bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of brain oxygen extraction fraction between normal cognition and mild cognitive impairment in an elderly cohort using quantitative BOLD
Authors: Le Linh N. N. , Wheeler Gregory J. , Fletcher Evan , Blockley Nicholas P. , Fan Audrey 
Published: December 2023

AbstractBackgroundThis study is to quantify oxygen extraction fraction (OEF), an important physiological biomarker for brain hemodynamic impairment, in the elderly cohort using non‐invasive quantitative BOLD (qBOLD) modeling, which is an MRI method for assessment of hemodynamic and metabolic properties.Method56 participants (75.4±7.0yo, 37F), including 46 cognitively normal (CN) individuals (75.2±7.3yo, 29F) and 10 mild‐cognitive‐impairment (MCI) patients (76.0±6.1yo, 7F), were recruited from UC Davis Alzheimer’s Disease Research Center cohort. MRI scans were acquired on a 3T scanner (Siemens Tim Trio) with a 32‐channel head coil, including an Asymmetric Spin Echo (ASE) scan (TR/TE, 2800/66ms; 2.3×2.3.x1.3mm3 resolution; FOV, 224mm; 13 values of the spin echo displacement time, from 0 to 52ms) and a T1‐weighted MPRAGE. T1‐weighted images were segmented into regions of interest (ROI), including gray matter (GM), white matter (WM), and cortical lobes (frontal, parietal, occipital, temporal) using FSL FAST. ASE signal curves were preprocessed and fit to two‐compartment qBOLD model with Variational Bayesian framework to estimate deoxygenated blood volume (DBV) and transverse relaxation rate (R2’), which are used to quantify OEF with known physiological constants (Cherukara et al., NeuroImage 2019). Average OEF values were calculated and compared for each ROI between CN and MCI groups using pairwise t‐tests with Bonferroni correction. The correlation between OEF and age were assessed using a linear mixed effects model that accounted for subject clustering.ResultCompared to CN, reduced DBV and increased OEF were observed throughout most of the brain on the group average level in MCI (Figure 1). In regional analysis, we observed a trend of increased OEF in MCI compared to CN in several ROIs, which was significant in the occipital lobe (p = 0.007) (Figure 2). In Figure 3, OEF was inversely correlated with age in the parietal lobe for CN (p = 0.026). In contrast, there was a positive trend of association between OEF and age for MCI across all regions.ConclusionThis study showed that age has an association with brain oxygen extraction that are differently affected by normal aging and MCI. Higher brain OEF in MCI individuals may reflect changes in oxygen metabolism because of underlying pathology, including vascular injury and compensation for hypoperfusion.
Functional connectivity of the frontoparietal control network is positively associated with cognition, independent of amyloid and tau burden, in the A4 study
Authors: Boyle Rory , Shirzadi Zahra , Coughlan Gillian T , Properzi Michael J , Chhatwal Jasmeer P. , Buckley Rachel F. , Sperling Reisa A. , Schultz Aaron P. 
Published: December 2023

