{"id":"https://openalex.org/W2074005617","doi":"https://doi.org/10.1109/mlsp.2015.7324375","title":"Sparse model learning for high dimensional diffusion MRI data in traumatic brain injury","display_name":"Sparse model learning for high dimensional diffusion MRI data in traumatic brain injury","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2074005617","doi":"https://doi.org/10.1109/mlsp.2015.7324375","mag":"2074005617"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2015.7324375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5001271123","display_name":"Matineh Shaker","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]},{"id":"https://openalex.org/I1283280774","display_name":"Brigham and Women's Hospital","ror":"https://ror.org/04b6nzv94","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283280774","https://openalex.org/I48633490"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Matineh Shaker","raw_affiliation_strings":["Brigham and Women's Hospital, Harvard Medical School, Boston, MA","ECE Department, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Brigham and Women's Hospital, Harvard Medical School, Boston, MA","institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I136199984"]},{"raw_affiliation_string":"ECE Department, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083261801","display_name":"Deniz Erdo\u011fmu\u015f","orcid":"https://orcid.org/0000-0002-1114-3539"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deniz Erdogmus","raw_affiliation_strings":["ECE Department, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"ECE Department, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042038501","display_name":"Jennifer Dy","orcid":"https://orcid.org/0000-0002-8430-134X"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jennifer Dy","raw_affiliation_strings":["ECE Department, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"ECE Department, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002424091","display_name":"Sylvain Bouix","orcid":"https://orcid.org/0000-0003-1326-6054"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]},{"id":"https://openalex.org/I1283280774","display_name":"Brigham and Women's Hospital","ror":"https://ror.org/04b6nzv94","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283280774","https://openalex.org/I48633490"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sylvain Bouix","raw_affiliation_strings":["Brigham and Women's Hospital, Harvard Medical School, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Brigham and Women's Hospital, Harvard Medical School, Boston, MA","institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5001271123"],"corresponding_institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I12912129","https://openalex.org/I136199984"],"apc_list":null,"apc_paid":null,"fwci":0.2635,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61764488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.9939000010490417,"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/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.6955727338790894},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.6243838667869568},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5796202421188354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5528532862663269},{"id":"https://openalex.org/keywords/traumatic-brain-injury","display_name":"Traumatic brain injury","score":0.5383924245834351},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5259188413619995},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5011215209960938},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49843621253967285},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4749222695827484},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4736163318157196},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4567928612232208},{"id":"https://openalex.org/keywords/matrix-normal-distribution","display_name":"Matrix normal distribution","score":0.4462057948112488},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44135791063308716},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.41645222902297974},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4124360978603363},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36744850873947144},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2749304473400116},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11601370573043823},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.11278766393661499},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09047728776931763}],"concepts":[{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.6955727338790894},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.6243838667869568},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5796202421188354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5528532862663269},{"id":"https://openalex.org/C2781017439","wikidata":"https://www.wikidata.org/wiki/Q1995526","display_name":"Traumatic brain injury","level":2,"score":0.5383924245834351},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5259188413619995},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5011215209960938},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49843621253967285},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4749222695827484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4736163318157196},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4567928612232208},{"id":"https://openalex.org/C58501165","wikidata":"https://www.wikidata.org/wiki/Q4820795","display_name":"Matrix normal distribution","level":4,"score":0.4462057948112488},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44135791063308716},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.41645222902297974},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4124360978603363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36744850873947144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2749304473400116},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11601370573043823},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.11278766393661499},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09047728776931763},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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":2,"locations":[{"id":"doi:10.1109/mlsp.2015.7324375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:espace2.etsmtl.ca:24750","is_oa":false,"landing_page_url":"https://espace2.etsmtl.ca/id/eprint/24750/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402392","display_name":"Espace \u00c9TS (ETS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1341030882","host_organization_name":"Educational Testing Service","host_organization_lineage":["https://openalex.org/I1341030882"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Compte rendu de conf\u00e9rence"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7599999904632568,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1586475594","https://openalex.org/W2004293194","https://openalex.org/W2004842662","https://openalex.org/W2061564920","https://openalex.org/W2079868698","https://openalex.org/W2122241942","https://openalex.org/W2132555912","https://openalex.org/W2144494407","https://openalex.org/W2144974984","https://openalex.org/W2147950750","https://openalex.org/W2151128232","https://openalex.org/W2166858899","https://openalex.org/W4250766106","https://openalex.org/W4388323202","https://openalex.org/W6682227116"],"related_works":["https://openalex.org/W3209122612","https://openalex.org/W242057822","https://openalex.org/W2087633920","https://openalex.org/W1480550861","https://openalex.org/W2002611209","https://openalex.org/W1984648871","https://openalex.org/W320706093","https://openalex.org/W4252836768","https://openalex.org/W2481638807","https://openalex.org/W1987270193"],"abstract_inverted_index":{"The":[0,65,114],"estimation":[1],"of":[2,5,18,34,41,73,81,104,116,133,150],"multivariate":[3,31],"distributions":[4],"from":[6],"high":[7],"dimensional":[8],"data":[9],"with":[10,60,199],"low":[11],"sample":[12],"size":[13],"is":[14,51],"at":[15],"the":[16,55,71,79,82,92,102,110,122,136,148,151,171],"core":[17],"many":[19],"image":[20],"analysis":[21],"applications.":[22],"Here,":[23],"we":[24],"present":[25],"a":[26,30,39,74,163,182,188],"method":[27],"to":[28,53,97,129,162,181],"estimate":[29],"Gaussian":[32],"distribution":[33,50,83,124],"diffusion":[35],"tensor":[36],"features":[37,128],"in":[38,58,70,156,159],"set":[40],"brain":[42,62,105,172],"regions":[43,106],"based":[44],"on":[45,91],"45":[46],"healthy":[47],"individuals.":[48],"This":[49,95],"used":[52,126],"identify":[54],"imaging":[56],"abnormalities":[57],"subjects":[59,120],"traumatic":[61],"injury":[63],"(TBI).":[64],"model":[66,93,100,112,164,184],"receives":[67],"apriori":[68],"knowledge":[69],"form":[72],"neighborhood":[75,152],"graph,":[76],"and":[77,118,206],"maximizes":[78],"likelihood":[80],"while":[84],"putting":[85],"an":[86,98],"additional":[87],"L1":[88],"sparsity":[89],"constraint":[90,154],"parameters.":[94],"leads":[96,180],"interpretable":[99],"where":[101],"interactions":[103],"are":[107,125],"encoded":[108],"into":[109,169],"estimated":[111,123],"coefficients.":[113],"likelihoods":[115],"normal":[117],"TBI":[119],"under":[121,138],"as":[127,193],"evaluate":[130],"discriminatory":[131],"power":[132],"model,":[134],"using":[135,187],"area":[137],"receiver":[139],"operating":[140],"characteristic":[141],"(ROC)":[142],"curves.":[143],"Our":[144],"experiments":[145],"show":[146],"that":[147],"addition":[149],"graph":[153,179,192],"results":[155],"significant":[157],"improvements":[158],"classification":[160,197],"compared":[161],"which":[165],"does":[166],"not":[167],"take":[168],"account":[170],"region":[173],"interactions.":[174],"In":[175],"addition,":[176],"our":[177],"prior":[178,191],"better":[183],"than":[185],"one":[186],"fully":[189],"connected":[190],"it":[194],"provides":[195],"similar":[196],"performance":[198],"10":[200],"times":[201],"fewer":[202],"parameters":[203],"(model":[204],"order),":[205],"significantly":[207],"lower":[208],"Bayesian":[209],"information":[210],"criterion":[211],"(BIC).":[212]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
