Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts.

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.

Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid.

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-ß ratio ( A ß 42 / A ß 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios ( p-tau181 / A ß 42 $\text{p-tau181}/\mathrm{A}{\beta}_{42}$ , p-tau231 / A ß 42 $\text{p-tau231}/\mathrm{A}{\beta}_{42}$ ) relative to 11 C-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 A ß 42 / A ß 40 ( ß = 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 A ß 42 / A ß 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ (-1%/year) preceded brain amyloid positivity by 41 years (95% CI = [32, 53]). DISCUSSION: Plasma A ß 42 / A ß 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time. HIGHLIGHTS Plasma A ß 42 / A ß 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ declines over time among PiB- but does not change among PiB+. Phosphorylated-tau to Aß42 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 A ß 42 / A ß 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may precede brain amyloid positivity by decades.

Structural cerebellar correlates of cognitive and motor dysfunctions in cerebellar degeneration.

See King et al. (doi:10.1093/aww348) for a scientific commentary on this article.Detailed mapping of clinical dysfunctions to the cerebellar lobules in disease populations is necessary to establish the functional significance of lobules implicated in cognitive and motor functions in normal subjects. This study constitutes the first quantitative examination of the lobular correlates of a broad range of cognitive and motor phenomena in cerebellar disease. We analysed cross-sectional data from 72 cases with cerebellar disease and 36 controls without cerebellar disease. Cerebellar lobule volumes were derived from a graph-cut based segmentation algorithm. Sparse partial least squares, a variable selection approach, was used to identify lobules associated with motor function, language, executive function, memory, verbal learning, perceptual organization and visuomotor coordination. Motor dysfunctions were chiefly associated with the anterior lobe and posterior lobule HVI. Confrontation naming, noun fluency, recognition, and perceptual organization did not have cerebellar associations. Verb and phonemic fluency, working memory, cognitive flexibility, immediate and delayed recall, verbal learning, and visuomotor coordination were variably associated with HVI, Crus I, Crus II, HVII B and/or HIX. Immediate and delayed recall also showed associations with the anterior lobe. These findings provide preliminary anatomical evidence for a functional topography of the cerebellum first defined in task-based functional magnetic resonance imaging studies of normal subjects and support the hypotheses that (i) cerebellar efferents target frontal lobe neurons involved in forming action representations and new search strategies; (ii) there is greater involvement of the cerebellum when immediate recall tasks involve more complex verbal stimuli (e.g. longer words versus digits); and (iii) it is involved in spontaneous retrieval of long-term memory. More generally, they provide an anatomical background for studies that seek the mechanisms by which cognitive and motor dysfunctions arise from cerebellar degeneration. Beyond replicating these findings, future research should employ experimental tasks to probe the integrity of specific functions in cerebellar disease, and new imaging methods to quantitatively map atrophy across the cerebellum.

A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging.

It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm(3). These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. We apply our method to studying cortical ß-amyloid deposition, a hallmark of preclinical Alzheimer's disease, as measured using positron emission tomography. Results on 104 individuals with a total of 300 visits suggest that precuneus is the earliest cortical region to accumulate amyloid, closely followed by the cingulate and frontal cortices, then by the lateral parietal cortex. The extracted progression scores reveal a pattern similar to mean cortical distribution volume ratio (DVR), an index of global brain amyloid levels. The proposed method can be applied to other types of longitudinal imaging data, including metabolism, blood flow, tau, and structural imaging-derived measures, to extract individualized summary scores indicating disease progression and to provide voxelwise trajectories that can be compared between brain regions.

Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.

The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes shows distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample.

BACKGROUND: The delineation of the relative temporal trajectories of specific cognitive measures associated with Alzheimer's disease (AD) is important for evaluating preclinical markers and monitoring disease progression. METHODS: We characterized the temporal trajectories of measures of verbal episodic memory, short-term visual memory, and mental status using data from 895 participants in the Baltimore Longitudinal Study of Aging. RESULTS: The California Verbal Learning Test (CVLT) immediate recall was the first measure to decline, followed by CVLT delayed recall. However, further along the disease progression scale, CVLT delayed recall and visual memory changed more rapidly than CVLT immediate recall. CONCLUSIONS: Our findings reconcile reports of early changes in immediate recall with greater reliance on delayed recall performance in clinical settings. Moreover, the utility of cognitive markers in evaluating AD progression depends on the stage of cognitive decline, suggesting that optimal endpoints in therapeutic trials may vary across different stages of the disease process.

Learning nonparametric ordinary differential equations from noisy data.

Learning nonparametric systems of Ordinary Differential Equations (ODEs) x˙=f(t,x) from noisy data is an emerging machine learning topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces (RKHS) to define candidates for f for which the solution of the ODE exists and is unique. Learning f consists of solving a constrained optimization problem in an RKHS. We propose a penalty method that iteratively uses the Representer theorem and Euler approximations to provide a numerical solution. We prove a generalization bound for the L2 distance between x and its estimator. Experiments are provided for the FitzHugh-Nagumo oscillator, the Lorenz system, and for predicting the Amyloid level in the cortex of aging subjects. In all cases, we show competitive results compared with the state-of-the-art.

Approaching expert results using a hierarchical cerebellum parcellation protocol for multiple inexpert human raters.

Volumetric measurements obtained from image parcellation have been instrumental in uncovering structure-function relationships. However, anatomical study of the cerebellum is a challenging task. Because of its complex structure, expert human raters have been necessary for reliable and accurate segmentation and parcellation. Such delineations are time-consuming and prohibitively expensive for large studies. Therefore, we present a three-part cerebellar parcellation system that utilizes multiple inexpert human raters that can efficiently and expediently produce results nearly on par with those of experts. This system includes a hierarchical delineation protocol, a rapid verification and evaluation process, and statistical fusion of the inexpert rater parcellations. The quality of the raters' and fused parcellations was established by examining their Dice similarity coefficient, region of interest (ROI) volumes, and the intraclass correlation coefficient of region volume. The intra-rater ICC was found to be 0.93 at the finest level of parcellation.

Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid.

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-ß ratio (Aß 42 /Aß 40 ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181/Aß 42 , p-tau231/Aß 42 ) relative to 11 C-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 Aß 42 /Aß 40 (ß = 5.41 × 10^ -4 , SE = 1.95 × 10 -4 , p = 0.0073). Change in brain amyloid was correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). Greatest relative decline in Aß 42 /Aß 40 (-1%/year) preceded brain amyloid positivity onset by 41 years (95% CI = [32, 53]). DISCUSSION: Plasma Aß 42 /Aß 40 may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time.

A computational neurodegenerative disease progression score: method and results with the Alzheimer's disease Neuroimaging Initiative cohort.

While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.

Structured layer surface segmentation for retina OCT using fully convolutional regression networks.

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.

Self-organization in high-density bacterial colonies: efficient crowd control.

Colonies of bacterial cells can display complex collective dynamics, frequently culminating in the formation of biofilms and other ordered super-structures. Recent studies suggest that to cope with local environmental challenges, bacterial cells can actively seek out small chambers or cavities and assemble there, engaging in quorum sensing behavior. By using a novel microfluidic device, we showed that within chambers of distinct shapes and sizes allowing continuous cell escape, bacterial colonies can gradually self-organize. The directions of orientation of cells, their growth, and collective motion are mutually correlated and dictated by the chamber walls and locations of chamber exits. The ultimate highly organized steady state is conducive to a more-organized escape of cells from the chambers and increased access of nutrients into and evacuation of waste out of the colonies. Using a computational model, we suggest that the lengths of the cells might be optimized to maximize self-organization while minimizing the potential for stampede-like exit blockage. The self-organization described here may be crucial for the early stage of the organization of high-density bacterial colonies populating small, physically confined growth niches. It suggests that this phenomenon can play a critical role in bacterial biofilm initiation and development of other complex multicellular bacterial super-structures, including those implicated in infectious diseases.

Spike inference from calcium imaging using sequential Monte Carlo methods.

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest--spike trains and/or time varying intracellular calcium concentrations--are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., approximately 5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.

Noninvasive pulmonary [18F]-2-fluoro-deoxy-D-glucose positron emission tomography correlates with bactericidal activity of tuberculosis drug treatment.

Tools for monitoring response to tuberculosis (TB) treatment are time-consuming and resource intensive. Noninvasive biomarkers have the potential to accelerate TB drug development, but to date, little progress has been made in utilizing imaging technologies. Therefore, in this study, we used noninvasive imaging to monitor response to TB treatment. BALB/c and C3HeB/FeJ mice were aerosol infected with Mycobacterium tuberculosis and administered bactericidal (standard and highly active) or bacteriostatic TB drug regimens. Serial pulmonary [(18)F]-2-fluoro-deoxy-D-glucose (FDG) positron emission tomography (PET) was compared with standard microbiologic methods to monitor the response to treatment. [(18)F]FDG-PET correctly identified the bactericidal activity of the drug regimens. Imaging required fewer animals; was available in real time, as opposed to having CFU counts 4 weeks later; and could also detect TB relapse in a time frame similar to that of the standard method. Lesion-specific [(18)F]FDG-PET activity also broadly correlated with TB treatment in C3HeB/FeJ mice that develop caseating lesions. These studies demonstrate the application of noninvasive imaging to monitor TB treatment response. By reducing animal numbers, these biomarkers will allow cost-effective studies of more expensive animal models of TB. Validated markers may also be useful as "point-of-care" methods to monitor TB treatment in humans.

Predicting time to dementia using a quantitative template of disease progression.

INTRODUCTION: Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring. METHODS: We used a multivariate Bayesian model to temporally align 1369 Alzheimer's disease Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution of cerebrospinal fluid A ß 1 - 42 , p- ta u 181 p , and t-tau and hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia and predicted AD dementia onset age in a disjoint sample. RESULTS: Quantitative template showed early changes in verbal memory, cerebrospinal fluid Aß1-42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was < 1.5 years. DISCUSSION: Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.

Fully Convolutional Boundary Regression for Retina OCT Segmentation.

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.

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps.

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 and , -, and -, and are also available from our Web site, http://www.baderzone.org.

Intensity inhomogeneity correction of SD-OCT data using macular flatspace.

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images.

The effect of rare variants in TREM2 and PLD3 on longitudinal cognitive function in the Wisconsin Registry for Alzheimer's Prevention.

Recent studies have found an association between functional variants in TREM2 and PLD3 and Alzheimer's disease (AD), but their effect on cognitive function is unknown. We examined the effect of these variants on cognitive function in 1449 participants from the Wisconsin Registry for Alzheimer's Prevention, a longitudinal study of initially asymptomatic adults, aged 36-73 years at baseline, enriched for a parental history of AD. A comprehensive cognitive test battery was performed at up to 5 visits. A factor analysis resulted in 6 cognitive factors that were standardized into z scores (∼N [0, 1]); the mean of these z scores was also calculated. In linear mixed models adjusted for age, gender, practice effects, and self-reported race/ethnicity, PLD3 V232M carriers had significantly lower mean z scores (p = 0.02) and lower z scores for story recall (p = 0.04), visual learning and memory (p = 0.049), and speed and flexibility (p = 0.02) than noncarriers. TREM2 R47H carriers had marginally lower z scores for speed and flexibility (p = 0.06). In conclusion, a functional variant in PLD3 was associated with significantly lower cognitive function in individuals carrying the variant than in noncarriers.