{"id":"https://openalex.org/W1988772041","doi":"https://doi.org/10.1109/acssc.2012.6489116","title":"Wavelet packet based clustering for the study of functional connectivity in the rat brain","display_name":"Wavelet packet based clustering for the study of functional connectivity in the rat brain","publication_year":2012,"publication_date":"2012-11-01","ids":{"openalex":"https://openalex.org/W1988772041","doi":"https://doi.org/10.1109/acssc.2012.6489116","mag":"1988772041"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2012.6489116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2012.6489116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)","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/A5042284505","display_name":"Alessio Medda","orcid":"https://orcid.org/0000-0002-0632-5338"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I4388482740","display_name":"Georgia Tech Research Institute","ror":"https://ror.org/04qfrh333","country_code":null,"type":"facility","lineage":["https://openalex.org/I130701444","https://openalex.org/I4388482740"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alessio Medda","raw_affiliation_strings":["Georgia Tech Research Institute, Atlanta, USA","Georgia Tech Research Institute, Atlanta, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Tech Research Institute, Atlanta, USA","institution_ids":["https://openalex.org/I130701444","https://openalex.org/I4388482740"]},{"raw_affiliation_string":"Georgia Tech Research Institute, Atlanta, USA#TAB#","institution_ids":["https://openalex.org/I130701444","https://openalex.org/I4388482740"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080980159","display_name":"Shella Keilholz","orcid":"https://orcid.org/0000-0001-5737-1660"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shella Keilholz","raw_affiliation_strings":["Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, USA","institution_ids":["https://openalex.org/I130701444","https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2895,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56460639,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"765","last_page":"769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9969000220298767,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9969000220298767,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9965999722480774,"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/T10320","display_name":"Neural Networks and Applications","score":0.9940000176429749,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7548597455024719},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6722550392150879},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6648380756378174},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.6034334897994995},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5767465829849243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5520855188369751},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.5447505712509155},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5444208383560181},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4531536102294922},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4379115402698517}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7548597455024719},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6722550392150879},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6648380756378174},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.6034334897994995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5767465829849243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5520855188369751},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.5447505712509155},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5444208383560181},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4531536102294922},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4379115402698517}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2012.6489116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2012.6489116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)","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":9,"referenced_works":["https://openalex.org/W1663998522","https://openalex.org/W1986754283","https://openalex.org/W2009494091","https://openalex.org/W2012739722","https://openalex.org/W2025911413","https://openalex.org/W2041119928","https://openalex.org/W2073279428","https://openalex.org/W2137526583","https://openalex.org/W2147721078"],"related_works":["https://openalex.org/W4245508182","https://openalex.org/W4233511069","https://openalex.org/W2046633342","https://openalex.org/W2370050053","https://openalex.org/W2372936409","https://openalex.org/W53954450","https://openalex.org/W2365287829","https://openalex.org/W2389645710","https://openalex.org/W2379553594","https://openalex.org/W2351059076"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,43,84],"data-driven":[4],"clustering":[5,62],"method":[6],"based":[7,86],"on":[8,87],"the":[9,16,23,47,53,76,88,95,112,116,136,146],"use":[10],"of":[11,18,32,46,75,115],"wavelet":[12,27,54,90],"packet":[13,55],"features":[14],"for":[15,37],"study":[17],"functionally":[19],"connected":[20],"regions":[21,110],"in":[22,40,111,145],"brain.":[24],"In":[25],"particular,":[26],"packets":[28],"are":[29,57,70,104],"used":[30],"because":[31],"their":[33],"optimal":[34],"whitening":[35],"properties":[36],"1/f-like":[38],"processes,":[39],"association":[41],"with":[42,63,72,80,135],"uniform":[44],"segmentation":[45],"frequency":[48],"axis.":[49],"Features":[50],"obtained":[51,69,82],"by":[52,94],"transform":[56,91],"grouped":[58],"together":[59],"using":[60],"agglomerative":[61],"standardize":[64],"Euclidian":[65],"distance.":[66],"The":[67,98],"results":[68,81],"compared":[71],"known":[73],"atlas":[74],"rat":[77,117],"brain":[78,118],"and":[79,119,131],"from":[83],"technique":[85],"standard":[89],"previously":[92],"presented":[93,99],"same":[96],"authors.":[97],"approach":[100],"produces":[101,121],"clusters":[102,122,138],"that":[103,123],"well":[105],"matched":[106],"to":[107,126],"classical":[108],"anatomical":[109],"sensorimotor":[113],"cortex":[114],"also":[120],"roughly":[124],"correspond":[125],"primary":[127,129],"motor,":[128],"somatosensory":[130,133],"secondary":[132],"areas,":[134],"subcortical":[137],"typically":[139],"comprising":[140],"one":[141],"or":[142],"two":[143],"groups":[144],"caudate":[147],"putamen":[148],"area.":[149]},"counts_by_year":[{"year":2013,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
