Esha Datta

Association of HLA genetic risk burden with disease phenotypes in multiple sclerosis

IMPORTANCE Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive. OBJECTIVES To investigate whether MS risk-associated HLA alleles also affect disease phenotypes. DESIGN, SETTING, AND PARTICIPANTS A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015. MAIN OUTCOMES AND MEASURES Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set. RESULTS Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [-0.3 to 0.5], respectively; P = 1.8 × 10-27). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard β = -1.20 × 10-1; P = 1.7 × 10-2 and standard β = -1.67 × 10-1; P = 2.3 × 10-4, respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard β = -1.28 × 10-1; P = 5.1 × 10-3 and standard β = 9.52 × 10-2; P = 3.6 × 10-2, respectively). CONCLUSIONS AND RELEVANCE We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.

Precision medicine in chronic disease management: The multiple sclerosis BioScreen

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine—that is, the application of information technology to medicine—has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real‐time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence‐based medicine, support shared decision making, and ultimately lead to improved outcomes. Ann Neurol 2014;76:633–642

Spinal cord grey matter segmentation challenge

ABSTRACT An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi‐ or fully‐automated segmentation methods for cervical cord cross‐sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross‐sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi‐centre and multi‐vendor dataset acquired with distinct 3D gradient‐echo sequences. This challenge aimed to characterize the state‐of‐the‐art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold‐standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality‐of‐segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication. HighlightsFirst grey matter spinal cord segmentation challenge.Six institutions participated in the challenge and compared their methods.Public available dataset from multiple vendors and sites.The challenge web site remains open to new submissions.

Gray matter segmentation of the spinal cord with active contours in MR images

Objective: Fully or partially automated spinal cord gray matter segmentation techniques for spinal cord gray matter segmentation will allow for pivotal spinal cord gray matter measurements in the study of various neurological disorders. The objective of this work was multi‐fold: (1) to develop a gray matter segmentation technique that uses registration methods with an existing delineation of the cord edge along with Morphological Geodesic Active Contour (MGAC) models; (2) to assess the accuracy and reproducibility of the newly developed technique on 2D PSIR T1 weighted images; (3) to test how the algorithm performs on different resolutions and other contrasts; (4) to demonstrate how the algorithm can be extended to 3D scans; and (5) to show the clinical potential for multiple sclerosis patients. Methods: The MGAC algorithm was developed using a publicly available implementation of a morphological geodesic active contour model and the spinal cord segmentation tool of the software Jim (Xinapse Systems) for initial estimate of the cord boundary. The MGAC algorithm was demonstrated on 2D PSIR images of the C2/C3 level with two different resolutions, 2D T2* weighted images of the C2/C3 level, and a 3D PSIR image. These images were acquired from 45 healthy controls and 58 multiple sclerosis patients selected for the absence of evident lesions at the C2/C3 level. Accuracy was assessed though visual assessment, Hausdorff distances, and Dice similarity coefficients. Reproducibility was assessed through interclass correlation coefficients. Validity was assessed through comparison of segmented gray matter areas in images with different resolution for both manual and MGAC segmentations. Results: Between MGAC and manual segmentations in healthy controls, the mean Dice similarity coefficient was 0.88 (0.82–0.93) and the mean Hausdorff distance was 0.61 (0.46–0.76) mm. The interclass correlation coefficient from test and retest scans of healthy controls was 0.88. The percent change between the manual segmentations from high and low‐resolution images was 25%, while the percent change between the MGAC segmentations from high and low resolution images was 13%. Between MGAC and manual segmentations in MS patients, the average Dice similarity coefficient was 0.86 (0.8–0.92) and the average Hausdorff distance was 0.83 (0.29–1.37) mm. Conclusion: We demonstrate that an automatic segmentation technique, based on a morphometric geodesic active contours algorithm, can provide accurate and precise spinal cord gray matter segmentations on 2D PSIR images. We have also shown how this automated technique can potentially be extended to other imaging protocols. HIGHLIGHTSThis technique uses a morphological geodesic active contours algorithm.The average Hausdorff distance between automated and manual segmentations is 0.61 mm.The interclass correlation coefficient for test and retest segmentations is 0.88.Images from controls and MS patients without lesions were successfully segmented.Images with various resolutions and contrasts were successfully segmented.

Mindcontrol: A web application for brain segmentation quality control

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study.

Mobile Learning and Digital Libraries

With the recent advancement of mobile technologies, such as smart phones, digital cameras and PDAs (Personal Digital Assistants), and tablet PCs mobile learning provides opportunities for formal and informal education in a wide range of settings. In particular, the use of mobile technologies to access digital libraries opens up doors for providing unique learning experiences, both inside and outside of the classroom. This paper presents the design and implementation of a mobile learning digital library infrastructure and test applications. We first conducted a user needs analysis of students, educators, and parents in order to understand desirable functional attributes and challenges associated with mobile learning. We translated this needs assessment into a list of twelve functional attributes for digital library infrastructures and mobile device applications that will facilitate informal learning. In order to test out the recommendations, a conceptual design was developed as a lesson plan that uses mobile devices and digital libraries to teach the concept of simple machines. This lesson was implemented during a workshop conducted with students in the TechBridge program, an after school program that introduces girls to technology. The students that participated in this workshop were from less affluent schools and were all members of ethnic groups that are typically underrepresented in the field of engineering. This paper summarizes the needs assessment research, implementation, testing and recommendations for future work. Our goal is to provide recommendations for mobile learning technologies that will increase access and enhance mobile learning experiences for students of all backgrounds.Copyright

Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1‐weighted brain MRI acquisitions

To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1‐weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor‐implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data.

Precision medicine in chronic disease management: the MS BioScreen

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine—that is, the application of information technology to medicine—has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real‐time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence‐based medicine, support shared decision making, and ultimately lead to improved outcomes. Ann Neurol 2014;76:633–642

Precision medicine in chronic disease management: The multiple sclerosis BioScreen: MS BioScreen

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine—that is, the application of information technology to medicine—has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real‐time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence‐based medicine, support shared decision making, and ultimately lead to improved outcomes. Ann Neurol 2014;76:633–642