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Rokem A, Benson NC. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Neuroinformatics 2024:10.1007/s12021-024-09666-6. [PMID: 38763989 DOI: 10.1007/s12021-024-09666-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2024] [Indexed: 05/21/2024]
Abstract
NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.
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Affiliation(s)
- Ariel Rokem
- Department of Psychology, University of Washington, 119 Guthrie Hall, Seattle, 98195, Washington, USA.
- eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, 98195, Washington, USA.
| | - Noah C Benson
- eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, 98195, Washington, USA
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Roy E, Van Rinsveld A, Nedelec P, Richie-Halford A, Rauschecker AM, Sugrue LP, Rokem A, McCandliss BD, Yeatman JD. Differences in educational opportunity predict white matter development. Dev Cogn Neurosci 2024; 67:101386. [PMID: 38676989 DOI: 10.1016/j.dcn.2024.101386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/05/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.
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Affiliation(s)
- Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, USA.
| | | | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Adam Richie-Halford
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Andreas M Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | | | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
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Kruper J, Richie-Halford A, Benson NC, Caffarra S, Owen J, Wu Y, Egan C, Lee AY, Lee CS, Yeatman JD, Rokem A. Convolutional neural network-based classification of glaucoma using optic radiation tissue properties. Commun Med (Lond) 2024; 4:72. [PMID: 38605245 PMCID: PMC11009254 DOI: 10.1038/s43856-024-00496-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Adam Richie-Halford
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Noah C Benson
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Sendy Caffarra
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
- University of Modena and Reggio Emilia, Modena, Italy
| | - Julia Owen
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | | | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA.
- eScience Institute, University of Washington, Seattle, WA, USA.
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Pogoncheff G, Hu Z, Rokem A, Beyeler M. Explainable machine learning predictions of perceptual sensitivity for retinal prostheses. J Neural Eng 2024; 21:026009. [PMID: 38452381 DOI: 10.1088/1741-2552/ad310f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/07/2024] [Indexed: 03/09/2024]
Abstract
Objective.Retinal prostheses evoke visual precepts by electrically stimulating functioning cells in the retina. Despite high variance in perceptual thresholds across subjects, among electrodes within a subject, and over time, retinal prosthesis users must undergo 'system fitting', a process performed to calibrate stimulation parameters according to the subject's perceptual thresholds. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking.Approach.To address these challenges, we (1) fitted machine learning models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical parameters ('predictors') and (2) leveraged explainable artificial intelligence (XAI) to reveal which of these predictors were most important.Main results.Our models accounted for up to 76% of the perceptual threshold response variance and enabled predictions of whether an electrode was deactivated in a given trial with F1 and area under the ROC curve scores of up to 0.732 and 0.911, respectively. Our models identified novel predictors of perceptual sensitivity, including subject age, time since blindness onset, and electrode-fovea distance.Significance.Our results demonstrate that routinely collected clinical measures and a single session of system fitting might be sufficient to inform an XAI-based threshold prediction strategy, which has the potential to transform clinical practice in predicting visual outcomes.
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Affiliation(s)
- Galen Pogoncheff
- Department of Computer Science, University of California, Santa Barbara, CA, United States of America
| | - Zuying Hu
- Department of Computer Science, University of California, Santa Barbara, CA, United States of America
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, United States of America
- eScience Institute, University of Washington, Seattle, WA, United States of America
| | - Michael Beyeler
- Department of Computer Science, University of California, Santa Barbara, CA, United States of America
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, United States of America
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Caffarra S, Kanopka K, Kruper J, Richie-Halford A, Roy E, Rokem A, Yeatman JD. Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance. J Neurosci 2024; 44:e0684232023. [PMID: 38124006 PMCID: PMC11059423 DOI: 10.1523/jneurosci.0684-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 11/21/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford 94305, California
- Stanford University Graduate School of Education, Stanford 94305, California
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Klint Kanopka
- Stanford University Graduate School of Education, Stanford 94305, California
| | - John Kruper
- Department of Psychology, University of Washington, Seattle 91905, Washington
- eScience Institute, University of Washington, Seattle 98195-1570, Washington
| | - Adam Richie-Halford
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford 94305, California
- Stanford University Graduate School of Education, Stanford 94305, California
| | - Ethan Roy
- Stanford University Graduate School of Education, Stanford 94305, California
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle 91905, Washington
- eScience Institute, University of Washington, Seattle 98195-1570, Washington
| | - Jason D Yeatman
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford 94305, California
- Stanford University Graduate School of Education, Stanford 94305, California
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Cieslak M, Cook PA, Shafiei G, Tapera TM, Radhakrishnan H, Elliott M, Roalf DR, Oathes DJ, Bassett DS, Tisdall MD, Rokem A, Grafton ST, Satterthwaite TD. Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme. Hum Brain Mapp 2024; 45:e26570. [PMID: 38339908 PMCID: PMC10826632 DOI: 10.1002/hbm.26570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 02/12/2024] Open
Abstract
Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.
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Affiliation(s)
- Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Philip A. Cook
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Tinashe M. Tapera
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Mark Elliott
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Desmond J. Oathes
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Dani S. Bassett
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of BioengineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Physics and AstronomyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Electrical and Systems EngineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Sante Fe InstituteSanta FeNew MexicoUnited States
| | - M. Dylan Tisdall
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Ariel Rokem
- Department of Psychology and the eScience InstituteUniversity of WashingtonSeattleWashingtonUnited States
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of California Santa BarbaraSanta BarbaraCaliforniaUnited States
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Penn‐CHOP Lifespan Brain InstitutePhiladelphiaPennsylvaniaUnited States
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7
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Roy E, Richie-Halford A, Kruper J, Narayan M, Bloom D, Nedelec P, Rauschecker AM, Sugrue LP, Brown TT, Jernigan TL, McCandliss BD, Rokem A, Yeatman JD. White matter and literacy: A dynamic system in flux. Dev Cogn Neurosci 2024; 65:101341. [PMID: 38219709 PMCID: PMC10825614 DOI: 10.1016/j.dcn.2024.101341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/24/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.
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Affiliation(s)
- Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, USA.
| | - Adam Richie-Halford
- Graduate School of Education, Stanford University, Stanford, CA, USA; Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - John Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Manjari Narayan
- Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - David Bloom
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andreas M Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Timothy T Brown
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | - Terry L Jernigan
- Center for Human Development, University of California San Diego, San Diego, CA, USA
| | | | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
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8
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Poldrack RA, Markiewicz CJ, Appelhoff S, Ashar YK, Auer T, Baillet S, Bansal S, Beltrachini L, Benar CG, Bertazzoli G, Bhogawar S, Blair RW, Bortoletto M, Boudreau M, Brooks TL, Calhoun VD, Castelli FM, Clement P, Cohen AL, Cohen-Adad J, D'Ambrosio S, de Hollander G, de la Iglesia-Vayá M, de la Vega A, Delorme A, Devinsky O, Draschkow D, Duff EP, DuPre E, Earl E, Esteban O, Feingold FW, Flandin G, Galassi A, Gallitto G, Ganz M, Gau R, Gholam J, Ghosh SS, Giacomel A, Gillman AG, Gleeson P, Gramfort A, Guay S, Guidali G, Halchenko YO, Handwerker DA, Hardcastle N, Herholz P, Hermes D, Honey CJ, Innis RB, Ioanas HI, Jahn A, Karakuzu A, Keator DB, Kiar G, Kincses B, Laird AR, Lau JC, Lazari A, Legarreta JH, Li A, Li X, Love BC, Lu H, Marcantoni E, Maumet C, Mazzamuto G, Meisler SL, Mikkelsen M, Mutsaerts H, Nichols TE, Nikolaidis A, Nilsonne G, Niso G, Norgaard M, Okell TW, Oostenveld R, Ort E, Park PJ, Pawlik M, Pernet CR, Pestilli F, Petr J, Phillips C, Poline JB, Pollonini L, Raamana PR, Ritter P, Rizzo G, Robbins KA, Rockhill AP, Rogers C, Rokem A, Rorden C, Routier A, Saborit-Torres JM, Salo T, Schirner M, Smith RE, Spisak T, Sprenger J, Swann NC, Szinte M, Takerkart S, Thirion B, Thomas AG, Torabian S, Varoquaux G, Voytek B, Welzel J, Wilson M, Yarkoni T, Gorgolewski KJ. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv 2024:arXiv:2309.05768v2. [PMID: 37744469 PMCID: PMC10516110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
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Affiliation(s)
| | | | | | - Yoni K Ashar
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford, UK
- Artificial Intelligence and Informatics group, Rosalind Franklin Institute, Harwell Campus, Didcot, UK
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Shashank Bansal
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Leandro Beltrachini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Wales, UK
| | - Christian G Benar
- Aix Marseille Université, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Giacomo Bertazzoli
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
- Brigham and Women's Hospital, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Ross W Blair
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Teon L Brooks
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Bioretics srl, Cesena, Italy
| | - Patricia Clement
- Department of Medical Imaging, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | | | - Sasha D'Ambrosio
- Dipartimento di Scienze della Salute dell'Università degli Studi di Milano, Milan, Italy
- Department of Clinical and Experimental Epilepsy, University College London, UK
| | - Gilles de Hollander
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | | | | | - Arnaud Delorme
- SCCN, University of California, San Diego, La Jolla CA USA
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Medical Center, New York, NY, USA
| | - Dejan Draschkow