AbstractBackgroundThe Anti‐Amyloid in Asymptomatic Alzheimer’s Disease (A4) study is the first secondary prevention trial of an anti‐amyloid therapy, solanezumab, in cognitively unimpaired older adults with elevated amyloid‐beta (Ab)‐PET burden. The inclusion of resting‐state functional connectivity MRI (fcMRI) as a secondary outcome measure is a novelty for clinical trials. Despite promising longitudinal findings, there is mixed evidence for cross‐sectional associations between functional connectivity in the default mode (DMN) and frontoparietal control (FPCN) networks, with Ab, tau, and cognition. Here, in a large cross‐sectional sample, we investigate these associations and assess whether fcMRI accounts for variability in cognition that is not attributable to Ab or tau.Method1,641 participants from A4 and the LEARN observational arm with fcMRI passed quality control and had complete data for sociodemographics, cognition, and Ab (Florbetapir)‐PET (Table 1). A subset had tau (Flortaucipir)‐PET (n = 420). Within‐network connectivity estimates of the default mode (DMN) and left and right frontoparietal control networks (FPCN) were obtained using template‐based rotation (Fig. 1). Using hierarchical regressions, we examined the independent associations of functional connectivity with Ab burden (mean cortical standardized uptake value [SUVr]), tau burden (mean of inferior temporal and entorhinal SUVrs), and cognition (Preclinical Alzheimer’s Cognitive Composite), independent of covariates (age, sex, education level, head motion, scanner and, where applicable, Ab and/or tau burden).ResultAb and tau burden were not independently associated with DMN or right FPCN connectivity. Higher tau was associated with reduced left FPCN connectivity, independent of Ab. Stronger left and right FPCN connectivity were associated with better cognition, independent of Ab and tau (Table 2). This association was stronger for the left FPCN.ConclusionFPCN functional connectivity is associated with better cognition, independent of Ab and tau burden. This association was strongest in the left FPCN and supports evidence that implicates FPCN connectivity in cognitive reserve and resilience. While this association is robust, weak effect sizes suggest that functional connectivity explains a limited amount of variability in cognition after accounting for covariates and AD pathology. Future release of the A4 study longitudinal data will enable quantification of the added value of fcMRI in AD clinical trials.
Amyloid‐PET Centiloid quantification predicts cognitive functioning in a pre‐dementia population: findings from AMYPAD‐PNHS
Authors: García David Vállez , Collij Lyduine E. , Mastenbroek Sophie E , Alves Isadora Lopes , Gispert Juan Domingo , Jessen Frank , Ritchie Craig W , Rovira Mercè Boada , Marquié Marta , Vandenberghe Rik , Schöll Michael , Frisoni Giovanni B , Hanseeuw Bernard J , Payoux Pierre , Vellas Bruno , Dubois Bruno , Martinez‐Lage Pablo , Scheltens Philip , Shekari Mahnaz , Wolz Robin , Grootoonk Sylke , Stephens Andrew W. , Gismondi Rossella , Schmidt Mark E , Buckley Christopher , Ford Lisa , Visser Pieter Jelle , Farrar Gill , Barkhof Frederik 
Published: December 2023

AbstractBackgroundThe prevention of symptomatic Alzheimer’s disease (AD) is a major endeavor currently in the field, with the prevalence of subjects on the AD continuum (i.e. presence of amyloid‐ß [Aß] pathology) being much higher than previously estimated. However, the extent to which Aß pathology and its interaction with common risk factors in the aging population relates to disease progression is still limited. The AMYPAD Prognostic and Natural History Study (PNHS) aims to evaluate the value of amyloid‐PET for predicting AD‐related disease progression.MethodA total of 1423 non‐demented subjects were included from the AMYPAD‐PNHS pan‐European cohort (17 sites across 10 Parent Cohorts), for who a baseline [18F]flutemetamol or [18F]florbetaben amyloid‐PET scans that passed quality control was available (Table‐1). Amyloid burden was assessed using the Centiloid (CL) method (whole cerebellum reference region). Independent linear mixed‐effect models were used to assess the predictive value of continuous CL, and its interaction with time, across cognitive domains: mini‐mental state examination (MMSE), memory, attention, language fluency, visuospatial, and executive functions. Years from baseline, age, sex, years of education, cognitive dementia rating (CDR), and APOE‐e4 were used as independent variables. Clinical follow‐up was 3.4 ± 1.8 years (up to ±10 years) on average.ResultAt baseline, 90% of the subjects were classified as cognitively unimpaired (CDR = 0). Based on CL, most subjects were categorized as amyloid‐negative (59%, 0±9CL), followed by grey‐zone (24%, 25±11CL), and amyloid‐positive (17%, 82±21CL). Baseline CL and its interaction with time were predictive of MMSE scores, memory, attention, and language fluency. Baseline CL, but not its interaction with time, was predictive of executive function. Finally, the interaction of baseline CL with time, but not its main effect, was predictive of visuospatial functioning (Figure‐1). Age and sex were significant contributors to nearly all models.ConclusionHigher baseline CL was associated with lower cognitive performance in all explored domains, suggesting that emerging cerebral amyloid accumulation in non‐demented individuals is predictive of overall cognitive function. Also, higher CL values were predictive of decline across cognitive domains, except for executive function. Further analysis is ongoing, including different confounding factors, longitudinal PET outcomes, and other biomarkers of the disease.
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‐ß (Aß) 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 Aß 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 Aß 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 Aß 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 Aß CSF from amyloid PET scans with high accuracy, and found that temporal and parietal lobes have the highest influence on Aß CSF predictions. In the next step, we will validate the model in independent data and aim to develop the model further for clinical use.
Telomeres are associated with markers and regions sensitive to aging and not Alzheimer’s disease in cognitively unimpaired older adults
Authors: Lehodey Asrar , de Flores Robin , Palix Cassandre , Touron Edelweiss , Mézenge Florence , Landeau Brigitte , Chocat Anne , Kaliman Perla , la Sayette Vincent De , Vivien Denis , Chetelat Gael , Poisnel Géraldine 
Published: December 2023

