title: Consensus between pipelines in structural brain networks.
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.
authors.affiliation: Centre for Medical Image Computing, University College London, London, United Kingdom; Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Parker
authors.affiliation: Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom.
authors.forename: Fani
authors.initials: F
authors.lastname: Deligianni
authors.affiliation: Centre for Medical Image Computing, University College London, London, United Kingdom.
authors.forename: M Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.affiliation: Centre for Medical Image Computing, University College London, London, United Kingdom.
authors.forename: Pankaj
authors.initials: P
authors.lastname: Daga
authors.affiliation: Centre for Medical Image Computing, University College London, London, United Kingdom.
authors.forename: Marc
authors.initials: M
authors.lastname: Modat
authors.affiliation: Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom; Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Dayan
authors.affiliation: Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom.
authors.forename: Chris A
authors.initials: CA
authors.lastname: Clark
authors.affiliation: Centre for Medical Image Computing, University College London, London, United Kingdom; Dementia Research Centre, University College London, London, United Kingdom.
authors.forename: Sebastien
authors.initials: S
authors.lastname: Ourselin
authors.affiliation: Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Clayden
mesh_terms: D000328:Adult; D000716:Anatomy, Artistic; D001271:Atlases as Topic; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping; D054022:Cerebrum / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*