bluuebunny/crossref_metadata_2025_split huggingface dataset:
A machine learning framework with uncertainty‐driven neuroimaging modality selection for data‐efficient prediction of Alzheimer’s Disease
Authors: Zheng Zhiyang , Su Yi , Chen Kewei , Weidman David , Wu Teresa , Lo ShihChung , Lure Fleming , Li Jing 
Published: December 2022

AbstractBackgroundEarly prediction of the AD risk for individuals with MCI has important clinical value. Machine Learning (ML) models integrating multi‐modality neuroimaging datasets have shown great promise. Existing approaches either require all the modalities or suffer from performance loss with fewer modalities. We proposed a data‐efficient ML framework, namely the Uncertainty‐driven Modality Selection (UMoS) framework, to sequentially add modalities for each patient on an as‐needed basis while at the same time ensuring high prediction accuracy.MethodThe dataset included 1319 T1‐MRI scans from MCI patients in ADNI and among these 612 additionally had amyloid‐PET. Regional volumetric and thickness measures were computed using FreeSurfer v7.1 from MRI and regional SUVR measures were computed using our in‐house pipeline from amyloid‐PET. MRI is used in the standard of care while amyloid‐PET has less accessibility and is more expensive. Thus, the goal of UMoS was to save a patient from needing PET if doing so did not hurt prediction accuracy. To achieve this, we first trained two elastic net logistic regression models to predict the risk of conversion to AD for MCI patients: one with MRI only; the other one additionally including amyloid‐PET. Uncertainty quantification (UQ) was performed to quantify the predictive uncertainty of the MRI model. Under the UMoS framework, each individual was first predicted using the MRI model. If the predictive uncertainty was high, the individual would be predicted using the MRI+PET model. The uncertainty threshold was selected by balancing the saving of PET in the patient cohort and the prediction accuracy.ResultUMoS achieved 0.851 Area Under the Curve (AUC) with 46.2% patients predicted by the MRI‐only model and 53.8% by the MRI+PET model. If all patients were predicted by the MRI model, the AUC was 0.794. If all patients were predicted by the MRI +PET model, the AUC was 0.875.ConclusionWe proposed an ML framework, UMoS, that used predictive uncertainty of the MRI model to drive the decision that if less‐accessible amyloid‐PET was needed for predicting MCI conversion to AD. This framework allows an individualized approach to make accurate prediction based on the data available and leverage additional data only when needed.
Predicting the pace of MCI progression to AD based on multi‐modality neuroimaging data by a novel hybrid ordinal learning algorithm
Authors: Wang Lujia , Su Yi , Zheng Zhiyang , Chen Kewei , Weidman David , Wu Teresa , Lo ShihChung , Lure Fleming , Li Jing 
Published: December 2022

AbstractBackgroundMachine learning (ML) has shown great promise for integrating multi‐modality neuroimaging datasets to predict the risk of converting to AD for individuals with Mild Cognitive Impairment (MCI). Most existing work aims to classify MCI patients into converters versus non‐converters. The limitation is a lack of granularity in differentiating MCI patients who convert at different paces. Pace prediction has important clinical values by allowing for more personalized interventional strategies, better preparing patients and their caregivers, and facilitating patient selection in clinical trials. We proposed a novel Hybrid Ordinal Learning (HOL) algorithm for pace prediction of MCI conversion to AD.MethodThis study focused on the amyloid‐positive MCI patient cohort in ADNI for the pace prediction, which included 282 samples. The datasets included baseline T1‐MRI, amyloid‐PET, MMSE, CDR, age, gender, years of education, and APOE status. ROI‐based structural measures were computed from T1‐MRI using FreeSurfer v7.1 and regional SUVR measures were computed using our in‐house pipeline from amyloid‐PET. HOL was trained to predict the conversion to AD for each MCI patient into four ordinal classes representing four fast‐to‐slow paces of conversion, e.g., conversion in the first year, not in the first but in the second year, not in the first two years but by the fifth year, and not in the first five years, respectively. The novelty of HOL compared to existing ordinal classification algorithms was its capability of leveraging imperfectly‐labeled samples in the training set to build a robust learner.ResultUnder cross validation, HOL achieved 0.83 accuracy, balanced across the different paces, based on a combination of 314 features from MRI, PET, and clinical data, while the conventional ordinal SVM classifier achieved only 0.68 accuracy.ConclusionWe demonstrated that a novel ML algorithm, HOC, could achieve a high accuracy in predicting the pace of conversion to AD for MCI patients. Future research is needed to validate the research finding with larger datasets.
Convergent and Discriminant Validity of NIH Toolbox Cognition Measures of Premorbid Intellectual Functioning in Older African American Adults With and Without Mild Cognitive Impairment
Authors: Halter Colt M , Moll Allison C. , Kero Katherine , Hanna Sophie , DiCerbo Loraine M. , Woodard John L. , Giordani Bruno , Kavcic Voyko 
Published: December 2022