AbstractBackgroundTelomeres (protective chromosomal ends) gradually shorten over the lifespan and have been associated with the development of age‐related diseases such as Alzheimer’s disease (AD). Most studies use median telomere length as a biomarker of cellular aging, but their dysfunction is rather reflected by the load of short telomere. No study to date has assessed the links between the percentage of short telomeres (ST) and blood, neuroimaging markers of aging, neurodegeneration, amyloid and tau pathologies in healthy elderly.MethodBaseline data from 125 cognitively unimpaired older adults (65 to 83 years old; mean ± SD: 68.9 ± 2.8) from the Age‐Well trial (NCT02977819), who completed multimodal neuroimaging and blood sampling, were analyzed. Neuroimaging data included structural T1‐weighted MRI, T2‐weighted high‐resolution scan of the hippocampus, DKI, FDG‐PET and AV45‐PET (perfusion and amyloid load). ST, as well as neurodegeneration, amyloid, and tau biomarkers (NFL, GFAP, Aß42/40 ratio and pTau181) were measured in blood samples. Multiple linear regressions between ST and each marker were conducted corrected for age, sex, education and body mass index. Additional adjustments for APOE and amyloid status were made. Statistical significance was set to p<0.05, except for voxelwise analyses (puncorrected<0.005, kuncorrected = 500 voxels).ResultST was associated with lower white matter integrity (measured by kurtosis fractional anisotropy (kFA)) in prefrontal and temporal regions (Fig.1), and with the hippocampal subiculum volume, but not with total gray matter volume, mean kurtosis, glucose metabolism, brain perfusion or amyloid load; nor with blood markers of neurodegeneration, amyloid, or tau. Additional adjustments for APOE/amyloid status gave similar results.ConclusionTelomere dysfunctions were associated with lower white matter integrity in regions vulnerable to aging, but not to AD, nor with neurodegeneration, amyloid or tau biomarkers, and neither with AD risk factors (APOE or amyloid status). When we focused on the hippocampus, a specific link was found with the subiculum volume, an hippocampal subfield known to be the most sensitive to aging, which is not the case in AD. These results give further evidence for the tight connection between cellular and cerebral aging, but not with AD in cognitively unimpaired older adults.
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

AbstractBackgroundAß deposition is thought to start decades prior to symptom onset. Previous studies have shown that mathematical modeling enables reconstruction of Aß‐PET trajectory and age estimation at Aß‐PET positivity in Aß‐positive patients. We aimed to apply this approach to determine Aß duration and estimate age at Aß‐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 Aß+ (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 Aß‐PET Centiloids and time (Figure 2). We estimated the Aß temporal trajectory by integrating a reciprocal of the spline model from the accumulation rate and Centiloidmid‐point. Finally, we used the resulting Aß trajectory to estimate Aß duration and age at Aß‐PET positivity onset for patients with EOAD and LOAD (Figure 3).ResultEOAD patients had higher baseline Aß 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 Aß trajectory, with a temporal shift. Aß trajectory was estimated in ADNI Florbetapir‐PET, showing an almost linear dependency between Centiloids and Aß duration (Figure 2). The model suggests that Aß‐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 Aß‐PET positive around 52yo, while 60yo patients with EOAD dementia were Aß‐PET positive around age 31 (Figure 3).ConclusionBased on available data, the dynamic of Aß accumulation seems similar in EOAD and LOAD, with a temporal shift indicating that EOAD patients might transition to Aß‐PET‐positive in their early 30s. Prospective studies on early phases of Aß accumulation in EOAD patients will be important for validation of these findings since existing data is limited.
Detecting cortical microstructural alterations secondary to white matter hyperintensity in amyloid positive cognitively normal subjects
Authors: Torso Mario , Ridgway Gerard R , Valotti Michele , Hardingham Ian , Chance Steven A 
Published: December 2023

