{"id":"https://openalex.org/W7148943333","doi":"https://doi.org/10.48550/arxiv.2604.01667","title":"M3D-BFS: a Multi-stage Dynamic Fusion Strategy for Sample-Adaptive Multi-Modal Brain Network Analysis","display_name":"M3D-BFS: a Multi-stage Dynamic Fusion Strategy for Sample-Adaptive Multi-Modal Brain Network Analysis","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7148943333","doi":"https://doi.org/10.48550/arxiv.2604.01667"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01667","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.01667","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132840675","display_name":"Rui Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132875601","display_name":"Xiaotong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaotong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132840906","display_name":"Jiaxing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiaxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132863176","display_name":"Yueying Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yueying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051420650","display_name":"Jiayin Wei","orcid":"https://orcid.org/0009-0009-1586-5123"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Jiayin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132887044","display_name":"Youyong Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Youyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.13199999928474426,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.13199999928474426,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.12110000103712082,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.11020000278949738,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.6334999799728394},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5076000094413757},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49230000376701355},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4763000011444092},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4456000030040741},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4320000112056732},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.40139999985694885},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39809998869895935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7803999781608582},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6334999799728394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5910000205039978},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49230000376701355},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4510999917984009},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.3179999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3149999976158142},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2939999997615814},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C3018390542","wikidata":"https://www.wikidata.org/wiki/Q1073","display_name":"Brain function","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01667","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.01667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01667","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-modal":[0],"fusion":[1,27,83,95,179],"is":[2,159,173],"of":[3,67,121,125,147,169,194],"great":[4],"significance":[5],"in":[6,22,29,46],"neuroscience":[7],"which":[8,32],"integrates":[9],"information":[10],"from":[11],"different":[12,50,99,188],"modalities":[13],"and":[14,39,104],"can":[15,109],"achieve":[16],"better":[17],"performance":[18],"than":[19],"uni-modal":[20,140],"methods":[21,28],"downstream":[23],"tasks.":[24],"Current":[25],"multi-modal":[26,88,105,156,181],"brain":[30,89,182],"networks,":[31],"mainly":[33],"focus":[34],"on":[35,187],"structural":[36],"connectivity":[37,41],"(SC)":[38],"functional":[40],"(FC)":[42],"modalities,":[43],"are":[44],"static":[45,94],"nature.":[47],"They":[48],"feed":[49],"samples":[51],"into":[52,134],"the":[53,152,163,167,174,192],"same":[54],"model":[55],"with":[56],"identical":[57],"computation,":[58],"ignoring":[59],"inherent":[60],"difference":[61],"between":[62],"input":[63,113],"samples.":[64],"This":[65],"lack":[66],"sample":[68,114],"adaptation":[69],"hinders":[70],"model's":[71],"further":[72],"performance.":[73],"To":[74,118,166],"this":[75,172],"end,":[76],"we":[77,97,130],"innovatively":[78],"propose":[79],"a":[80],"multi-stage":[81],"dynamic":[82,178],"strategy":[84],"(M3D-BFS)":[85],"for":[86,102,177,180],"sample-adaptive":[87],"network":[90,183],"analysis.":[91,184],"Unlike":[92],"other":[93],"methods,":[96],"design":[98],"mixture-of-experts":[100],"(MoEs)":[101],"uni-":[103],"representations":[106],"where":[107,123],"modules":[108],"adaptively":[110],"change":[111],"as":[112],"changes":[115],"during":[116],"inference.":[117],"alleviate":[119],"issue":[120],"MoE":[122],"training":[124],"experts":[126,146],"may":[127],"be":[128],"collapsed,":[129],"divide":[131],"our":[132,170],"method":[133],"3":[135],"stages.":[136],"We":[137],"first":[138,175],"train":[139],"encoders":[141],"respectively,":[142],"then":[143],"pretrain":[144],"single":[145],"MoEs":[148],"before":[149],"finally":[150],"finetuning":[151],"whole":[153],"model.":[154],"A":[155],"disentanglement":[157],"loss":[158],"designed":[160],"to":[161],"enhance":[162],"final":[164],"representations.":[165],"best":[168],"knowledge,":[171],"work":[176],"Extensive":[185],"experiments":[186],"real-world":[189],"datasets":[190],"demonstrates":[191],"superiority":[193],"M3D-BFS.":[195]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-04T00:00:00"}