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<li>Low, D. M., Rao, V., Randolph, G., Song, P. C., & Ghosh, S. S. (2024). Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings. doi: <a href="https://doi.org/10.1101/2020.11.23.20235945">10.1101/2020.11.23.20235945</a>.</li>
<li>Burdinski, D., Kodibagkar, A., Potter, K., Schuster, R., Evins, A. E., Ghosh, S., & Gilman, J. (2024). Impact of year-long cannabis use for medical symptoms on brain activation during cognitive processes. doi: <a href="https://doi.org/10.1101/2024.04.29.24306516">10.1101/2024.04.29.24306516</a>.</li>
<li>Lin, D. J., Backus, D., Chakraborty, S., Liew, S.-L., Valero-Cuevas, F. J., Patten, C., & Cotton, R. J. (2024). Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation. Journal of NeuroEngineering and Rehabilitation, 21(1). doi: <a href="https://doi.org/10.1186/s12984-024-01309-w">10.1186/s12984-024-01309-w</a>.</li>
<li>Renton, A. I., Dao, T. T., Johnstone, T., Civier, O., Sullivan, R. P., White, D. J., Lyons, P., Slade, B. M., Abbott, D. F., Amos, T. J., Bollmann, S., Botting, A., Campbell, M. E. J., Chang, J., Close, T. G., Dörig, M., Eckstein, K., Egan, G. F., Evas, S., … Bollmann, S. (2024). Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nature Methods, 21(5), 804–808. doi: <a href="https://doi.org/10.1038/s41592-023-02145-x">10.1038/s41592-023-02145-x</a>.</li>
<li>Szczepanik, M., Wagner, A. S., Heunis, S., Waite, L. K., Eickhoff, S. B., & Hanke, M. (2024). Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Neuroinformatics, 22(4), 635–645. doi: <a href="https://doi.org/10.1007/s12021-024-09665-7">10.1007/s12021-024-09665-7</a>.</li>
<li>Sokołowski, A., Bhagwat, N., Chatelain, Y., Dugré, M., Hanganu, A., Monchi, O., McPherson, B., Wang, M., Poline, J.-B., Sharp, M., & Glatard, T. (2024). Longitudinal brain structure changes in Parkinson’s disease: A replication study. PLOS ONE, 19(1), e0295069. doi: <a href="https://doi.org/10.1371/journal.pone.0295069">10.1371/journal.pone.0295069</a>.</li>
<li>Torabi, M., Mitsis, G. D., & Poline, J.-B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13. doi: <a href="https://doi.org/10.1093/gigascience/giae009">10.1093/gigascience/giae009</a>.</li>
<li>Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Benar, C. G., Bertazzoli, G., Bhogawar, S., Blair, R. W., Bortoletto, M., Boudreau, M., Brooks, T. L., Calhoun, V. D., Castelli, F. M., Clement, P., Cohen, A. L., … Gorgolewski, K. J. (2024). The past, present, and future of the brain imaging data structure (BIDS). Imaging Neuroscience, 2, 1–19. doi: <a href="https://doi.org/10.1162/imag_a_00103">10.1162/imag_a_00103</a>.</li>
<li>Low, D. M., Rao, V., Randolph, G., Song, P. C., & Ghosh, S. S. (2024). Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings. PLOS Digital Health, 3(5), e0000516. doi: <a href="https://doi.org/10.1371/journal.pdig.0000516">10.1371/journal.pdig.0000516</a>.</li>
<li>Halchenko, Y. O., Goncalves, M., Ghosh, S., Velasco, P., Visconti di Oleggio Castello, M., Salo, T., Wodder, J. T., Hanke, M., Sadil, P., Gorgolewski, K. J., Ioanas, H.-I., Rorden, C., Hendrickson, T. J., Dayan, M., Houlihan, S. D., Kent, J., Strauss, T., Lee, J., To, I., … Kennedy, D. N. (2024). HeuDiConv — flexible DICOM conversion into structured directory layouts. Journal of Open Source Software, 9(99), 5839. doi: <a href="https://doi.org/10.21105/joss.05839">10.21105/joss.05839</a>.</li>
<li>Hubbard, N. A., Bauer, C. C. C., Siless, V., Auerbach, R. P., Elam, J. S., Frosch, I. R., Henin, A., Hofmann, S. G., Hodge, M. R., Jones, R., Lenzini, P., Lo, N., Park, A. T., Pizzagalli, D. A., Vaz-DeSouza, F., Gabrieli, J. D. E., Whitfield-Gabrieli, S., Yendiki, A., & Ghosh, S. S. (2024). The Human Connectome Project of adolescent anxiety and depression dataset. Scientific Data, 11(1). doi: <a href="https://doi.org/10.1038/s41597-024-03629-x">10.1038/s41597-024-03629-x</a>.</li>
