{"id":"https://openalex.org/W4401749599","doi":"https://doi.org/10.1109/isbi56570.2024.10635716","title":"MCI Detection Using FMRI Time Series Embeddings of Recurrence Plots","display_name":"MCI Detection Using FMRI Time Series Embeddings of Recurrence Plots","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401749599","doi":"https://doi.org/10.1109/isbi56570.2024.10635716"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","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/A5093402711","display_name":"Ninad Aithal","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ninad Aithal","raw_affiliation_strings":["Indian Institute of Science,Centre for Brain Research,Bengaluru"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Centre for Brain Research,Bengaluru","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chakka Sai Pradeep","orcid":null},"institutions":[{"id":"https://openalex.org/I181514455","display_name":"International Institute of Information Technology Bangalore","ror":"https://ror.org/05h9eqy10","country_code":"IN","type":"education","lineage":["https://openalex.org/I181514455"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chakka Sai Pradeep","raw_affiliation_strings":["International Institute of Information Technology,Bengaluru"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"International Institute of Information Technology,Bengaluru","institution_ids":["https://openalex.org/I181514455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014371833","display_name":"Neelam Sinha","orcid":"https://orcid.org/0000-0001-8164-8412"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neelam Sinha","raw_affiliation_strings":["Indian Institute of Science,Centre for Brain Research,Bengaluru"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Centre for Brain Research,Bengaluru","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5029,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.90911879,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10244","display_name":"Chaos control and synchronization","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9945999979972839,"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/series","display_name":"Series (stratigraphy)","score":0.6533089876174927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.585259735584259},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5287268757820129},{"id":"https://openalex.org/keywords/recurrence-plot","display_name":"Recurrence plot","score":0.4881778359413147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4510582387447357},{"id":"https://openalex.org/keywords/recurrence-quantification-analysis","display_name":"Recurrence quantification analysis","score":0.4312877953052521},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37999922037124634},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14234280586242676},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08073309063911438}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6533089876174927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.585259735584259},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5287268757820129},{"id":"https://openalex.org/C173134143","wikidata":"https://www.wikidata.org/wiki/Q2797953","display_name":"Recurrence plot","level":3,"score":0.4881778359413147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4510582387447357},{"id":"https://openalex.org/C43456602","wikidata":"https://www.wikidata.org/wiki/Q7303254","display_name":"Recurrence quantification analysis","level":3,"score":0.4312877953052521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37999922037124634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14234280586242676},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08073309063911438},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W207372120","https://openalex.org/W1515147372","https://openalex.org/W2063404606","https://openalex.org/W2078524519","https://openalex.org/W2081681829","https://openalex.org/W2101713460","https://openalex.org/W2116649573","https://openalex.org/W2123927491","https://openalex.org/W2133665775","https://openalex.org/W2963304300","https://openalex.org/W3095114157","https://openalex.org/W3210535667","https://openalex.org/W4312220613","https://openalex.org/W4376866854","https://openalex.org/W4386934914","https://openalex.org/W6675357634"],"related_works":["https://openalex.org/W4236831073","https://openalex.org/W4385146532","https://openalex.org/W2128285247","https://openalex.org/W4309197050","https://openalex.org/W4310481254","https://openalex.org/W2950325250","https://openalex.org/W1870609992","https://openalex.org/W4297688580","https://openalex.org/W2889018780","https://openalex.org/W2952356350"],"abstract_inverted_index":{"The":[0,116],"human":[1],"brain":[2,63,149],"can":[3,18],"be":[4,39],"conceptualized":[5],"as":[6],"a":[7],"dynamical":[8],"system.":[9],"Utilizing":[10],"resting":[11],"state":[12],"fMRI":[13,125],"time":[14,88,93],"series":[15,94],"imaging,":[16],"we":[17,60],"study":[19],"the":[20,43,119,147,159],"underlying":[21],"dynamics":[22],"at":[23,95],"ear-marked":[24],"Regions":[25],"of":[26,77,87,118,127,144,153],"Interest":[27],"(ROIs)":[28],"to":[29,41,47,84,100,110],"understand":[30,85],"structure":[31],"or":[32],"lack":[33],"thereof.":[34],"This":[35],"differential":[36],"behavior":[37],"could":[38],"key":[40],"understanding":[42],"neurodegeneration":[44],"and":[45,51],"also":[46],"classify":[48],"between":[49],"healthy":[50],"Mild":[52],"Cognitive":[53],"Impairment":[54],"(MCI)":[55],"subjects.":[56],"In":[57],"this":[58],"study,":[59],"consider":[61],"6":[62,148],"networks":[64],"spanning":[65],"over":[66],"160":[67],"ROIs":[68],"derived":[69],"from":[70,133],"Dosenbach":[71],"template,":[72],"where":[73],"each":[74,96],"network":[75],"consists":[76],"25-30":[78],"ROIs.":[79],"Recurrence":[80],"plot,":[81],"extensively":[82],"used":[83],"evolution":[86],"series,":[89],"is":[90,98,107,122],"employed.":[91],"Representative":[92],"ROI":[97],"converted":[99],"its":[101],"corresponding":[102],"recurrence":[103],"plot":[104],"visualization,":[105],"which":[106],"subsequently":[108],"condensed":[109],"low-dimensional":[111],"feature":[112],"embeddings":[113],"through":[114],"Autoencoders.":[115],"performance":[117],"proposed":[120,160],"method":[121],"shown":[123],"on":[124],"volumes":[126],"100":[128],"subjects":[129],"(balanced":[130],"data),":[131],"taken":[132],"publicly":[134],"available":[135],"ADNI":[136],"dataset.":[137],"Results":[138],"obtained":[139],"show":[140],"peak":[141],"classification":[142],"accuracy":[143,152],"93%":[145],"among":[146],"networks,":[150],"mean":[151],"89.3%":[154],"thereby":[155],"illustrating":[156],"promise":[157],"in":[158],"approach.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}