paper.bib

@article{karakuzu2022vendor,
  title={Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI},
  author={Karakuzu, Agah and Biswas, Labonny and Cohen-Adad, Julien and Stikov, Nikola},
  journal={Magnetic Resonance in Medicine},
  volume={88},
  number={3},
  pages={1212--1228},
  doi={10.1002/mrm.29292},
  year={2022},
  publisher={Wiley Online Library}
}

@article{karakuzu2022qmri,
  title={qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data},
  author={Karakuzu, Agah and Appelhoff, Stefan and Auer, Tibor and Boudreau, Mathieu and Feingold, Franklin and Khan, Ali R and Lazari, Alberto and Markiewicz, Chris and Mulder, Martijn and Phillips, Christophe and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={517},
  doi={10.1038/s41597-022-01571-4},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@misc{karakuzu_nl,
 title={NeuroLibre : A preprint server for full-fledged reproducible neuroscience},
 url={osf.io/h89js_v1},
 DOI={10.31219/osf.io/h89js},
 publisher={OSF Preprints},
 author={Karakuzu, Agah and DuPre, Elizabeth and Tetrel, Loic and Bermudez, Patrick and Boudreau, Mathieu and Chin, Mary and Poline, Jean-Baptiste and Das, Samir and Bellec, Lune and Stikov, Nikola},
 year={2022},
 month={Apr}
}

@article{harding2023canadian,
  title={The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community},
  author={Harding, Rachel J and Bermudez, Patrick and Bernier, Alexander and Beauvais, Michael and Bellec, Pierre and Hill, Sean and Karakuzu, Ag{\^a}h and Knoppers, Bartha M and Pavlidis, Paul and Poline, Jean-Baptiste and others},
  journal={PLOS Computational Biology},
  volume={19},
  number={7},
  pages={e1011230},
  year={2023},
  doi={10.1371/journal.pcbi.1011230},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{karakuzu2025,
  doi = {10.55458/neurolibre.00041},
  url = {https://doi.org/10.55458/neurolibre.00041},
  year = {2025},
  publisher = {NeuroLibre},
  pages = {41},
  author = {Agah Karakuzu},
  title = {Toward a woven literature: Open-source infrastructure for reproducible publishing},
  journal = {NeuroLibre Reproducible Preprints}
}

@article{dupre2022beyond,
  title={Beyond advertising: New infrastructures for publishing integrated research objects},
  author={DuPre, Elizabeth and Holdgraf, Chris and Karakuzu, Agah and Tetrel, Lo{\"\i}c and Bellec, Pierre and Stikov, Nikola and Poline, Jean-Baptiste},
  journal={PLOS Computational Biology},
  volume={18},
  number={1},
  pages={e1009651},
  year={2022},
  doi={10.1371/journal.pcbi.1009651},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{boudreau2024repeat,
author = {Boudreau, Mathieu and Karakuzu, Agah and Cohen-Adad, Julien and Bozkurt, Ecem and Carr, Madeline and Castellaro, Marco and Concha, Luis and Doneva, Mariya and Dual, Seraina A. and Ensworth, Alex and Foias, Alexandru and Fortier, Véronique and Gabr, Refaat E. and Gilbert, Guillaume and Glide-Hurst, Carri K. and Grech-Sollars, Matthew and Hu, Siyuan and Jalnefjord, Oscar and Jovicich, Jorge and Keskin, Kübra and Koken, Peter and Kolokotronis, Anastasia and Kukran, Simran and Lee, Nam G. and Levesque, Ives R. and Li, Bochao and Ma, Dan and Mädler, Burkhard and Maforo, Nyasha G. and Near, Jamie and Pasaye, Erick and Ramirez-Manzanares, Alonso and Statton, Ben and Stehning, Christian and Tambalo, Stefano and Tian, Ye and Wang, Chenyang and Weiss, Kilian and Zakariaei, Niloufar and Zhang, Shuo and Zhao, Ziwei and Stikov, Nikola and { the ISMRM Reproducible Research Study Group and the ISMRM Quantitative MR Study Group }},
title = {Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge},
journal = {Magnetic Resonance in Medicine},
volume = {92},
number = {3},
pages = {1115-1127},
doi = {10.1002/mrm.30111},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.30111},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.30111},
year = {2024}
}

