{"id":"https://openalex.org/W4379806249","doi":"https://doi.org/10.1145/3591106.3592213","title":"SPAE: Spatial Preservation-based Autoencoder for ADHD functional brain networks modelling","display_name":"SPAE: Spatial Preservation-based Autoencoder for ADHD functional brain networks modelling","publication_year":2023,"publication_date":"2023-06-08","ids":{"openalex":"https://openalex.org/W4379806249","doi":"https://doi.org/10.1145/3591106.3592213"},"language":"en","primary_location":{"id":"doi:10.1145/3591106.3592213","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3591106.3592213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081126476","display_name":"Chunhong Cao","orcid":"https://orcid.org/0000-0002-3812-6536"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhong Cao","raw_affiliation_strings":["Xiangtan University, China"],"raw_orcid":"https://orcid.org/0000-0002-3812-6536","affiliations":[{"raw_affiliation_string":"Xiangtan University, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101885350","display_name":"Gai Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gai Li","raw_affiliation_strings":["Xiangtan University, China"],"raw_orcid":"https://orcid.org/0000-0003-3439-4813","affiliations":[{"raw_affiliation_string":"Xiangtan University, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004946269","display_name":"Huawei Fu","orcid":"https://orcid.org/0000-0002-8620-2062"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawei Fu","raw_affiliation_strings":["Xiangtan University, China"],"raw_orcid":"https://orcid.org/0000-0002-8620-2062","affiliations":[{"raw_affiliation_string":"Xiangtan University, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005840310","display_name":"Xingxing Li","orcid":"https://orcid.org/0009-0003-1665-4244"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingxing Li","raw_affiliation_strings":["Xiangtan University, China"],"raw_orcid":"https://orcid.org/0009-0003-1665-4244","affiliations":[{"raw_affiliation_string":"Xiangtan University, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010870600","display_name":"Xieping Gao","orcid":"https://orcid.org/0000-0002-7764-3616"},"institutions":[{"id":"https://openalex.org/I173759888","display_name":"Hunan Normal University","ror":"https://ror.org/053w1zy07","country_code":"CN","type":"education","lineage":["https://openalex.org/I173759888"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xieping Gao","raw_affiliation_strings":["Hunan Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-7764-3616","affiliations":[{"raw_affiliation_string":"Hunan Normal University, China","institution_ids":["https://openalex.org/I173759888"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0559,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.86116729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"370","last_page":"377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9998999834060669,"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":0.9998999834060669,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9958000183105469,"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":0.993399977684021,"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/autoencoder","display_name":"Autoencoder","score":0.761378288269043},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6950456500053406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.640266478061676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6261793375015259},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.5341832637786865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.502002477645874},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.4807579219341278},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4726509153842926},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4677194654941559},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4570051431655884},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4328625202178955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39825767278671265},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3100624084472656},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.1512121558189392},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1490345597267151},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1250576674938202}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.761378288269043},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6950456500053406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.640266478061676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6261793375015259},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.5341832637786865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.502002477645874},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.4807579219341278},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4726509153842926},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4677194654941559},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4570051431655884},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4328625202178955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39825767278671265},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3100624084472656},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.1512121558189392},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1490345597267151},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1250576674938202},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3591106.3592213","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3591106.3592213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7632203563","display_name":null,"funder_award_id":"62272404, 61972333","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2137526583","https://openalex.org/W2139708397","https://openalex.org/W2143112732","https://openalex.org/W2147252463","https://openalex.org/W2751986434","https://openalex.org/W2765820969","https://openalex.org/W2898352483","https://openalex.org/W2950568021","https://openalex.org/W2964226462","https://openalex.org/W2981381476","https://openalex.org/W3090884046","https://openalex.org/W3119205652","https://openalex.org/W3139088197","https://openalex.org/W4206280419","https://openalex.org/W4283785073","https://openalex.org/W4312560592"],"related_works":["https://openalex.org/W4310873165","https://openalex.org/W2355395139","https://openalex.org/W4285596704","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810"],"abstract_inverted_index":{"Spatio-temporal":[0],"modelling":[1,126],"based":[2],"on":[3,112,207],"resting-state":[4],"functional":[5,128],"magnetic":[6],"resonance":[7],"imaging":[8],"(rsfMRI)":[9],"of":[10,27,61,78,194],"ADHD":[11,127,218,233],"has":[12],"been":[13],"a":[14,121,161],"major":[15],"concern":[16],"in":[17,24,82,96,216,229],"the":[18,22,25,55,76,83,107,136,147,150,155,169,172,176,180,192,198,204,208,213,221,227],"neuroimaging":[19],"community,":[20],"given":[21],"differences":[23,228],"role":[26],"brain":[28,62,84,129],"regions":[29],"between":[30,171,232],"attention":[31],"deficit":[32],"hyperactivity":[33],"disorder":[34],"(ADHD)":[35],"patients":[36],"versus":[37],"typical":[38],"developmental":[39],"control":[40],"group":[41],"(TC).":[42],"Several":[43],"spatio-temporal":[44,108,195],"deep":[45],"learning":[46],"models":[47,65],"are":[48,188,201],"proposed":[49,95,133],"for":[50,71,125],"rsfMRI,":[51],"however,":[52],"due":[53],"to":[54,145,167,190,203],"high":[56],"dimensionality":[57],"and":[58,91,103,139,143,175,184,234],"few":[59],"samples":[60],"data,":[63],"most":[64],"use":[66],"dimension-reduced":[67,151,177],"data":[68,152,174],"as":[69],"input":[70],"modelling,":[72],"which":[73,211],"suffer":[74],"from":[75],"loss":[77],"original":[79,116,156,173],"spatial":[80,118,122,137,158,162],"relationships":[81,109],"data.":[85,178],"Although":[86],"Recurrent":[87],"Neural":[88],"Network":[89],"(RNN)":[90],"Attention":[92],"mechanism":[93],"(Attention)":[94],"recent":[97],"years":[98],"can":[99],"extract":[100],"local":[101],"correlations":[102,231],"long-distance":[104],"dependency":[105],"(LDD),":[106],"they":[110],"rely":[111],"have":[113],"lost":[114],"their":[115],"high-dimensional":[117,157],"relevance.":[119],"Therefore,":[120],"preservation-based":[123],"autoencoder":[124],"networks":[130],"(FBNs)":[131],"is":[132,165],"by":[134],"embedding":[135],"information":[138],"combining":[140],"both":[141],"RNN":[142],"Transformer":[144],"address":[146],"issue":[148],"that":[149],"cannot":[153],"preserve":[154],"correlations.":[159,196],"Firstly,":[160],"preservation":[163],"module":[164,183,187],"designed":[166,189],"fill":[168],"gap":[170],"Secondly,":[179],"dimension":[181],"reduction":[182],"feature":[185],"extraction":[186],"improve":[191],"representation":[193],"Thirdly,":[197],"extracted":[199],"FBNs":[200],"applied":[202],"disease":[205],"classification":[206],"ADHD-200":[209],"dataset,":[210],"show":[212],"model\u2019s":[214],"effectiveness":[215],"classifying":[217],"compared":[219],"with":[220],"state-of-the-art":[222],"methods.":[223],"Finally,":[224],"we":[225],"investigate":[226],"regional":[230],"TC.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}