AbstractBackgroundEstimation of premorbid IQ (PMIQ) among older adults is crucial to characterize cognitive decline properly, particularly among older adults at risk for Alzheimer’s disease. Three scores from the NIH Toolbox‐Cognition Battery (NIHTB‐C) ostensibly may estimate premorbid functioning: Crystallized Cognition (CC) and its component subtests, Picture Vocabulary (PV) and Oral Reading Recognition (ORR). However, they have not yet been validated in a sample of older African American adults at risk for cognitive impairment. We investigated whether performance on the Crystallized NIHTB‐C measures was related to the WTAR, a gold standard word recognition test associated with PMIQ, in older African Americans with and without mild cognitive impairment (MCI). We also compared WTAR scores to the summary score from the NIHTB‐C Fluid Cognition (FC) battery, where no relationship was expected.MethodsThe WTAR and NIHTB‐C were administered to 36 cognitively normal and 18 MCI participants (diagnoses determined by consensus conference following NACC UDS guidelines) who were community‐dwelling, older African Americans aged 65–86 years. Data analysis used Bayesian bivariate correlations and hierarchical multiple regression to adjust for education effects.ResultsIn the full sample, Bayesian bivariate correlations showed that WTAR performance was positively associated with performance on ORR (r = .86, BF10>100, 95%CI[.76, .92]), PV (r = .66, BF10>100, 95%CI[.47, .78]), and CC (r = .84, BF10>100, 95%CI[.72, .90]), and not associated with FC performance (r = .08, BF10 = 0.196, 95%CI[‐.19, .33]). This pattern held in analyses restricted to the cognitively normal (r = .84, BF10>100, 95%CI[.68, .91]; r = .58, BF10>100, 95%CI[.29, .75]; r = .81, BF10>100, 95%CI[.64, .90]; r = .12, BF10 = 0.22, 95%CI[‐.23, .37], respectively) and the MCI groups (r = .88, BF10>100, 95%CI[.64, .95]; r = .71, BF10 = 41.6, 95%CI[.31, .87]; r = .84, BF10>100, 95%CI[.55, .93]; r = ‐.33, BF10 = 0.70, 95%CI[‐.66, .15], respectively). Although education was positively related to the four measures (p<.05), hierarchical multiple regression revealed that education did not predict NIHTB‐C performance and did not improve model fit beyond WTAR performance alone.ConclusionWTAR scores are strongly positively associated with NIHTB‐C CC scores and unrelated to FC scores, suggesting appropriate convergent and discriminant validity. Our findings support our hypothesis that NIHTB‐C Crystalized Cognition scores represent reasonable, valid estimates of PMIQ in older African Americans with and without MCI.
A blood‐based diagnostic for Alzheimer’s disease using spectral microscopy of immuno‐enriched Aβ from frozen PBMCs
Authors: Tsutsui Shigeki , Templeton George W , Ota Tomoko , Black Stefanie AG , Stys Peter K 
Published: December 2022

AbstractBackgroundIn Alzheimer’s disease (AD), toxic Aβ peptides aggregate into higher molecular weight assemblies and accumulate not only in the extracellular space, but also in the walls of blood vessels in the brain, increasing their permeability, and promoting immune cell migration and activation. As immune cells contact these pathological brain materials, they may act as “sentinels” that are detectable once they return to the circulation. Previously, we have demonstrated that leukocytes, when stained with an amyloid sensitive probe, K114 (Tocris Bioscience), display distinct spectral changes in subjects with AD and mild cognitive impairment (MCI) (Black et al. 2022). Here we extend this work by introducing an enhanced method that combines the specificity of immunocapture of Aβ peptides together with spectral interrogation to reveal their amyloid character from easily obtainable frozen peripheral blood mononuclear cells (PBMCs).Methods30 frozen PBMC samples were obtained from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) with 15 clinically‐ and histologically‐confirmed MCI/AD and 15 cognitively normal subjects based on the National Alzheimer’s Coordinating Center (NACC) database. To adapt our method to frozen samples, we used immunoprecipitation to purify target amyloid proteins by anti‐Aβ antibody (4G8)‐coated magnetic beads (Protein G SureBeads™, BioRad Laboratories), which were then labelled with K114, and imaged with a spectral confocal microscope.ResultsComparing subjects with neuropathologically‐confirmed AD (n=6), Braak stage for neurofibrillary degeneration (B score) >5 and density of neocortical neuritic plaques (C score) >2 to cognitively normal controls (n=6), our technique detected highly significant differences between groups (P<0.005, Fig. 1). Correlation between our AD Score and B scores was R = 0.824, and with cognitive status was R = 0.735 (Fig. 2).ConclusionsThese observations indicate that our method is capable of detecting AD from circulating human PBMCs, potentially mirroring the “Aβ load” in the brain. Our technique could constitute a reliable and inexpensive biomarker for early AD and AD‐related MCI.
The Association between Brain Age and Alcohol Use
Authors: Funk‐White Makaya , Wing David , Eyler Lisa T. , Moore Alison A. , Reas Emilie T. , McEvoy Linda K. 
Published: December 2022

