{"id":"https://openalex.org/W79730163","doi":null,"title":"Machine Learning Methods for Magnetic Resonance Imaging Analysis.","display_name":"Machine Learning Methods for Magnetic Resonance Imaging Analysis.","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W79730163","mag":"79730163"},"language":"en","primary_location":{"id":"mag:79730163","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/96103","pdf_url":null,"source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Deep Blue (University of Michigan)","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/2027.42/96103","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086500327","display_name":"Cen Guo","orcid":"https://orcid.org/0000-0002-4545-1062","observed_orcids":["https://orcid.org/0000-0002-4545-1062"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cen Guo","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4545-1062","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5086500327"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9359999895095825,"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":0.9359999895095825,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.0478999987244606,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.0027000000700354576,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/fmri","display_name":"functional MRI","score":0.9369999766349792},{"id":"https://openalex.org/keywords/anatomical-mri","display_name":"anatomical MRI","score":0.9789999723434448},{"id":"https://openalex.org/keywords/brain-connectivity","display_name":"brain connectivity","score":0.5559999942779541},{"id":"https://openalex.org/keywords/gaussian-processes","display_name":"Gaussian processes","score":0.7730000019073486},{"id":"https://openalex.org/keywords/spatiotemporal-correlation","display_name":"spatio-temporal correlation","score":0.9549999833106995},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"multiple kernel learning","score":0.9330000281333923},{"id":"https://openalex.org/keywords/automated-disease-classification","display_name":"automated disease classification","score":0.6990000009536743},{"id":"https://openalex.org/keywords/brain-structure","display_name":"brain structure","score":0.5220000147819519},{"id":"https://openalex.org/keywords/neuronal-activity","display_name":"neuronal activity","score":0.4180000126361847}],"concepts":[{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.6268991827964783},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.5354620814323425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40572211146354675},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.3205028176307678},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2557636797428131},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.19688019156455994},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.09935703873634338}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:79730163","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/96103","pdf_url":null,"source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Deep Blue (University of Michigan)","raw_type":null},{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/96103","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/96103","pdf_url":"https://hdl.handle.net/2027.42/96103","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"mag:79730163","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/96103","pdf_url":null,"source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Deep Blue (University of Michigan)","raw_type":null},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6240000128746033,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W79730163.pdf","grobid_xml":"https://content.openalex.org/works/W79730163.grobid-xml"},"referenced_works_count":81,"referenced_works":["https://openalex.org/W7508719","https://openalex.org/W1519178192","https://openalex.org/W1528119265","https://openalex.org/W1746819321","https://openalex.org/W1964777591","https://openalex.org/W1967419852","https://openalex.org/W1970798317","https://openalex.org/W1973990950","https://openalex.org/W1978660262","https://openalex.org/W1979062697","https://openalex.org/W1983924090","https://openalex.org/W1990257603","https://openalex.org/W1997194251","https://openalex.org/W1997677703","https://openalex.org/W1999651610","https://openalex.org/W2000338428","https://openalex.org/W2001648635","https://openalex.org/W2005238835","https://openalex.org/W2012267789","https://openalex.org/W2016444985","https://openalex.org/W2018136225","https://openalex.org/W2019161608","https://openalex.org/W2028989537","https://openalex.org/W2036864541","https://openalex.org/W2050616618","https://openalex.org/W2054092091","https://openalex.org/W2058046532","https://openalex.org/W2059007296","https://openalex.org/W2063237661","https://openalex.org/W2065445371","https://openalex.org/W2065815171","https://openalex.org/W2071797780","https://openalex.org/W2073021981","https://openalex.org/W2078524519","https://openalex.org/