{"id":"https://openalex.org/W7213315003","doi":"https://doi.org/10.46889/jnor.2026.6307","title":"Optimizing Brain Neuronal Network Topology with Maximum-Flow and Minimum-Cut Algorithms","display_name":"Optimizing Brain Neuronal Network Topology with Maximum-Flow and Minimum-Cut Algorithms","publication_year":2026,"publication_date":"2026-11-01","ids":{"openalex":"https://openalex.org/W7213315003","doi":"https://doi.org/10.46889/jnor.2026.6307"},"language":"en","primary_location":{"id":"doi:10.46889/jnor.2026.6307","doi":"https://doi.org/10.46889/jnor.2026.6307","is_oa":true,"landing_page_url":"https://doi.org/10.46889/jnor.2026.6307","pdf_url":"https://athenaeumpub.com/wp-content/uploads/Optimizing-Brain-Neuronal-Network-Topology-with-Maximum-Flow-and-Minimum-Cut-Algorithms.pdf","source":{"id":"https://openalex.org/S5407055784","display_name":"Journal of Neuro and Oncology Research","issn_l":"3068-918X","issn":["3068-918X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Neuro and Oncology Research","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://athenaeumpub.com/wp-content/uploads/Optimizing-Brain-Neuronal-Network-Topology-with-Maximum-Flow-and-Minimum-Cut-Algorithms.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139571430","display_name":"Yunhao Xie","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhao Xie","raw_affiliation_strings":["Fudan University, Beijing West District Baiyun Load 10th, Shanghai, 100045, China","South True Co., Limited, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, Beijing West District Baiyun Load 10th, Shanghai, 100045, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"South True Co., Limited, Hong Kong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5151745184","display_name":"Xueyin Ma","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xueyin Ma","raw_affiliation_strings":["South True Co., Limited, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South True Co., Limited, Hong Kong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5151775408","display_name":"Peijia Wu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I82514191","display_name":"Honeywell (United States)","ror":"https://ror.org/02t71h845","country_code":"US","type":"company","lineage":["https://openalex.org/I82514191"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peijia Wu","raw_affiliation_strings":["Honeywell"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Honeywell","institution_ids":["https://openalex.org/I82514191"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5151787302","display_name":"Yanan Dai","orcid":"https://orcid.org/0000-0002-8833-4667","observed_orcids":["https://orcid.org/0000-0002-8833-4667"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanan Dai","raw_affiliation_strings":["Fudan University, 180th Fenglin Road, Shanghai, Shanghai 100045, China"],"raw_orcid":"https://orcid.org/0000-0002-8833-4667","affiliations":[{"raw_affiliation_string":"Fudan University, 180th Fenglin Road, Shanghai, Shanghai 100045, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5151787302"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6583605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"3","first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.8833000063896179,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.8833000063896179,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.10480000078678131,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.008700000122189522,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/connectomics","display_name":"connectomics","score":0.7760000228881836},{"id":"https://openalex.org/keywords/maximum-flow","display_name":"maximum flow","score":0.6869999766349792},{"id":"https://openalex.org/keywords/minimum-cut","display_name":"minimum cut","score":0.9940000176429749},{"id":"https://openalex.org/keywords/network-topology","display_name":"network topology","score":0.7540000081062317},{"id":"https://openalex.org/keywords/bottlenecks","display_name":"bottlenecks","score":0.6449999809265137},{"id":"https://openalex.org/keywords/network-robustness","display_name":"network robustness","score":0.9150000214576721},{"id":"https://openalex.org/keywords/network-efficiency","display_name":"network efficiency","score":0.6309999823570251},{"id":"https://openalex.org/keywords/network-hubs","display_name":"network hubs","score":0.5},{"id":"https://openalex.org/keywords/diffusion-tractography","display_name":"diffusion tractography","score":0.7329999804496765},{"id":"https://openalex.org/keywords/functional-connectivity","display_name":"functional