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Background and objectivesAn unmet need exists for validated quantitative tools to measure multiple sclerosis (MS) disease activity and progression. We developed a custom immunoassay-based MS disease activity (MSDA) Test incorporating 18 protein concentrations into an algorithm to calculate four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score. The objective was to clinically validate the MSDA Test based on associations between scores and clinical/radiographic assessments.MethodsSerum samples (N=614) from patients with MS at multiple sites were split into Train (n=426; algorithm development) and Test (n=188; evaluation) subsets. Subsets were stratified by demographics, sample counts per site, and gadolinium-positive (Gd+) lesion counts; age and sex were used to demographically adjust protein concentrations. MSDA Test results were evaluated for potential association with Gd+ lesion presence/absence, new and enlarging (N/E) T2 lesion presence, and active versus stable disease status (composite endpoint combining radiographic and clinical evidence of disease activity).ResultsA multi-protein model was developed (trained and cross-validated) using the Train subset. When applied to the Test subset, the model classified the Gd+ lesion presence/absence, N/E T2 lesion presence, and active versus stable disease status assessments with an area under the receiver operating characteristic (AUROC) of 0.781, 0.750, and 0.768, respectively. In each case, the multi-protein model had significantly (bootstrapped, one-sidedpDiscussionThe MSDA Test was clinically validated; the multi-protein model had greater performance compared with the top-performing single-protein model. The MSDA Test may serve as a quantitative and objective tool to enhance care for MS.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images. QSIPrep is a software platform for processing of most diffusion MRI datasets and ensures that adequate workflows are used.

A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS).From a single-center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12-year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion.Patients who developed SPMS showed faster cord atrophy rates (-2.19%/yr) at least 4 years before conversion compared to their RRMS matches (-0.88%/yr, p 0.001). Spinal cord atrophy rates decelerated after conversion (-1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (-1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p 0.0001) and 53% (p 0.0001) shorter time to silent progression and SPMS conversion, respectively.Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 2022;91:268-281.

Available online 30 April 2021. Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress. The present manuscript is part of a growing community effort to collate Brainhack-related insights and expertise into a Jupyter Book (http://brainhack.org/brainhack_jupyter_book/) that will serve as a centralized set of resources for the community; we acknowledge all the individuals who contributed and will make ongoing contributions to these resources. A pre-print version of the present manuscript is available as part of the Jupyter Book. Moreover, we would like to acknowledge all Brainhack organizers, supporters, presenters, and participants for their contribution to growing and maintaining this community. The benefits described in this manuscript would not be possible without them. We also thank all institutions, labs, and organizations who have helped this community grow, meet in stimulating environments, and add an excellent educational resource pool and agenda. With an expanding community, Brainhack’s support network keeps growing, and we thank all labs and individual researchers for their dedication and expertise offered to this community (see http://brainhack.org/brainhack_jupyter_book/acknowledgments.html for a full list of individual acknowledgments; an updated list will be maintained in the Jupyter Book). Grants and funding bodies: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg; from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut; and from NIH K00MH122372 for Stephanie Noble. The Brainhack Community member list and contributions of the different authors are detailed at http://brainhack.org/brainhack_jupyter_book/contributors.html. Our crediting system is described here: http://brainhack.org/brainhack_jupyter_book/neuroview_authorship-agreement.html.

BACKGROUND Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. OBJECTIVE This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. METHODS We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform’s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. RESULTS Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users’ data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser–based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. CONCLUSIONS We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health–related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

Background and purpose Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded. Methods We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. Results The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure. Conclusions Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.

The neural circuitry mediating the influence of motivation on long-term declarative or episodic memory formation is delineated in young adults, but its status is unknown in healthy aging. We examined the effect of reward and punishment anticipation on intentional declarative memory formation for words using an event-related functional magnetic resonance imaging (fMRI) monetary incentive encoding task in twenty-one younger and nineteen older adults. At 24-hour memory retrieval testing, younger adults were significantly more likely to remember words associated with motivational cues than neutral cues. Motivational enhancement of memory in younger adults occurred only for recollection (“remember” responses) and not for familiarity (“familiar” responses). Older adults had overall diminished memory and did not show memory gains in association with motivational cues. Memory encoding associated with monetary rewards or punishments activated motivational (substantia nigra/ventral tegmental area) and memory-related (hippocampus) brain regions in younger, but not older, adults during the target word periods. In contrast, older and younger adults showed similar activation of these brain regions during the anticipatory motivational cue interval. In a separate monetary incentive delay task that did not require learning, we found evidence for relatively preserved striatal reward anticipation in older adults. This supports a potential dissociation between incidental and intentional motivational processes in healthy aging. The finding that motivation to obtain rewards and avoid punishments had reduced behavioral and neural influence on intentional episodic memory formation in older compared to younger adults is relevant to life-span theories of cognitive aging including the dopaminergic vulnerability hypothesis. Keywords: monetary incentive encoding; monetary incentive delay; reward; punishment; aging; fMRI; memory; learning; motivation; hippocampus; ventral tegmental area; striatum

