{"id":"https://openalex.org/W7141002721","doi":"https://doi.org/10.48550/arxiv.2603.24603","title":"Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders","display_name":"Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7141002721","doi":"https://doi.org/10.48550/arxiv.2603.24603"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24603","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24603","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.24603","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130735910","display_name":"Jinlong Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jinlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124456782","display_name":"Jiatong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jiatong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130760868","display_name":"Zijian Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Zijian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9853000044822693,"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":0.9853000044822693,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.006099999882280827,"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/T10581","display_name":"Neural dynamics and brain function","score":0.0008999999845400453,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.6766999959945679},{"id":"https://openalex.org/keywords/amplitude","display_name":"Amplitude","score":0.6615999937057495},{"id":"https://openalex.org/keywords/dynamic-functional-connectivity","display_name":"Dynamic functional connectivity","score":0.6345999836921692},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6053000092506409},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.5953999757766724},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5467000007629395},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.5110999941825867},{"id":"https://openalex.org/keywords/phase-synchronization","display_name":"Phase synchronization","score":0.42719998955726624},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42660000920295715},{"id":"https://openalex.org/keywords/synchronization","display_name":"Synchronization (alternating current)","score":0.4253999888896942}],"concepts":[{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.6766999959945679},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.6615999937057495},{"id":"https://openalex.org/C2781312939","wikidata":"https://www.wikidata.org/wiki/Q17088721","display_name":"Dynamic functional connectivity","level":3,"score":0.6345999836921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6205999851226807},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6053000092506409},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5953999757766724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5590000152587891},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C194027367","wikidata":"https://www.wikidata.org/wiki/Q4420475","display_name":"Phase synchronization","level":3,"score":0.42719998955726624},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.4253999888896942},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C3018011982","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Functional connectivity","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35010001063346863},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3057999908924103},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.29980000853538513},{"id":"https://openalex.org/C120843803","wikidata":"https://www.wikidata.org/wiki/Q4955807","display_name":"Brain activity and meditation","level":3,"score":0.2994999885559082},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C2778538070","wikidata":"https://www.wikidata.org/wiki/Q1436063","display_name":"Autism spectrum disorder","level":3,"score":0.2842999994754791},{"id":"https://openalex.org/C2777670902","wikidata":"https://www.wikidata.org/wiki/Q492038","display_name":"Human brain","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.2759999930858612},{"id":"https://openalex.org/C2987132901","wikidata":"https://www.wikidata.org/wiki/Q193147","display_name":"Phase coherence","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.25209999084472656},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24603","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24603","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.24603","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24603","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dynamic":[0],"functional":[1,7],"connectivity":[2],"(dFC)":[3],"derived":[4,90],"from":[5,44,91,162],"resting-state":[6],"magnetic":[8],"resonance":[9],"imaging":[10],"(fMRI)":[11],"has":[12],"been":[13],"extensively":[14],"utilized":[15],"in":[16,115],"brain":[17,47,72,169],"science":[18],"research.":[19],"The":[20,134],"sliding":[21],"window":[22],"correlation":[23,36],"(SWC)":[24],"method":[25],"is":[26],"a":[27,77],"widely":[28],"used":[29],"approach":[30,56],"for":[31],"constructing":[32],"dFC":[33,88,160],"by":[34],"computing":[35],"coefficients":[37],"between":[38],"amplitude":[39,60,100],"time":[40],"series":[41],"of":[42,46,64,71,113,159],"signals":[43,66],"pairs":[45],"regions.":[48],"In":[49],"this":[50],"study,":[51],"we":[52,75,145],"propose":[53],"an":[54],"integrated":[55],"that":[57,137,156],"incorporates":[58],"both":[59,157],"and":[61,93,120,131,164],"phase":[62,94,105],"information":[63],"fMRI":[65],"to":[67,167],"improve":[68],"the":[69,111,151],"detection":[70],"disorders.":[73,170],"Specifically,":[74],"introduce":[76],"multi-scale":[78],"fusion":[79],"learning":[80],"framework,":[81,153],"namely":[82],"MSFL,":[83],"which":[84,154],"leverages":[85],"two":[86,125],"complementary":[87],"features":[89,161],"SWC":[92,98,163],"synchronization":[95],"(PS).":[96],"Here,":[97],"captures":[99],"correlations,":[101],"while":[102],"PS":[103,165],"measures":[104],"coherence":[106],"within":[107],"dFC.":[108],"We":[109],"evaluated":[110],"efficacy":[112],"MSFL":[114,138],"classifying":[116],"autism":[117],"spectrum":[118],"disorder":[119,123],"major":[121],"depressive":[122],"using":[124,150],"publicly":[126],"available":[127],"datasets:":[128],"ABIDE":[129],"I":[130],"REST-meta-MDD,":[132],"respectively.":[133],"results":[135],"indicate":[136],"significantly":[139],"outperforms":[140],"existing":[141],"comparative":[142],"models.":[143],"Moreover,":[144],"performed":[146],"model":[147],"explanation":[148],"analysis":[149],"SHAP":[152],"showed":[155],"types":[158],"contribute":[166],"detecting":[168]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-28T00:00:00"}