connectivity","score":0.6809999942779541},{"id":"https://openalex.org/keywords/neuromodulation","display_name":"neuromodulation","score":0.7900000214576721},{"id":"https://openalex.org/keywords/multi-scale-network","display_name":"multi-scale network","score":0.46700000762939453},{"id":"https://openalex.org/keywords/betweenness-centrality","display_name":"betweenness centrality","score":0.7699999809265137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7178000211715698},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.6904000043869019},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6722000241279602},{"id":"https://openalex.org/C119701452","wikidata":"https://www.wikidata.org/wiki/Q5165881","display_name":"Control reconfiguration","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4104999899864197},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C3018011982","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Functional connectivity","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C45715564","wikidata":"https://www.wikidata.org/wiki/Q1292103","display_name":"Connectome","level":3,"score":0.350600004196167},{"id":"https://openalex.org/C118403218","wikidata":"https://www.wikidata.org/wiki/Q43283","display_name":"Biological neural network","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3337000012397766},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.32350000739097595},{"id":"https://openalex.org/C2779136372","wikidata":"https://www.wikidata.org/wiki/Q10283002","display_name":"Information flow","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2779097318","wikidata":"https://www.wikidata.org/wiki/Q2993446","display_name":"Connectomics","level":4,"score":0.29980000853538513},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C2780196419","wikidata":"https://www.wikidata.org/wiki/Q1762173","display_name":"Nerve net","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C74254510","wikidata":"https://www.wikidata.org/wiki/Q7240508","display_name":"Premovement neuronal activity","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C87868495","wikidata":"https://www.wikidata.org/wiki/Q750843","display_name":"Information processing","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.46889/jnor.2026.6307","doi":"https://doi.org/10.46889/jnor.2026.6307","is_oa":true,"landing_page_url":"https://doi.org/10.46889/jnor.2026.6307","pdf_url":"https://athenaeumpub.com/wp-content/uploads/Optimizing-Brain-Neuronal-Network-Topology-with-Maximum-Flow-and-Minimum-Cut-Algorithms.pdf","source":{"id":"https://openalex.org/S5407055784","display_name":"Journal of Neuro and Oncology Research","issn_l":"3068-918X","issn":["3068-918X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Neuro and Oncology Research","raw_type":"journal-article"},{"id":"doi:10.2139/ssrn.7457600","doi":"https://doi.org/10.2139/ssrn.7457600","is_oa":true,"landing_page_url":"https://doi.org/10.2139/ssrn.7457600","pdf_url":null,"source":{"id":"https://openalex.org/S4210172589","display_name":"SSRN Electronic Journal","issn_l":"1556-5068","issn":["1556-5068"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1318003438","host_organization_name":"RELX Group (Netherlands)","host_organization_lineage":["https://openalex.org/I1318003438"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.46889/jnor.2026.6307","doi":"https://doi.org/10.46889/jnor.2026.6307","is_oa":true,"landing_page_url":"https://doi.org/10.46889/jnor.2026.6307","pdf_url":"https://athenaeumpub.com/wp-content/uploads/Optimizing-Brain-Neuronal-Network-Topology-with-Maximum-Flow-and-Minimum-Cut-Algorithms.pdf","source":{"id":"https://openalex.org/S5407055784","display_name":"Journal of Neuro and Oncology Research","issn_l":"3068-918X","issn":["3068-918X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Neuro and Oncology Research","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213315003.pdf","grobid_xml":"https://content.openalex.org/works/W7213315003.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1636205509","https://openalex.org/W1834060250","https://openalex.org/W1921607560","https://openalex.org/W2020519533","https://openalex.org/W2049130783","https://openalex.org/W2061042699","https://openalex.org/W2093353037","https://openalex.org/W2103912979","https://openalex.org/W2110423157","https://openalex.org/W2110893883","https://openalex.org/W2114899912","https://openalex.org/W2118383750","https://openalex.org/W2138793275","https://openalex.org/W2150318080","https://openalex.org/W2157151515","https://openalex.org/W2160542921","https://openalex.org/W2274452385","https://openalex.org/W2307937785","https://openalex.org/W2504084072","https://openalex.org/W2582996697","https://openalex.org/W2604323123","https://openalex.org/W2606136976","https://openalex.org/W2610332124