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Eugene Paul Duff
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Elizabeth DuPre
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Eric Earl
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, England, UK
| | - Anthony Galassi
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Giuseppe Gallitto
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Melanie Ganz
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rémi Gau
- Origamin Lab, The Neuro, McGill University, Montreal, Quebec, Canada
| | - James Gholam
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | | | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England, UK
| | - Ashley G Gillman
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Queensland, Australia
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, England, UK
| | | | - Samuel Guay
- Université de Montréal, Montréal, QC, Canada
| | - Giacomo Guidali
- Department of Psychology & NeuroMI - Milan Centre for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Daniel A Handwerker
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Nell Hardcastle
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Peer Herholz
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Christopher J Honey
- Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Robert B Innis
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Horea-Ioan Ioanas
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Andrew Jahn
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, USA
| | - Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
| | - David B Keator
- Change Your Brain Change Your Life Foundation, Costa Mesa, CA, USA
- Amen Clinics, Costa Mesa, CA, USA
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY USA
| | - Balint Kincses
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Jonathan C Lau
- Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston, MA, USA
| | - Adam Li
- Columbia University, New York, NY, USA
| | - Xiangrui Li
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | | | - Hanzhang Lu
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Eleonora Marcantoni
- School for Psychology and Neuroscience and Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Giacomo Mazzamuto
- National Research Council - National Institute of Optics (CNR-INO), Florence, Italy
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Mark Mikkelsen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Henk Mutsaerts
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Swedish National Data Service, Gothenburg University, Gothenburg, Sweden
| | | | - Martin Norgaard
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Eduard Ort
- Heinrich Heine University, Department of Biological Psychology of Decision Making, Düsseldorf, Germany
| | | | - Mateusz Pawlik
- Paris-Lodron-University of Salzburg, Department of Psychology, Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | | | - Jean-Baptiste Poline
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montréal, Canada
| | - Luca Pollonini
- Department of Engineering Technology, University of Houston, Houston, TX
- Basque Center on Cognition, Brain and Language, Donostia-San Sebastián, Spain
| | | | - Petra Ritter
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Gaia Rizzo
- Invicro, London, UK
- Division of Brain Sciences, Imperial College London, London, UK
| | - Kay A Robbins
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA
| | - Alexander P Rockhill
- Department of Neurosurgery, Oregon Health & Science University, Portland, OR, USA
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ariel Rokem
- University of Washington, Department of Psychology and eScience Institute, Seattle, WA, USA
| | - Chris Rorden
- University of South Carolina, Department of Psychology, Columbia, SC, USA
| | | | | | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Schirner
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- The Florey Department of Neuroscience and Mental Heath, The University of Melbourne, Parkville, Victoria, Australia
| | - Tamas Spisak
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Julia Sprenger
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Nicole C Swann
- University of Oregon, Department of Human Physiology, Eugene, OR, USA
| | - Martin Szinte
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Sylvain Takerkart
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | | | - Adam G Thomas
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | | | | | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, and Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | | | - Martin Wilson
- University of Birmingham, Centre for Human Brain Health and School of Psychology, Birmingham, UK
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9
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Yücel EI, Mohan V, Boynton GM, Rokem A, Fine I. Contributed Session I: The perceptual experience of optogenetic vision. J Vis 2023; 23:71. [PMID: 38109577 DOI: 10.1167/jov.23.15.71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Abstract
Optogenetic therapy for retinal degenerative diseases aims to elicit light response in remaining retinal cells (bipolar and/or ganglion cells). Animal models suggest that these proteins have lower sensitivity, and slower kinetics compared to neurotypical vision. Here we describe a framework for simulating 'virtual patients' to quantify the predicted perceptual experience of optogenetic vision. We simulated the neural responses of rd1 mouse retina expressing 4xBGAG12,460:SNAP-mGluR2 (Holt et al., 2022) and used this simulation to generate virtual patients: sighted participants viewing the visual stimulus filtered through our simulations. We measured the visual performance of these virtual patients (n=6) using temporal contrast sensitivity functions. Virtual patients had a 10x fold loss of sensitivity, which was exacerbated at higher temporal frequencies, corresponding to a loss of Snellen acuity from ~20/40 at low temporal frequencies to ~20/100-20/200 at high temporal frequencies. We predict that the ability to process fast-moving objects may be impaired in optogenetic vision, and patients with uncontrollable nystagmus may be poor candidates for optogenetic treatments with sluggish kinetics. Our virtual patient framework can easily be extended to simulate any optogenetic protein, and thereby provides a way to quantify and compare the expected perceptual performance of different opto-proteins based on in vitro retinal data.
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Affiliation(s)
| | | | | | - Ariel Rokem
- Department of Psychology, Center for Human Neuroscience, University of Washington, Seattle
| | - Ione Fine
- Department of Psychology, Center for Human Neuroscience, University of Washington, Seattle
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10
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Kruper J, Richie-Halford A, Benson N, Caffarra S, Owen J, Wu Y, Lee A, Lee C, Yeatman J, Rokem A. Contributed Session I: Specific and non-linear effects of glaucoma on optic radiation tissue properties. J Vis 2023; 23:73. [PMID: 38109575 DOI: 10.1167/jov.23.15.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Abstract
Changes in sensory input with aging and disease affect brain tissue properties. To establish the link between glaucoma, the most prevalent cause of irreversible blindness, and changes in major brain connections, we characterized white matter tissue properties in diffusion MRI measurements in a large sample of subjects with glaucoma (N=905; age 49-80) and healthy controls (N=5,292; age 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. A convolutional neural network (CNN) accurately classified whether a subject has glaucoma using information from the primary visual connection to cortex (the optic radiations, OR), but not from non-visual brain connections. On the other hand, regularized linear regression could not classify glaucoma, and the CNN did not generalize to classification of age-group or of age-related macular degeneration. This suggests a unique non-linear signature of glaucoma in OR tissue properties.
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11
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Grotheer M, Bloom D, Kruper J, Richie-Halford A, Zika S, Aguilera González VA, Yeatman JD, Grill-Spector K, Rokem A. Human white matter myelinates faster in utero than ex utero. Proc Natl Acad Sci U S A 2023; 120:e2303491120. [PMID: 37549280 PMCID: PMC10438384 DOI: 10.1073/pnas.2303491120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/27/2023] [Indexed: 08/09/2023] Open
Abstract
The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large (n = 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N = 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.
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Affiliation(s)
- Mareike Grotheer
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - John Kruper
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - Adam Richie-Halford
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - Stephanie Zika
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - Vicente A. Aguilera González
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - Jason D. Yeatman
- Department of Psychology, Stanford University, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
- Graduate School of Education, Stanford University, Stanford, CA94305
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA94305
| | - Kalanit Grill-Spector
- Department of Psychology, Stanford University, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
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12
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Kruper J, Benson NC, Caffarra S, Owen J, Wu Y, Lee AY, Lee CS, Yeatman JD, Rokem A. Optic radiations representing different eccentricities age differently. Hum Brain Mapp 2023; 44:3123-3135. [PMID: 36896869 PMCID: PMC10171550 DOI: 10.1002/hbm.26267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/10/2023] [Accepted: 02/16/2023] [Indexed: 03/11/2023] Open
Abstract
The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.
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Affiliation(s)
- John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Noah C. Benson
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Sendy Caffarra
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Julia Owen
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Yue Wu
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Aaron Y. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Cecilia S. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
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13
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2023; 10:247. [PMID: 37117243 PMCID: PMC10147723 DOI: 10.1038/s41597-023-02137-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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14
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Abstract
To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual's perceptual thresholds ('system fitting'), despite thresholds varying drastically across subjects, across electrodes within a subject, and over time. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking. To address these challenges, we 1) fitted machine learning (ML) models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical parameters ('predictors') and 2) leveraged explainable artificial intelligence (XAI) to reveal which of these predictors were most important. Our models accounted for up to 77% of the perceptual threshold response variance and enabled predictions of whether an electrode was deactivated in a given trial with F1 and AUC scores of up to 0.740 and 0.913, respectively. Deactivation and threshold models identified novel predictors of perceptual sensitivity, including subject age, time since blindness onset, and electrode-fovea distance. Our results demonstrate that routinely collected clinical measures and a single session of system fitting might be sufficient to inform an XAI-based threshold prediction strategy, which may transform clinical practice in predicting visual outcomes.