Background: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. Objective: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. Methods: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform’s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. Results: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users’ data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser–based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. Conclusions: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health–related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population., Journal of Medical Internet Research, 23 (11), ISSN:1438-8871

BACKGROUND Diffusion-weighted magnetic resonance imaging tractography can be used to create models of white matter fascicles. Anatomical and pathological variability between subjects can drastically alter the tractography output, so standardizing results across a cohort is nontrivial. Furthermore, tractography methods have inherently low reproducibility due to stochasticity (for probabilistic methods) and subjective decisions, since the final fascicle model often requires a manual intervention step performed by an expert human operator to control both outliers and systematic false-positive pathways, as defined by prior knowledge of anatomy. METHODS We present an approach that computationally assigns a cluster confidence index (CCI) reflecting the reproducibility of that pathway in the context of a streamline dataset. This metric is a tractography algorithm-agnostic tool that can be applied to any dataset of streamlines. RESULTS Applications of this metric include systematic elimination of outlier streamlines using a CCI threshold and interactive filtering by CCI to facilitate manual segmentation of fascicle models. CONCLUSIONS This method is intended to replace the application of a streamline density threshold so that outliers are eliminated based on low pathway density instead of voxel-wise density.

Background Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. Objective This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. Methods We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board. Results This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. Conclusions After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.

BACKGROUND Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. OBJECTIVE This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. METHODS We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board. RESULTS This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. CONCLUSIONS After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.

Brain volumetric measurements in multiple sclerosis (MS) reflect not only disease-specific processes but also other sources of variability. The latter has to be considered especially in multicenter and longitudinal studies. Here, we compare data generated by three different 3-Tesla magnetic resonance scanners (Philips Achieva; Siemens Verio; GE Signa MR750). We scanned two patients diagnosed with relapsing remitting MS six times per scanner within three weeks (T1w and FLAIR, 3D). We assessed T2-hyperintense lesions by an automated lesion segmentation tool and determined volumes of grey matter (GM), white matter (WM) and whole brain (GM+WM) from the lesion-filled T1-weighted images using voxel-based morphometry (SPM8/VBM8) and SIENAX (FSL). We measured cortical thickness using FreeSurfer from both, lesion-filled and original T1-weighted images. We quantified brain volume changes with SIENA. In both patients, we found significant differences in total lesion volume, global brain tissue volumes and cortical thickness measures between the scanners. Morphometric measures varied remarkably between repeated scans at each scanner, independent of the brain imaging software tool used. We conclude that for cross-sectional multicenter studies, the effect of different scanners has to be taken into account. For longitudinal monocentric studies, the expected effect size should exceed the size of false positive findings observed in this study. Assuming a physiological loss of brain volume of about 0.3% per year in healthy adult subjects (Good et al., 2001), which may double in MS (De Stefano et al., 2010; De Stefano et al., 2015), with current tools reliable estimation of brain atrophy in individual patients is only possible over periods of several years.

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.

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts., Data sharing is an important component of reproducible research, but meaningful data sharing can be difficult. Here authors describe a new open source tool, AFQ-Browser, that builds an interactive website allowing visualization and exploratory data analysis of published diffusion MRI data.

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is an important component of reproducible research, it is not always clear how to usefully share data in a manner that allows other labs to understand and reproduce published findings. Here we describe a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a published diffusion MRI study. Because AFQ-browser is portable -- it runs in any modern web-browser -- it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of a diffusion MRI dataset (anatomy, quantitative diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new scientific discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, sharing and aggregating data across labs, and publishing data alongside manuscripts.

Neurosurgical resection is one of the few opportunities researchers have to image the human brain both prior to and following focal damage. One of the challenges associated with studying brains undergoing surgical resection is that they often do not fit the brain templates most image-processing methodologies are based on, so manual intervention is required to reconcile the pathology and the most extreme cases must be excluded. Manual intervention requires significant time investment and introduces reproducibility concerns. We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration in the patient space, which aligns images using white matter contrast. Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms., United States. National Institutes of Health (NIH-NIBIB R01 EB020740)

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available., National Institute of Mental Health (U.S.) (R01 MH084029), National Institute of Mental Health (U.S.) (U01 MH074813), National Institute of Mental Health (U.S.) (3U01MH092250-03S1), United States. National Institutes of Health (1R01EB020740)

Urbanization presents unique environmental challenges to human commensal species. The Afrotropical Anopheles gambiae complex contains a number of synanthropic mosquito species that are major vectors of malaria. To examine ongoing cryptic diversification within the complex, we performed reduced representation sequencing on 941 mosquitoes collected across four ecogeographic zones in Cameroon. We find evidence for clear subdivision within An. coluzzii and An. gambiae s.s. - the two most significant malaria vectors in the region. Importantly, in both species rural and urban populations of mosquitoes were genetically differentiated. Genome scans of cryptic subgroups reveal pervasive signatures of selection centered on genes involved in xenobiotic resistance. Notably, a selective sweep containing eight detoxification enzymes is unique to urban mosquitoes that exploit polluted breeding sites. Overall, our study reveals that anthropogenic environmental modification is driving population differentiation and local adaptation in African malaria mosquitoes with potentially significant consequences for malaria epidemiology.

