1,056 research outputs found
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
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