{"id":"https://openalex.org/W6943988210","doi":"https://doi.org/10.17615/416y-8232","title":"NOVEL STATISTICAL METHODS FOR MODELING BRAIN AND OTHER DENSE, WEIGHTED NETWORKS","display_name":"NOVEL STATISTICAL METHODS FOR MODELING BRAIN AND OTHER DENSE, WEIGHTED NETWORKS","publication_year":2022,"publication_date":"2022-05-27","ids":{"openalex":"https://openalex.org/W6943988210","doi":"https://doi.org/10.17615/416y-8232"},"language":"en","primary_location":{"id":"doi:10.17615/416y-8232","is_oa":true,"landing_page_url":"https://doi.org/10.17615/416y-8232","pdf_url":null,"source":{"id":"https://openalex.org/S7407051488","display_name":"UNC Libraries","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"thesis"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.17615/416y-8232","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Leinwand, Benjamin","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Leinwand, Benjamin","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114027177"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28808814,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9519000053405762,"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":0.9519000053405762,"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":0.005400000140070915,"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/T13283","display_name":"Mental Health Research Topics","score":0.005200000014156103,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6082000136375427},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.5},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4219000041484833},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4169999957084656},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.41040000319480896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3865000009536743},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.38530001044273376},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3598000109195709},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.35749998688697815}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6082000136375427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5658000111579895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.558899998664856},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.5},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4625000059604645},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39489999413490295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3598000109195709},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3019999861717224},{"id":"https://openalex.org/C144559511","wikidata":"https://www.wikidata.org/wiki/Q2986279","display_name":"Principal (computer security)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.17615/416y-8232","is_oa":true,"landing_page_url":"https://doi.org/10.17615/416y-8232","pdf_url":null,"source":{"id":"https://openalex.org/S7407051488","display_name":"UNC Libraries","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"thesis"}],"best_oa_location":{"id":"doi:10.17615/416y-8232","is_oa":true,"landing_page_url":"https://doi.org/10.17615/416y-8232","pdf_url":null,"source":{"id":"https://openalex.org/S7407051488","display_name":"UNC Libraries","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"thesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Advances":[0],"in":[1,108,195,324,425,442],"brain":[2,47,214,285,325],"imaging":[3],"have":[4,498],"led":[5],"to":[6,30,115,239,248,255,266,280,304,313,321,344,394,431,454,526,535],"the":[7,62,67,80,88,93,116,121,134,146,149,196,213,256,267,305,395,402,413,451,481,487,510],"generation":[8],"of":[9,12,17,22,37,52,64,69,71,84,118,123,125,148,189,212,223,264,271,294,307,349,387,416,437,450,464,471,483,545],"new":[10,23,361,410],"kinds":[11,51],"datasets.":[13],"The":[14,58,137,220,273,309,379,462],"novel":[15,42,232],"structures":[16],"these":[18,438],"datasets":[19],"necessitate":[20],"development":[21],"modeling":[24],"paradigms":[25],"for":[26,45,66,120,178,260,317,363,408,519],"analysis":[27,102,235],"better":[28],"suited":[29],"this":[31,224,241,479,528],"domain.":[32],"This":[33],"work":[34],"is":[35,104,112,168,193,202,237,276,377,418,429,459,466],"composed":[36],"different":[38,165,187,347,371,375,543],"projects":[39],"that":[40,207,300],"propose":[41],"statistical":[43,274],"methods":[44,439],"analyzing":[46,520],"data":[48,160,434],"and":[49,106,278,287,365,445,473,492,530],"other":[50,216],"networks,":[53,286],"which":[54,253,389,421],"are":[55,90,140,172,227,298