AbstractBackgroundAlcohol has been shown to have both beneficial and adverse associations with cognitive health in aging, dependent upon amount consumed, but associations with structural brain measures are less well understood. Here, we investigated whether predicted brain age, calculated using a voxel‐based algorithm trained on a large dataset, differs by alcohol use among a cohort of healthy older adults.MethodParticipants included 163 community dwelling older adults (aged 56‐97 years, mean 77 years, 60% females). We categorized individuals as: non‐drinkers (30.7%), light drinkers (1‐7 drinks/week; 47.2%) moderate drinkers (> 7‐14 drinks/week; 12.3%), or heavy drinkers (> 14 drinks/week; 9.8%). In secondary analyses, we explored the effect of differentiating the non‐drinker group into never drinkers (5.5%), former drinkers (10.4%), and occasional drinkers (14.7%). Brain age was calculated using structural MRIs processed with the BrainAgeR program (Cole et al, 2018). We calculated predicted brain age difference (PBAD) by subtracting predicted brain age (PBA) from chronological age. We used analysis of variance (ANOVA) to determine if PBAD differed across groups.ResultsPBA was highly correlated with actual age (r = .713). PBAD differed significantly across groups (F (3,150) = 3.20; p = 0.025): PBAD was significantly higher among heavy drinkers (average PBAD = 3.0 years) compared to all other groups. Non‐drinkers, light drinkers, and moderate drinkers showed younger than actual brain age, by 1.3, 2.9, and 3.3 years, respectively, with no significant differences across groups. Our secondary analysis showed that PBAD among former drinkers was not significantly different from that of heavy drinkers while never drinkers and occasional drinkers showed significantly younger PBAD relative to heavy drinkers.ConclusionAmong this sample of healthy older adults, consumption of more than 14 drinks/week was associated with older than expected brain age. This is consistent with prior literature demonstrating that drinking above recommended guidelines is associated with brain volume loss. In contrast, never drinking and lighter drinking was associated with younger than expected brain age. While we found little evidence for a beneficial association of light or moderate drinking, drinking within guidelines does not appear to adversely affect brain health even among older adults.
Biological variation of Alzheimer’s disease plasma biomarkers in healthy individuals
Authors: Brum Wagner S. , van Doorn William , Ashton Nicholas J. , Montoliu‐Gaya Laia , Janelidze Shorena , Zimmer Eduardo R. , Zetterberg Henrik , Karikari Thomas K , Hansson Oskar , Meex Steven JR , Carobene Anna , Blennow Kaj 
Published: December 2022

