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INTRODUCTION: Understanding longitudinal plasma biomarker trajectories relative to brain amyloid changes can help devise Alzheimer's progression assessment strategies. METHODS: We examined the temporal order of changes in plasma amyloid‐β ratio (Aβ42/Aβ40${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181/Aβ42$\text{p-tau181}/\mathrm{A}{\beta}_{42}$, p-tau231/Aβ42$\text{p-tau231}/\mathrm{A}{\beta}_{42}$) relative to 11C‐Pittsburgh compound B (PiB) positron emission tomography (PET) cortical amyloid burden (PiB−/+). Participants (n = 199) were cognitively normal at index visit with a median 6.1‐year follow‐up. RESULTS: PiB groups exhibited different rates of longitudinal change in Aβ42/Aβ40(β=5.41×10−4,SE=1.95×10−4,p=0.0073)${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}\ ({\beta \ = \ 5.41 \times {{10}}^{ - 4},{\rm{\ SE\ }} = \ 1.95 \times {{10}}^{ - 4},\ p\ = \ 0.0073})$. Change in brain amyloid correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). The greatest relative decline in Aβ42/Aβ40${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ (−1%/year) preceded brain amyloid positivity by 41 years (95% CI = [32, 53]). DISCUSSION: Plasma Aβ42/Aβ40${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may begin declining decades prior to brain amyloid accumulation, whereas p‐tau ratios, GFAP, and NfL increase closer in time. HIGHLIGHTS: Plasma Aβ42/Aβ40${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ declines over time among PiB− but does not change among PiB+.Phosphorylated‐tau to Aβ42 ratios increase over time among PiB+ but do not change among PiB−.Rate of change in brain amyloid is correlated with change in GFAP and neurofilament light chain.The greatest decline in Aβ42/Aβ40${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may precede brain amyloid positivity by decades. [ABSTRACT FROM AUTHOR]

Alzheimer's disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: New methods have recently been developed to model longitudinal amyloid biomarkers in AD. This work compares three methods in three different cohorts, and investigates associations of APOE‐e4 status with amyloid onset age and amyloid accumulation rates. Method: Participants from ADNI (N=1213), BLSA (N=205) and WRAP (N=272) with available amyloid PET imaging and APOE genotype were included in the study (Table 1). Amyloid (A) burden was quantified using established methods (ADNI: BAI processed SUVR, WM reference region; BLSA: SRTM‐LRSC DVR, PVC, cerebellum GM reference region; WRAP: graphical analysis DVR, cerebellum GM reference region). Group‐based trajectory modeling (GBTM), ordinary differential equation‐Gaussian process (ODE‐GP) and sampled iterative local linear approximation (SILLA) methods were used to model longitudinal amyloid accumulation, estimate A+ onset age, and align amyloid values by years A+ for each cohort. Forward and backward SUVR/DVR prediction, estimated age of A+ onset, and associations of DVR/SUVR residuals with age, time from reference scan, and SUVR/DVR, were compared for each model and cohort. The impact of APOE genotype on A+ onset and amyloid PET accumulation trajectories was investigated using discrete rate sample, Kaplan‐Meier curves, and accelerated failure time models. Result: All methods produced similar amyloid vs. years A+ curves for each cohort (Figure 1) with most notable discrepancies occurring in the A‐ range. DVR/SUVR prediction performance was similar across methods and was better for retrospective vs. prospective prediction. Amyloid accumulation rates and trajectories were similar for APOE‐e4+ and APOE‐e4‐ genotypes (Figure 2). Kaplan‐Meier curves were different between the APOE groups (log‐rank test, p<0.005 for each test; Figure 3), with the e4+ group exhibiting earlier A+ onset ages compared to e4‐ (Table 2) for all methods and cohorts. Conclusion: This work demonstrates similar performance of three novel methods for modeling longitudinal amyloid PET burden and estimating individualized A+ onset age. Results suggest that APOE‐e4 status affects the A+ onset age, but not the rate of amyloid PET accumulation. Ongoing work is investigating potential differences in amyloid accumulation trajectories and A+ onset age between APOE genotypes (e.g. e3e3, e3e4, e4e4,...) and is investigating relationships between A+ onset, APOE genotype and onset of cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: The analysis of the longitudinal progression of plasma biomarkers and amyloid imaging might lead to new insights in the early pattern of biomarker changes. Method: Plasma Aβ42/Aβ40, pTau231/Aβ42, pTau181/Aβ42, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) were measured for n = 223 subjects and 1673 total visits in the Wisconsin Registry for Alzheimer's Prevention (WRAP) using Quanterix Simoa assays at the University of Gothenburg. Global distribution volume ratio (DVR) was computed from 11C‐Pittsburgh compound B PET scans. Inclusion criteria included Amyloid or CSF Amyloid positive status, and at least one plasma serial value. Trajectories spanning age 46.9 to 80.3 years old for the DVR and plasma biomarkers were estimated by fitting a vector field to the longitudinal data (https://arxiv.org/abs/2206.15215). A Louvain community clustering analysis was performed. The time from Amyloid positivity (DVR = 1.19; centiloid of 22) was also computed for all individual trajectories that crossed this threshold. Result: The Louvain community clustering algorithm resulted in three distinct communities (Fig 1): no increase in DVR or plasma biomarkers (Green, n = 88(39.5%)); increase in DVR and all plasma biomarkers except NFL (Red, 41(18.4%)); and mixed DVR plus GFAP and NFL increase (Blue, 94(42.2%)). Time since Amyloid positivity (TSAP) was computed for the subset of subjects (n = 84) expected to cross the Amyloid positivity threshold. The Louvain communities as a function of TSAP are shown in Fig 2. Conclusion: We identified three communities regarding the longitudinal progression of amyloid PET and plasma biomarker levels. Plasma biomarkers were dynamic up to 30 years before Amyloid positivity. [ABSTRACT FROM AUTHOR]

