bluuebunny/crossref_metadata_2025_split huggingface dataset:
An experimental approach to disentangle neurodevelopmental and current factors of influence on brain and cognition: an intervention twin study
Authors: Walhovd Kristine B , Markus Sneve H , Sørensen Øystein , Bråthen Anne Cecilie Sjøli , Falch Emilie Sogn , Garrido Pablo F. , Kransberg Jonas , Slivka Maksim , Alatorre‐Warren José‐Luis , Overbye Knut , Czajkowski Nikolai Olavi , Fjell Anders M 
Published: January 2025

AbstractBackgroundNeurodevelopmental origins of functional variation through the lifespan are acknowledged, but pathways need to be identified. The objectives of the project Set‐to‐change is to test whether and how early life environmental factors and genetic makeup regulate brain and cognition and its change, as well as neurocognitive plasticity in response to training through the lifespan.MethodPreliminary analyses for the first months are presented. We investigate differences in brain and cognition and their change, in adult mono‐ (MZ) and dizygotic (DZ) twins (total n ∼220 individuals, age range 16‐79 years, mean 36), with varying degrees of prenatal environmental variance, as indexed by their extent of discordance in birth weight (BW). Half of the sample trained in a novel navigation intervention utilizing true locomotion in virtual reality, assessed with brain MRI and cognitive measures at pre‐ and post‐ 10 weeks intervention in an AB/BA crossover design. Effects of training on hippocampus volume are analyzed using generalized additive mixed models (GAMM4) accounting for condition (pre‐train, post‐train, post‐rest), sex, age (smooth), time, and time x age. Anatomical brainprints from T1 and T2W scans are used to assess effects of genetic (MZ/DZ) and early environmental (birth weight; BW) differences.ResultsPreliminary analyses indicate a positive effect of training on hippocampal volumes. Differences in behavioral level reached in training, related to differences in BW among MZ twins, but the higher BW twin tended to train more. Using “brainprints” (Valizadeh et al. Sci Rep 2018, 8, 5611) from multicontrast MRI data, we observed that across 10 weeks, self‐similarity was high and could be used for perfect identification of one self and MZ twin, DZ‐similarity also identifiable. Brainprint similarity at baseline varied as a function of BW in MZ twins (Figure 1). BW discordance in MZ twins (n for analysis: 27 pairs) related to degree of functional plasticity, as measured by more whole brain connectome change after training being higher with higher BW.ConclusionTwin designs sampling early and current life environmental conditions experimentally, may further enhance our understanding of the impact of early life factors on current outcomes and in adulthood.
Imaging the impact of early and late life risk factors on the brain: a comparison
Authors: Gordon Brian A. 
Published: January 2025

AbstractBackgroundAlzheimer disease (AD) is a chronic progressive neurodegenerative disorder that presents with cognitive dysfunction, memory loss, language difficulties, emotion dysregulation, and the eventual loss of motor function and death. Magnetic resonance imaging (MRI) shows early atrophy in the medial temporal lobes, which then spreads to the posterior temporal lobe, parietal lobe, and finally the frontal lobe with relative sparing of the sensorimotor cortex. Social disadvantage has been shown to have potentially additive impacts on aging trajectories.Over the first years of life the brain undergoes rapid expansion and maturation. Whereas sensory regions are relatively stable from birth, there is tremendous change in the temporal, parietal, and frontal regions which are responsible for higher order cognitive function. This change is paralleled by the development of motor, language, emotion regulation, and executive function. Early life events such as low birth weight, poverty, and adversity can impact neurodevelopment, including cortical expansion and connectivity.Given this mirroring between regional brain development and degeneration, researchers have hypothesized a “last in first out” mechanism underlying progressive neurodegeneration, but minimum direct comparisons have been performed. longitudinally studies older adults.MethodsThis work brings together unique cohorts spanning the two ends of the lifespan. The Early Life Adversity, Biological Embedding (eLABE) study scans infants scanned longitudinally between birth and age three years of age. The Charles F. and Joann Knight ADRC and ADNI longitudinal follow middle and older‐aged individuals. Structural change is estimated a unique multimodal surface matching (MSM) pipeline that estimates the deformation of pairwise cortical surfaces to estimate cortical expansion or atrophy over time. Analyses will include all children with longitudinal MRI data as well as longitudinal MRI data to characterize healthy aging, individuals who convert to AD, as well as longitudinal change in within individuals with an AD diagnosis.ResultsPreliminary results in each cohort (Figure 1) demonstrate highly similar spatial patterns between early life development and AD related cortical thinning.ConclusionsAlthough there has long been speculation that there is a reciprocity between regions that develop in childhood and those vulnerable to age and disease related decline, this work test this theory.
Adult lifespan neurodegeneration is associated with memory decline independent of amyloid deposition
Authors: Roe James M , Jagust William J. , Landau Susan M. , Harrison Theresa M. , Grydeland Håkon , Slivka Maksim , Alatorre‐Warren José‐Luis , Sørensen Øystein , Walhovd Kristine B , Fjell Anders M , Vidal‐Piñeiro Didac , Wang Yunpeng 
Published: January 2025