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Affiliation(s)
- Galen Pogoncheff
- Department of Computer Science, University of California, Santa Barbara
| | - Zuying Hu
- Department of Computer Science, University of California, Santa Barbara
| | - Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, WA
| | - Michael Beyeler
- Department of Computer Science, University of California, Santa Barbara; Department of Psychological & Brain Sciences, University of California, Santa Barbara
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15
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Yucel E, Beyeler M, Sadeghi R, Kartha A, Dagnelie G, Rokem A, Fine I. Factors affecting two-point discrimination thresholds in Argus II patients. J Vis 2022. [DOI: 10.1167/jov.22.14.3754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Ezgi Yucel
- Department of Psychology, University of Washington, USA
| | - Michael Beyeler
- Department of Computer Science, UC Santa Barbara, Santa Barbara, USA
| | - Roksana Sadeghi
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Arathy Kartha
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gislin Dagnelie
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, USA
| | - Ione Fine
- Department of Psychology, University of Washington, USA
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16
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Abbott NJ, Anderson JAE, Gagana B, Bleile M, Bloomfield PS, Bottom V, Bourque J, Boyle R, Brynildsen JK, Calarco N, Castrellon JJ, Chaku N, Chen B, Chopra S, Coffey EBJ, Colenbier N, Cox DJ, Crippen JE, Crouse JJ, David S, Leener BD, Delap G, Deng ZD, Dugre JR, Eklund A, Ellis K, Ered A, Farmer H, Faskowitz J, Finch JE, Flandin G, Flounders MW, Fonville L, Frandsen SB, Garic D, Garrido-Vásquez P, Gonzalez-Escamilla G, Grogans SE, Grotheer M, Gruskin DC, Guberman GI, Haggerty EB, Hahn Y, Hall EH, Hanson JL, Harel Y, Vieira BH, Hettwer MD, Hobday H, Horien C, Huang F, Huque ZM, James AR, Kahhale I, Kamhout SLH, Keller AS, Khera HS, Kiar G, Kirk PA, Kohl SH, Korenic SA, Korponay C, Kozlowski AK, Kraljevic N, Lazari A, Leavitt MJ, Li Z, Liberati G, Lorenc ES, Lossin AJ, Lotter LD, Lydon-Staley DM, Madan CR, Magielse N, Marusak HA, Mayor J, McGowan AL, Mehta KP, Meisler SL, Michael C, Mitchell ME, Morand-Beaulieu S, Newman BT, Nielsen JA, O’Mara SM, Ojha A, Omary A, Özarslan E, Parkes L, Peterson M, Pines AR, Pisanu C, Rich RR, Sahoo AK, Samara A, Sayed F, Schneider JT, Shaffer LS, Shatalina E, Sims SA, Sinclair S, Song JW, Hogrogian GS, Tamnes CK, Tooley UA, Tripathi V, Turker HB, Valk SL, Wall MB, Walther CK, Wang Y, Wegmann B, Welton T, Wiesman AI, Wiesman AG, Wiesman M, Winters DE, Yuan R, Zacharek SJ, Zajner C, Zakharov I, Zammarchi G, Zhou D, Zimmerman B, Zoner K, Satterthwaite TD, Rokem A. Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:709. [PMID: 36396653 PMCID: PMC9671885 DOI: 10.1038/s41597-022-01816-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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17
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:616. [PMID: 36224186 PMCID: PMC9556519 DOI: 10.1038/s41597-022-01695-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
Abstract
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.
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Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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Yücel EI, Sadeghi R, Kartha A, Montezuma SR, Dagnelie G, Rokem A, Boynton GM, Fine I, Beyeler M. Factors affecting two-point discrimination in Argus II patients. Front Neurosci 2022; 16:901337. [PMID: 36090266 PMCID: PMC9448992 DOI: 10.3389/fnins.2022.901337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
Two of the main obstacles to the development of epiretinal prosthesis technology are electrodes that require current amplitudes above safety limits to reliably elicit percepts, and a failure to consistently elicit pattern vision. Here, we explored the causes of high current amplitude thresholds and poor spatial resolution within the Argus II epiretinal implant. We measured current amplitude thresholds and two-point discrimination (the ability to determine whether one or two electrodes had been stimulated) in 3 blind participants implanted with Argus II devices. Our data and simulations show that axonal stimulation, lift and retinal damage all play a role in reducing performance in the Argus 2, by either limiting sensitivity and/or reducing spatial resolution. Understanding the relative role of these various factors will be critical for developing and surgically implanting devices that can successfully subserve pattern vision.
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Affiliation(s)
- Ezgi I. Yücel
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Roksana Sadeghi
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Arathy Kartha
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Sandra Rocio Montezuma
- Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, United States
| | - Gislin Dagnelie
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, United States,eScience Institute, University of Washington, Seattle, WA, United States
| | - Geoffrey M. Boynton
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Ione Fine
- Department of Psychology, University of Washington, Seattle, WA, United States,*Correspondence: Ione Fine,
| | - Michael Beyeler
- Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, United States,Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, United States
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Yucel EI, Beyeler M, Sadeghi R, Rokem A, Fine I, Kartha A, Dagnelie G. What limits the spatial resolution of artificial vision in epiretinal implant patients? J Vis 2022. [PMID: 35120248 DOI: 10.1167/jov.22.3.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Retinal implants provide artificial vision to blind individuals through electrically stimulating remaining non-photoreceptor retinal cells. For epiretinal implants, placed over the ganglion cell layer, individual electrodes produce elongated 'streaks' due to the unselective stimulation of underlying ganglion axons (Beyeler, 2019). Here, to examine whether these axonal streaks explain the poor spatial acuity of prosthetic patients, we measured two-point discrimination performance in three patients implanted with an Argus 2 epiretinal implant (Second Sight Medical Products Inc). METHODS On each trial two electrodes were simultaneously stimulated (0.45 um pulse width, 6-20 Hz pulse train, 250-500ms duration, current amplitude 2x threshold). Participants verbally reported the number of distinct percepts they saw. RESULTS A regression analysis found that current amplitude, physical distance, distance along the axon, and distance between axons all played a significant role in determining whether participants saw one or two percepts. CONCLUSIONS Participants were less likely to see two distinct percepts when electrodes were physically close or lay close to the same axon bundle. Electrodes with high stimulation thresholds were also less likely to produce distinct percepts. Thus electrode pairs can merge into a single percept when (1) current fields overlap, (2) their current fields stimulate the same axonal bundle, or (3) the elongated percepts overlap.
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Affiliation(s)
| | | | | | - Ariel Rokem
- Department of Psychology, University of Washington, USA
| | - Ione Fine
- Department of Psychology, University of Washington, USA
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20
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Fadnavis S, Endres S, Wen Q, Wu YC, Cheng H, Koudoro S, Rane S, Rokem A, Garyfallidis E. Bifurcated Topological Optimization for IVIM. Front Neurosci 2021; 15:779025. [PMID: 34975382 PMCID: PMC8714828 DOI: 10.3389/fnins.2021.779025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/16/2021] [Indexed: 12/02/2022] Open
Abstract
In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.
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Affiliation(s)
- Shreyas Fadnavis
- Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
- *Correspondence: Shreyas Fadnavis
| | - Stefan Endres
- Faculty of Production Engineering, Leibniz Institute of Materials Engineering (IWT), Bremen, Germany
- Department of Chemical Engineering, Institute of Applied Materials, University of Pretoria, Pretoria, South Africa
| | - Qiuting Wen
- Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Yu-Chien Wu
- Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hu Cheng
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Serge Koudoro
- Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States
| | - Swati Rane
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, United States
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21
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Caffarra S, Joo SJ, Bloom D, Kruper J, Rokem A, Yeatman JD. Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex. Hum Brain Mapp 2021; 42:5785-5797. [PMID: 34487405 PMCID: PMC8559498 DOI: 10.1002/hbm.25654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 12/24/2022] Open
Abstract
The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
- Basque Center on Cognition Brain and LanguageSan SebastianSpain
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Sung Jun Joo
- Department of PsychologyPusan National UniversityPusanRepublic of Korea
| | - David Bloom
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Jason D. Yeatman
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
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22
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Kiar G, Chatelain Y, de Oliveira Castro P, Petit E, Rokem A, Varoquaux G, Misic B, Evans AC, Glatard T. Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks. PLoS One 2021; 16:e0250755. [PMID: 34724000 PMCID: PMC8559953 DOI: 10.1371/journal.pone.0250755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/25/2021] [Indexed: 11/19/2022] Open
Abstract
The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.