In this article, we proved that the filter bank multi-carrier (FBMC) with offset quadrature amplitude modulation (OQAM) signals asymptotically converges to a white Gaussian random process as the number of subcarriers increase to infinity. Hence, a band-limited flat spectrum model, with a large number of subcarriers, is proposed to simplify the prediction of the spectral regrowth of amplified FBMC-OQAM signals in terms of the output intermodulation products. [ABSTRACT FROM AUTHOR]

Background: High‐frequency biomarkers (HFB) are measurements collected weekly, on a daily basis or even more frequently. They are designed to detect early progression from one cognitive state to a later stage. Examples include home‐based measurements, e.g., computer usage and sleep patterns. In randomized controlled trials (RCT), we aim to detect the difference in trajectories of HFB between placebo and experimental group. Current statistical analysis e.g. linear mixed effect with random intercept models detect changes in the mean trajectory but fail to detect changes in the covariance structure, thus requiring longer trials. We examined whether more careful modeling, using historical data, would reduce the sample size needed for a RCT? Objective: (1) to present a novel statistical methodology for analyzing RCTs whose outcomes are HFB based on historical data of cognitively normal subjects, and (2) to evaluate this methodology using simulated data first, then using data from the Oregon Center for Aging and Technology (ORCATECH). Methods: The statistical methodology proposed proceeds in three phases (Figure 1). Highlights include the use of historical data for cognitively normal subjects and the use of a Gaussian process with parameterized mean function and covariance structure. Examples of results from simulated data are shown in Figure 2, demonstrating the superior power of the methodology compared to the linear mixed‐effects model. ORCATECH's weekly computer usage data was used (Figure 3). We computed an RCT effect size with 86 cognitively normal subjects and 11 subjects who were cognitively normal at baseline, but would eventually develop MCI within 139 weeks on average (sd=72). Results: Using this new approach, we found an effect size of W=0.616 (Area Under the curve), outperforming a baseline method which yielded an effect size of W=0.515. Respective total sample sizes for an RCT with alpha=0.05 and power=0.8 are 74 and 1028, for the new versus the conventional approach, respectively. Conclusions: We have shown that a careful statistical procedure allows for maximally benefiting from HFB by reducing the sample size needed for a 100 week RCT. More experiments will be necessary to validate these findings. [ABSTRACT FROM AUTHOR]