AbstractBackgroundPlasma biomarkers for Alzheimer’s disease (AD) have demonstrated great performance to identify AD pathology in research cohorts, and recent studies also support their potential to monitor effects of disease‐modifying treatments. To properly interpret these biomarkers in research settings and to inform power calculations of future trials using them as surrogate outcomes, information on their physiological variability over time is needed. We conducted a 10‐week biological variation (BV) study to assess the within‐individual (CVI; CV: coefficient of variation), between‐individual (CVG) and analytical variability (CVA) of blood‐based AD biomarkers. These parameters can provide the reference change value (RCV), which determines how much biomarker values must change to represent a significant abnormality‐related change, i.e. exceeding analytical and biological variation.MethodPlasma samples were collected weekly for 10 weeks from 20 healthy individuals from the European Biological Variation Study (n=20; 50% female, median age 46.4). Biomarkers for tau pathology (p‐tau181), brain amyloidosis (Aβ42, Aβ40, Aβ42/40), glial activation (GFAP) and neurodegeneration (NfL) were quantified in duplicate using the Simoa technology. The CV‐ANOVA statistical method was used to compute BV estimates. To evaluate diurnal rhythm of these biomarkers, we also conducted a study with non‐demented older adults (n=24; 58‐82yo), with hourly blood collections during 26h in strictly‐controlled environmental conditions (NCT02091427). P‐tau217 (Lily) analyses are ongoing for both studies.ResultIndividual‐level (Figure 1) and pooled biomarker levels are shown (Figure 2), as well as BV estimates (Table 1). The 10‐week within‐individual physiological variation (CVI) was lower for Aβ42/40 (4.3%), followed by Aβ42 (6.0%), Aβ40 (6.4%), NfL (7.9%), GFAP (9.3%) and p‐tau181 (15.8%). Between‐individual variation (CVG) was lower for Aβ42/40 (8.8%), followed by Aβ42 (13.3%), Aβ40 (17.0%), p‐tau181 (20.5%), NfL (23.0%) and GFAP (31.5%). Analytical variation was low for all biomarkers (CVA range: 2.5‐6.6%). The RCV was higher for p‐tau181 (45.6%), followed by GFAP (31.7%), NfL (27.6%), Aβ40 (19.0%), Aβ42 (18.8%) and Aβ42/40 (15.6%).ConclusionBlood‐based AD biomarkers demonstrate good week‐to‐week stability, but their CVG and RCV’s can be rather high, with implications both for use in clinical routine (misclassification risk) and for future trial designs (high numbers of participants needed). Results for the diurnal variation study will also be presented.
Kidney function is associated to Alzheimer’s related blood biomarkers (NfL, P‐tau181, and GFAP) but not dementia risk in a community‐based cohort followed over 17 years
Authors: Stocker Hannah , Beyer Léon , Trares Kira , Perna Laura , Rujescu Dan , Holleczek Bernd , Beyreuther Konrad , Schöttker Ben , Gerwert Klaus Bernhard , Brenner Hermann 
Published: December 2022

AbstractBackgroundIn order to determine the future of Alzheimer’s disease (AD) related blood biomarkers in the clinical setting, it is crucial to identify factors that could affect the interpretation of biomarker levels. Kidney function has been shown to influence biomarker levels, but has also exhibited associations to dementia risk and it is unclear whether kidney function plays a role in AD pathology or is merely a factor influencing blood biomarker levels. Therefore, the aim of this study was to explore the association of kidney function with risk of incident AD/dementia diagnosis within 17 years and with the blood biomarkers, neurofilament light (NfL), phosphorylated tau (p‐tau181), and glial fibrillary acidic protein (GFAP), in a community‐based cohort study.MethodsKidney function was assessed through the estimated glomerular filtration rate test (2021‐CKD‐EPI creatinine‐cystatin C equation (eGFRcr‐cys)) at baseline in 6,256 participants aged 50‐75 years, of whom 510 received a dementia diagnosis throughout 17 years of follow‐up in a community‐based prospective cohort study in Germany (Figure 1). In a subset of participants (n=766, n=261 dementia cases), NfL, p‐tau181, and GFAP blood concentrations were measured at baseline using Simoa technology. Cox regression and linear regression models adjusted for age and sex were used to assess the association of kidney function with incident dementia risk and the log‐transformed blood biomarker concentrations.ResultsImpaired kidney function (eGFRcr< 60 mL/min/1.73m2) at baseline was not associated to incident dementia (p=.73) or AD diagnosis (p=.94) within 17 years (Figure 2). However, the blood biomarkers, NfL and P‐tau181, did exhibit significant associations to impaired kidney function (β, p‐value: NfL, 0.47, <.0001; P‐tau181, 0.21, <.01) and gender‐specific associations were evident in the association to GFAP (β, p‐value: Males, 0.31, <.01; Females, ‐.12, .11) (Table 1).ConclusionsReduced kidney function may result in increased concentrations of AD related blood biomarkers but not necessarily increased AD risk. Kidney function might influence the accuracy of AD blood biomarkers and should be considered in clinical translation. Additionally, the interaction between kidney function and sex in the association to GFAP including possible explanatory biological mechanisms should be further explored.
Results From a Multiple Ascending Dose Study in Healthy Volunteers of ACD856, a Positive Modulator of Neurotrophin Trk‐Receptors
Authors: Önnestam Kristin , Nilsson Boel , Rother Matthias , Rein‐Hedin Erik , Bylund Johan , Halldin Magnus , Forsell Pontus , Nordvall Gunnar , Sandin Johan , Segerdahl Märta 
Published: December 2022