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Affiliation(s)
- Gregory Kiar
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Yohan Chatelain
- Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
| | | | - Eric Petit
- Exascale Computing Lab, Intel, Paris, France
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, United States of America
| | - Gaël Varoquaux
- Parietal Project-team, INRIA Saclay-ile de France, Paris, France
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alan C Evans
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
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23
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Mehta P, Petersen CA, Wen JC, Banitt MR, Chen PP, Bojikian KD, Egan C, Lee SI, Balazinska M, Lee AY, Rokem A. Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images. Am J Ophthalmol 2021; 231:154-169. [PMID: 33945818 DOI: 10.1016/j.ajo.2021.04.021] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 01/17/2023]
Abstract
PURPOSE To develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]). RESULTS Results show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function. CONCLUSIONS The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease.
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Affiliation(s)
- Parmita Mehta
- From the Paul G. Allen School of Computer Science and Engineering, Seattle, Washington, USA (PM, S-IL, MB)
| | - Christine A Petersen
- Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL)
| | - Joanne C Wen
- Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL)
| | - Michael R Banitt
- Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL)
| | - Philip P Chen
- Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL)
| | - Karine D Bojikian
- Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL)
| | | | - Su-In Lee
- From the Paul G. Allen School of Computer Science and Engineering, Seattle, Washington, USA (PM, S-IL, MB)
| | - Magdalena Balazinska
- From the Paul G. Allen School of Computer Science and Engineering, Seattle, Washington, USA (PM, S-IL, MB); eScience Institute, Seattle, Washington, USA (MB, AR)
| | - Aaron Y Lee
- Department of Ophthalmology, Seattle, Washington, USA (CAP, JCW, MRB, PPC, KDB, AYL)
| | - Ariel Rokem
- eScience Institute, Seattle, Washington, USA (MB, AR); Department of Psychology, Seattle, Washington, USA (AR).
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De Luca A, Ianus A, Leemans A, Palombo M, Shemesh N, Zhang H, Alexander DC, Nilsson M, Froeling M, Biessels GJ, Zucchelli M, Frigo M, Albay E, Sedlar S, Alimi A, Deslauriers-Gauthier S, Deriche R, Fick R, Afzali M, Pieciak T, Bogusz F, Aja-Fernández S, Özarslan E, Jones DK, Chen H, Jin M, Zhang Z, Wang F, Nath V, Parvathaneni P, Morez J, Sijbers J, Jeurissen B, Fadnavis S, Endres S, Rokem A, Garyfallidis E, Sanchez I, Prchkovska V, Rodrigues P, Landman BA, Schilling KG. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge. Neuroimage 2021; 240:118367. [PMID: 34237442 PMCID: PMC7615259 DOI: 10.1016/j.neuroimage.2021.118367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/09/2021] [Accepted: 07/04/2021] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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Affiliation(s)
- Alberto De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Enes Albay
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France; Istanbul Technical University, Istanbul, Turkey
| | - Sara Sedlar
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Abib Alimi
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Maryam Afzali
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | | | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Derek K Jones
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Haoze Chen
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Mingwu Jin
- Department of Physics, University of Texas at Arlington, Arlington, USA
| | - Zhijie Zhang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Fengxiang Wang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | | | | | - Jan Morez
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Shreyas Fadnavis
- Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
| | - Stefan Endres
- Leibniz Institute for Materials Engineering - IWT, Faculty of Production Engineering, University of Bremen, Bremen, Germany
| | - Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, Seattle, WA USA
| | | | | | | | | | - Bennet A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA; Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
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25
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Caffarra S, Joo SJ, Bloom D, Kruper J, Rokem A, Yeatman JD. Development of the visual pathways predicts changes in electrophysiological responses in visual cortex. J Vis 2021. [DOI: 10.1167/jov.21.9.2127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Sendy Caffarra
- Stanford University School of Medicine, Division of Developmental-Behavioral Pediatrics, Stanford, CA, USA
- Stanford University Graduate School of Education, Stanford, CA, USA
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Sung Jun Joo
- Department of Psychology, Pusan National University, Busan, Republic of Korea
| | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - John Kruper
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Jason D. Yeatman
- Stanford University School of Medicine, Division of Developmental-Behavioral Pediatrics, Stanford, CA, USA
- Stanford University Graduate School of Education, Stanford, CA, USA
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26
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Rokem A. Detect-ing brain anomalies with autoencoders. Nat Comput Sci 2021; 1:569-570. [PMID: 38217132 DOI: 10.1038/s43588-021-00128-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA.
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27
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Levitis E, van Praag CDG, Gau R, Heunis S, DuPre E, Kiar G, Bottenhorn KL, Glatard T, Nikolaidis A, Whitaker KJ, Mancini M, Niso G, Afyouni S, Alonso-Ortiz E, Appelhoff S, Arnatkeviciute A, Atay SM, Auer T, Baracchini G, Bayer JMM, Beauvais MJS, Bijsterbosch JD, Bilgin IP, Bollmann S, Bollmann S, Botvinik-Nezer R, Bright MG, Calhoun VD, Chen X, Chopra S, Chuan-Peng H, Close TG, Cookson SL, Craddock RC, De La Vega A, De Leener B, Demeter DV, Di Maio P, Dickie EW, Eickhoff SB, Esteban O, Finc K, Frigo M, Ganesan S, Ganz M, Garner KG, Garza-Villarreal EA, Gonzalez-Escamilla G, Goswami R, Griffiths JD, Grootswagers T, Guay S, Guest O, Handwerker DA, Herholz P, Heuer K, Huijser DC, Iacovella V, Joseph MJE, Karakuzu A, Keator DB, Kobeleva X, Kumar M, Laird AR, Larson-Prior LJ, Lautarescu A, Lazari A, Legarreta JH, Li XY, Lv J, Mansour L S, Meunier D, Moraczewski D, Nandi T, Nastase SA, Nau M, Noble S, Norgaard M, Obungoloch J, Oostenveld R, Orchard ER, Pinho AL, Poldrack RA, Qiu A, Raamana PR, Rokem A, Rutherford S, Sharan M, Shaw TB, Syeda WT, Testerman MM, Toro R, Valk SL, Van Den Bossche S, Varoquaux G, Váša F, Veldsman M, Vohryzek J, Wagner AS, Walsh RJ, White T, Wong FT, Xie X, Yan CG, Yang YF, Yee Y, Zanitti GE, Van Gulick AE, Duff E, Maumet C. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective. Gigascience 2021; 10:6355274. [PMID: 34414422 DOI: 10.1093/gigascience/giab051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
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Affiliation(s)
- Elizabeth Levitis
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Cassandra D Gould van Praag
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Rémi Gau
- Institute of Psychology, Université Catholique de Louvain, Louvain la Neuve 1348, Belgium
| | - Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
| | - Elizabeth DuPre
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, QC, H3A 2B4, Canada.,Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
| | - Aki Nikolaidis
- Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, BN1 9RR, UK.,Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK.,NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada
| | - Guiomar Niso
- Departement of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA.,ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, 28040 Madrid, Spain
| | - Soroosh Afyouni
- Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.,Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Eva Alonso-Ortiz
- Department of Electrical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1J4, Canada
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Selim Melvin Atay
- Neuroscience and Neurotechnology, Middle East Technical University, Ankara 06800, Turkey
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford GU2 7XH, UK
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, H3A 2B4, Canada.,Montréal Neurological Institute, Montréal, QC, H3A 2B4, Canada
| | - Johanna M M Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, 3010, Parkville, Melbourne, Australia.,Orygen Youth Health, Melbourne, VIC, 3052, Royal Park, Melbourne, Australia
| | | | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Isil P Bilgin
- Department of Biomedical Engineering, Cybernetics, The School of Biological Sciences, The University of Reading, Reading, RG6 6AH, UK
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Steffen Bollmann
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.,Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, 100101, Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, Beijing, China
| | - Sidhant Chopra
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing 210024, China
| | - Thomas G Close
- Department of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.,National Imaging Facility, The University of Sydney, Sydney, NSW 2006, Australia
| | - Savannah L Cookson
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX 78712, USA
| | - Alejandro De La Vega
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.,Research Centre, Sainte-Justine University Hospital Center, Montreal, QC, H3T 1C5, Canada
| | - Damion V Demeter
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Paola Di Maio
- Center for Systems, Knowledge Representation and Neuroscience, Edinburgh and Taipei, UK and Taiwan.,Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Erin W Dickie
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne 1003, Switzerland
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń 87-100, Poland
| | - Matteo Frigo
- Athena Project Team, Université Côte D'Azur, Inria, 06103 Nice, France
| | - Saampras Ganesan
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen DK-2100, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen DK-2100, Denmark
| | - Kelly G Garner