AbstractBackgroundCurrent models of AD posit neurodegeneration and cognitive decline occur downstream in a pathophsyiological cascade initiated by amyloid (Aβ), yet lifespan research suggests the brain regions and cognitive functions impacted most by AD exhibit the steepest, steady decline rates across life. We hypothesised adult lifespan neurodegeneration in AD‐vulnerable brain regions would predict memory decline rates detectable in healthy adults as they age, independent of Aβ.MethodWe combined MRI scans across three large longitudinal cohorts of cognitively healthy adults (age 30‐96 years) to estimate brain change relative to the change expected given a person’s age (2‐14 timepoints; 4125 scans of 1027 individuals; cohorts: LCBC, the Berkeley Aging Cohort Study [BACS]; ADNI [stable cognitively healthy]). We similarly estimated episodic memory change, as measured by the California Verbal Learning Test (LCBC and BACS; 2356 observations of 601 individuals). Using a set of features we previously determined classifies AD patients from controls (AUC=.952), we ran multivariate models to test associations between brain structure change and memory change. Specifically, we calculated the principal component of change across different subsets of brain features using a sliding window through AD feature importance, iteratively testing its association with memory change. In a subset with PET data (537 Aβ observations of 262 individuals), we tested whether brain‐memory change associations were independent of Aβ. Finally, we tested the spatial correlation between adult lifespan brain change rates in only Aβ‐negative individuals (adults >40 years confirmed Aβ‐negative at every timepoint) and an AD‐control effect size based on change.ResultAssociations between brain structure change and memory change were detectable in healthy adult lifespan data (p=7.0e‐10), present in comparatively young adults (in 30–65‐year‐olds), and strongest in regions changing faster in AD. Correcting for Aβ did not attenuate brain‐memory change associations, though Aβ was independently associated with more memory decline (p=2.7‐3). The spatial correlation between adult lifespan change rates (in Aβ‐negative) and accelerated change in AD was r=.74, p=10‐63.ConclusionOur results show neurodegeneration in AD‐sensitive regions exists independently of Aβ, and that such changes are not benign, but track with more memory decline across the healthy adult lifespan.
Association between education and multimodal neuroimaging in men and women across the lifespan
Authors: Gonneaud Julie , TURPIN Anne‐Laure , Vivien Denis , la Sayette Vincent De , Chételat Gael ,   Medit‐Ageing Research Group
Published: January 2025

AbstractBackgroundEducation has been associated with reserve mechanisms and lower dementia risk, but the literature shows inconsistent results on the association between education and brain outcomes across the lifespan. Considering that both dementia risk and education are likely to differ between sexes, our study aims at understanding the association between education and brain outcomes across the lifespan and whether it differs by sex.MethodIn 207 healthy individuals (110women) aged 19‐84 years old (47.98±18.75), we investigated the association between years of education and multimodal neuroimaging (structural‐MRI, FDG‐PET, Florbetapir‐PET) and how this association was modulated by age and sex. Analyses were restricted to regions involved in Alzheimer’s disease and/or reserve mechanisms (hippocampal volume, temporoparietal metabolism, neocortical amyloid, anterior and posterior cingulate cortices [ACC/PCC] for each modality).ResultThere was no main effect of years of education on neuroimaging (ps>.09) nor interaction between education and the other variables. However, exploratory interactions conducted within young (18‐40), middle‐aged (40‐60) and older (60+yo) groups separately showed that, only in the older group, sex interacted with education on ACC volume, revealing that education was associated with greater volume only in older women (Figure; pinteraction=.006). Complementary analyses conducted in an independent and larger sample of older adults (n=135, 83women, >65yo) in whom lifestyle was assessed retrospectively, suggest that midlife and late‐late‐life mental engagement, more than early‐life engagement (reflecting mainly education), are related to greater brain outcomes in late‐life (ACC volume, PCC amyloid burden, ACC metabolism). We assessed the moderative effect of sex on these associations and found that the association between grey matter volume and occupation (midlife engagement) was mainly driven by women (pinteraction=.02). The interaction between early‐life engagement and sex on ACC volume highlighted in the main cohort was replicated, yet at a trend‐level (pinteraction=.08).ConclusionOur study suggests that education is not strongly associated with age‐related differences in structure, metabolism or amyloid burden. Older women with higher levels of education, however, showed higher ACC volumes. These results suggest that education could promote brain reserve in late life in a sex‐dependent manner. Future studies should investigate the mechanisms behind these differences in brain reserve.
The impact of polygenic risks on neurotransmitter‐related grey matter atrophy in Alzheimer’s disease
Authors: Manca Riccardo , Mitolo Micaela , Bacalini Maria Giulia , Capellari Sabina , Venneri Annalena 
Published: January 2025