<li>Ioanas, H.-I., Macdonald, A., & Halchenko, Y. O. (2024). Neuroimaging article reexecution and reproduction assessment system. Frontiers in Neuroinformatics, 18. doi: <a href="https://doi.org/10.3389/fninf.2024.1376022">10.3389/fninf.2024.1376022</a>.</li>
<li>Queder, N., Tien, V. B., Abraham, S. A., Urchs, S. G. W., Helmer, K. G., Chaplin, D., van Erp, T. G. M., Kennedy, D. N., Poline, J.-B., Grethe, J. S., Ghosh, S. S., & Keator, D. B. (2023). NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query. Frontiers in Neuroinformatics, 17. doi: <a href="https://doi.org/10.3389/fninf.2023.1174156">10.3389/fninf.2023.1174156</a>.</li>
<li>Bollmann, S., Renton, A., Dao, T., Johnstone, T., Civier, O., Sullivan, R., White, D., Lyons, P., Slade, B., Abbott, D., Amos, T., Bollmann, S., Botting, A., Campbell, M., Chang, J., Close, T., Eckstein, K., Egan, G., Evas, S., … Narayanan, A. (2023). Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. doi: <a href="https://doi.org/10.21203/rs.3.rs-2649734/v1">10.21203/rs.3.rs-2649734/v1</a>.</li>
<li>Larivière, S., Bayrak, Ş., Vos de Wael, R., Benkarim, O., Herholz, P., Rodriguez-Cruces, R., Paquola, C., Hong, S.-J., Misic, B., Evans, A. C., Valk, S. L., & Bernhardt, B. C. (2023). BrainStat: A toolbox for brain-wide statistics and multimodal feature associations. NeuroImage, 266, 119807. doi: <a href="https://doi.org/10.1016/j.neuroimage.2022.119807">10.1016/j.neuroimage.2022.119807</a>.</li>
<li>Poline, J.-B., Das, S., Glatard, T., Madjar, C., Dickie, E. W., Lecours, X., Beaudry, T., Beck, N., Behan, B., Brown, S. T., Bujold, D., Beauvais, M., Caron, B., Czech, C., Dharsee, M., Dugré, M., Evans, K., Gee, T., Ippoliti, G., … Evans, A. C. (2023). Data and Tools Integration in the Canadian Open Neuroscience Platform. Scientific Data, 10(1). doi: <a href="https://doi.org/10.1038/s41597-023-01946-1">10.1038/s41597-023-01946-1</a>.</li>
<li>Kiar, G., Clucas, J., Feczko, E., Goncalves, M., Jarecka, D., Markiewicz, C. J., Halchenko, Y. O., Hermosillo, R., Li, X., Miranda-Dominguez, O., Ghosh, S., Poldrack, R. A., Satterthwaite, T. D., Milham, M. P., & Fair, D. (2023). Align with the NMIND consortium for better neuroimaging. Nature Human Behaviour, 7(7), 1027–1028. doi: <a href="https://doi.org/10.1038/s41562-023-01647-0">10.1038/s41562-023-01647-0</a>.</li>
<li>Torabian, S., Vélez, N., Sochat, V., Halchenko, Y. O., & Grossman, E. D. (2023). The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data. Frontiers in Neuroscience, 17. doi: <a href="https://doi.org/10.3389/fnins.2023.1233416">10.3389/fnins.2023.1233416</a>.</li>
<li>Wang, Q., Aljassar, M., Bhagwat, N., Zeighami, Y., Evans, A. C., Dagher, A., Pike, G. B., Sadikot, A. F., & Poline, J.-B. (2023). Reproducibility of cerebellar involvement as quantified by consensus structural MRI biomarkers in advanced essential tremor. Scientific Reports, 13(1). doi: <a href="https://doi.org/10.1038/s41598-022-25306-y">10.1038/s41598-022-25306-y</a>.</li>
<li>Peraza, J. A., Salo, T., Riedel, M. C., Bottenhorn, K. L., Poline, J.-B., Dockès, J., Kent, J. D., Bartley, J. E., Flannery, J. S., Hill-Bowen, L. D., Lobo, R. P., Poudel, R., Ray, K. L., Robinson, J. L., Laird, R. W., Sutherland, M. T., de la Vega, A., & Laird, A. R. (2023). Methods for decoding cortical gradients of functional connectivity. doi: <a href="https://doi.org/10.1101/2023.08.01.551505">10.1101/2023.08.01.551505</a>.</li>
