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Ghosh, Satrajit

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Ghosh

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Satrajit

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Ghosh, Satrajit
Author's Publications: search.filters.isAuthorOfPublication.16716a64-558f-4054-82ee-7f4a9dd3189f

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Now showing 1 - 6 of 6
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    A very simple, re-executable neuroimaging publication
    (F1000Research, 2017) Ghosh, Satrajit; Poline, Jean-Baptiste; Keator, David B.; Halchenko, Yaroslav O.; Thomas, Adam G.; Kessler, Daniel A.; Kennedy, David N.
    Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.
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    The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments
    (Nature Publishing Group, 2016) Gorgolewski, Krzysztof J.; Auer, Tibor; Calhoun, Vince D.; Craddock, R. Cameron; Das, Samir; Duff, Eugene P.; Flandin, Guillaume; Ghosh, Satrajit; Glatard, Tristan; Halchenko, Yaroslav O.; Handwerker, Daniel A.; Hanke, Michael; Keator, David; Li, Xiangrui; Michael, Zachary; Maumet, Camille; Nichols, B. Nolan; Nichols, Thomas E.; Pellman, John; Poline, Jean-Baptiste; Rokem, Ariel; Schaefer, Gunnar; Sochat, Vanessa; Triplett, William; Turner, Jessica A.; Varoquaux, Gaël; Poldrack, Russell A.
    The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
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    Decreased Cerebellar-Orbitofrontal Connectivity Correlates with Stuttering Severity: Whole-Brain Functional and Structural Connectivity Associations with Persistent Developmental Stuttering
    (Frontiers Media S.A., 2016) Sitek, Kevin; Cai, Shanqing; Beal, Deryk S.; Perkell, Joseph S.; Guenther, Frank H.; Ghosh, Satrajit
    Persistent developmental stuttering is characterized by speech production disfluency and affects 1% of adults. The degree of impairment varies widely across individuals and the neural mechanisms underlying the disorder and this variability remain poorly understood. Here we elucidate compensatory mechanisms related to this variability in impairment using whole-brain functional and white matter connectivity analyses in persistent developmental stuttering. We found that people who stutter had stronger functional connectivity between cerebellum and thalamus than people with fluent speech, while stutterers with the least severe symptoms had greater functional connectivity between left cerebellum and left orbitofrontal cortex (OFC). Additionally, people who stutter had decreased functional and white matter connectivity among the perisylvian auditory, motor, and speech planning regions compared to typical speakers, but greater functional connectivity between the right basal ganglia and bilateral temporal auditory regions. Structurally, disfluency ratings were negatively correlated with white matter connections to left perisylvian regions and to the brain stem. Overall, we found increased connectivity among subcortical and reward network structures in people who stutter compared to controls. These connections were negatively correlated with stuttering severity, suggesting the involvement of cerebellum and OFC may underlie successful compensatory mechanisms by more fluent stutterers.
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    Mindboggling morphometry of human brains
    (Public Library of Science, 2017) Klein, Arno; Ghosh, Satrajit; Bao, Forrest S.; Giard, Joachim; Häme, Yrjö; Stavsky, Eliezer; Lee, Noah; Rossa, Brian; Reuter, Martin; Chaibub Neto, Elias; Keshavan, Anisha
    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.
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    Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age
    (Elsevier BV, 2010) Ghosh, Satrajit; Kakunoori, Sita; Augustinack, Jean; Nieto-Castanon, Alfonso; Kovelman, Ioulia; Gaab, Nadine; Christodoulou, Joanna; Triantafyllou, Christina; Gabrieli, John; Fischl, Bruce
    Understanding the neurophysiology of human cognitive development relies on methods that enable accurate comparison of structural and functional neuroimaging data across brains from people of different ages. A fundamental question is whether the substantial brain growth and related changes in brain morphology that occur in early childhood permit valid comparisons of brain structure and function across ages. Here we investigated whether valid comparisons can be made in children from ages 4 to 11, and whether there are differences in the use of volume-based versus surface-based registration approaches for aligning structural landmarks across these ages. Regions corresponding to the calcarine sulcus, central sulcus, and Sylvian fissure in both the hemispheres were manually labeled on T1-weighted structural magnetic resonance images from 31 children ranging in age from 4.2 to 11.2 years old. Quantitative measures of shape similarity and volumetric-overlap of these manually labeled regions were calculated when brains were aligned using a 12-parameter affine transform, SPM's nonlinear normalization, a diffeomorphic registration (ANTS), and FreeSurfer's surface-based registration. Registration error for normalization into a common reference framework across participants in this age range was lower than commonly used functional imaging resolutions. Surface-based registration provided significantly better alignment of cortical landmarks than volume-based registration. In addition, registering children's brains to a common space does not result in an age-associated bias between older and younger children, making it feasible to accurately compare structural properties and patterns of brain activation in children from ages 4 to 11.
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    BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
    (Public Library of Science, 2017) Gorgolewski, Krzysztof J.; Alfaro-Almagro, Fidel; Auer, Tibor; Bellec, Pierre; Capotă, Mihai; Chakravarty, M. Mallar; Churchill, Nathan W.; Cohen, Alexander Li; Craddock, R. Cameron; Devenyi, Gabriel A.; Eklund, Anders; Esteban, Oscar; Flandin, Guillaume; Ghosh, Satrajit; Guntupalli, J. Swaroop; Jenkinson, Mark; Keshavan, Anisha; Kiar, Gregory; Liem, Franziskus; Raamana, Pradeep Reddy; Raffelt, David; Steele, Christopher J.; Quirion, Pierre-Olivier; Smith, Robert E.; Strother, Stephen C.; Varoquaux, Gaël; Wang, Yida; Yarkoni, Tal; Poldrack, Russell A.
    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.