Citation (Click to expand/minimize)
Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gaël Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” Frontiers in Neuroinformatics. Frontiers, 14.

Avants, Brian B, Nick Tustison, Gang Song, and others. 2009. “Advanced Normalization Tools (Ants).” Insight J 2 (365): 1–35.

Brett, Matthew, Christopher J. Markiewicz, Michael Hanke, Marc-Alexandre Côté, Ben Cipollini, Paul McCarthy, Dorota Jarecka, et al. 2022. Nipy/Nibabel: (version 4.0.0). Zenodo. https://doi.org/10.5281/zenodo.591597.

Ciric, Rastko, Adon F. G. Rosen, Guray Erus, Matthew Cieslak, Azeez Adebimpe, Philip A. Cook, Danielle S. Bassett, Christos Davatzikos, Daniel H. Wolf, and Theodore D. Satterthwaite. 2018. “Mitigating Head Motion Artifact in Functional Connectivity MRI.” Nature Protocols 13 (12): 2801–26. https://doi.org/10.1038/s41596-018-0065-y.

Ciric, Rastko, William H Thompson, Romy Lorenz, Mathias Goncalves, Eilidh MacNicol, Christopher J Markiewicz, Yaroslav O Halchenko, et al. 2022. “TemplateFlow: FAIR-Sharing of Multi-Scale, Multi-Species Brain Models.” bioRxiv. Cold Spring Harbor Laboratory, 2021–02. https://doi.org/10.1101/2021.02.10.430678.

Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. “Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage 154 (July): 174–87. https://doi.org/10.1016/j.neuroimage.2017.03.020.

Cox, Robert W. 1996. “AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages.” Computers and Biomedical Research 29 (3). Elsevier: 162–73.

Cox, Robert W, and James S Hyde. 1997. “Software Tools for Analysis and Visualization of fMRI Data.” NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance in Vivo 10 (4-5). Wiley Online Library: 171–78.

Esteban, Oscar, Rastko Ciric, Karolina Finc, Ross W Blair, Christopher J Markiewicz, Craig A Moodie, James D Kent, et al. 2020. “Analysis of Task-Based Functional Mri Data Preprocessed with fMRIPrep.” Nature Protocols 15 (7). Nature Publishing Group: 2186–2202. https://doi.org/10.1038/s41596-020-0327-3.

Esteban, Oscar, Christopher J Markiewicz, Ross W Blair, Craig A Moodie, A Ilkay Isik, Asier Erramuzpe, James D Kent, et al. 2019. “FMRIPrep: A Robust Preprocessing Pipeline for Functional Mri.” Nature Methods 16 (1). Nature Publishing Group: 111–16. https://doi.org/10.1038/s41592-018-0235-4.

Glasser, Matthew F., Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, et al. 2016. “A Multi-Modal Parcellation of Human Cerebral Cortex.” Nature 536 (7615): 171–78. https://doi.org/10.1038/nature18933.

Glasser, Matthew F., Stamatios N. Sotiropoulos, J. Anthony Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. Andersson, Junqian Xu, et al. 2013. “The Minimal Preprocessing Pipelines for the Human Connectome Project.” NeuroImage 80 (October): 105–24. https://doi.org/10.1016/j.neuroimage.2013.04.127.

Gordon, Evan M., Timothy O. Laumann, Babatunde Adeyemo, Jeremy F. Huckins, William M. Kelley, and Steven E. Petersen. 2016. “Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations.” Cerebral Cortex 26 (1): 288–303. https://doi.org/10.1093/cercor/bhu239.

Gorgolewski, Krzysztof, Christopher D. Burns, Cindee Madison, Dav Clark, Yaroslav O. Halchenko, Michael L. Waskom, and Satrajit S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5. https://doi.org/10.3389/fninf.2011.00013.

Harris, Charles R., Jarrod K. Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, et al. 2020. “Array Programming with NumPy.” Nature 585 (7825): 357–62. https://doi.org/10.1038/s41586-020-2649-2.

Hermosillo, Robert JM, Lucille A Moore, Eric Fezcko, Ally Dworetsky, Adam Pines, Gregory Conan, Michael A Mooney, et al. 2022. “A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography.” bioRxiv. Cold Spring Harbor Laboratory, 2022–01. https://doi.org/10.1101/2022.01.12.475422.

Hunter, John D. 2007. “Matplotlib: A 2D Graphics Environment.” Computing in Science & Engineering 9 (03). IEEE Computer Society: 90–95.

Jiang, Lili, and Xi-Nian Zuo. 2016. “Regional Homogeneity: A Multimodal, Multiscale Neuroimaging Marker of the Human Connectome.” The Neuroscientist 22 (5). Sage Publications Sage CA: Los Angeles, CA: 486–505.

King, Maedbh, Carlos R Hernandez-Castillo, Russell A Poldrack, Richard B Ivry, and Jörn Diedrichsen. 2019. “Functional Boundaries in the Human Cerebellum Revealed by a Multi-Domain Task Battery.” Nature Neuroscience 22 (8). Nature Publishing Group US New York: 1371–8. https://doi.org/10.1038/s41593-019-0436-x.

