{"id":"https://openalex.org/W2508035651","doi":"https://doi.org/10.1109/prni.2016.7552348","title":"Addressing the \"problem\" of temporal correlations in MVPA analysis","display_name":"Addressing the \"problem\" of temporal correlations in MVPA analysis","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2508035651","doi":"https://doi.org/10.1109/prni.2016.7552348","mag":"2508035651"},"language":"en","primary_location":{"id":"doi:10.1109/prni.2016.7552348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2016.7552348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","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/A5019680783","display_name":"Roee Gilron","orcid":null},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Roee Gilron","raw_affiliation_strings":["TAU, School of Psychological Sciences, Tel Aviv, Israel"],"affiliations":[{"raw_affiliation_string":"TAU, School of Psychological Sciences, Tel Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025273171","display_name":"Jonathan D. Rosenblatt","orcid":"https://orcid.org/0000-0003-3116-1782"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonathan D. Rosenblatt","raw_affiliation_strings":["Department of Industrial, Zlotowsky Center for Neuroscience, Beer Sheva, Israel"],"affiliations":[{"raw_affiliation_string":"Department of Industrial, Zlotowsky Center for Neuroscience, Beer Sheva, Israel","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077458180","display_name":"Roy Mukamel","orcid":"https://orcid.org/0000-0001-9359-8950"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Roy Mukamel","raw_affiliation_strings":["TAU, School of Psychological Sciences, Tel Aviv, Israel"],"affiliations":[{"raw_affiliation_string":"TAU, School of Psychological Sciences, Tel Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019680783"],"corresponding_institution_ids":["https://openalex.org/I16391192"],"apc_list":null,"apc_paid":null,"fwci":0.325,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6122735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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/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/T10581","display_name":"Neural dynamics and brain function","score":0.9940999746322632,"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/T11094","display_name":"Face Recognition and Perception","score":0.991599977016449,"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/permutation","display_name":"Permutation (music)","score":0.6191580295562744},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6152393221855164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.541463315486908},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.5188875794410706},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5034143328666687},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5000348091125488},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49761179089546204},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4840937852859497},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.48025158047676086},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.46873530745506287},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4586704969406128},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.4344835579395294},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.41968071460723877},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3942137360572815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3701944947242737},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.342207133769989},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2746518552303314}],"concepts":[{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.6191580295562744},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6152393221855164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.541463315486908},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.5188875794410706},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5034143328666687},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5000348091125488},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49761179089546204},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4840937852859497},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.48025158047676086},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.46873530745506287},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4586704969406128},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4344835579395294},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.41968071460723877},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3942137360572815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3701944947242737},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.342207133769989},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2746518552303314},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/prni.2016.7552348","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2016.7552348","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1735013697","https://openalex.org/W1991237518","https://openalex.org/W2015682160","https://openalex.org/W2029671524","https://openalex.org/W2047704070","https://openalex.org/W2061170062","https://openalex.org/W2084942536","https://openalex.org/W2109883003","https://openalex.org/W2129651491","https://openalex.org/W2138790588","https://openalex.org/W2151803133","https://openalex.org/W2154272340","https://openalex.org/W2169005503","https://openalex.org/W2258584225","https://openalex.org/W2379496151","https://openalex.org/W6709277177"],"related_works":["https://openalex.org/W3216236747","https://openalex.org/W2035724348","https://openalex.org/W2190571313","https://openalex.org/W4226226594","https://openalex.org/W2148189552","https://openalex.org/W2049286649","https://openalex.org/W2007006185","https://openalex.org/W2793178588","https://openalex.org/W2028366770","https://openalex.org/W2508035651"],"abstract_inverted_index":{"The":[0,103,160],"use":[1,161],"of":[2,22,43,50,68,85,162],"multivariate":[3],"pattern":[4],"analysis":[5],"(MVPA)":[6],"has":[7],"grown":[8],"substantially":[9],"over":[10],"the":[11,20,38,66,83,121,134],"past":[12],"few":[13],"years.":[14],"Many":[15],"studies":[16],"using":[17,30],"MVPA":[18,173],"estimate":[19],"response":[21],"individual":[23],"trial":[24],"activity":[25,69],"and":[26],"perform":[27],"hypothesis":[28,79],"testing":[29,80,138],"a":[31,124,157],"non-parametric":[32,78],"approach.":[33],"Here":[34],"we":[35],"show":[36,64],"that":[37,56,65,82,108],"default":[39],"auto":[40,127],"regression":[41,128],"model":[42],"order":[44,145],"1":[45],"used":[46],"for":[47,95,139,171],"temporal":[48,165],"whitening":[49,120,166],"BOLD":[51],"data":[52,111,122],"is":[53,106,148],"problematic":[54],"in":[55,77,109,136,151,175],"it":[57],"leads":[58],"to":[59],"biased":[60],"permutation":[61,137],"tests.":[62],"We":[63],"correlation":[67],"estimates":[70],"across":[71],"trials":[72],"can":[73,132],"cause":[74],"extreme":[75],"bias":[76,135],"so":[81],"proportion":[84],"type":[86,89],"I":[87],"or":[88],"II":[90],"errors":[91],"are":[92,116],"inflated.":[93],"Crucially":[94],"MVPA,":[96],"this":[97],"inflation":[98],"increases":[99],"with":[100,123],"sphere":[101],"size.":[102],"error":[104],"magnitude":[105],"such":[107],"our":[110],"set,":[112],"strong":[113],"univariate":[114],"effects":[115],"completely":[117],"missed.":[118],"By":[119],"more":[125,163],"general":[126],"(AR)":[129],"model,":[130],"one":[131],"correct":[133],"better":[140],"signal":[141],"detection.":[142],"Applying":[143],"higher":[144],"AR":[146],"models":[147],"already":[149],"implemented":[150],"many":[152],"neuroimaging":[153],"software":[154],"packages":[155],"as":[156],"non-default":[158],"option.":[159],"aggressive":[164],"may":[167],"also":[168],"prove":[169],"crucial":[170],"valid":[172],"inference":[174],"fast":[176],"event":[177],"related":[178],"designs.":[179]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}