","https://openalex.org/W2789905221","https://openalex.org/W2792315573","https://openalex.org/W2803664483","https://openalex.org/W2809684781","https://openalex.org/W2903807505","https://openalex.org/W2903808414","https://openalex.org/W2912581782","https://openalex.org/W2949857658","https://openalex.org/W2951635356","https://openalex.org/W2952216243","https://openalex.org/W3000313970","https://openalex.org/W3023042104","https://openalex.org/W3045710579","https://openalex.org/W3090789943","https://openalex.org/W3102100346","https://openalex.org/W3154409837","https://openalex.org/W3176923149","https://openalex.org/W3208687975","https://openalex.org/W4205798776","https://openalex.org/W4283017342","https://openalex.org/W4311060730","https://openalex.org/W4322733206","https://openalex.org/W4367049415","https://openalex.org/W4376226279","https://openalex.org/W4387568575","https://openalex.org/W4389132751","https://openalex.org/W4396780205"],"related_works":["https://openalex.org/W2775381142","https://openalex.org/W2265973855","https://openalex.org/W6977099039","https://openalex.org/W647071796","https://openalex.org/W4403661643","https://openalex.org/W4255674892","https://openalex.org/W7100619062","https://openalex.org/W7098960497","https://openalex.org/W4405032424","https://openalex.org/W4415310965"],"abstract_inverted_index":{"Brain":[0],"neuronal":[1],"networks":[2],"combine":[3],"dense":[4,178],"local":[5],"processing,":[6],"sparse":[7],"long-range":[8],"projections,":[9],"heterogeneous":[10],"synaptic":[11],"weights,":[12],"and":[13,33,49,69,89,130,158,161,180,189,203,214],"constantly":[14],"changing":[15],"activity,":[16],"making":[17],"topology":[18],"a":[19,26,113,118,124],"mechanistic":[20],"determinant":[21],"of":[22,103,186],"communication":[23],"rather":[24,122,218],"than":[25,123,219],"static":[27],"description.":[28],"Against":[29],"this":[30],"backdrop,":[31],"maximumflow":[32],"minimum-cut":[34],"algorithms":[35],"offer":[36],"complementary":[37,184],"questions:":[38],"how":[39],"much":[40],"information":[41],"or":[42,53,105,170],"activity":[43],"can":[44,64,108,165],"move":[45],"between":[46],"selected":[47],"populations,":[48],"which":[50],"lowest-capacity":[51],"edge":[52],"node":[54],"sets":[55],"constrain":[56],"that":[57],"transfer?":[58],"Applied":[59],"to":[60,136,156],"connectomic":[61],"graphs,":[62],"they":[63],"localize":[65],"bottlenecks,":[66],"critical":[67],"connectors,":[68],"vulnerable":[70,155],"partitions,":[71],"while":[72],"extending":[73],"centrality-based":[74],"analyses":[75],"beyond":[76],"simple":[77],"hub":[78],"identification.":[79],"As":[80],"evidence":[81],"accumulated,":[82],"their":[83],"value":[84],"became":[85],"clearest":[86],"when":[87],"efficiency":[88],"robustness":[90],"were":[91],"considered":[92],"together.":[93],"High-capacity":[94],"routes":[95,188],"may":[96,116],"support":[97],"functional":[98],"integration,":[99],"yet":[100],"focal":[101],"failure":[102],"hubs":[104],"intermodular":[106],"connectors":[107],"cause":[109],"disproportionate":[110],"disruption;":[111],"conversely,":[112],"minimum":[114],"cut":[115],"represent":[117],"transient,":[119],"state-dependent":[120],"constraint":[121],"permanently":[125],"indispensable":[126],"structure.":[127],"Neuromodulatory":[128],"reconfiguration":[129],"task":[131],"dependence":[132],"therefore":[133],"require":[134],"capacities":[135,202],"be":[137],"interpreted":[138],"dynamically.":[139],"Biological":[140],"validation":[141],"further":[142],"complicates":[143],"the":[144],"picture:":[145],"diffusion":[146],"tractography":[147],"varies":[148],"with":[149,199],"processing":[150],"choices,":[151],"electron-microscopy":[152],"reconstructions":[153],"remain":[154],"segmentation":[157],"coverage":[159],"errors,":[160],"aggregation":[162],"across":[163],"scales":[164],"either":[166],"obscure":[167],"cellular":[168],"bottlenecks":[169],"exaggerate":[171],"regional":[172],"ones.":[173],"Functional":[174],"connectivity,":[175],"pathology-linked":[176],"propagation,":[177],"recordings,":[179],"targeted":[181],"reconstruction":[182],"provide":[183],"tests":[185],"predicted":[187],"cuts.":[190],"Future":[191],"progress":[192],"depends":[193],"on":[194],"multiscale,":[195],"temporally":[196],"resolved":[197],"graphs":[198],"biologically":[200],"justified":[201],"uncertainty-aware":[204],"optimization,":[205],"using":[206],"flow":[207],"measures":[208],"alongside":[209],"modularity,":[210],"rich-club":[211],"structure,":[212],"betweenness,":[213],"other":[215],"network":[216],"descriptors":[217],"as":[220],"universal":[221],"proxies":[222],"for":[223],"neural":[224],"function.":[225]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-09-16T00:00:00"}