Background: Accumulation of beta‐amyloid plaques, an early hallmark neuropathology of Alzheimer's disease (AD), begin more than a decade prior to the onset of clinical symptoms. Understanding the temporal progression of early amyloid accumulation is important for predicting who will become amyloid‐positive and when. Based on observations from several longitudinal amyloid positron emission tomography (PET) studies that the rate of overall amyloid burden is closely associated with baseline level of amyloid, we develop a formal mathematical model capturing this phenomenon and use it to (i) align temporal trajectories of amyloid accumulation across individuals, (ii) create a fine‐grained timeline of amyloid accumulation, and (iii) estimate the age at onset of amyloid positivity per individual. Method: We used (PiB) PET scans from the Wisconsin Registry for Alzheimer's Prevention (WRAP) and the Baltimore Longitudinal Study of Aging (BLSA) to assess global amyloid burden cDVR, see Figure 1. A first‐order autonomous model for the slope of cDVR as a function of cDVR was fit to the data. We used Bayesian modeling with a Gaussian process (GP) prior and independent homoscedastic noise. Result: The best fit was obtained with a quadratic GP kernel (Figure 2). Since the system is autonomous, the trajectories are parallel and thus can be registered by translation. The progression of the Amyloid burden is thus visualized as a function of the time (in years) from PIB positivity in Figure 3. The estimated age when the PIB cDVR becomes positive is estimated. This age depends on APOE status, with a significant difference between the APOE groups [3,3] and [3,4]. p‐value=.008 in WRAP, p‐value=.054 In BLSA. Conclusion: Large studies on aging allow studying the time‐progression of the amyloid burden. We found that an autonomous ordinary differential equation allows for a good explanatory model of the PIB‐DVR data in the WRAP as well as in the BLSA studies. Still, a few participants do not fit the data closely. It remains to understand if this is due to noisy measurements or to specific conditions. Also, alternative dynamical systems, including Hamiltonian, still need to be investigated and compared. [ABSTRACT FROM AUTHOR]

Investigation of the temporal trajectories of currently used neuropsychological tests is critical to identifying earliest changing measures on the path to dementia due to Alzheimer's disease (AD). We used the Progression Score (PS) method to characterize the temporal trajectories of measures of verbal memory, executive function, attention, processing speed, language, and mental status using data spanning normal cognition, mild cognitive impairment, and AD from 1,661 participants with a total of 7,839 visits (age at last visit 77.6 SD 9.2) in the Baltimore Longitudinal Study of Aging (BLSA) and 1510 participants with a total of 3,473 visits (age at last visit 59.5 SD 7.4) in the Wisconsin Registry for Alzheimer's Prevention (WRAP). This method aligns individuals in time based on the similarity of their longitudinal measurements to reveal temporal trajectories. As a validation of our methodology, we explored the associations between the individualized cognitive progression scores (Cog-PS) computed by our method and clinical diagnosis. Digit span tests were the first to show declines in both data sets, and were detected mainly among cognitively normal individuals. These were followed by tests of verbal memory, which were in turn followed by Trail Making Tests, Boston Naming Test, and Mini-Mental State Examination. Differences in Cog-PS across the clinical diagnosis and APOEɛ4 groups were statistically significant, highlighting the potential use of Cog-PS as individualized indicators of disease progression. Identifying cognitive measures that are changing in preclinical AD can lead to the development of novel cognitive tests that are finely tuned to detecting earliest changes. [ABSTRACT FROM AUTHOR]

