The Case for Optimized Edge-Centric Tractography at Scale
The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connect...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2022.752471/full |
| _version_ | 1828796171816009728 |
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| author | Joseph Y. Moon Pratik Mukherjee Ravi K. Madduri Amy J. Markowitz Lanya T. Cai Eva M. Palacios Geoffrey T. Manley Peer-Timo Bremer |
| author_facet | Joseph Y. Moon Pratik Mukherjee Ravi K. Madduri Amy J. Markowitz Lanya T. Cai Eva M. Palacios Geoffrey T. Manley Peer-Timo Bremer |
| author_sort | Joseph Y. Moon |
| collection | DOAJ |
| description | The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population. |
| first_indexed | 2024-12-12T04:18:02Z |
| format | Article |
| id | doaj.art-42c14f1ab570427086f5ff977a995ab4 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-12T04:18:02Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-42c14f1ab570427086f5ff977a995ab42022-12-22T00:38:24ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962022-05-011610.3389/fninf.2022.752471752471The Case for Optimized Edge-Centric Tractography at ScaleJoseph Y. Moon0Pratik Mukherjee1Ravi K. Madduri2Amy J. Markowitz3Lanya T. Cai4Eva M. Palacios5Geoffrey T. Manley6Peer-Timo Bremer7Lawrence Livermore National Laboratory, Livermore, CA, United StatesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United StatesArgonne National Laboratory, Lemont, IL, United StatesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United StatesLawrence Livermore National Laboratory, Livermore, CA, United StatesThe anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.https://www.frontiersin.org/articles/10.3389/fninf.2022.752471/fullconnectomesidentifiabilitytractographydiffusion MRIoptimizationEDI |
| spellingShingle | Joseph Y. Moon Pratik Mukherjee Ravi K. Madduri Amy J. Markowitz Lanya T. Cai Eva M. Palacios Geoffrey T. Manley Peer-Timo Bremer The Case for Optimized Edge-Centric Tractography at Scale Frontiers in Neuroinformatics connectomes identifiability tractography diffusion MRI optimization EDI |
| title | The Case for Optimized Edge-Centric Tractography at Scale |
| title_full | The Case for Optimized Edge-Centric Tractography at Scale |
| title_fullStr | The Case for Optimized Edge-Centric Tractography at Scale |
| title_full_unstemmed | The Case for Optimized Edge-Centric Tractography at Scale |
| title_short | The Case for Optimized Edge-Centric Tractography at Scale |
| title_sort | case for optimized edge centric tractography at scale |
| topic | connectomes identifiability tractography diffusion MRI optimization EDI |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2022.752471/full |
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