AbstractBackgroundPrevious research showed that cortical diffusivity is sensitive to primary cortical microstructural changes due to amyloid deposition (1). The present study aimed to investigate the cortical microstructural alterations secondary to white matter damage in cognitively normal participants, amyloid positive and negative.MethodThree hundred and thirty‐five cognitively normal (MMSE > 24) participants with baseline CSF Aß42 and MRI scans were obtained from the European Prevention of Alzheimer’s Dementia (EPAD) cohort. Participants were classified as Aß42‐ (N = 231) or Aß42+ (N = 104), based on a published cut‐off (2). The Fazekas scale deep white matter (FSDWM) score was used to further classify participants as LOW (0 and 1; N = 281) or HIGH (2 and 3; N = 54) FSDWM. Structural and diffusion MRI (dMRI) were used to calculate whole brain cortical volume fraction, cortical thickness and three neuropathology‐inspired cortical diffusivity measures: the angle between the radial minicolumnar direction and the principal diffusion direction (AngleR); the principal diffusion component parallel with the minicolumns (ParlPD), and the diffusion components perpendicular to the minicolumns (PerpPD+). Group differences in structural and diffusion metrics were tested using GLM adjusting for scanner manufacturer, number of dMRI volumes, age and sex, with Bonferroni’s correction (p<0.05/5).ResultThe interaction between Aß42 status and FSDWM revealed significant differences in cortical microstructural measures (GLM: AngleR F17,317 = 7.065, p = 0.008; PerpPD+ F17,317 = 7.976, p = 0.005). The pairwise comparisons showed significantly lower AngleR values in Aß42‐ HIGH compared to Aß42‐ LOW and significantly higher PerpPD+ values in Aß42+ HIGH compared to Aß42+ LOW (Figure). No significant differences in structural measures (thickness or volume) were detected.ConclusionCortical diffusivity measures can detect differences in cortical microstructure of amyloid positive and negative participants with different WM damage severity. The reduced AngleR value in Aß42‐ HIGH participants was potentially due to neuroinflammatory processes, consistent with previous findings(1). The higher PerpPD+ values in Aß42+ HIGH may indicate the secondary cortical microstructural damage. References: (1) Torso et al. 2022, PMID:36281682 (2) Ingala et al. 2021 PMID:33811742
Associations between tau‐PET and neurodegeneration with cognitive and behavioural changes over time in amyloid‐positive individuals
Authors: Singleton Ellen Hanna , Binette Alexa Pichet , Strandberg Olof , Stomrud Erik , Mattsson‐Carlgren Niklas , Ossenkoppele Rik , Hansson Oskar 
Published: December 2023

AbstractBackgroundTau pathology is strongly associated with clinical symptoms, possibly (partly) through the manifestation of neurodegeneration. However, independent pathways between tau pathology and clinical symptoms have been suggested. Longitudinal studies investigating the role of tau vs neurodegeneration on cognitive and behavioral changes across the AD clinical spectrum are relatively lacking.MethodWe included n = 482 individuals from the Swedish‐BioFINDER‐2‐study who had undergone longitudinal neuropsychological assessments and [18F]RO948‐tau‐PET and 3T‐MRI, of whom n = 157 were amyloid‐beta positive (A+) cognitively unimpaired (CU), and n = 326 A+ cognitively impaired (CI) individuals, and calculated SUVRs and cortical thickness in multiple regions(i.e. medial‐temporal, lateral‐temporal, medial‐parietal, lateral‐parietal, frontal, occipital, whole‐brain). Mean follow‐up time was 1.63(1.29) years. Linear‐Mixed‐Effects‐models with random‐intercepts and fixed‐slopes were performed, adjusting for age, sex, and education, applying FDR‐correction, with both baseline and rate‐of‐change in tau‐PET or MRI (the annualized difference between baseline and 2 year follow‐up) as predictors and MMSE, mPACC5 or the Mild Behavioral Inventory (MBI) as outcomes. Post‐hoc analyses included corrections for the other modality and serial mediation analyses (n = 1000 bootstrap iterations).ResultLinear‐Mixed‐Effects‐models with baseline tau‐PET or MRI as predictors showed stronger associations between tau‐PET and cognitive and behavioral changes over time than cortical thickness, which were more pronounced in CI than CU individuals (Figure 1). Analyses with rate‐of‐change in tau‐PET or MRI as predictors showed stronger associations for tau‐PET in CU individuals, but superiority of MRI in CI individuals, which was confirmed when correcting for the other modality, i.e. assessing independent effects (Figure 2). No significant associations were found between rate‐of‐change in tau‐PET or MRI and longitudinal MBI (all p>0.05; data not shown). Serial mediation analyses revealed no mediating effect of rate‐of‐change in tau‐PET or MRI between baseline tau‐PET and cognition over time in CU, while in CI, rate‐of‐change in MRI did show a significant mediating effect (p = 0.002; Figure 3).ConclusionTau‐PET showed stronger associations with cognitive and behavioral changes over time than cortical thickness at baseline across the AD clinical spectrum. However, assessing rate‐of‐change, MRI showed superiority over tau‐PET in CI individuals, suggesting differential contributions of tau pathology vs neurodegeneration in different disease stages.