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD 4072, Australia.,School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.,School of Psychology, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Eduardo A Garza-Villarreal
- Laboratorio Nacional de Imagenología por Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Qro 76230, Mexico
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Rohit Goswami
- Faculty of Physical Sciences, University of Iceland, 102 Reykjavík, Iceland.,Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - John D Griffiths
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Tijl Grootswagers
- The MARCS Institute for Brain, Behaviour & Development, Western Sydney University, Sydney 2751, NSW, Australia
| | - Samuel Guay
- Department of Psychology, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Olivia Guest
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, 6525 EN, Netherlands
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA
| | - Peer Herholz
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorien C Huijser
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, Rotterdam 3062, the Netherlands.,Developmental and Educational Psychology, Leiden University, Leiden 2333, the Netherlands
| | - Vittorio Iacovella
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto 38068, Italy
| | - Michael J E Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1N8, Canada.,Montréal Heart Institute, University of Montréal, Montréal, QC, H1T 1C8, Canada
| | - David B Keator
- Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Xenia Kobeleva
- Department of Neurology, University Hospital Bonn, 53127 Bonn, Germany.,Clinical Research, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany
| | - Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL 33199, USA
| | - Linda J Larson-Prior
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.,Arkansas Children's Nutrition Center, Little Rock, AR, USA.,Department of Neurology, Pediatrics, Neuroscience & Developmental Sciences, Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Alexandra Lautarescu
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 7EH, UK
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Jon Haitz Legarreta
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Xue-Ying Li
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing101408, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.,Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China.,CFIN and PET Center, Aarhus University, 8000 Aarhus, Denmark
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, NSW 2006, Australia
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - David Meunier
- Aix Marseille Univ, CNRS, INT, Institut de Neurosciences de la Timone, 13005 Marseille, France
| | | | - Tulika Nandi
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 7LF, UK
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Matthias Nau
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA.,Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Stephanie Noble
- Radiology & Biomedical Imaging, Yale University, New Haven, CT 06519, USA
| | - Martin Norgaard
- Center for Reproducible Neuroscience, Department of Psychology, Stanford University, Stanford, CA 94305Ci, USA.,Neurobiology Research Unit, Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Johnes Obungoloch
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara City, Uganda
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6500 GL, The Netherlands.,NatMEG, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Edwina R Orchard
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France
| | | | - Anqi Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, Smart Systems Institute, National University of Singapore, Singapore 117583, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Ariel Rokem
- Department of Psychology & eScience Institute, University of Washington, Seattle, WA 98195, USA
| | - Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands.,Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Thomas B Shaw
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Warda T Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, VIC 3053, Australia
| | | | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Neuroscience Department, Institut Pasteur, 75015 Paris, France
| | - Sofie L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany.,Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France.,Montreal Neurological Institute, McGill, Montreal, QC, H3A 2B4, Canada
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry Psychology & Neuroscience, King's College London SE5 8AF, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxfordshire, OX2 6GG, Oxford, UK
| | - Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark
| | - Adina S Wagner
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Reubs J Walsh
- Department of Clinical, Neuro-, and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam,1081BT, The Netherlands.,Center for Applied Transgender Studies , Chicago, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, 3000CB, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam 3000CB, The Netherlands
| | - Fu-Te Wong
- Institute of Linguistics, Academia Sinica, Taipei, Taiwan.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School, New York City, NY 10065, USA
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, 100101 Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yu-Fang Yang
- Department of Psychology, University of Würzburg, Würzburg 97074, Germany
| | - Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.,Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
| | | | - Ana E Van Gulick
- Figshare, Cambridge, MA 02139, USA.,University Libraries, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Paediatrics, University of Oxford, Oxford, OX3 9DU, UK
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, 35042 Rennes, France
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28
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Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, Yeatman JD, Garyfallidis E, Rokem A. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2021; 15:675433. [PMID: 34349631 PMCID: PMC8327208 DOI: 10.3389/fnhum.2021.675433] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.
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Affiliation(s)
| | - Marta M. Correia
- Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Maurizio Marrale
- Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy
- National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy
| | - Elizabeth Huber
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
| | - John Kruper
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
| | - Serge Koudoro
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Jason D. Yeatman
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
- Department of Pediatrics, Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Ariel Rokem
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
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29
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Cieslak M, Cook PA, He X, Yeh FC, Dhollander T, Adebimpe A, Aguirre GK, Bassett DS, Betzel RF, Bourque J, Cabral LM, Davatzikos C, Detre JA, Earl E, Elliott MA, Fadnavis S, Fair DA, Foran W, Fotiadis P, Garyfallidis E, Giesbrecht B, Gur RC, Gur RE, Kelz MB, Keshavan A, Larsen BS, Luna B, Mackey AP, Milham MP, Oathes DJ, Perrone A, Pines AR, Roalf DR, Richie-Halford A, Rokem A, Sydnor VJ, Tapera TM, Tooley UA, Vettel JM, Yeatman JD, Grafton ST, Satterthwaite TD. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat Methods 2021; 18:775-778. [PMID: 34155395 PMCID: PMC8596781 DOI: 10.1038/s41592-021-01185-5] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 05/17/2021] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
| | | | - Xiaosong He
- University of Pennsylvania, Philadelphia, PA, USA
| | | | - Thijs Dhollander
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | | | | | | | | | | | | | | | - John A Detre
- University of Pennsylvania, Philadelphia, PA, USA
| | - Eric Earl
- Oregon Health and Science University, Portland, OR, USA
| | | | | | | | - Will Foran
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | - Ruben C Gur
- University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- University of Pennsylvania, Philadelphia, PA, USA
| | - Max B Kelz
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | - Anders Perrone
- Oregon Health and Science University, Portland, OR, USA
- University of Minnesota, Minneapolis, MN, USA
| | - Adam R Pines
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | | | - Scott T Grafton
- University of California, Santa Barbara, Santa Barbara, CA, USA
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Richie-Halford A, Yeatman JD, Simon N, Rokem A. Multidimensional analysis and detection of informative features in human brain white matter. PLoS Comput Biol 2021; 17:e1009136. [PMID: 34181648 PMCID: PMC8270416 DOI: 10.1371/journal.pcbi.1009136] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/09/2021] [Accepted: 05/31/2021] [Indexed: 12/20/2022] Open
Abstract
The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.
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Affiliation(s)
- Adam Richie-Halford
- eScience Institute, University of Washington, Seattle, Washington, United States of America
| | - Jason D. Yeatman
- Graduate School of Education and Division of Developmental and Behavioral Pediatrics, Stanford University, Stanford, California, United States of America
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, Washington, United States of America
- Department of Psychology, University of Washington, Seattle, Washington, United States of America
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31
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Gau R, Noble S, Heuer K, Bottenhorn KL, Bilgin IP, Yang YF, Huntenburg JM, Bayer JMM, Bethlehem RAI, Rhoads SA, Vogelbacher C, Borghesani V, Levitis E, Wang HT, Van Den Bossche S, Kobeleva X, Legarreta JH, Guay S, Atay SM, Varoquaux GP, Huijser DC, Sandström MS, Herholz P, Nastase SA, Badhwar A, Dumas G, Schwab S, Moia S, Dayan M, Bassil Y, Brooks PP, Mancini M, Shine JM, O'Connor D, Xie X, Poggiali D, Friedrich P, Heinsfeld AS, Riedl L, Toro R, Caballero-Gaudes C, Eklund A, Garner KG, Nolan CR, Demeter DV, Barrios FA, Merchant JS, McDevitt EA, Oostenveld R, Craddock RC, Rokem A, Doyle A, Ghosh SS, Nikolaidis A, Stanley OW, Uruñuela E. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience. Neuron 2021; 109:1769-1775. [PMID: 33932337 DOI: 10.1016/j.neuron.2021.04.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 03/23/2021] [Accepted: 04/01/2021] [Indexed: 11/25/2022]
Abstract
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.
