevent the hippocampal atrophy. The correlation between WMH severity of brain MRI and the CRI was not statistically significant.ConclusionCognitive reserve is associated with the cognitive function in dementia free homozygous APOE ε4 allele carriers and education might prevent hippocampal atrophy.
Structural modeling of clinical factors for simultaneous validation and prediction of future conversion to mild cognitive impairment
Authors: Ram Sebastiaan R , Strange Bryan A , Zhang Linda , Ser Teodoro , Lorenzo Elizabeth Lucia Valeriano , Valentí Meritxell , Zea‐Sevilla María Ascensión , Frades Belén , Heskes Tom , Larrañaga Pedro , Bielza Concha , Sanchez‐Juan Pascual 
Published: December 2023

AbstractBackgroundIdentifying risk factors of future conversion from cognitively normal to mild cognitive impairment is crucial for dementia prevention and understanding the clinical processes that result in the development of cognitive deterioration. One challenge which is faced with this task is that, when identifying these factors, not only the individual contribution but also its coherency with other indicators must be measured.MethodTo map the relationship between clinical risk factors and conversion to mild cognitive impairment, we created and validated a set of structural equation and growth mixture models based on input from clinical experts in the field of dementia research. These models are powerful as they allow the researcher to test hypotheses between observed variables and their underlying latent constructs. In addition to verification of the factor structure, we integrated a method for predicting individual factors, such as mild cognitive impairment, on out‐of‐sample converters and controls directly on the structural model, a feature which is normally uncommon in structural equation modeling partly due to its sensitivity for model misspecification.ResultIn a study of 1213 elderly participants we found that these structural models perform similar to current state‐of‐the‐art logistic regression models in terms of Area‐Under‐the‐Curve (AUC) score on a 1‐year prediction (AUC > 0.9), yet offer a significant increase in flexibility and insight into the direct and indirect effects of endogenous variables on conversion to mild cognitive impairment. We also observe a substantial increase in predictive power for models fitted with baseline data compared to baseline with follow‐up visit for both 5 and 9‐year prediction on neuropsychological and demographic data alone (AUC increase +‐ 0.05), allowing us to monitor the development of cognitive health in subjects over time.ConclusionThe results from this study suggest a powerful technique for creating structural models with the ability to perform robust path analysis, longitudinal monitoring of cognitive health and prediction of future conversion to mild cognitive impairment. We expect that clinical experts can use this method to continue testing and validating new hypotheses in dementia research.
Estimated Age of Amyloid Onset in Participants with Early‐ versus Late‐Onset Alzheimer’s Disease: Results from the LEADS & ADNI Studies
Authors: Blazhenets Ganna , Mundada Nidhi , Lee JiaQie , Landau Susan M. , Koeppe Robert A. , Carrillo Maria C. , Dickerson Brad C. , Jagust William J. , Apostolova Liana G. , Rabinovici Gil D. , La Joie Renaud 
Published: December 2023

AbstractBackgroundAβ deposition is thought to start decades prior to symptom onset. Previous studies have shown that mathematical modeling enables reconstruction of Aβ‐PET trajectory and age estimation at Aβ‐PET positivity in Aβ‐positive patients. We aimed to apply this approach to determine Aβ duration and estimate age at Aβ‐PET positivity in sporadic early‐ (<65yo) and late‐ (≥65yo) onset AD (EOAD/LOAD).MethodWe combined PET data from the Longitudinal Early‐Onset AD Study (LEADS, n = 288) and ADNeuroimaging Initiative (ADNI, n = 915). First, we selected Aβ+ (baseline Centiloids ≥24) cognitively impaired patients with ≥2 Florbetaben‐PET scans to compare baseline and longitudinal amyloid‐PET in EOAD versus LOAD using linear mixed effects regression (Figure 1). Then, we used ADNI participants with ≥2 Florbetapir‐PET scans to assess the relationship between Aβ‐PET Centiloids and time (Figure 2). We estimated the Aβ temporal trajectory by integrating a reciprocal of the spline model from the accumulation rate and Centiloidmid‐point. Finally, we used the resulting Aβ trajectory to estimate Aβ duration and age at Aβ‐PET positivity onset for patients with EOAD and LOAD (Figure 3).ResultEOAD patients had higher baseline Aβ burden than LOAD patients. Amyloid‐PET increased at similar rates in the two groups (Figure 1), allowing us to operate under the assumption that EOAD and LOAD follow the same Aβ trajectory, with a temporal shift. Aβ trajectory was estimated in ADNI Florbetapir‐PET, showing an almost linear dependency between Centiloids and Aβ duration (Figure 2). The model suggests that Aβ‐PET positivity (CL = 24) is reached after 7 years, and an additional 26 years are needed to reach 100 CL. When applied to the Florbetaben sample, we determined that 75yo LOAD patients became Aβ‐PET positive around 52yo, while 60yo patients with EOAD dementia were Aβ‐PET positive around age 31 (Figure 3).ConclusionBased on available data, the dynamic of Aβ accumulation seems similar in EOAD and LOAD, with a temporal shift indicating that EOAD patients might transition to Aβ‐PET‐positive in their early 30s. Prospective studies on early phases of Aβ accumulation in EOAD patients will be important for validation of these findings since existing data is limited.