Scipediacontent: Scipediacontent moved page Draft Content 329147724 to 238,295iu

2021-03-22T21:49:50Z

Scipediacontent moved page Draft Content 329147724 to 238,295iu

Scipediacontent: Created page with " == Abstract == Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological o..."

2021-03-22T21:48:47Z

Created page with " == Abstract == Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological o..."

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== Abstract ==

Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_329147724-beopen1254-6152-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://eprints.gla.ac.uk/208068/1/208068.pdf http://eprints.gla.ac.uk/208068/1/208068.pdf] under the license http://creativecommons.org/licenses/by/4.0

* [http://europepmc.org/articles/PMC4214749 http://europepmc.org/articles/PMC4214749] under the license http://creativecommons.org/licenses/by/4.0

* [https://dx.doi.org/10.1371/journal.pone.0111262 https://dx.doi.org/10.1371/journal.pone.0111262]

* [https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0111262&type=printable https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0111262&type=printable]

* [http://dx.plos.org/10.1371/journal.pone.0111262 http://dx.plos.org/10.1371/journal.pone.0111262],
: [http://dx.doi.org/10.1371/journal.pone.0111262 http://dx.doi.org/10.1371/journal.pone.0111262] under the license cc-by

* [http://europepmc.org/articles/PMC4214749 http://europepmc.org/articles/PMC4214749],
: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214749 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214749],
: [https://ui.adsabs.harvard.edu/abs/2014PLoSO...9k1262P/abstract https://ui.adsabs.harvard.edu/abs/2014PLoSO...9k1262P/abstract],
: [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111262 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111262],
: [http://eprints.gla.ac.uk/208068 http://eprints.gla.ac.uk/208068],
: [https://paperity.org/p/60453848/consensus-between-pipelines-in-structural-brain-networks https://paperity.org/p/60453848/consensus-between-pipelines-in-structural-brain-networks],
: [http://discovery.ucl.ac.uk/1469321 http://discovery.ucl.ac.uk/1469321],
: [http://discovery.ucl.ac.uk/1469321/5/Clayden.1469321_journal.pone.0111262.pdf http://discovery.ucl.ac.uk/1469321/5/Clayden.1469321_journal.pone.0111262.pdf],
: [https://core.ac.uk/display/29416145 https://core.ac.uk/display/29416145],
: [https://academic.microsoft.com/#/detail/1975363200 https://academic.microsoft.com/#/detail/1975363200] under the license http://creativecommons.org/licenses/by/4.0/

* [http://europepmc.org/articles/PMC4214749?pdf=render http://europepmc.org/articles/PMC4214749?pdf=render],
: [https://doaj.org/toc/1932-6203 https://doaj.org/toc/1932-6203]

* [https://discovery.ucl.ac.uk/id/eprint/1469321 https://discovery.ucl.ac.uk/id/eprint/1469321],
: [https://discovery.ucl.ac.uk/id/eprint/1469321/5/Clayden.1469321_journal.pone.0111262.pdf https://discovery.ucl.ac.uk/id/eprint/1469321/5/Clayden.1469321_journal.pone.0111262.pdf]

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Scipediacontent: Scipediacontent moved page Draft Content 329147724 to 238,295iu

Scipediacontent: Created page with " == Abstract == Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological o..."