,342,440,507,524,533],"described":[56],"below.":[57],"first":[59],"project":[60,311,453],"studies":[61],"problem":[63],"testing":[65,119,152],"equality":[68,122],"autocovariances":[70,124],"two":[72,126,135,164],"independent":[73],"high-dimensional":[74,131],"time":[75],"series.":[76],"Tests":[77],"based":[78,98],"on":[79,99,142,289,355,383,412],"suprema":[81],"or":[82],"sums":[83],"suitable":[85],"averages":[86],"across":[87,374],"dimensions":[89],"adapted":[91],"from":[92,133,161],"available":[94],"literature.":[95],"Another":[96],"test":[97],"principal":[100],"component":[101],"(PCA)":[103],"introduced":[105,150],"studied":[107],"theory.":[109],"An":[110,156,448],"extension":[111,449],"also":[113,419,460],"considered":[114],"setting":[117],"populations,":[127],"having":[128],"multiple":[129,433],"individual":[130,391],"series":[132],"populations.":[136],"proposed":[138,238,380],"methodologies":[139],"assessed":[141],"simulated":[143],"data,":[144],"with":[145,329,370,501],"performance":[147],"PCA":[151],"being":[153],"superior":[154],"overall.":[155],"application":[157,463],"using":[158],"fMRI":[159],"individuals":[162],"experiencing":[163],"emotional":[166],"states":[167],"provided.":[169],"These":[170],"tests":[171],"further":[173],"developed":[174,518],"into":[175],"a":[176,183,191,203,315,334,384,405,490],"method":[177,236,275],"detecting":[179,190],"change":[180,192],"points":[181],"within":[182],"single":[184],"session.":[185],"A":[186,231,292,360],"notion":[188],"explored":[194],"second":[197],"project.":[198],"Alzheimer's":[199],"disease":[200,206],"(AD)":[201],"multi-factor":[204],"neurodegenerative":[205],"selectively":[208],"affects":[209],"certain":[210],"regions":[211],"while":[215],"areas":[217],"remain":[218],"unaffected.":[219],"underlying":[221,511],"mechanisms":[222],"selectivity,":[225],"however,":[226],"still":[228,514],"largely":[229],"elusive.":[230],"longitudinal":[233,284],"network":[234,258],"address":[240],"challenge":[242],"by":[243,538],"employing":[244],"sparse":[245],"logistic":[246],"regression":[247],"identify":[249],"frequency-specific":[250],"oscillation":[251],"patterns":[252,348,373],"contribute":[254],"selective":[257],"vulnerability":[259],"patients":[261],"at":[262,542],"risk":[263],"advancing":[265],"more":[268,281],"severe":[269],"stage":[270],"dementia.":[272],"fit":[277],"applied":[279],"than":[282],"100":[283],"validated":[288],"synthetic":[290],"data.":[291,447],"set":[293,415],"critical":[295],"connectome":[296],"pathways":[297],"identified":[299],"exhibit":[301,333],"strong":[302],"association":[303],"progression":[306],"AD.":[308],"third":[310],"attempts":[312],"create":[314],"mechanism":[316],"generating":[318,364,409],"networks":[319,328,369,411,458,523,541],"similar":[320],"those":[322,398,505,536],"observed":[323,456],"scans.":[326],"Dense":[327],"weighted":[330,368,476,522],"connections":[331],"often":[332],"community":[335,358],"like":[336],"structure,":[337],"where":[338,427,469],"although":[339],"most":[340,496,502],"nodes":[341,470],"connected":[343],"each":[345,356,484],"other,":[346],"edge":[350,485],"weights":[351],"may":[352,422,513],"emerge":[353],"depending":[354],"node's":[357],"membership.":[359],"framework":[362],"estimating":[366],"dense,":[367],"connectivity":[372],"groups":[376],"introduced.":[378],"model":[381],"relies":[382],"particular":[385],"class":[386],"functions":[388],"map":[390],"node":[392],"characteristics":[393],"edges":[396,506],"connecting":[397],"nodes.":[399],"By":[400],"leveraging":[401],"estimation":[403,531],"techniques,":[404],"bootstrap":[406],"methodology":[407],"same":[414],"vertices":[417],"developed,":[420],"be":[423,515],"useful":[424],"circumstances":[426],"it":[428],"costly":[430],"collect":[432],"sets.":[435],"Performance":[436],"analyzed":[441],"theory,":[443],"simulations,":[444],"real":[446],"previous":[452],"partially":[455],"bipartite":[457],"discussed.":[461],"interest":[465],"recommender":[467],"systems,":[468],"users":[472,497],"items":[474],"share":[475],"edges.":[477],"In":[478,494],"construct,":[480],"weight":[482],"reflects":[486],"affinity":[488,512],"between":[489],"user":[491],"item.":[493],"general,":[495],"not":[499],"interacted":[500],"items,":[503],"so":[504],"unobserved,":[508],"but":[509],"estimated.":[516],"Tools":[517],"dense":[521],"updated":[525],"match":[527],"setting,":[529],"results":[532],"compared":[534],"achieved":[537],"graph":[539],"neural":[540],"levels":[544],"missingness.":[546]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}