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Affiliation(s)
- Rémi Gau
- Institute of Psychology, Université Catholique de Louvain, Louvain la Neuve, Belgium.
| | - Stephanie Noble
- Radiology & Biomedical Imaging, Yale University, New Haven CT, USA
| | - Katja Heuer
- Center for Research and Interdisciplinarity, Université of Paris, Paris, France; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Isil P Bilgin
- Biomedical Engineering, Cybernetics, University of Reading, Reading, UK; Allied Health Professions Institute, University of the West of England, Bristol, UK
| | - Yu-Fang Yang
- Department of Psychology, University of Würzburg, Würzburg, Germany
| | | | - Johanna M M Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia; Orygen Youth Health, Melbourne, Australia
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Shawn A Rhoads
- Department of Psychology, Georgetown University, Washington DC, USA
| | - Christoph Vogelbacher
- Laboratory for Multimodal Neuroimaging, Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Valentina Borghesani
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada
| | - Elizabeth Levitis
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Hao-Ting Wang
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK; Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK; Sussex Neuroscience, University of Sussex, Brighton, UK
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | - Xenia Kobeleva
- Department of Neurology, University of Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Samuel Guay
- Université de Montréal, Montréal, QC, Canada
| | - Selim Melvin Atay
- Neuroscience and Neurotechnology, Middle East Technical University, Ankara, Turkey
| | - Gael P Varoquaux
- Parietal, INRIA, Saclay, France; Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Dorien C Huijser
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands; Developmental and Educational Psychology, Leiden University, Leiden, the Netherlands
| | | | - Peer Herholz
- NeuroDataScience - ORIGAMI laboratory, Faculty of Medicine and Health Sciences McGill University Montréal, QC Canada
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - AmanPreet Badhwar
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada; Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Université de Montréal, Montréal, QC, Canada; Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, QC, Canada
| | - Guillaume Dumas
- Department of Psychiatry, Université de Montréal, Montréal, QC, Canada; Mila, Université de Montréal, Montréal, QC, Canada
| | - Simon Schwab
- Department of Biostatistics & Center for Reproducible Science, University of Zurich, Zurich, Switzerland
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, San Sebastián-Donostia, Spain; University of the Basque Country (EHU UPV), San Sebastián-Donostia, Spain
| | - Michael Dayan
- Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland
| | - Yasmine Bassil
- Graduate Division of Biological & Biomedical Sciences, Emory University, Atlanta, GA, USA
| | - Paula P Brooks
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK; Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK; NeuroPoly Lab, Polytechnique Montréal, Montréal, QC, Canada
| | - James M Shine
- Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Medicine, New York City, NY, USA
| | - Davide Poggiali
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Anibal S Heinsfeld
- Computational Neuroimaging Lab, University of Texas at Austin, Austin, TX, USA; Department of Computer Science, University of Texas at Austin, Austin, TX, USA
| | - Lydia Riedl
- Department of Psychiatry and Psychotherapy, Philipps Universität, Marburg, Germany
| | - Roberto Toro
- Center for Research and Interdisciplinarity, Université of Paris, Paris, France; Neuroscience Department, Institut Pasteur, Paris, France
| | | | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Department of Computer and Information Science, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Kelly G Garner
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia; School of Psychology, University of Birmingham, Birmingham, UK; School of Psychology, The University of Queensland, St Lucia, Australia
| | | | - Damion V Demeter
- Psychology Department, The University of Texas at Austin, Austin, TX, USA
| | - Fernando A Barrios
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - Junaid S Merchant
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA; Department of Psychology, University of Maryland, College Park, MD, USA
| | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX, USA
| | - Ariel Rokem
- Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Andrew Doyle
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, QC, Canada
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Olivia W Stanley
- Centre for Functional and Metabolic Mapping, University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
| | - Eneko Uruñuela
- Basque Center on Cognition, Brain and Language, San Sebastián-Donostia, Spain; University of the Basque Country (EHU UPV), San Sebastián-Donostia, Spain
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Abstract
For high-dimensional supervised learning, it is often beneficial to use domain-specific knowledge to improve the performance of statistical learning models. When the problem contains covariates which form groups, researchers can include this grouping information to find parsimonious representations of the relationship between covariates and targets. These groups may arise artificially, as from the polynomial expansion of a smaller feature space, or naturally, as from the anatomical grouping of different brain regions or the geographical grouping of different cities. When the number of features is large compared to the number of observations, one seeks a subset of the features which is sparse at both the group and global level.
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Affiliation(s)
| | - Manjari Narayan
- Department of Psychiatry and Behavioral Sciences, Stanford University
| | - Noah Simon
- Department of Biostatistics, University of Washington
| | - Jason Yeatman
- Graduate School of Education and Division of Developmental and Behavioral Pediatrics, Stanford University
| | - Ariel Rokem
- Department of Psychology, University of Washington
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Kruper J, Yeatman JD, Richie-Halford A, Bloom D, Grotheer M, Caffarra S, Kiar G, Karipidis II, Roy E, Chandio BQ, Garyfallidis E, Rokem A. Evaluating the Reliability of Human Brain White Matter Tractometry. Apert Neuro 2021; 1:10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669. [PMID: 35079748 PMCID: PMC8785971 DOI: 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Mareike Grotheer
- Center for Mind, Brain and Behavior - CMBB, Hans-Meerwein-Straße 6, Marburg 35032, Germany
- Department of Psychology, University of Marburg, Marburg 35039, Germany
| | - Sendy Caffarra
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, BCBL, 20009, Spain
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, H3A 0E9, Canada
| | - Iliana I Karipidis
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine,Stanford, CA, 94305, USA
| | - Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
| | - Bramsh Q Chandio
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
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Rokem A, Kay K. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. Gigascience 2020; 9:giaa133. [PMID: 33252656 PMCID: PMC7702219 DOI: 10.1093/gigascience/giaa133] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/28/2020] [Accepted: 11/02/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. RESULTS The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. CONCLUSION Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.
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Affiliation(s)
- Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, Guthrie Hall 119A, Seattle, WA, 98195, USA
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Twin Cities, 2021 6th St SE, Minneapolis, MN, 55455, USA
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Chandio BQ, Risacher SL, Pestilli F, Bullock D, Yeh FC, Koudoro S, Rokem A, Harezlak J, Garyfallidis E. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 2020; 10:17149. [PMID: 33051471 PMCID: PMC7555507 DOI: 10.1038/s41598-020-74054-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 09/22/2020] [Indexed: 11/08/2022] Open
Abstract
Tractography has created new horizons for researchers to study brain connectivity in vivo. However, tractography is an advanced and challenging method that has not been used so far for medical data analysis at a large scale in comparison to other traditional brain imaging methods. This work allows tractography to be used for large scale and high-quality medical analytics. BUndle ANalytics (BUAN) is a fast, robust, and flexible computational framework for real-world tractometric studies. BUAN combines tractography and anatomical information to analyze the challenging datasets and identifies significant group differences in specific locations of the white matter bundles. Additionally, BUAN takes the shape of the bundles into consideration for the analysis. BUAN compares the shapes of the bundles using a metric called bundle adjacency which calculates shape similarity between two given bundles. BUAN builds networks of bundle shape similarities that can be paramount for automating quality control. BUAN is freely available in DIPY. Results are presented using publicly available Parkinson's Progression Markers Initiative data.
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Affiliation(s)
- Bramsh Qamar Chandio
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, USA.
| | | | - Franco Pestilli
- Department of Psychology, The University of Texas, Austin, TX, USA
| | - Daniel Bullock
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Serge Koudoro
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, USA
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Washington, DC, USA
| | - Jaroslaw Harezlak
- School of Public Health, Indiana University Bloomington, Bloomington, IN, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, USA
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Beyeler M, Boynton GM, Fine I, Rokem A. Model-Based Recommendations for Optimal Surgical Placement of Epiretinal Implants. Med Image Comput Comput Assist Interv 2019; 11768:394-402. [PMID: 35373219 PMCID: PMC8975247 DOI: 10.1007/978-3-030-32254-0_44] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual 'streaks' that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept and the retinal location of the stimulating electrode. However, this knowledge has yet to be incorporated into the surgical placement of retinal implants. Here we systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of the electrode array. Using a psychophysically validated computational model, we demonstrate that better implant placement has the potential to reduce the spatial extent of axonal activation in existing implant users by up to ~55%. Importantly, the best implant location, as inferred from a population of simulated virtual patients, is both surgically feasible and is relatively stable across individuals. This study is a first step towards the use of computer simulations in patient-specific planning of retinal implant surgery.
