{"doi":"10.1016/j.neuroimage.2017.06.050","title":"Improved tractography using asymmetric fibre orientation distributions","abstract":null,"journal":"NeuroImage","year":2017,"id":617612,"datarank":1.6952085980510943,"base_score":4.02535169073515,"endowment":4.02535169073515,"self_citation_contribution":0.6038027536102726,"citation_network_contribution":1.0914058444408217,"self_endowment_contribution":0.6038027536102726,"citer_contribution":1.0914058444408217,"corpus_percentile":null,"corpus_rank":null,"citation_count":55,"citer_count":42,"citers_with_citation_signal":36,"citers_with_endowment":36,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":472981,"name":"Michiel Cottaar","orcid":"0000-0003-4679-7724","position":1,"is_corresponding":false},{"id":479504,"name":"Krikor Dikranian","orcid":"0000-0002-0215-3815","position":2,"is_corresponding":false},{"id":276227,"name":"Aurobrata Ghosh","orcid":null,"position":3,"is_corresponding":false},{"id":1416232,"name":"Hui Zhang","orcid":"0000-0002-4442-2788","position":4,"is_corresponding":false},{"id":244799,"name":"Daniel C. Alexander","orcid":"0000-0003-2439-350X","position":5,"is_corresponding":false},{"id":1592880,"name":"Timothy E. Behrens","orcid":null,"position":6,"is_corresponding":false},{"id":12948,"name":"Saad Jbabdi","orcid":null,"position":7,"is_corresponding":false},{"id":12949,"name":"Stamatios N. Sotiropoulos","orcid":"0000-0003-4735-5776","position":8,"is_corresponding":false},{"id":250901,"name":"Matteo Bastiani","orcid":"0000-0002-8436-2919","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Improved tractography using asymmetric fibre orientation distributions","abstract":"Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and -x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.","is_dataset_classified":null,"base_score":4.02535169073515,"endowment":4.02535169073515,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"28669902","pmcid":"PMC6318223","openalex_id":"https://openalex.org/W2726812203","authors":[],"funders":[{"funder_name":"EPSRC","grant_id":"EP/L023067/1","title":"Anatomy-Driven Brain Connectivity Mapping"},{"funder_name":"EPSRC","grant_id":"EP/L022680/1","title":"Anatomy-Driven Brain Connectivity Mapping"},{"funder_name":"European Union's Seventh Framework Programme","grant_id":"FP/2007-2013","title":null},{"funder_name":"ERC","grant_id":"319456","title":"The Developing Human Connectome Project"},{"funder_name":"MRC","grant_id":"MR/L009013/1","title":"Imaging the spatial organization of brain connections"},{"funder_name":"Wellcome Trust","grant_id":"104765/Z/14/Z","title":null},{"funder_name":"Engineering and Physical Sciences Research Council","grant_id":"EP/M020533/1","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"U54 MH091657","title":null},{"funder_name":"Wellcome Trust","grant_id":"203139","title":null},{"funder_name":"Engineering and Physical Sciences Research Council","grant_id":"EP/G007748/1","title":null},{"funder_name":"National Institutes of Health","grant_id":"3U54MH091657-03S1","title":"Mapping the Human Connectome: Structure, Function, and Heritability"}],"total_grants":11,"fwci":4.4502,"citation_percentile":0.95081929,"influential_citations":0,"citation_trend":[{"year":2017,"count":2},{"year":2018,"count":6},{"year":2019,"count":8},{"year":2020,"count":7},{"year":2021,"count":9},{"year":2022,"count":4},{"year":2023,"count":3},{"year":2024,"count":5},{"year":2025,"count":9},{"year":2026,"count":2}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://www.sciencedirect.com/science/article/pii/S1053811917305219/pdf","host_type":"journal"},{"url":"https://www.sciencedirect.com/science/article/pii/S1053811917305219/pdf","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1053811917305219?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1053811917305219?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.neuroimage.2017.06.050","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/28669902","host_type":"repository"},{"url":"http://eprints.nottingham.ac.uk/44034/","host_type":"repository"},{"url":"https://discovery.ucl.ac.uk/id/eprint/1562777/","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6318223","host_type":"repository"},{"url":"https://nottingham-repository.worktribe.com/output/885520","host_type":"repository"},{"url":"https://ora.ox.ac.uk/objects/uuid:6ccde7fe-1c81-4ce3-9f89-44b00bb00d77","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC6318223","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1016/j.neuroimage.2017.06.050","host_type":""},{"url":"https://dx.doi.org/10.1016/j.neuroimage.2017.06.050","host_type":""}],"fields_of_study":["Advanced Neuroimaging Techniques and Applications","Fetal and Pediatric Neurological Disorders","Voice and Speech Disorders","03 medical and health sciences","0302 clinical medicine","Algorithms","Animals","Brain","Brain Mapping","Diffusion Tensor Imaging","Humans","Image Processing, Computer-Assisted","Macaca","Models, Neurological","Nerve Fibers","Pattern Recognition, Automated"],"mesh_terms":["Algorithms","Animals","Brain","Brain Mapping","Humans","Image Processing, Computer-Assisted","Macaca","Models, Neurological","Nerve Fibers","Pattern Recognition, Automated","Diffusion Tensor Imaging"],"keywords":["Tractography","Orientation (vector space)","Computer science","Artificial intelligence","Diffusion MRI","Mathematics","Medicine","Radiology","Geometry","Magnetic resonance imaging","Asymmetry","Connectome","Structural Connectivity","Brain Mapping","Models, Neurological","Brain","Diffusion MRI, Tractography, Structural connectivity, Asymmetry, Connectome","Article","Pattern Recognition, Automated","Sir Peter Mansfield Imaging Centre (SPMIC)","Diffusion Tensor Imaging","Nerve Fibers","Image Processing, Computer-Assisted","Animals","Humans","Macaca","Beacon - Precision Imaging","Algorithms"],"sdg_mappings":[{"sdg_number":4,"sdg_label":"4. 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