IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007549.html
   My bibliography  Save this article

BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

Author

Listed:
  • Manoj Kumar
  • Cameron T Ellis
  • Qihong Lu
  • Hejia Zhang
  • Mihai Capotă
  • Theodore L Willke
  • Peter J Ramadge
  • Nicholas B Turk-Browne
  • Kenneth A Norman

Abstract

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.Author summary: The analysis of brain activity, as measured using functional magnetic resonance imaging (fMRI), has led to significant discoveries about how the brain processes information and how this is affected by disease. However, exhaustive multivariate analyses in space and time, run across a large number of subjects, can be complex and computationally intensive, creating a high barrier for entry into this field. Furthermore, the materials available to learn these methods do not encompass all the methods used, work is often published with no publicly available code, and the analyses are often difficult to run on large datasets without cluster computing. We have created interactive software tutorials that make it easy to understand and execute advanced analyses on fMRI data using the BrainIAK package—an open-source package built in Python. We have released these tutorials freely to the public and have significantly reduced computational roadblocks for users by making it possible to run the tutorials with a web browser and internet connection. We hope that this facilitated access and the usability of the underlying code—a compendium for how to program and optimize the latest fMRI analyses—will accelerate training, reproducibility, and discovery in cognitive neuroscience.

Suggested Citation

  • Manoj Kumar & Cameron T Ellis & Qihong Lu & Hejia Zhang & Mihai Capotă & Theodore L Willke & Peter J Ramadge & Nicholas B Turk-Browne & Kenneth A Norman, 2020. "BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-12, January.
  • Handle: RePEc:plo:pcbi00:1007549
    DOI: 10.1371/journal.pcbi.1007549
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007549
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007549&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1007549?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Erez Simony & Christopher J Honey & Janice Chen & Olga Lositsky & Yaara Yeshurun & Ami Wiesel & Uri Hasson, 2016. "Dynamic reconfiguration of the default mode network during narrative comprehension," Nature Communications, Nature, vol. 7(1), pages 1-13, November.
    2. Hamed Nili & Cai Wingfield & Alexander Walther & Li Su & William Marslen-Wilson & Nikolaus Kriegeskorte, 2014. "A Toolbox for Representational Similarity Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valentina Krenz & Arjen Alink & Tobias Sommer & Benno Roozendaal & Lars Schwabe, 2023. "Time-dependent memory transformation in hippocampus and neocortex is semantic in nature," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Satoko Amemori & Ann M. Graybiel & Ken-ichi Amemori, 2024. "Cingulate microstimulation induces negative decision-making via reduced top-down influence on primate fronto-cingulo-striatal network," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Julia Berezutskaya & Zachary V Freudenburg & Umut Güçlü & Marcel A J van Gerven & Nick F Ramsey, 2020. "Brain-optimized extraction of complex sound features that drive continuous auditory perception," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-34, July.
    4. Hamed Nili & Alexander Walther & Arjen Alink & Nikolaus Kriegeskorte, 2020. "Inferring exemplar discriminability in brain representations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    5. Katherine R. Storrs & Barton L. Anderson & Roland W. Fleming, 2021. "Unsupervised learning predicts human perception and misperception of gloss," Nature Human Behaviour, Nature, vol. 5(10), pages 1402-1417, October.
    6. Agustin Lage-Castellanos & Giancarlo Valente & Elia Formisano & Federico De Martino, 2019. "Methods for computing the maximum performance of computational models of fMRI responses," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-25, March.
    7. Sreejan Kumar & Theodore R. Sumers & Takateru Yamakoshi & Ariel Goldstein & Uri Hasson & Kenneth A. Norman & Thomas L. Griffiths & Robert D. Hawkins & Samuel A. Nastase, 2024. "Shared functional specialization in transformer-based language models and the human brain," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    8. Ming Bo Cai & Nicolas W Schuck & Jonathan W Pillow & Yael Niv, 2019. "Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-30, May.
    9. Cai Wingfield & Li Su & Xunying Liu & Chao Zhang & Phil Woodland & Andrew Thwaites & Elisabeth Fonteneau & William D Marslen-Wilson, 2017. "Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-25, September.
    10. Michael F Bonner & Russell A Epstein, 2018. "Computational mechanisms underlying cortical responses to the affordance properties of visual scenes," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-31, April.
    11. Sebastian P. H. Speer & Laetitia Mwilambwe-Tshilobo & Lily Tsoi & Shannon M. Burns & Emily B. Falk & Diana I. Tamir, 2024. "Hyperscanning shows friends explore and strangers converge in conversation," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    12. Máté Aller & Agoston Mihalik & Uta Noppeney, 2022. "Audiovisual adaptation is expressed in spatial and decisional codes," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    13. Jörn Diedrichsen & Nikolaus Kriegeskorte, 2017. "Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.
    14. Haider Al-Tahan & Yalda Mohsenzadeh, 2021. "Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-19, March.
    15. Benjamin Lahner & Kshitij Dwivedi & Polina Iamshchinina & Monika Graumann & Alex Lascelles & Gemma Roig & Alessandro Thomas Gifford & Bowen Pan & SouYoung Jin & N. Apurva Ratan Murty & Kendrick Kay & , 2024. "Modeling short visual events through the BOLD moments video fMRI dataset and metadata," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    16. Annika Garlichs & Helen Blank, 2024. "Prediction error processing and sharpening of expected information across the face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    17. Kean Ming Tan & Junwei Lu & Tong Zhang & Han Liu, 2021. "Estimating and inferring the maximum degree of stimulus‐locked time‐varying brain connectivity networks," Biometrics, The International Biometric Society, vol. 77(2), pages 379-390, June.
    18. Kristjan Kalm & Dennis Norris, 2021. "Sequence learning recodes cortical representations instead of strengthening initial ones," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-34, May.
    19. Hongmi Lee & Janice Chen, 2022. "Predicting memory from the network structure of naturalistic events," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    20. Alexander J Barnett & Walter Reilly & Halle R Dimsdale-Zucker & Eda Mizrak & Zachariah Reagh & Charan Ranganath, 2021. "Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain," PLOS Biology, Public Library of Science, vol. 19(6), pages 1-34, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007549. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.