{"id":"https://openalex.org/W2995161523","doi":"https://doi.org/10.1109/tencon.2019.8929396","title":"Estimation of Functional Connectivity in Cognitive Impaired Brain Using Non-Homogeneous Dynamic Bayesian Model","display_name":"Estimation of Functional Connectivity in Cognitive Impaired Brain Using Non-Homogeneous Dynamic Bayesian Model","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2995161523","doi":"https://doi.org/10.1109/tencon.2019.8929396","mag":"2995161523"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"},"type":"article","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/A5021135216","display_name":"J. Ramakrishna","orcid":"https://orcid.org/0000-0003-0673-055X"},"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":true,"raw_author_name":"J. Siva Ramakrishna","raw_affiliation_strings":["International Institute of Information Technology, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"International Institute of Information Technology, Bengaluru, India","institution_ids":["https://openalex.org/I181514455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081995395","display_name":"Hariharan Ramasangu","orcid":"https://orcid.org/0000-0002-7590-1749"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hariharan Ramasangu","raw_affiliation_strings":["Relecura Inc., Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Relecura Inc., Bengaluru, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021135216"],"corresponding_institution_ids":["https://openalex.org/I181514455"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.1768776,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2154","last_page":"2159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9994999766349792,"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":0.9994999766349792,"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/T13283","display_name":"Mental Health Research Topics","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9891999959945679,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.736900269985199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6979248523712158},{"id":"https://openalex.org/keywords/dynamic-functional-connectivity","display_name":"Dynamic functional connectivity","score":0.6668193340301514},{"id":"https://openalex.org/keywords/default-mode-network","display_name":"Default mode network","score":0.5574668049812317},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5413259863853455},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5333333611488342},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5208823680877686},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.5131712555885315},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5080561637878418},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.49108052253723145},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.47942298650741577},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47797200083732605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39471691846847534},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39008980989456177},{"id":"https://openalex.org/keywords/functional-connectivity","display_name":"Functional connectivity","score":0.27821677923202515},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17528453469276428},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.1752253770828247},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13631215691566467},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12568655610084534}],"concepts":[{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.736900269985199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6979248523712158},{"id":"https://openalex.org/C2781312939","wikidata":"https://www.wikidata.org/wiki/Q17088721","display_name":"Dynamic functional connectivity","level":3,"score":0.6668193340301514},{"id":"https://openalex.org/C141516989","wikidata":"https://www.wikidata.org/wiki/Q1182555","display_name":"Default mode network","level":3,"score":0.5574668049812317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5413259863853455},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5333333611488342},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5208823680877686},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.5131712555885315},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5080561637878418},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.49108052253723145},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.47942298650741577},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47797200083732605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39471691846847534},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39008980989456177},{"id":"https://openalex.org/C3018011982","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Functional connectivity","level":2,"score":0.27821677923202515},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17528453469276428},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.1752253770828247},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13631215691566467},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12568655610084534},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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":19,"referenced_works":["https://openalex.org/W1490480470","https://openalex.org/W2106706098","https://openalex.org/W2131681506","https://openalex.org/W2594648503","https://openalex.org/W2611370172","https://openalex.org/W2757072205","https://openalex.org/W2777412843","https://openalex.org/W2784043705","https://openalex.org/W2788481234","https://openalex.org/W2793980368","https://openalex.org/W2795897604","https://openalex.org/W2892685897","https://openalex.org/W2900341503","https://openalex.org/W2913944140","https://openalex.org/W2952828710","https://openalex.org/W3099768174","https://openalex.org/W4295193448","https://openalex.org/W6756379444","https://openalex.org/W6764596438"],"related_works":["https://openalex.org/W3120263506","https://openalex.org/W2343802789","https://openalex.org/W3139092759","https://openalex.org/W3069116891","https://openalex.org/W3128072696","https://openalex.org/W2437514924","https://openalex.org/W2183461225","https://openalex.org/W2325228252","https://openalex.org/W3158029635","https://openalex.org/W2578973671"],"abstract_inverted_index":{"Probabilistic":[0],"graph":[1,20,91],"models":[2,21,92],"have":[3,120],"been":[4,37,121],"viewed":[5],"as":[6,94,133],"an":[7],"essential":[8],"tool":[9],"for":[10,67,87,123,161],"learning":[11],"networks":[12,25],"from":[13],"data.":[14,29,70],"The":[15,30,89,147],"dynamic":[16],"functional":[17,41,61],"connectivity":[18,48,62,148],"using":[19],"depends":[22],"on":[23,46],"time-varying":[24],"of":[26,32,56,60,76,107,128,156,159,172],"Functional":[27],"MRI":[28],"structure":[31],"the":[33,54,57,74,80,153,170,176],"brain":[34,83],"organization":[35],"has":[36],"studied":[38],"by":[39],"exploring":[40],"network":[42,77],"analysis.":[43],"Network-oriented":[44],"studies":[45],"whole-brain":[47],"analysis":[49],"are":[50],"helpful":[51],"to":[52],"infer":[53],"dynamics":[55,110],"brain.":[58],"Estimation":[59],"is":[63,84],"a":[64,100,108,166],"challenging":[65],"task":[66],"large":[68],"dimensional":[69],"Understanding":[71],"and":[72,111,142,150],"discovering":[73],"arrangement":[75],"data":[78],"about":[79],"complicated":[81],"human":[82],"fundamentally":[85],"significant":[86,167],"diagnosis.":[88],"template-based":[90],"such":[93,132],"Dynamic":[95],"Bayesian":[96],"Networks":[97],"(DBN)":[98],"provides":[99],"single":[101],"compact":[102],"model":[103],"which":[104],"captures":[105],"characteristics":[106],"system":[109],"distributions":[112],"over":[113],"time.":[114],"In":[115],"this":[116],"work,":[117],"non-homogeneous":[118],"DBNs":[119],"constructed":[122],"Default":[124],"Mode":[125],"Network":[126],"(DMN)":[127],"three":[129,162],"category":[130,163],"subjects":[131,164],"Alzheimer's":[134],"Disease":[135],"(AD),":[136],"Early":[137],"Mild":[138],"Cognitive":[139],"Impairment":[140],"(EMCI)":[141],"Control":[143],"Normal":[144],"(CN)":[145],"subjects.":[146],"interactions":[149],"weights":[151],"between":[152,175],"eleven":[154],"Region":[155],"Interests":[157],"(ROI)":[158],"DMN":[160],"show":[165],"difference":[168],"in":[169],"form":[171],"information":[173],"flow":[174],"ROIs.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}