{"id":"https://openalex.org/W7117247961","doi":"https://doi.org/10.1109/access.2025.3648664","title":"Hi-STMNA-GCN: A Hierarchical Spatio-Temporal Multi-Neighbor Graph Convolutional Network for Robust Alzheimer\u2019s Disease Detection From fMRI Data","display_name":"Hi-STMNA-GCN: A Hierarchical Spatio-Temporal Multi-Neighbor Graph Convolutional Network for Robust Alzheimer\u2019s Disease Detection From fMRI Data","publication_year":2025,"publication_date":"2025-12-25","ids":{"openalex":"https://openalex.org/W7117247961","doi":"https://doi.org/10.1109/access.2025.3648664"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3648664","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3648664","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3648664","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121268784","display_name":"Neeti Sangwan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Neeti Sangwan","raw_affiliation_strings":["Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055795425","display_name":"Veena Mittal","orcid":"https://orcid.org/0000-0001-5319-8514"},"institutions":[{"id":"https://openalex.org/I155125381","display_name":"Galgotias University","ror":"https://ror.org/02w8ba206","country_code":"IN","type":"education","lineage":["https://openalex.org/I155125381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Veena Mittal","raw_affiliation_strings":["Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India","institution_ids":["https://openalex.org/I155125381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047878382","display_name":"Deepak Sinwar","orcid":"https://orcid.org/0000-0001-9597-6206"},"institutions":[{"id":"https://openalex.org/I73779912","display_name":"Manipal University Jaipur","ror":"https://ror.org/040h76494","country_code":null,"type":"education","lineage":["https://openalex.org/I73779912"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepak Sinwar","raw_affiliation_strings":["Department of IoT and Intelligent Systems, Manipal University Jaipur, Jaipur, Rajasthan, India"],"affiliations":[{"raw_affiliation_string":"Department of IoT and Intelligent Systems, Manipal University Jaipur, Jaipur, Rajasthan, India","institution_ids":["https://openalex.org/I73779912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019218760","display_name":"Navdeep Bohra","orcid":"https://orcid.org/0000-0002-9132-1963"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Navdeep Bohra","raw_affiliation_strings":["Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5121268784"],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68384079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"1812","last_page":"1832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.5800999999046326,"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":0.5800999999046326,"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.290800005197525,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.051500000059604645,"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/discriminative-model","display_name":"Discriminative model","score":0.7865999937057495},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6079999804496765},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47510001063346863},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.44519999623298645},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42500001192092896},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.3806999921798706},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.364300012588501}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8039000034332275},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7865999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6442000269889832},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6079999804496765},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44119998812675476},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3578000068664551},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C2991673738","wikidata":"https://www.wikidata.org/wiki/Q5062122","display_name":"Brain