AbstractBackgroundACD856 is a novel positive allosteric modulator of Trk‐receptors in clinical development for the treatment of Alzheimer’s disease and other disorders where cognition is impaired. The aim of this study was to assess the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics (PD) of multiple ascending doses of ACD856.Method24 healthy volunteer subjects were administered ACD856 or placebo as a daily oral solution over 7 days in 3 cohorts of stepwise ascending doses. After each dose, the safety, tolerability, and pharmacokinetics of ACD856 were assessed by an internal Safety Review Committee to decide on the escalation of dose to the next cohort.ResultThere were no clinically significant changes from baseline in mean ECG, vital signs, physical examination findings, clinical chemistry, haematology, coagulation, or urinalysis parameters after receiving the 7 daily doses of ACD856. Occasional individual abnormal laboratory values were observed but all were assessed as not clinically significant. Most of the reported adverse events were of mild intensity, only a few were of moderate intensity and none of severe intensity. No serious adverse events were reported.The pharmacokinetic data confirmed the findings from the previously performed single ascending dose (SAD) study, including that ACD856 has a rapid absorption, long half‐life, high bioavailability, and a linear dose‐dependent exposure. The Cmax at steady state was approximately 1.6‐fold higher than the initial dose administration and no time‐dependent clearance was observed. Moreover, initial CSF data confirms CNS exposure of ACD856. Further PK, CSF and pharmacodynamic data will be shown at the time of presentation.ConclusionACD856 was shown to be safe and well tolerated in man at the tested dose levels and with a suitable pharmacokinetic profile for further clinical development. In the next step, ACD856 will be evaluated in a phase Ib study with the aim to further assess pharmacodynamic effects.
AlzoSure® Predict, a simple minimally‐invasive blood test to predict the early onset of Alzheimer’s disease before the manifestation of clinical symptoms
Authors: Piccirella Simona , Van Neste Leander , Fowler Christopher , Masters Colin L , Fripp Jurgen , Doecke James D , Xiong Chengjie , Uberti Daniela , Kinnon Paul 
Published: December 2022

AbstractBackgroundOngoing research seeks to identify blood‐based biomarkers able to predict the onset and progression of Alzheimer’s disease (AD). AlzoSure® Predict is a rapid, minimally‐invasive blood test that predicts the early onset of AD up to, at least, 6 years in advance of clinical symptoms.MethodsThe AlzoSure® Predict test, an IP‐LC‐MS/MS based method, was performed to quantify the AZ284® peptide of an unfolded variant of the p53 protein (U‐p53AZ). The data were compared with the neuropsychological diagnosis in a longitudinal cohort of 479 subjects between 60 and 85 years old from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. The cohort consists of 237 cognitive normal, including both subjects with no and subjective memory complaints (NMC/SMC) and 98 mild cognitive impairment (MCI), 141 AD, and 3 other dementia patients that were followed‐up every 18 months.ResultsThe data confirms the strong diagnostic value of U‐p53AZ with a sensitivity of 95% for AD, discriminating it from NMC and SMC subjects with an AUC of 99.8% (95% CI: 99.6‐100.0%) and a specificity of 99.6%, and from MCI patients at the early symptomatic stage with an AUC of 95.7% (95% CI: 92.8‐98.5%) and a specificity of 91.8%.In the prognostic setting, U‐p53AZ showed a significantly better performance (P<0.0001) with an AUC of 99.0% (95%CI: 98.2‐99.8%) compared to amyloid b‐PET (Ab‐PET) with an AUC of 83.5% (95% CI: 79.5‐87.5%). Due to unavailability of data, Ab‐PET performance was determined on a subset of 422 subjects. In a multifactorial Cox proportional hazards model, U‐p53AZ proved to be the major contributor and and the best performing, independent predictor for cognitive decline to neuropsychological AD over time. In this model with normalized continuous risk factors, U‐p53AZ was the most significant contributor (P<.0001) with a hazards ratio of 2.92, compared to Aβ‐PET (P<.0155), APOE ε4 (P<.2323), and age (P<.0106).ConclusionThe present study confirms the diagnostic performance of U‐p53AZ for AD and expands its potential into the prognostic field. Additional studies are going to be performed based on both ADRC and ADNI samples to further prove the prognostic power and the utility of the AlzoSure® Predict test.