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Affiliation(s)
- Michael Beyeler
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
- Institute for Neuroengineering (UWIN), University of Washington, Seattle, WA 98195, USA
- eScience Institute, University of Washington, Seattle, WA 98195, USA
| | - Geoffrey M Boynton
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
| | - Ione Fine
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
- Institute for Neuroengineering (UWIN), University of Washington, Seattle, WA 98195, USA
| | - Ariel Rokem
- Institute for Neuroengineering (UWIN), University of Washington, Seattle, WA 98195, USA
- eScience Institute, University of Washington, Seattle, WA 98195, USA
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Bressler DW, Rokem A, Silver MA. Slow Endogenous Fluctuations in Cortical fMRI Signals Correlate with Reduced Performance in a Visual Detection Task and Are Suppressed by Spatial Attention. J Cogn Neurosci 2019; 32:85-99. [PMID: 31560268 DOI: 10.1162/jocn_a_01470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Spatial attention improves performance on visual tasks, increases neural responses to attended stimuli, and reduces correlated noise in visual cortical neurons. In addition to being visually responsive, many retinotopic visual cortical areas exhibit very slow (<0.1 Hz) endogenous fluctuations in functional magnetic resonance imaging signals. To test whether these fluctuations degrade stimulus representations, thereby impairing visual detection, we recorded functional magnetic resonance imaging responses while human participants performed a target detection task that required them to allocate spatial attention to either a rotating wedge stimulus or a central fixation point. We then measured the effects of spatial attention on response amplitude at the frequency of wedge rotation and on the amplitude of endogenous fluctuations at nonstimulus frequencies. We found that, in addition to enhancing stimulus-evoked responses, attending to the wedge also suppressed slow endogenous fluctuations that were unrelated to the visual stimulus in topographically defined areas in early visual cortex, posterior parietal cortex, and lateral occipital cortex, but not in a nonvisual cortical control region. Moreover, attentional enhancement of response amplitude and suppression of endogenous fluctuations were dissociable across cortical areas and across time. Finally, we found that the amplitude of the stimulus-evoked response was not correlated with a perceptual measure of visual target detection. Instead, perceptual performance was accounted for by the amount of suppression of slow endogenous fluctuations. Our results indicate that the amplitude of slow fluctuations of cortical activity is influenced by spatial attention and suggest that these endogenous fluctuations may impair perceptual processing in topographically organized visual cortical areas.
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Yucel EI, Benson NC, Tong Y, Frederick B, Fine I, Rokem A. The Alignment of Systemic Low Frequency Oscillations with V1 Retinotopic Organization. J Vis 2019. [DOI: 10.1167/19.10.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Ezgi I Yucel
- Department of Psychology, University of Washington, Seattle, WA
- UW Institute of Neuroengineering, Seattle, WA
- The University of Washington eScience Institute, Seattle, WA
| | - Noah C Benson
- Department of Psychology, New York University, New York, NY
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN
| | - Blaise Frederick
- McLean Hospital Brain Imaging Center, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Ione Fine
- Department of Psychology, University of Washington, Seattle, WA
- UW Institute of Neuroengineering, Seattle, WA
| | - Ariel Rokem
- UW Institute of Neuroengineering, Seattle, WA
- The University of Washington eScience Institute, Seattle, WA
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39
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Bain JS, Yeatman JD, Schurr R, Rokem A, Mezer AA. Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines. Hum Brain Mapp 2019; 40:3695-3711. [PMID: 31106944 DOI: 10.1002/hbm.24626] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/18/2019] [Accepted: 05/01/2019] [Indexed: 11/11/2022] Open
Abstract
The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging-based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.
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Affiliation(s)
- Jonathan S Bain
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jason D Yeatman
- Institute for Learning & Brain Sciences and Department of Speech and Hearing Science, The University of Washington, Seattle, Washington, USA
| | - Roey Schurr
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ariel Rokem
- The University of Washington eScience Institute, The University of Washington, Seattle, Washington, USA
| | - Aviv A Mezer
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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40
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Keshavan A, Yeatman JD, Rokem A. Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging. Front Neuroinform 2019; 13:29. [PMID: 31139070 PMCID: PMC6517786 DOI: 10.3389/fninf.2019.00029] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 04/01/2019] [Indexed: 01/02/2023] Open
Abstract
Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination. We propose to (1) start with expertly labeled data, (2) amplify labels through web applications that engage citizen scientists, and (3) train machine learning on amplified labels, to emulate the experts. Demonstrating this, we developed a system to quality control brain magnetic resonance images. Expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99). Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist.
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Affiliation(s)
- Anisha Keshavan
- eScience Institute, University of Washington, Seattle, WA, United States
- Institute for Neuroengineering, University of Washington, Seattle, WA, United States
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing, University of Washington, Seattle, WA, United States
| | - Jason D. Yeatman
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing, University of Washington, Seattle, WA, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States
- Institute for Neuroengineering, University of Washington, Seattle, WA, United States
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41
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Huber E, Henriques RN, Owen JP, Rokem A, Yeatman JD. Applying microstructural models to understand the role of white matter in cognitive development. Dev Cogn Neurosci 2019; 36:100624. [PMID: 30927705 PMCID: PMC6969248 DOI: 10.1016/j.dcn.2019.100624] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 12/18/2018] [Accepted: 01/29/2019] [Indexed: 11/25/2022] Open
Abstract
Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a typical multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular modeling techniques to examine individual differences in white matter structure. We first assess scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon water fraction in poor readers. Both models support this interpretation, highlighting the utility of these approaches for testing specific hypotheses about cognitive development.
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Affiliation(s)
- Elizabeth Huber
- Institute for Learning & Brain Sciences and Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, 98195, United States.
| | - Rafael Neto Henriques
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Julia P Owen
- Department of Radiology, University of Washington, Seattle, WA, 98195, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, 98195, United States
| | - Jason D Yeatman
- Institute for Learning & Brain Sciences and Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, 98195, United States
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42
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Tian Q, Yang G, Leuze C, Rokem A, Edlow BL, McNab JA. Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator. Neuroimage 2019; 189:497-515. [PMID: 30684636 DOI: 10.1016/j.neuroimage.2019.01.038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Revised: 12/06/2018] [Accepted: 01/14/2019] [Indexed: 01/14/2023] Open
Abstract
Diffusion spectrum MRI (DSI) provides model-free estimation of the diffusion ensemble average propagator (EAP) and orientation distribution function (ODF) but requires the diffusion data to be acquired on a Cartesian q-space grid. Multi-shell diffusion acquisitions are more flexible and more commonly acquired but have, thus far, only been compatible with model-based analysis methods. Here, we propose a generalized DSI (GDSI) framework to recover the EAP from multi-shell diffusion MRI data. The proposed GDSI approach corrects for q-space sampling density non-uniformity using a fast geometrical approach. The EAP is directly calculated in a preferable coordinate system by multiplying the sampling density corrected q-space signals by a discrete Fourier transform matrix, without any need for gridding. The EAP is demonstrated as a way to map diffusion patterns in brain regions such as the thalamus, cortex and brainstem where the tissue microstructure is not as well characterized as in white matter. Scalar metrics such as the zero displacement probability and displacement distances at different fractions of the zero displacement probability were computed from the recovered EAP to characterize the diffusion pattern within each voxel. The probability averaged across directions at a specific displacement distance provides a diffusion property based image contrast that clearly differentiates tissue types. The displacement distance at the first zero crossing of the EAP averaged across directions orthogonal to the primary fiber orientation in the corpus callosum is found to be larger in the body (5.65 ± 0.09 μm) than in the genu (5.55 ± 0.15 μm) and splenium (5.4 ± 0.15 μm) of the corpus callosum, which corresponds well to prior histological studies. The EAP also provides model-free representations of angular structure such as the diffusion ODF, which allows estimation and comparison of fiber orientations from both the model-free and model-based methods on the same multi-shell data. For the model-free methods, detection of crossing fibers is found to be strongly dependent on the maximum b-value and less sensitive compared to the model-based methods. In conclusion, our study provides a generalized DSI approach that allows flexible reconstruction of the diffusion EAP and ODF from multi-shell diffusion data and data acquired with other sampling patterns.
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Affiliation(s)
- Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States.
| | - Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
| | - Christoph Leuze
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Jennifer A McNab
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
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43
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Abstract
The diff_classifier package seeks to address the issue of scale-up in multi-particle tracking (MPT) analyses via a parallelization approach. MPT is a powerful analytical tool that has been used in fields ranging from aeronautics to oceanography (Pulford, 2005) allowing researchers to collect spatial and velocity information of moving objects from video datasets. Examples include: Tracking tracers in ocean currents to study fluid flowTracking molecular motors (e.g., myosin, kinesin) to assess motile activityMeasuring intracellular trafficking by tracking membrane vesiclesAssessing microrheological properties by tracking nanoparticle movement.
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Affiliation(s)
- Chad Curtis
- Department of Chemical Engineering, University of Washington
| | - Ariel Rokem
- eScience Institute, University of Washington
| | - Elizabeth Nance
- Department of Chemical Engineering, University of Washington
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44
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Beyeler M, Nanduri D, Weiland J, Rokem A, Boynton G, Fine I. Optimizing stimulation protocols for prosthetic vision based on retinal anatomy. J Vis 2018. [DOI: 10.1167/18.10.205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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45
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Yeatman JD, Richie-Halford A, Smith JK, Keshavan A, Rokem A. A browser-based tool for visualization and analysis of diffusion MRI data. Nat Commun 2018; 9:940. [PMID: 29507333 PMCID: PMC5838108 DOI: 10.1038/s41467-018-03297-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 02/02/2018] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Jason D Yeatman
- Institute for Learning & Brain Sciences, University of Washington, Portage Bay Building, Box 357988, Seattle, WA, 98195, USA.
