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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 nu...

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 modeling 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 modeling amyloid accumulation using 10-fold cross-validation and hold-out validation where applicable. Estimated amyloid onset age was compared across all three modeling 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 prior to amyloid onset.Model prediction and estimated amyloid onset age were similar across all three amyloid modeling 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 nonlinear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modeling 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.

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials., Comment: Presents final results of the TADPOLE competition. 60 pages, 7 tables, 14 figures

Monitoring retinal thickness of persons with multiple sclerosis (MS) provides important bio-markers for disease progression. However, changes in retinal thickness can be small and concealed by noise in the acquired data. Consistent longitudinal retinal layer segmentation methods for optical coherence tomography (OCT) images are crucial for identifying the real longitudinal retinal changes of individuals with MS. In this paper, we propose an iterative registration and deep learning based segmentation method for longitudinal 3D OCT scans. Since 3D OCT scans are usually anisotropic with large slice separation, we extract B-scan features using 2D deep networks and utilize inter-B-scan context with convolutional long-short-term memory (LSTM). To incorporate longitudinal information, we perform fundus registration and interpolate the smooth retinal surfaces of the previous visit to use as a prior on the current visit.

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.

Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.

It is not known whether computerized cognitive assessments, like the CogState battery, are sensitive to preclinical cognitive changes or pathology in people at risk for Alzheimer’s disease (AD). In 469 late middle-aged participants from the Wisconsin Registry for Alzheimer’s Prevention (mean age 63.8±7 years at testing; 67% female; 39% APOE4+), we examined relationships between a CogState abbreviated battery (CAB) of seven tests and demographic characteristics, traditional paper-based neuropsychological tests as well as a composite cognitive impairment index, cognitive impairment status (determined by consensus review); and biomarkers for amyloid and tau (CSF phosphorylated-tau/Aβ42 and global PET-PiB burden) and neural injury (CSF neurofilament light protein). CSF and PET-PiB were collected in n=71 and n=91 participants, respectively, approximately four years prior to CAB testing. For comparison, we examined three traditional tests of delayed memory in parallel. Similar to studies in older samples, the CAB was less influenced by demographic factors than traditional tests. CAB tests were generally correlated with most paper-based cognitive tests examined and mapped onto the same cognitive domains. Greater composite cognitive impairment index was associated with worse performance on all CAB tests. Cognitively impaired participants performed significantly worse compared to normal controls on all but one CAB test. Poorer One Card Learning test performance was associated with higher levels of CSF phosphorylated-tau/Aβ42. These results support the use of the CogState battery as measures of early cognitive impairment in studies of people at risk for AD.

A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer surfaces, with correct hierarchy (topology) are desired for monitoring disease progression. State-of-the-art methods use a trained classifier to label each pixel into background, layer, or surface pixels. The final step of extracting the desired smooth surfaces with correct topology are mostly performed by graph methods (e.g. shortest path, graph cut). However, manually building a graph with varying constraints by retinal region and pathology and solving the minimization with specialized algorithms will degrade the flexibility and time efficiency of the whole framework. In this paper, we directly model the distribution of surface positions using a deep network with a fully differentiable soft argmax to obtain smooth, continuous surfaces in a single feed forward operation. A special topology module is used in the deep network both in the training and testing stages to guarantee the surface topology. An extra deep network output branch is also used for predicting lesion and layers in a pixel-wise labeling scheme. The proposed method was evaluated on two publicly available data sets of healthy controls, subjects with multiple sclerosis, and diabetic macular edema; it achieves state-of-the art sub-pixel results.

Understanding the time-dependent changes of biomarkers related to Alzheimer’s disease (AD) is a key to assessing disease progression and to measuring the outcomes of disease-modifying therapies. In this paper, we validate an Alzheimer’s disease progression score model which uses multiple biomarkers to quantify the AD progression of subjects following three assumptions: (1) there is a unique disease progression for all subjects, (2) each subject has a different age of onset and rate of progression, and (3) each biomarker is sigmoidal as a function of disease progression. Fitting the parameters of this model is a challenging problem which we approach using an alternating least squares optimization algorithm. In order to validate this optimization scheme under realistic conditions, we use the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. With the help of Monte Carlo simulations, we show that most of the global parameters of the model are tightly estimated, thus enabling an ordering of the biomarkers that fit the model well, ordered as: the Rey auditory verbal learning test with 30 minutes delay, the sum of the two lateral hippocampal volumes divided by the intra-cranial volume, followed by (the clinical dementia rating sum of boxes score and the mini mental state examination score) in no particular order and lastly the Alzheimer’s disease assessment scale-cognitive subscale.