Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this voxel does not enhance", to a well-defined and numerically testable null hypothesis based on existing data. We call the procedure "soft null hypothesis" testing as opposed to the standard "hard null hypothesis" testing. This problem is fundamentally different from: 1) testing when a quantitative null hypothesis is given; 2) clustering using a mixture distribution; or 3) identifying a reasonable threshold with a parametric null assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the largest population of such clinical brain images

Leptomeningeal enhancement in multiple sclerosis and other neurological diseases: A systematic review and Meta-Analysis

BACKGROUND The lack of systematic evidence on leptomeningeal enhancement (LME) on MRI in neurological diseases, including multiple sclerosis (MS), hampers its interpretation in clinical routine and research settings. PURPOSE To perform a systematic review and meta-analysis of MRI LME in MS and other neurological diseases. MATERIALS AND METHODS In a comprehensive literature search in Medline, Scopus, and Embase, out of 2292 publications, 459 records assessing LME in neurological diseases were eligible for qualitative synthesis. Of these, 135 were included in a random-effects model meta-analysis with subgroup analyses for MS. RESULTS Of eligible publications, 161 investigated LME in neoplastic neurological (n = 2392), 91 in neuroinfectious (n = 1890), and 75 in primary neuroinflammatory diseases (n = 4038). The LME-proportions for these disease classes were 0.47 [95%-CI: 0.37-0.57], 0.59 [95%-CI: 0.47-0.69], and 0.26 [95%-CI: 0.20-0.35], respectively. In a subgroup analysis comprising 1605 MS cases, LME proportion was 0.30 [95%-CI 0.21-0.42] with lower proportions in relapsing-remitting (0.19 [95%-CI 0.13-0.27]) compared to progressive MS (0.39 [95%-CI 0.30-0.49], p = 0.002) and higher proportions in studies imaging at 7 T (0.79 [95%-CI 0.64-0.89]) compared to lower field strengths (0.21 [95%-CI 0.15-0.29], p < 0.001). LME in MS was associated with longer disease duration (mean difference 2.2 years [95%-CI 0.2-4.2], p = 0.03), higher Expanded Disability Status Scale (mean difference 0.6 points [95%-CI 0.2-1.0], p = 0.006), higher T1 (mean difference 1.6 ml [95%-CI 0.1-3.0], p = 0.04) and T2 lesion load (mean difference 5.9 ml [95%-CI 3.2-8.6], p < 0.001), and lower cortical volume (mean difference -21.3 ml [95%-CI -34.7--7.9], p = 0.002). CONCLUSIONS Our study provides high-grade evidence for the substantial presence of LME in MS and a comprehensive panel of other neurological diseases. Our data could facilitate differential diagnosis of LME in clinical settings. Additionally, our meta-analysis corroborates that LME is associated with key clinical and imaging features of MS. PROSPERO No: CRD42021235026

LONGITUDINAL HIGH-DIMENSIONAL DATA ANALYSIS

We develop a flexible framework for modeling high-dimensional functional and imaging data observed longitudinally. The approach decomposes the observed variability of high-dimensional observations measured at multiple visits into three additive components: a subject-specific functional random intercept that quantifies the cross-sectional variability, a subject-specific functional slope that quantifies the dynamic irreversible deformation over multiple visits, and a subject-visit specific functional deviation that quantifies exchangeable or reversible visit-to-visit changes. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels

Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles

NONLINEAR TUBE-FITTING FOR THE ANALYSIS OF ANATOMICAL AND FUNCTIONAL STRUCTURES

We are concerned with the estimation of the exterior surface of tube-shaped anatomical structures. This interest is motivated by two distinct scientific goals, one dealing with the distribution of HIV microbicide in the colon and the other with measuring degradation in white-matter tracts in the brain. Our problem is posed as the estimation of the support of a distribution in three dimensions from a sample from that distribution, possibly measured with error. We propose a novel tube-fitting algorithm to construct such estimators. Further, we conduct a simulation study to aid in the choice of a key parameter of the algorithm, and we test our algorithm with validation study tailored to the motivating data sets. Finally, we apply the tube-fitting algorithm to a colon image produced by single photon emission computed tomography (SPECT)and to a white-matter tract image produced using diffusion tensor `imaging (DTI)

Assembly of multicellular constructs and microarrays of cells using magnetic nanowires

An approach is described for controlling the spatial organization of mammalian cells using ferromagnetic nanowires in conjunction with patterned micromagnet arrays. The nanowires are fabricated by electrodeposition in nanoporous templates, which allows for precise control of their size and magnetic properties. The high aspect ratio and large remanent magnetization of the nanowires enable suspensions of cells bound to Ni nanowires to be controlled with low magnetic fields. This was used to produce one- and two-dimensional field-tuned patterning of suspended 3T3 mouse fibroblasts. Self-assembled one-dimensional chains of cells were obtained through manipulation of the wires\u27 dipolar interactions. Ordered patterns of individual cells in two dimensions were formed through trapping onto magnetic microarrays of ellipsoidal permalloy micromagnets. Cell chains were formed on the arrays by varying the spacing between the micromagnets or the strength of fluid flow over the arrays. The positioning of cells on the array was further controlled by varying the direction of an external magnetic field. These results demonstrate the possibility of using magnetic nanowires to organize cells