Marcus, Daniel S, John Harwell, Timothy Olsen, Michael Hodge, Matthew F Glasser, Fred Prior, Mark Jenkinson, Timothy Laumann, Sandra W Curtiss, and David C Van Essen. 2011. “Informatics and Data Mining Tools and Strategies for the Human Connectome Project.” Frontiers in Neuroinformatics 5. Frontiers Research Foundation: 4.

Mehta, Kahini, Taylor Salo, Thomas J Madison, Azeez Adebimpe, Danielle S Bassett, Max Bertolero, Matthew Cieslak, et al. 2024. "XCP-d: A Robust Pipeline for the Post-Processing of fMRI Data." Imaging Neuroscience 2. MIT Press: 1–26. https://doi.org/10.1162/imag_a_00257.

Najdenovska, Elena, Yasser Alemán-Gómez, Giovanni Battistella, Maxime Descoteaux, Patric Hagmann, Sebastien Jacquemont, Philippe Maeder, Jean-Philippe Thiran, Eleonora Fornari, and Meritxell Bach Cuadra. 2018. “In-Vivo Probabilistic Atlas of Human Thalamic Nuclei Based on Diffusion-Weighted Magnetic Resonance Imaging.” Scientific Data 5 (1). Nature Publishing Group: 1–11. https://doi.org/10.1038/sdata.2018.270.

Pauli, Wolfgang M, Amanda N Nili, and J Michael Tyszka. 2018. “A High-Resolution Probabilistic in Vivo Atlas of Human Subcortical Brain Nuclei.” Scientific Data 5 (1). Nature Publishing Group: 1–13. https://doi.org/10.1038/sdata.2018.63.

Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data.” NeuroImage 64 (January): 240–56. https://doi.org/10.1016/j.neuroimage.2012.08.052.

Schaefer, Alexander, Ru Kong, Evan M. Gordon, Timothy O. Laumann, Xi-Nian Zuo, Avram J. Holmes, Simon B. Eickhoff, and B. T. Thomas Yeo. 2018. “Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.” Cerebral Cortex (New York, N.Y.: 1991) 28 (9): 3095–3114. https://doi.org/10.1093/cercor/bhx179.

Taylor, Paul A, and Ziad S Saad. 2013. “FATCAT:(An Efficient) Functional and Tractographic Connectivity Analysis Toolbox.” Brain Connectivity 3 (5). Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA: 523–35.

Tian, Ye, Daniel S Margulies, Michael Breakspear, and Andrew Zalesky. 2020. “Topographic Organization of the Human Subcortex Unveiled with Functional Connectivity Gradients.” Nature Neuroscience 23 (11). Nature Publishing Group: 1421–32. https://doi.org/10.1038/s41593-020-00711-6.

Virtanen, Pauli, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, et al. 2020. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.” Nature Methods 17 (3): 261–72. https://doi.org/10.1038/s41592-019-0686-2.

Yarkoni, Tal, Christopher J Markiewicz, Alejandro de la Vega, Krzysztof J Gorgolewski, Taylor Salo, Yaroslav O Halchenko, Quinten McNamara, et al. 2019. “PyBIDS: Python Tools for Bids Datasets.” Journal of Open Source Software 4 (40). NIH Public Access.

Zhang, Bo, Fei Wang, Hao-Ming Dong, Xiao-Wei Jiang, Sheng-Nan Wei, Miao Chang, Zhi-Yang Yin, et al. 2019. “Surface-Based Regional Homogeneity in Bipolar Disorder: A Resting-State fMRI Study.” Psychiatry Research 278 (August): 199–204. https://doi.org/10.1016/j.psychres.2019.05.045.

Zou, Qi-Hong, Chao-Zhe Zhu, Yihong Yang, Xi-Nian Zuo, Xiang-Yu Long, Qing-Jiu Cao, Yu-Feng Wang, and Yu-Feng Zang. 2008. “An Improved Approach to Detection of Amplitude of Low-Frequency Fluctuation (ALFF) for Resting-State fMRI: Fractional ALFF.” Journal of Neuroscience Methods 172 (1): 137–41. https://doi.org/10.1016/j.jneumeth.2008.04.012.


Functional data were processed using XCP-D.

Preprocessed BOLD data (fMRIPrep outputs) underwent nuisance regression following the removal of non-steady-state volumes. The data were then despiked, band-pass filtered, and smoothed. For each atlas (e.g., Glasser, Gordon, and multiple resolutions of the Schaefer parcellation), parcellated time series were extracted from the residual BOLD signal. From these, pairwise functional connectivity (Pearson’s correlation) was computed between parcels, along with regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF).

Quality Control

(coming soon/need finalize)

Functional data quality assurance included measures of in-scanner motion (framewise displacement, FD) and atlas-dependent coverage (the percentage of each parcel containing valid data). fMRI runs were excluded if their median FD exceeded Q3+3*IQR across the sample. In addition, parcels with less than 50% coverage were excluded from analyses, and runs with more than XXX excluded parcels were excluded. We provide FD, coverage, and many other quality metrics for all scans.