W2091202088","https://openalex.org/W2093583661","https://openalex.org/W2094102206","https://openalex.org/W2099741732","https://openalex.org/W2103681669","https://openalex.org/W2104289741","https://openalex.org/W2105778736","https://openalex.org/W2106219595","https://openalex.org/W2106664807","https://openalex.org/W2108384452","https://openalex.org/W2108900027","https://openalex.org/W2109743529","https://openalex.org/W2110065044","https://openalex.org/W2111609296","https://openalex.org/W2116649573","https://openalex.org/W2123649031","https://openalex.org/W2124588234","https://openalex.org/W2124757684","https://openalex.org/W2127384340","https://openalex.org/W2128058309","https://openalex.org/W2128157506","https://openalex.org/W2133188546","https://openalex.org/W2134363988","https://openalex.org/W2139376466","https://openalex.org/W2139896607","https://openalex.org/W2139906140","https://openalex.org/W2139940714","https://openalex.org/W2141622014","https://openalex.org/W2143132653","https://openalex.org/W2143360274","https://openalex.org/W2143826137","https://openalex.org/W2145295623","https://openalex.org/W2150825673","https://openalex.org/W2151635492","https://openalex.org/W2154462399","https://openalex.org/W2155513557","https://openalex.org/W2156676059","https://openalex.org/W2156909104","https://openalex.org/W2157026617","https://openalex.org/W2157840151","https://openalex.org/W2163274894","https://openalex.org/W2166035259","https://openalex.org/W2169011823","https://openalex.org/W2171831801","https://openalex.org/W2523130748","https://openalex.org/W2544883878","https://openalex.org/W3142828467"],"related_works":["https://openalex.org/W7098730142","https://openalex.org/W2099192642","https://openalex.org/W2938933990","https://openalex.org/W3163491696","https://openalex.org/W7096304748","https://openalex.org/W2077406389","https://openalex.org/W1492564612","https://openalex.org/W6921333198","https://openalex.org/W4405920699","https://openalex.org/W2512705261"],"abstract_inverted_index":{"The":[0,76,103,110,191,222],"study":[1,94,123,269],"of":[2,13,37,57,67,78,118,127,197,206,219,224,272,283],"the":[3,38,55,58,70,95,116,152,166,175,182,195,237,257,265,270,273,281],"brain":[4,98,207],"and":[5,89,100,144,180,260,279],"its":[6],"connection":[7,96],"to":[8,15,33,53,82,87,93,115,150,164,184,229],"human":[9,74],"activities":[10],"has":[11],"been":[12,31],"interest":[14],"scientists":[16],"for":[17,211],"centuries.":[18],"However,":[19],"it":[20],"is":[21,44,63,81,105,113,228],"only":[22],"in":[23,73,253,256,264],"recent":[24],"years":[25],"that":[26,48,234],"medical":[27],"imaging":[28],"methods":[29,86,135,186],"have":[30],"developed":[32],"allow":[34],"a":[35,46,50,64,137,145,160,170,203,217,244],"visualization":[36],"brain.":[39,59,75],"Magnetic":[40],"Resonance":[41],"Imaging":[42],"(MRI)":[43],"such":[45],"technique":[47],"provides":[49,202],"noninvasive":[51],"way":[52],"view":[54],"structure":[56],"Functional":[60],"MRI":[61,91,201,226],"(fMRI)":[62],"special":[65],"type":[66],"MRI,":[68],"measuring":[69],"neural":[71],"activity":[72],"aim":[77],"this":[79,156],"dissertation":[80,104],"apply":[83],"machine":[84],"learning":[85,247],"functional":[88],"anatomical":[90,198,214,225],"data":[92,129,179,189,278],"between":[97],"regions":[99,255],"their":[101],"functions.":[102],"divided":[106],"into":[107],"two":[108],"parts.":[109],"first":[111],"part":[112,193],"devoted":[114],"analysis":[117,196,227],"fMRI.":[119],"A":[120],"standard":[121],"fMRI":[122],"produces":[124],"massive":[125],"amount":[126],"noisy":[128],"with":[130],"strong":[131],"spatio-temporal":[132,142],"correlation.":[133],"Existing":[134],"include":[136],"model-based":[138,171],"approach":[139],"which":[140,148,249],"assumes":[141],"independence":[143],"data-driven":[146],"method":[147,176,275],"fails":[149],"exploit":[151],"experimental":[153],"design.":[154],"In":[155],"work":[157],"we":[158],"propose":[159,243],"Gaussian":[161],"process":[162],"model":[163],"incorporate":[165],"temporal":[167],"correlation":[168],"through":[169,187],"approach.":[172],"We":[173,242,268],"validate":[174],"on":[177,286],"simulated":[178,277],"compare":[181],"results":[183],"other":[185],"real":[188],"analysis.":[190],"second":[192],"covers":[194],"MRI.":[199],"Anatomical":[200],"detailed":[204],"map":[205],"structure,":[208],"especially":[209],"useful":[210],"detecting":[212],"small":[213,254],"changes":[215],"as":[216],"result":[218],"disease":[220],"process.":[221],"goal":[223],"train":[230],"an":[231],"automated":[232],"classifier":[233,248,285],"can":[235],"identify":[236],"patients":[238],"from":[239],"healthy":[240],"controls.":[241],"multiple":[245],"kernel":[246],"will":[250],"build":[251],"classifiers":[252],"segregating":[258],"step":[259],"then":[261],"group":[262],"them":[263],"integrating":[266],"step.":[267],"performance":[271],"new":[274],"using":[276],"demonstrate":[280],"power":[282],"our":[284],"disease-related":[287],"data.":[288]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