<li>Zhao, C., Jarecka, D., Covitz, S., Chen, Y., Eickhoff, S. B., Fair, D. A., Franco, A. R., Halchenko, Y. O., Hendrickson, T. J., Hoffstaedter, F., Houghton, A., Kiar, G., Macdonald, A., Mehta, K., Milham, M. P., Salo, T., Hanke, M., Ghosh, S. S., Cieslak, M., & Satterthwaite, T. D. (2023). A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. doi: <a href="https://doi.org/10.1101/2023.08.16.552472">10.1101/2023.08.16.552472</a>.</li>
<li>Notter, M. P., Herholz, P., Da Costa, S., Gulban, O. F., Isik, A. I., Gaglianese, A., & Murray, M. M. (2022). fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines. Brain Topography, 36(2), 172–191. doi: <a href="https://doi.org/10.1007/s10548-022-00935-8">10.1007/s10548-022-00935-8</a>.</li>
<li>Ferris, J. K., Lo, B. P., Khlif, M. S., Brodtmann, A., Boyd, L. A., & Liew, S.-L. (2023). Optimizing automated white matter hyperintensity segmentation in individuals with stroke. Frontiers in Neuroimaging, 2. doi: <a href="https://doi.org/10.3389/fnimg.2023.1099301">10.3389/fnimg.2023.1099301</a>.</li>
<li>Makris, N., Rushmore, R., Kaiser, J., Albaugh, M., Kubicki, M., Rathi, Y., Zhang, F., O’Donnell, L. J., Yeterian, E., Caviness, V. S., & Kennedy, D. N. (2023). A Proposed Human Structural Brain Connectivity Matrix in the Center for Morphometric Analysis Harvard-Oxford Atlas Framework: A Historical Perspective and Future Direction for Enhancing the Precision of Human Structural Connectivity with a Novel Neuroanatomical Typology. Developmental Neuroscience, 45(4), 161–180. Portico. doi: <a href="https://doi.org/10.1159/000530358">10.1159/000530358</a>.</li>
<li>Modarres, M., Cochran, D., Kennedy, D. N., & Frazier, J. A. (2023). Comparison of comprehensive quantitative EEG metrics between typically developing boys and girls in resting state eyes-open and eyes-closed conditions. Frontiers in Human Neuroscience, 17. doi: <a href="https://doi.org/10.3389/fnhum.2023.1237651">10.3389/fnhum.2023.1237651</a>.</li>
<li>Poline, J.-B., Kennedy, D. N., Sommer, F. T., Ascoli, G. A., Van Essen, D. C., Ferguson, A. R., Grethe, J. S., Hawrylycz, M. J., Thompson, P. M., Poldrack, R. A., Ghosh, S. S., Keator, D. B., Athey, T. L., Vogelstein, J. T., Mayberg, H. S., & Martone, M. E. (2022). Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. Neuroinformatics, 20(2), 507–512. doi: <a href="https://doi.org/10.1007/s12021-021-09557-0">10.1007/s12021-021-09557-0</a>.</li>
<li>Royer, J., Rodríguez-Cruces, R., Tavakol, S., Larivière, S., Herholz, P., Li, Q., Vos de Wael, R., Paquola, C., Benkarim, O., Park, B., Lowe, A. J., Margulies, D., Smallwood, J., Bernasconi, A., Bernasconi, N., Frauscher, B., & Bernhardt, B. C. (2022). An Open MRI Dataset For Multiscale Neuroscience. Scientific Data, 9(1). doi: <a href="https://doi.org/10.1038/s41597-022-01682-y">10.1038/s41597-022-01682-y</a>.</li>
<li>Torres-Espín, A., Almeida, C. A., Chou, A., Huie, J. R., Chiu, M., Vavrek, R., Sacramento, J., Orr, M. B., Gensel, J. C., Grethe, J. S., Martone, M. E., Fouad, K., Ferguson, A. R., Alilain, W., Bacon, M., Batty, N., Beattie, M., Bresnahan, J., … Zholudeva, L. (2021). Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org). Neuroinformatics, 20(1), 203–219. doi: <a href="https://doi.org/10.1007/s12021-021-09533-8">10.1007/s12021-021-09533-8</a>.</li>
<li>Abrams, M. B., Bjaalie, J. G., Das, S., Egan, G. F., Ghosh, S. S., Goscinski, W. J., Grethe, J. S., Kotaleski, J. H., Ho, E. T. W., Kennedy, D. N., Lanyon, L. J., Leergaard, T. B., Mayberg, H. S., Milanesi, L., Mouček, R., Poline, J. B., Roy, P. K., Strother, S. C., Tang, T. B., … Martone, M. E. (2021). A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility. Neuroinformatics, 20(1), 25–36. doi: <a href="https://doi.org/10.1007/s12021-020-09509-0">10.1007/s12021-020-09509-0</a>.</li>