@article{stikov2023relaxometry,
  title={The relaxometry hype cycle},
  author={Stikov, Nikola and Karakuzu, Ag{\^a}h},
  journal={Frontiers in Physiology},
  volume={14},
  pages={1281147},
  year={2023},
  doi={10.3389/fphys.2023.1281147},
  publisher={Frontiers Media SA}
}


@article{karakuzu2024reproducible,
  title={Reproducible research practices in magnetic resonance neuroimaging: A review informed by advanced language models},
  author={Karakuzu, Agah and Boudreau, Mathieu and Stikov, Nikola},
  journal={Magnetic Resonance in Medical Sciences},
  volume={23},
  number={3},
  pages={252--267},
  year={2024},
  publisher={Japanese Society for Magnetic Resonance in Medicine},
  doi={10.2463/mrms.rev.2023-0174}
}

@article{karakuzu2025rethinking,
  title={Rethinking MRI as a measurement device through modular and portable pipelines},
  author={Karakuzu, Agah and Blostein, Nadia and Caron, Alex Valcourt and Bor{\'e}, Arnaud and Rheault, Fran{\c{c}}ois and Descoteaux, Maxime and Stikov, Nikola},
  journal={Magnetic Resonance Materials in Physics, Biology and Medicine},
  pages={1--17},
  year={2025},
  doi = {10.1007/s10334-025-01245-3},
  publisher={Springer}
}

@article{10.1162/imag_a_00409,
    author = {Boudreau, Mathieu and Karakuzu, Agah and Boré, Arnaud and Pinsard, Basile and Zelenkovski, Kiril and Alonso-Ortiz, Eva and Boyle, Julie and Bellec, Lune and Cohen-Adad, Julien},
    title = {Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers},
    journal = {Imaging Neuroscience},
    volume = {3},
    pages = {imag_a_00409},
    year = {2025},
    month = {01},
    abstract = {Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV \< 1\%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4\% and 6.9\%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.},
    issn = {2837-6056},
    doi = {10.1162/imag_a_00409},
    url = {https://doi.org/10.1162/imag\_a\_00409},
    eprint = {https://direct.mit.edu/imag/article-pdf/doi/10.1162/imag\_a\_00409/2483306/imag\_a\_00409.pdf},
}

@article{niso2022open,
title = {Open and reproducible neuroimaging: From study inception to publication},
journal = {NeuroImage},
volume = {263},
pages = {119623},
year = {2022},
issn = {1053-8119},
doi = {10.1016/j.neuroimage.2022.119623},
url = {https://www.sciencedirect.com/science/article/pii/S1053811922007388},
author = {Guiomar Niso and Rotem Botvinik-Nezer and Stefan Appelhoff and Alejandro {De La Vega} and Oscar Esteban and Joset A. Etzel and Karolina Finc and Melanie Ganz and Rémi Gau and Yaroslav O. Halchenko and Peer Herholz and Agah Karakuzu and David B. Keator and Christopher J. Markiewicz and Camille Maumet and Cyril R. Pernet and Franco Pestilli and Nazek Queder and Tina Schmitt and Weronika Sójka and Adina S. Wagner and Kirstie J. Whitaker and Jochem W. Rieger},
keywords = {Open science, Reproducibility, MRI, PET, MEG, EEG},
abstract = {Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.}
}

@article{layton2017pulseq,
  title={Pulseq: a rapid and hardware-independent pulse sequence prototyping framework},
  author={Layton, Kelvin J and Kroboth, Stefan and Jia, Feng and Littin, Sebastian and Yu, Huijun and Leupold, Jochen and Nielsen, Jon-Fredrik and St{\"o}cker, Tony and Zaitsev, Maxim},
  journal={Magnetic resonance in medicine},
  volume={77},
  number={4},
  pages={1544--1552},
  doi = {10.1002/mrm.26235},
  year={2017},
  publisher={Wiley Online Library}
}

@article{hoinkiss2023ai,
  title={AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language},
  author={Hoinkiss, Daniel Christopher and Huber, J{\"o}rn and Plump, Christina and L{\"u}th, Christoph and Drechsler, Rolf and G{\"u}nther, Matthias},
  journal={Frontiers in Neuroimaging},
  volume={2},
  pages={1090054},
  year={2023},
  doi={10.3389/fnimg.2023.1090054},
  publisher={Frontiers Media SA}
}