disease","level":3,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3648664","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3648664","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3648664","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3648664","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7817395329475403,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2025786233","https://openalex.org/W2157331557","https://openalex.org/W2522924024","https://openalex.org/W2569531558","https://openalex.org/W2779020697","https://openalex.org/W2806489700","https://openalex.org/W2905035821","https://openalex.org/W2942882625","https://openalex.org/W2964199361","https://openalex.org/W3047443805","https://openalex.org/W3095479837","https://openalex.org/W3099518117","https://openalex.org/W3189601601","https://openalex.org/W3207511726","https://openalex.org/W3209415657","https://openalex.org/W4224246814","https://openalex.org/W4224999602","https://openalex.org/W4225156123","https://openalex.org/W4226092233","https://openalex.org/W4306910185","https://openalex.org/W4307940494","https://openalex.org/W4308616331","https://openalex.org/W4309096516","https://openalex.org/W4309627396","https://openalex.org/W4311769512","https://openalex.org/W4320521693","https://openalex.org/W4321457755","https://openalex.org/W4324135730","https://openalex.org/W4361801748","https://openalex.org/W4362665062","https://openalex.org/W4367174198","https://openalex.org/W4376605176","https://openalex.org/W4381188046","https://openalex.org/W4385496892","https://openalex.org/W4386024312","https://openalex.org/W4387059604","https://openalex.org/W4387717514","https://openalex.org/W4400905597","https://openalex.org/W4401358030","https://openalex.org/W4401806028","https://openalex.org/W4403674966","https://openalex.org/W4406322220","https://openalex.org/W4406387918","https://openalex.org/W4407736145"],"related_works":[],"abstract_inverted_index":{"Early":[0],"detection":[1,198],"of":[2,112,134,172],"Alzheimer\u2019s":[3],"Disease":[4],"(AD),":[5],"particularly":[6],"at":[7,74,77],"the":[8,51,78,109,116,130,135,152,191],"Mild":[9],"Cognitive":[10],"Impairment":[11],"(MCI)":[12],"stage,":[13],"is":[14,122,144],"essential":[15],"for":[16,66,194],"effective":[17],"intervention":[18],"and":[19,33,183,199],"disease":[20],"management.":[21],"Traditional":[22],"deep":[23,62],"learning":[24,63],"models":[25],"often":[26],"struggle":[27],"to":[28,88,124,146],"simultaneously":[29],"capture":[30,90],"both":[31],"local":[32],"global":[34,131],"brain":[35,113,155],"connectivity":[36],"patterns":[37],"from":[38,96],"functional":[39],"Magnetic":[40],"Resonance":[41],"Imaging":[42],"(fMRI)":[43],"data.":[44],"To":[45],"address":[46],"these":[47],"challenges,":[48],"we":[49],"propose":[50],"Hierarchical":[52],"Spatio-Temporal":[53],"Multi-Neighbour":[54],"Aggregation":[55],"Graph":[56],"Convolutional":[57],"Network":[58],"(Hi-STMNA-GCN),":[59],"a":[60,119,138],"novel":[61],"framework":[64],"designed":[65],"robust":[67],"AD":[68,197],"classification.":[69],"The":[70],"Hi-STMNA-GCN":[71,164],"model":[72,108],"operates":[73],"two":[75],"levels:":[76],"Region":[79],"Level,":[80,118],"it":[81],"employs":[82],"multi-neighbour":[83],"aggregation":[84],"graph":[85],"convolutional":[86],"networks":[87],"comprehensively":[89],"spatial":[91],"dependencies":[92],"by":[93],"aggregating":[94],"information":[95],"neighbouring":[97],"nodes":[98],"across":[99],"multiple":[100],"scales,":[101],"while":[102],"Gated":[103],"Recurrent":[104],"Units":[105],"(GRU)":[106],"effectively":[107],"temporal":[110],"dynamics":[111],"activity.":[114],"At":[115],"Population":[117],"hypergraph-based":[120],"module":[121],"introduced":[123],"learn":[125],"complex":[126],"inter-subject":[127],"relationships,":[128],"enhancing":[129],"discriminative":[132],"power":[133],"model.":[136],"Additionally,":[137],"Masked":[139],"Relation":[140],"Learning":[141],"(MRL)":[142],"mechanism":[143],"integrated":[145],"prune":[147],"redundant":[148],"connections,":[149],"ensuring":[150],"that":[151,163],"most":[153],"informative":[154],"region":[156],"interactions":[157],"are":[158],"preserved.":[159],"Extensive":[160],"experiments":[161],"demonstrate":[162],"significantly":[165],"outperforms":[166],"existing":[167],"methods,":[168],"achieving":[169],"classification":[170],"accuracies":[171],"96%":[173],"(AD":[174,180],"vs.":[175,181,186],"Cognitively":[176],"Normal":[177],"(CN)),":[178],"94.8%":[179],"MCI),":[182],"98%":[184],"(MCI":[185],"CN).":[187],"These":[188],"results":[189],"highlight":[190],"model\u2019s":[192],"potential":[193],"early,":[195],"reliable":[196],"its":[200],"applicability":[201],"in":[202],"advancing":[203],"neuroimaging-based":[204],"diagnostic":[205],"tools.":[206]},"counts_by_year":[],"updated_date":"2026-01-08T20:05:33.558190","created_date":"2025-12-25T00:00:00"}