AbstractBackgroundTau protein tangles have been recently shown to accumulate in multiple brainstem nuclei in pre‐cortical Alzheimer’s disease (AD) stages. The impact of neurotransmission alterations on brain atrophy and their genetic correlates in AD remain unexplored. Therefore, the aims of this study were: 1) to investigate associations between grey matter (GM) loss across the AD continuum and the distribution of multiple neurotransmitter receptors/transporters; 2) to investigate the impact of polygenic risk scores for AD (PRSs) on such associations.MethodT1‐weighted MRI scans, genetic and clinical data were selected for 800 ADNI participants (age = 74.1±7.6; M = 55.3%) including 203 cognitively unimpaired older adults (CU), 442 with mild cognitive impairment (MCI) and 155 with AD. GM atrophy was investigated in MCI and AD groups, compared with CU. JuSpace was used to calculate correlations between GM atrophy and the distribution of several neurotransmitters. Two PRSs, with (AD‐PRS) and without APOE (AD‐PRSnoAPOE) were calculated using a Bayesian approach and investigated as predictors of the strength of correlation between GM volume (GMV) and neurotransmitters in general linear models.ResultGM atrophy was primarily in medio‐temporal areas in MCI, while was widespread in AD participants. In both groups, atrophy was negatively associated with serotoninergic and dopaminergic receptors/transporters. In the whole sample, AD‐PRS and AD‐PRSnoAPOE were negatively associated with the strength of correlation between GMV and 2 serotonin receptors (5‐HT1a and 5‐HT4). AD‐PRS was also associated with GMV‐FDOPA (negatively) and with GMV‐5‐HT1b (positively) correlation coefficients. In amyloid positive participants, AD‐PRS was associated with GMV‐5‐HT1b (b = 0.012, p < 0.001) and AD‐PRSnoAPOE with GMV‐5‐HT4 (b = ‐0.013, p < 0.001) correlation strength only.ConclusionGM loss due to AD may be particularly affected by the alterations in inhibitory serotoninergic and in presynaptic dopaminergic activity, that are known to affect acetylcholine function and memory decline. Such alterations appear to be driven by polygenic risk for AD, with potential specific effects dependent on APOE genotype and amyloid status. Investigating further the impact of AD‐PRS on various neurotransmitter‐related neural alterations may help clarifying neuropathological changes in pre‐clinical AD and support early detection of people at risk of dementia.
Demonstration of an open‐source toolbox for network spreading models: regional amyloid burden promotes tau production in Alzheimer's disease
Authors: Thompson Elinor , Schroder Anna , He Tiantian , Legouhy Antoine , Zhao Xin , Cole James H. , Oxtoby Neil P , Alexander Daniel C ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

AbstractBackgroundConnectome‐based models of disease propagation are used to probe mechanisms of pathology spread in neurodegenerative disease. We present our network spreading model toolbox that allows the user to compare model fits across different models and parameters. We apply the toolbox to assess whether local amyloid levels affect production of pathological tau.MethodsData: Regional tau‐PET and amyloid‐PET SUVRs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study were downloaded in September 2023 (demographics in Figure 1). Subjects were separated into groups based on AT disease stage: 1) amyloid‐negative, tau‐negative; 2) amyloid‐positive, tau‐negative; 3) amyloid‐positive, tau‐positive. Positivity thresholds were defined using Gaussian mixture modelling. Data were averaged within groups to produce stage‐specific maps.Spreading modelsWe compared three models: the network diffusion model, Fisher‐Kolmogorov‐Petrovsky‐Piscounov (FKPP) and weighted‐FKPP models (Figure 2). Previously we showed that weighting the production rate of FKPP with regional vectors improves the model fit by accounting for different regional characteristics (He et al, MICCAI 2023). Here, we demonstrate this new “weighted‐FKPP” model, using regional amyloid as the fixed production weight. Models were initialised with unity pathology in a bilateral seed region, zero elsewhere.Parameter optimisationFree parameters were fitted using Gaussian processes optimisation (α and seed region).NDM toolboxAll models are available in our toolbox: https://github.com/ethompson93/network_spreading_models.Null Modellingwe repeated the weighted‐FKPP model optimisation with a distribution of 100 randomly shuffled amyloid maps to demonstrate the spatial significance of the amyloid weighting.ResultAdding amyloid weighting to the production term in the FKPP model improved the model fit to the data for each group. The reduction in squared model residuals with the weighted FKPP model was significant only in the amyloid‐positive tau‐positive group (Figure 3). For all groups, the results from the FKPP weighted with amyloid had significantly improved model fit (Pearson’s R) compared to the null model (all p‐values<1×10‐10).ConclusionUsing our toolbox, we found that incorporating regional amyloid levels improves pathology‐spreading model fit to measured tau‐PET data, particularly at later stages of AD. This supports the hypothesis that amyloid promotes tau aggregation.
Circulating metabolome in relation to cognitive impairment: a community‐based cohort of older adults
Authors: Huang Yuhui , Sun Xuehui , Huang Qingxia , Huang Qiumin , Chen Xiao , Zhou Xiaofeng , Chen Hui , Shen Jie , Gao Mengyan , Zhang Hui , Tang Huiru , Wang Xiaofeng , Jiang Xiaoyan , Zheng Yan , Yuan Changzheng 
Published: January 2025