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, 98195, USA.
| | | | - Josh K Smith
- Department of Chemical Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Anisha Keshavan
- Institute for Learning & Brain Sciences, University of Washington, Portage Bay Building, Box 357988, Seattle, WA, 98195, USA
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, WRF Data Science Studio, University of Washington, Physics/Astronomy Tower (PAT), 6th Floor 3910 15th Ave NE, Seattle, WA, 98195, USA
| | - Ariel Rokem
- eScience Institute, WRF Data Science Studio, University of Washington, Physics/Astronomy Tower (PAT), 6th Floor 3910 15th Ave NE, Seattle, WA, 98195, USA.
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46
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Smith AM, Niemeyer KE, Katz DS, Barba LA, Githinji G, Gymrek M, Huff KD, Madan CR, Cabunoc Mayes A, Moerman KM, Prins P, Ram K, Rokem A, Teal TK, Valls Guimera R, Vanderplas JT. Journal of Open Source Software (JOSS): design and first-year review. PeerJ Prepr 2018; 4:e147. [PMID: 32704456 PMCID: PMC7340488 DOI: 10.7717/peerj-cs.147] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 01/24/2018] [Indexed: 06/01/2023]
Abstract
This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).
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Affiliation(s)
- Arfon M. Smith
- Data Science Mission Office, Space Telescope Science Institute, Baltimore, MD, United States of America
| | - Kyle E. Niemeyer
- School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, United States of America
| | - Daniel S. Katz
- National Center for Supercomputing Applications & Department of Computer Science & Department of Electrical and Computer Engineering & School of Information Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
| | - Lorena A. Barba
- Department of Mechanical & Aerospace Engineering, The George Washington University, Washington, D.C., United States of America
| | | | - Melissa Gymrek
- Departments of Medicine & Computer Science and Engineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Kathryn D. Huff
- Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
| | | | | | - Kevin M. Moerman
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Trinity Centre for Bioengineering, Trinity College, The University of Dublin, Dublin, Ireland
| | - Pjotr Prins
- University of Tennessee Health Science Center, Memphis, TN, United States of America
- University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Karthik Ram
- Berkeley Institute for Data Science, University of California, Berkeley, CA, United States of America
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States of America
| | | | - Roman Valls Guimera
- University of Melbourne Centre for Cancer Research, University of Melbourne, Melbourne, Australia
| | - Jacob T. Vanderplas
- eScience Institute, University of Washington, Seattle, WA, United States of America
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47
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Lee C, Rokem A, Lee A. Reply. Ophthalmol Retina 2018; 2:e3. [PMID: 31047354 DOI: 10.1016/j.oret.2017.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 11/07/2017] [Indexed: 06/09/2023]
Affiliation(s)
- Cecilia Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, Washington
| | - Aaron Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington.
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48
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Xiao S, Bucher F, Wu Y, Rokem A, Lee CS, Marra KV, Fallon R, Diaz-Aguilar S, Aguilar E, Friedlander M, Lee AY. Fully automated, deep learning segmentation of oxygen-induced retinopathy images. JCI Insight 2017; 2:97585. [PMID: 29263301 PMCID: PMC5752269 DOI: 10.1172/jci.insight.97585] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 11/15/2017] [Indexed: 12/29/2022] Open
Abstract
Oxygen-induced retinopathy (OIR) is a widely used model to study ischemia-driven neovascularization (NV) in the retina and to serve in proof-of-concept studies in evaluating antiangiogenic drugs for ocular, as well as nonocular, diseases. The primary parameters that are analyzed in this mouse model include the percentage of retina with vaso-obliteration (VO) and NV areas. However, quantification of these two key variables comes with a great challenge due to the requirement of human experts to read the images. Human readers are costly, time-consuming, and subject to bias. Using recent advances in machine learning and computer vision, we trained deep learning neural networks using over a thousand segmentations to fully automate segmentation in OIR images. While determining the percentage area of VO, our algorithm achieved a similar range of correlation coefficients to that of expert inter-human correlation coefficients. In addition, our algorithm achieved a higher range of correlation coefficients compared with inter-expert correlation coefficients for quantification of the percentage area of neovascular tufts. In summary, we have created an open-source, fully automated pipeline for the quantification of key values of OIR images using deep learning neural networks.
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Affiliation(s)
- Sa Xiao
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Felicitas Bucher
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
- Eye Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, Washington, USA
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Kyle V. Marra
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
- Department of Bioengineering, University of California, San Diego, San Diego, California, USA
| | - Regis Fallon
- Lowy Medical Research Institute, La Jolla, California, USA
| | - Sophia Diaz-Aguilar
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
| | - Edith Aguilar
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
| | - Martin Friedlander
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
- Lowy Medical Research Institute, La Jolla, California, USA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
- eScience Institute, University of Washington, Seattle, Washington, USA
- Department of Ophthalmology, Puget Sound Veteran Affairs, Seattle, Washington, USA
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49
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Glusman G, Rose PW, Prlić A, Dougherty J, Duarte JM, Hoffman AS, Barton GJ, Bendixen E, Bergquist T, Bock C, Brunk E, Buljan M, Burley SK, Cai B, Carter H, Gao J, Godzik A, Heuer M, Hicks M, Hrabe T, Karchin R, Leman JK, Lane L, Masica DL, Mooney SD, Moult J, Omenn GS, Pearl F, Pejaver V, Reynolds SM, Rokem A, Schwede T, Song S, Tilgner H, Valasatava Y, Zhang Y, Deutsch EW. Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework. Genome Med 2017; 9:113. [PMID: 29254494 PMCID: PMC5735928 DOI: 10.1186/s13073-017-0509-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.
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Affiliation(s)
| | - Peter W Rose
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 98093, USA
| | - Andreas Prlić
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 98093, USA.,RCSB Protein Data Bank, University of California San Diego, La Jolla, CA, 98093, USA
| | | | - José M Duarte
- RCSB Protein Data Bank, University of California San Diego, La Jolla, CA, 98093, USA
| | - Andrew S Hoffman
- Human Centered Design & Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Geoffrey J Barton
- Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
| | - Emøke Bendixen
- Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus, Denmark
| | - Timothy Bergquist
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA
| | - Christian Bock
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA
| | - Elizabeth Brunk
- University of California San Diego, La Jolla, CA, 92093, USA
| | - Marija Buljan
- Institute of Molecular Systems Biology, ETH Zurich, CH-8093, Zurich, Switzerland
| | - Stephen K Burley
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 98093, USA.,RCSB Protein Data Bank, University of California San Diego, La Jolla, CA, 98093, USA.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Binghuang Cai
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA
| | - Hannah Carter
- University of California San Diego, La Jolla, CA, 92093, USA
| | - JianJiong Gao
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Adam Godzik
- SBP Medical Discovery Institute, La Jolla, CA, 92037, USA
| | - Michael Heuer
- AMPLab, University of California, Berkeley, CA, 94720, USA
| | | | - Thomas Hrabe
- SBP Medical Discovery Institute, La Jolla, CA, 92037, USA
| | - Rachel Karchin
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.,Department of Oncology, Johns Hopkins Medicine, Baltimore, MD, 21287, USA
| | - Julia Koehler Leman
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, 10010, USA.,Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics and University of Geneva, CH-1211, Geneva, Switzerland
| | - David L Masica
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, 20850, USA.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA
| | - Gilbert S Omenn
- Institute for Systems Biology, Seattle, WA, 98109, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA
| | - Frances Pearl
- School of Life Sciences, University of Sussex, Brighton, BN1 9QG, UK
| | - Vikas Pejaver
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA.,The University of Washington eScience Institute, Seattle, WA, 98195, USA
| | | | - Ariel Rokem
- The University of Washington eScience Institute, Seattle, WA, 98195, USA
| | - Torsten Schwede
- SIB Swiss Institute of Bioinformatics and Biozentrum University of Basel, CH-4056, Basel, Switzerland
| | - Sicheng Song
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98109, USA
| | - Hagen Tilgner
- Brain and Mind Research Institute, Weill Cornell Medicine, New York City, NY, 10021, USA
| | - Yana Valasatava
- RCSB Protein Data Bank, University of California San Diego, La Jolla, CA, 98093, USA
| | - Yang Zhang
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA
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50
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Beyeler M, Rokem A, Boynton GM, Fine I. Improving retinal prostheses using the “virtual patient”. J Vis 2017. [DOI: 10.1167/17.15.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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