Learning nonparametric ordinary differential equations from noisy data.

Learning nonparametric systems of Ordinary Differential Equations (ODEs) 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 for which the solution of the ODE exists and is unique. Learning consists of solving a constrained optimization problem in an RKHS.

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-β ratio ( ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios ( , ) relative to 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.

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-β ratio (Aβ /Aβ ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181/Aβ , p-tau231/Aβ ) relative to 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.

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.

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.

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.

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.

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 , 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.

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.

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.

Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs.

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for ophthalmological assessment. In particular, OCT is used to study the changes in layer thickness across various pathologies. The automated image analysis of these OCT images has primarily been performed with graph based methods.

Temporal Order of Alzheimer's Disease-Related Cognitive Marker Changes in BLSA and WRAP Longitudinal Studies.

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.

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.

Associations between Performance on an Abbreviated CogState Battery, Other Measures of Cognitive Function, and Biomarkers in People at Risk for Alzheimer's Disease.

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).

INTENSITY INHOMOGENEITY CORRECTION OF MACULAR OCT USING N3 AND RETINAL FLATSPACE.

As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images.

Landmark Based Shape Analysis for Cerebellar Ataxia Classification and Cerebellar Atrophy Pattern Visualization.

Cerebellar dysfunction can lead to a wide range of movement disorders. Studying the cerebellar atrophy pattern associated with different cerebellar disease types can potentially help in diagnosis, prognosis, and treatment planning. In this paper, we present a landmark based shape analysis pipeline to classify healthy control and different ataxia types and to visualize the characteristic cerebellar atrophy patterns associated with different types.

Voxel Based Morphometry in Optical Coherence Tomography: Validation & Core Findings.

Optical coherence tomography (OCT) of the human retina is now becoming established as an important modality for the detection and tracking of various ocular diseases. Voxel based morphometry (VBM) is a long standing neuroimaging analysis technique that allows for the exploration of the regional differences in the brain. There has been limited work done in developing registration based methods for OCT, which has hampered the advancement of VBM analyses in OCT based population studies.

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).

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction.

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems.

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.

Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging.

Abstract. Cortical β-amyloid deposition begins in Alzheimer's disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD.

A computational method for computing an Alzheimer's disease progression score; experiments and validation with the ADNI data set.

Understanding the time-dependent changes of biomarkers related to Alzheimer's disease (AD) is a key to assessing disease progression and measuring the outcomes of disease-modifying therapies. In this article, we validate an AD progression score model which uses multiple biomarkers to quantify the AD progression of subjects following 3 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.

Quantitative Multi-modal Brain Autoradiography of Glutamatergic, Dopaminergic, Cannabinoid, and Nicotinic Receptors in Mutant Disrupted-In-Schizophrenia-1 (DISC1) Mice.

Purpose: Disrupted-in-schizophrenia-1 (DISC1) is a promising genetic susceptibility factor for major psychiatric conditions, such as schizophrenia. We hypothesized that the mutant DISC1 alters the homeostasis of multi-receptor interactions between dopaminergic [dopamine 2/3 (D(2/3)R)], glutamatergic [metabotropic glutamate 5 (mGluR5)], cannabinoid 1 (CB(1)R), and nicotinic acetylcholine (α4β2-nAChR) receptors in the brains of mice with inducible forebrain neuronal expression of dominant-negative mutant DISC1.

Procedures: The quantitative in vitro autoradiography was performed with positron emission tomography (PET) ligands using [(11)C]raclopride (D2/3R), [(11)C]ABP688 (mGluR5), [(11)C]OMAR (CB(1)R), and [(18)F]AZAN (nAChR).

Imaging the evolution of reactivation pulmonary tuberculosis in mice using 18F-FDG PET.

Unlabelled: Latent tuberculosis infection affects one third of the world's population and can reactivate (relapse) decades later. However, current technologies, dependent on postmortem analyses, cannot follow the temporal evolution of disease.

Methods: C3HeB/FeJ mice, which develop necrotic and hypoxic tuberculosis lesions, were aerosol-infected with Mycobacterium tuberculosis.

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.