{"id":"https://openalex.org/W7128502894","doi":"https://doi.org/10.48550/arxiv.2602.08262","title":"Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification","display_name":"Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7128502894","doi":"https://doi.org/10.48550/arxiv.2602.08262"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.08262","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08262","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":null,"license_id":null,"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.2602.08262","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024712502","display_name":"Guoqi Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yu, Guoqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102223980","display_name":"Dan Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Xiaowei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125588655","display_name":"Angelica I. Aviles-Rivero","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aviles-Rivero, Angelica I.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125507755","display_name":"Anqi Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Anqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100602070","display_name":"Shujun Wang","orcid":"https://orcid.org/0000-0001-8161-1695"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shujun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024712502"],"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":0.972100019454956,"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":0.972100019454956,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.009700000286102295,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0017000000225380063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.691100001335144},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.6890000104904175},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48350000381469727},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4560999870300293},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4171999990940094},{"id":"https://openalex.org/keywords/brain-activity-and-meditation","display_name":"Brain activity and meditation","score":0.4034999907016754},{"id":"https://openalex.org/keywords/magnetoencephalography","display_name":"Magnetoencephalography","score":0.35040000081062317},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3086000084877014}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.691100001335144},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.6890000104904175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6823999881744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6230000257492065},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48350000381469727},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4171999990940094},{"id":"https://openalex.org/C120843803","wikidata":"https://www.wikidata.org/wiki/Q4955807","display_name":"Brain activity and meditation","level":3,"score":0.4034999907016754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C556910895","wikidata":"https://www.wikidata.org/wiki/Q384188","display_name":"Magnetoencephalography","level":3,"score":0.35040000081062317},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C2993858690","wikidata":"https://www.wikidata.org/wiki/Q744385","display_name":"Temporal cortex","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.08262","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08262","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.08262","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08262","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":null,"license_id":null,"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":{"Functional":[0],"magnetic":[1],"resonance":[2],"imaging":[3],"(fMRI)":[4],"enables":[5],"non-invasive":[6],"brain":[7,178],"disorder":[8],"classification":[9,150],"by":[10],"capturing":[11,40],"blood-oxygen-level-dependent":[12],"(BOLD)":[13],"signals.":[14],"However,":[15],"most":[16],"existing":[17],"methods":[18],"rely":[19],"on":[20,62,98],"functional":[21],"connectivity":[22],"(FC)":[23],"via":[24],"Pearson":[25],"correlation,":[26],"which":[27],"reduces":[28],"4D":[29],"BOLD":[30,64],"signals":[31,65],"to":[32,119,133,155,174],"static":[33],"2D":[34],"matrices,":[35],"discarding":[36],"temporal":[37,51,85,159,169],"dynamics":[38],"and":[39,60,92,116,122,130,140,152,158],"only":[41],"linear":[42],"inter-regional":[43],"relationships.":[44],"In":[45],"this":[46,99],"work,":[47],"we":[48,101],"benchmark":[49],"state-of-the-art":[50],"models":[52,55,73],"(e.g.,":[53],"time-series":[54],"such":[56,87],"as":[57,88],"PatchTST,":[58],"TimesNet,":[59],"TimeMixer)":[61],"raw":[63],"across":[66],"five":[67],"public":[68],"datasets.":[69],"Results":[70],"show":[71],"these":[72],"consistently":[74],"outperform":[75],"traditional":[76],"FC-based":[77,157],"approaches,":[78],"highlighting":[79],"the":[80],"value":[81],"of":[82,128],"directly":[83],"modeling":[84,170],"information":[86],"cycle-like":[89],"oscillatory":[90],"fluctuations":[91],"drift-like":[93],"slow":[94],"baseline":[95],"trends.":[96],"Building":[97],"insight,":[100],"propose":[102],"DeCI,":[103],"a":[104,165],"simple":[105],"yet":[106],"effective":[107],"framework":[108],"that":[109,146],"integrates":[110],"two":[111],"key":[112],"principles:":[113],"(i)":[114],"Cycle":[115],"Drift":[117],"Decomposition":[118],"disentangle":[120],"cycle":[121],"drift":[123],"within":[124],"each":[125,135],"ROI":[126,136],"(Region":[127],"Interest);":[129],"(ii)":[131],"Channel-Independence":[132],"model":[134],"separately,":[137],"improving":[138],"robustness":[139],"reducing":[141],"overfitting.":[142],"Extensive":[143],"experiments":[144],"demonstrate":[145],"DeCI":[147],"achieves":[148],"superior":[149],"accuracy":[151],"generalization":[153],"compared":[154],"both":[156],"baselines.":[160],"Our":[161],"findings":[162],"advocate":[163],"for":[164],"shift":[166],"toward":[167],"end-to-end":[168],"in":[171],"fMRI":[172],"analysis":[173],"better":[175],"capture":[176],"complex":[177],"dynamics.":[179],"The":[180],"code":[181],"is":[182],"available":[183],"at":[184],"https://github.com/Levi-Ackman/DeCI.":[185]},"counts_by_year":[],"updated_date":"2026-02-11T14:45:31.134243","created_date":"2026-02-11T00:00:00"}