AbstractBackgroundThe role of circulating metabolome in cognitive impairment is limited and inconclusive. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors.MethodIn the community‐based Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance (NMR) spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired group according to their performance in two objective cognitive tests. A two‐step strategy of cross‐sectional discovery followed by prospective validation was applied to identify cognitive impairment‐related metabolites. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately‐to‐severely impaired) during the 2‐year follow‐up to 204 cognitively normal controls by age and sex with a 1:3 ratio and conducted conditional logistic regression. Metabolite set enrichment analysis was performed to identify cognitive impairment‐related metabolic pathways. We constructed prediction models for incident cognitive impairment using Lasso regression.ResultWe identified 28 metabolites cross‐sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were each prospectively associated with 28%, 28%, 29%, and 33% lower risk of incident cognitive impairment. Based on incident cognitive impairment‐related metabolites, three enriched metabolic pathways (valine, leucine, and isoleucine biosynthesis; valine, leucine, and isoleucine degradation; and aminoacyl‐tRNA biosynthesis) were found. Incorporating 13 metabolite biomarkers selected by Lasso regression into the traditional risk factors‐based prediction model substantially improved the prediction performance on incident cognitive impairment (AUCs: 0.839 vs. 0.703, P <0.001).ConclusionThe study identified specific plasma metabolites and potential enriched metabolic pathways related to cognitive impairment. Utilizing the selected metabolites substantially improved the prediction performance for cognitive impairment.
Baseline plasma NfL, p‐tau217 and tau‐PET SUVr predict cognitive decline in non‐demented persons from a memory clinic
Authors: Mendes Augusto J. , Ribaldi Federica , Lathuiliere Aurelien , Ashton Nicholas J. , Janelidze Shorena , Zetterberg Henrik , Scheffler Max , Assal Frederic , Garibotto Valentina , Blennow Kaj , Hansson Oskar , Frisoni Giovanni B. 
Published: January 2025

AbstractBackgroundPlasma biomarkers have been increasingly studied in Alzheimer’s disease due to their potentially high accessibility, affordability, and low invasiveness. Recent studies have shown that baseline plasma levels are capable of predicting cognitive decline in cognitively unimpaired subjects (CU) and with mild cognitive impairment (MCI). Despite the fact that neuroimaging biomarkers are also strong predictors, it is still unclear how well they perform when compared to plasma biomarkers in predicting cognitive deterioration.MethodOur goal was to evaluate how plasma (i.e., p‐tau181, p‐tau231, p‐tau217, GFAP, and NfL) and traditional neuroimaging biomarkers (i.e., amyloid‐PET centiloid, tau‐PET global‐SUVr, and hippocampal volume) are able to predict cognitive decline in non‐demented individuals. A total of 100 subjects were included in this study, namely 67 MCI and 33 CU from the Geneva Memory Center cohort. The study applied linear mixed‐effect regression models to evaluate how baseline biomarker levels predict cognitive decline. Moreover, sample sizes for future AD clinical trials were calculated for a subset of amyloid‐positive subjects based on how well each biomarker could predict the disease.ResultCognitive decline was only significantly predicted in the MCI subsample by baseline plasma NfL (β = ‐0.53, p = 0.02) p‐tau217 (β = ‐0.59, p = 0.009), and tau‐SUVR (β = ‐0.91, p = 0.002). Lastly, we have proven that the sample sizes for future treatment trials targeting AD can be reduced by up to 70% in individuals who exhibit both amyloid‐PET positivity by incorporating NfL as a requirement for inclusion. On the other hand, the inclusion of p‐tau217 might decrease amyloid‐positive sample sizes only by 10%.ConclusionTau‐SUVR was the best predictor of cognitive decline in the MCI stage, followed by plasma p‐tau217, and NfL. However, when the goal is to identify at‐risk subjects for future AD clinical trials with amyloid‐PET positivity in the inclusion criteria, the NfL seems to be the most appropriate biomarker to evaluate. This implies a strong association between amyloid‐PET and p‐tau217, whereas NfL may be more appropriate to identify individuals at‐risk for developing cognitive decline.