We consider the problem of group testing with sum observations and noiseless answers, in which we aim to locate multiple objects by querying the number of objects in each of a sequence of chosen sets. We study a probabilistic setting with entropy loss, in which we assume a joint Bayesian prior density on the locations of the objects and seek to choose the sets queried to minimize the expected entropy of the Bayesian posterior distribution after a fixed number of questions. We present a new non-adaptive policy, called the dyadic policy, show that it is optimal among non-adaptive policies, and is within a factor of two of optimal among adaptive policies. This policy is quick to compute, its nonadaptive nature makes it easy to parallelize, and our bounds show that it performs well even when compared with adaptive policies. We also study an adaptive greedy policy, which maximizes the one-step expected reduction in entropy, and show that it performs at least as well as the dyadic policy, offering greater query efficiency but reduced parallelism. Numerical experiments demonstrate that both procedures outperform a divide-and-conquer benchmark policy from the literature, called sequential bifurcation, and show how these procedures may be applied in a stylized computer vision problem. [ABSTRACT FROM AUTHOR]

Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution. [ABSTRACT FROM PUBLISHER]

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low overlap because each set is contaminated by a large number of stochastic false-positive interactions) or a high false-negative rate (interaction sets have low overlap because each misses many true interactions). We extend capture-recapture theory to provide the first unified model for false-positive and false-negative rates for two-hybrid screens. Analysis of yeast, worm, and fly data indicates that 25% to 45% of the reported interactions are likely false positives. Membrane proteins have higher false-discovery rates on average, and signal transduction proteins have lower rates. The overall false-negative rate ranges from 75% for worm to 90% for fly, which arises from a roughly 50% false-negative rate due to statistical undersampling and a 55% to 85% false-negative rate due to proteins that appear to be systematically lost from the assays. Finally, statistical model selection conclusively rejects the Erdös-Rényi network model in favor of the power law model for yeast and the truncated power law for worm and fly degree distributions. Much as genome sequencing coverage estimates were essential for planning the human genome sequencing project, the coverage estimates developed here will be valuable for guiding future proteomic screens. All software and datasets are available in Datasets S1 and S2, Figures S1-S5, and Tables S1-S6, and are also available from our Web site, http://www.baderzone.org. [ABSTRACT FROM AUTHOR]

In nature, bacteria often found themselves in high-density colonies. The combination of a novel microfluidic device and computational analysis reveals an unexpected self-organization behavior of tightly packed bacterial cells. [ABSTRACT FROM AUTHOR]

Abstract: We consider a sequence of three models for skin detection built from a large collection of labeled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from practitioners. Pixels are considered independent. Performance, measured by the ROC curve on the Compaq Database is impressive for such a simple model. However, single image examination reveals very irregular results. The second model is a Hidden Markov model, which includes constraints that force smoothness of the solution. The ROC curve obtained shows better performance than the baseline model. Finally, color gradient is included. Thanks to, Bethe tree approximation, we obtain a simple analytical expression for the coefficients of the associated maximum entropy model. Performance, compared with previous model is once more improved. [Copyright &y& Elsevier]

We propose a new method for estimating the probability mass function (pmf) of a discrete and finite random variable from a small sample. We focus on the observed counts—the number of times each value appears in the sample—and define the maximum likelihood set (MLS) as the set of pmfs that put more mass on the observed counts than on any other set of counts possible for the same sample size. We characterize the MLS in detail in this article. We show that the MLS is a diamond-shaped subset of the probability simplex [0,1]k bounded by at most k × (k–1) hyper-planes, where k is the number of possible values of the random variable. The MLS always contains the empirical distribution, as well as a family of Bayesian estimators based on a Dirichlet prior, particularly the well-known Laplace estimator. We propose to select from the MLS the pmf that is closest to a fixed pmf that encodes prior knowledge. When using Kullback-Leibler distance for this selection, the optimization problem comprises finding the minimum of a convex function over a domain defined by linear inequalities, for which standard numerical procedures are available. We apply this estimate to language modeling using Zipf's law to encode prior knowledge and show that this method permits obtaining state-of-the-art results while being conceptually simpler than most competing methods. [ABSTRACT FROM AUTHOR]

Demonstrates that the performance of classification trees based on recursive designs can be inferior to those based on global designs. Tree-structured classification; Testing strategies; Bounds on the information gain; Experiments and discussion.

We provide a novel search technique which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images. We show exponential gains in computation over traditional sliding window approaches, while keeping similar performance levels. [ABSTRACT FROM AUTHOR]