<li>Perez-Lebel, A., Varoquaux, G., Le Morvan, M., Josse, J., & Poline, J.-B. (2022). Benchmarking missing-values approaches for predictive models on health databases. GigaScience, 11. doi: <a href="https://doi.org/10.1093/gigascience/giac013">10.1093/gigascience/giac013</a>.</li>
<li>Kumar, A., Crowley, A., Queder, N., Poline, J., Ghosh, S. S., Kennedy, D., Grethe, J. S., Helmer, K. G., & Keator, D. B. (2022). The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project. F1000Research, 11, 228. doi: <a href="https://doi.org/10.12688/f1000research.108008.2">10.12688/f1000research.108008.2</a>.</li>
<li>DuPre, E., Holdgraf, C., Karakuzu, A., Tetrel, L., Bellec, P., Stikov, N., & Poline, J.-B. (2022). Beyond advertising: New infrastructures for publishing integrated research objects. PLOS Computational Biology, 18(1), e1009651. doi: <a href="https://doi.org/10.1371/journal.pcbi.1009651">10.1371/journal.pcbi.1009651</a>.</li>
<li>Satz, S., Halchenko, Y. O., Ragozzino, R., Lucero, M. M., Phillips, M. L., Swartz, H. A., & Manelis, A. (2022). The Relationship Between Default Mode and Dorsal Attention Networks Is Associated With Depressive Disorder Diagnosis and the Strength of Memory Representations Acquired Prior to the Resting State Scan. Frontiers in Human Neuroscience, 16. doi: <a href="https://doi.org/10.3389/fnhum.2022.749767">10.3389/fnhum.2022.749767</a>.</li>
<li>Cruces, R. R., Royer, J., Herholz, P., Larivière, S., Vos de Wael, R., Paquola, C., Benkarim, O., Park, B., Degré-Pelletier, J., Nelson, M. C., DeKraker, J., Leppert, I. R., Tardif, C., Poline, J.-B., Concha, L., & Bernhardt, B. C. (2022). Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. NeuroImage, 263, 119612. doi: <a href="https://doi.org/10.1016/j.neuroimage.2022.119612">10.1016/j.neuroimage.2022.119612</a>.</li>
<li>Niso, G., Botvinik-Nezer, R., Appelhoff, S., De La Vega, A., Esteban, O., Etzel, J. A., Finc, K., Ganz, M., Gau, R., Halchenko, Y. O., Herholz, P., Karakuzu, A., Keator, D. B., Markiewicz, C. J., Maumet, C., Pernet, C. R., Pestilli, F., Queder, N., Schmitt, T., … Rieger, J. W. (2022). Open and reproducible neuroimaging: From study inception to publication. NeuroImage, 263, 119623. doi: <a href="https://doi.org/10.1016/j.neuroimage.2022.119623">10.1016/j.neuroimage.2022.119623</a>.</li>
<li>Manelis, A., Halchenko, Y. O., Bonar, L., Stiffler, R. S., Satz, S., Miceli, R., Ladouceur, C. D., Bebko, G., Iyengar, S., Swartz, H. A., & Phillips, M. L. (2022). Working memory updating in individuals with bipolar and unipolar depression: fMRI study. Translational Psychiatry, 12(1). doi: <a href="https://doi.org/10.1038/s41398-022-02211-6">10.1038/s41398-022-02211-6</a>.</li>
<li>Modarres, M., Cochran, D., Kennedy, D. N., Schmidt, R., Fitzpatrick, P., & Frazier, J. A. (2021). Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in Autism (Preliminary Results). Neuroinformatics, 20(1), 53–62. doi: <a href="https://doi.org/10.1007/s12021-021-09517-8">10.1007/s12021-021-09517-8</a>.</li>
<li>McNaughton, R., Pieper, C., Sakai, O., Rollins, J. V., Zhang, X., Kennedy, D. N., Frazier, J. A., Douglass, L., Heeren, T., Fry, R. C., O’Shea, T. M., Kuban, K. K., Jara, H., Rollins, J. V., Shah, B., Singh, R., Vaidya, R., Van Marter, L., … Vogt, K. (2022). Quantitative MRI Characterization of the Extremely Preterm Brain at Adolescence: Atypical versus Neurotypical Developmental Pathways. Radiology, 304(2), 419–428. doi: <a href="https://doi.org/10.1148/radiol.210385">10.1148/radiol.210385</a>.</li>
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