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Optimized partial-coverage functional analysis pipeline (OPFAP): a semi-automated pipeline for skull stripping and co-registration of partial-coverage, ultra-high-field functional images

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Abstract

Objective

Ultra-high-field functional MRI (UHF-fMRI) allows for higher spatiotemporal resolution imaging. However, higher-resolution imaging entails coverage limitations. Processing partial-coverage images using standard pipelines leads to sub-optimal results. We aimed to develop a simple, semi-automated pipeline for processing partial-coverage UHF-fMRI data using widely used image processing algorithms.

Materials and methods

We developed automated pipelines for optimized skull stripping and co-registration of partial-coverage UHF functional images, using built-in functions of the Centre for Functional Magnetic Resonance Imaging of the Brain's (FMRIB’s) Software library (FSL) and advanced normalization tools. We incorporated the pipelines into the FSL’s functional analysis pipeline and provide a semi-automated optimized partial-coverage functional analysis pipeline (OPFAP).

Results

Compared to the standard pipeline, the OPFAP yielded images with 15 and 30% greater volume of non-zero voxels after skull stripping the functional and anatomical images, respectively (all p = 0.0004), which reflected the conservation of cortical voxels lost when the standard pipeline was used. The OPFAP yielded the greatest Dice and Jaccard coefficients (87 and 80%, respectively; all p < 0.0001) between the co-registered participant gyri maps and the template gyri maps, demonstrating the goodness of the co-registration results. Furthermore, the greatest volume of group-level activation in the most number of functionally relevant regions was observed when the OPFAP was used. Importantly, group-level activations were not observed when using the standard pipeline.

Conclusion

These results suggest that the OPFAP should be used for processing partial-coverage UHF-fMRI data for detecting high-resolution macroscopic blood oxygenation level-dependent activations.

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Acknowledgements

This work was supported by US Defense Advanced Research Projects Agency (DARPA) Microsystems Technology Office contract N66001-12-1-4045; Office
of Naval Research (ONR) Global N62909-14-1-N020; National Health and Medical Research Council of Australia (NHMRC) project grant APP1062532 and development grant APP1075117; Defence Health Foundation, Australia (booster grant); and Defence Science Institute, Australia, grant. Author P.E.Y. acknowledges the Faculty of Medicine, University of Melbourne for the Leslie Eric Paddle Scholarship in Neurology and the Melbourne Neuroscience Institute for the Strategic Australian Postgraduate Award. Author J.O.C was funded by the University of Melbourne McKenzie Fellowship. Author B.A.M acknowledges the Australian National Imaging Facility (NIF) fellowship. We acknowledge the facilities and the scientific and technical assistance of the NIF at the Melbourne Brain Centre Imaging Unit.

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Authors and Affiliations

  1. Department of Medicine and Radiology, Melbourne Medical School, The University of Melbourne, Kenneth Myer Building, 30 Royal Parade, Parkville, VIC, 3052, Australia

    Peter E. Yoo, Jon O. Cleary, Scott C. Kolbe, Roger J. Ordidge & Bradford A. Moffat

  2. Department of Biomedical and Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia

    Nicholas L. Opie & Sam E. John

  3. Vascular Bionics Laboratory, Department of Medicine, Melbourne Brain Centre, The University of Melbourne, Melbourne, VIC, Australia

    Peter E. Yoo, Nicholas L. Opie, Sam E. John & Thomas J. Oxley

  4. Center for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia

    Nicholas L. Opie, Sam E. John & Thomas J. Oxley

  5. The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia

    Scott C. Kolbe, Sam E. John & Thomas J. Oxley

  6. Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia

    Terence J. O’Brien & Thomas J. Oxley

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  1. Peter E. Yoo
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Contributions

P.E.Y conceived and designed the study, acquired the data, and performed the analyses. P.E.Y, B.A.M, J.O.C, and S.C.K interpreted the data. P.E.Y drafted the manuscript. J.O.C, S.C.K, N.L.O, R.J.O, T.J.O, S.E.J, T.J.O, and B.A.M provided critical revision of the manuscript. T.J.O and B.A.M are joint last authors.

Corresponding author

Correspondence to Peter E. Yoo.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Thomas J. Oxley and Bradford A. Moffat are joint last authors.

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Yoo, P.E., Cleary, J.O., Kolbe, S.C. et al. Optimized partial-coverage functional analysis pipeline (OPFAP): a semi-automated pipeline for skull stripping and co-registration of partial-coverage, ultra-high-field functional images. Magn Reson Mater Phy 31, 621–632 (2018). https://doi.org/10.1007/s10334-018-0690-z

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  • DOI: https://doi.org/10.1007/s10334-018-0690-z

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