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions. publisher: Elsevier articletitle: Power estimation for non-standardized multisite studies journaltitle: NeuroImage articlelink: http://dx.doi.org/10.1016/j.neuroimage.2016.03.051 content_type: article copyright: © 2016 The Authors. Published by Elsevier Inc. ispartof: NeuroImage vol:134 pages:281-294 ispartof: location:United States status: published

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.

© 2015 American Medical Association. All rights reserved. Importance In multiple sclerosis (MS), upper cervical cord gray matter (GM) atrophy correlates more strongly with disability than does brain or cord white matter (WM) atrophy. The corresponding relationships in the thoracic cord are unknown owing to technical difficulties in assessing GMandWMcompartments by conventional magnetic resonance imaging techniques. Objectives To investigate the associations between MS disability and disease type with lower thoracic cord GM andWMareas using phase-sensitive inversion recovery magnetic resonance imaging at 3 T, as well as to compare these relationships with those obtained at upper cervical levels. Design, Setting, and Participants Between July 2013 and March 2014, a total of 142 patients with MS (aged 25-75 years; 86 women) and 20 healthy control individuals were included in this cross-sectional observational study conducted at an academic university hospital. Main Outcomes and Measures Total cord areas (TCAs), GM areas, andWMareas at the disc levels C2/C3, C3/C4, T8/9, and T9/10. Area differences between groups were assessed, with age and sex as covariates. Results Patients with relapsingMS (RMS) had smaller thoracic cord GM areas than did ageand sex-matched control individuals (mean differences [coefficient of variation (COV)]: 0.98 mm2 [9.2%]; P =.003 at T8/T9 and 0.93mm2[8.0%]; P =.01 at T9/T10); however, there were no significant differences in either theWMarea or TCA. Patients with progressiveMS showed smaller GM areas (mean differences [COV]: 1.02mm2[10.6%]; P

Cluster-viz is a web application that provides a platform for cluster-based interactive quality-control of tractography algorithm outputs. This tool facilitates the creation of white matter fascicle models by employing a cluster-based approach to allow the user to select streamline bundles for inclusion/exclusion in the final fascicle model. This project was started at the 2016 Neurohackweek and BrainHack events and is still under development. We welcome contributions to the Cluster-viz github repository (https://github.com/kesshijordan/Cluster-viz).

Mindcontrol is an open-source web-based dashboard to quality control and curate neuroimaging data. At Neurohackweek 2016, a group assembled to add new features to the Mindcontrol interface. Contributors used Python, Javascript, and Git to configure Mindcontrol for the ABIDE and CoRR open datasets, and add new types of plots to the interface. All contributions are freely available online, and the code is being actively maintained at http://www.github.com/akeshavan/mindcontrol.

Resting state fMRI reveals intrinsic network characteristics present in the brain. They are correlated with behavioral measures, and have made surprising insights in the brains' connectivity structure possible. At the core of many of those studies is the correlation of behavioral measures, and the characteristics of networks among a set of brain regions. In this paper we evaluate methods that identify functional networks in resting state fMRI in light of predicting treatment response of patients suffering from social anxiety disorder. Results illustrate differences in prediction when obtaining network labelings by population-wide-clustering, subject-specific parcellation, transferring anatomical region labels, or mapping networks from a previous large scale resting state study.

Context: Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective: To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design: Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting: Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients: Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions: Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures: Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results: Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient.

MicroRNAs (miRNAs) are conserved non-coding small RNAs with potent post-transcriptional gene regulatory functions. Recent computational approaches and sequencing of small RNAs had indicated the existence of about 80 Ciona intestinalis miRNAs, although it was not clear whether other miRNA genes were present in the genome. We undertook an alternative computational approach to look for Ciona miRNAs. Conserved non-coding sequences from the C. intestinalis genome were extracted and computationally folded to identify putative hairpin-like structures. After applying additional criteria, we obtained 458 miRNA candidates whose sequences were used to design a custom microarray. Over 100 of our predicted hairpins were identified in this array when probed with RNA from various Ciona stages. We also compared our predictions to recently deposited sequences of Ciona small RNAs and report that 170 of our predicted hairpins are represented in this data set. Altogether, about 250 of our 458 predicted miRNAs were represented in either our array data or the small-RNA sequence database. These results suggest that Ciona has a large number of genomically encoded miRNAs that play an important role in modulating gene activity in developing embryos and adults.