{"id":"https://openalex.org/W7161288363","doi":"https://doi.org/10.48550/arxiv.2605.13933","title":"Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling","display_name":"Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161288363","doi":"https://doi.org/10.48550/arxiv.2605.13933"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13933","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13933","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.13933","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136231053","display_name":"Gaurav Rudravaram","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rudravaram, Gaurav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003957722","display_name":"Lianrui Zuo","orcid":"https://orcid.org/0000-0002-5923-9097"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuo, Lianrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012851448","display_name":"Karthik Ramadass","orcid":"https://orcid.org/0000-0002-4610-3860"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramadass, Karthik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136194228","display_name":"Elyssa McMaster","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McMaster, Elyssa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134121512","display_name":"Jongyeon Yoon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon, Jongyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082765146","display_name":"Aravind R. Krishnan","orcid":"https://orcid.org/0009-0000-1829-8245"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishnan, Aravind R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136266560","display_name":"Adam M. Saunders","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saunders, Adam M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136192021","display_name":"Chenyu Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Chenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007290834","display_name":"Nancy R. Newlin","orcid":"https://orcid.org/0000-0003-3714-4684"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Newlin, Nancy R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136229538","display_name":"Praitayini Kanakaraj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kanakaraj, Praitayini","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136223827","display_name":"Lori L. Beason Held","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Held, Lori L. Beason","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136257867","display_name":"Murat Bilgel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bilgel, Murat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136189305","display_name":"Laura A. Barquero","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barquero, Laura A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080996593","display_name":"Micah D'Archangel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"DArchangel, Micah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136233779","display_name":"Tin Q. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Tin Q.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136246059","display_name":"Laurie B. Cutting","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cutting, Laurie B.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136212673","display_name":"Derek Archer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Archer, Derek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136257834","display_name":"Timothy J. Hohman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hohman, Timothy J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136206293","display_name":"Daniel C. Moyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moyer, Daniel C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136208280","display_name":"Bennett A. Landman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Landman, Bennett A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.984499990940094,"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":0.984499990940094,"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":0.003100000089034438,"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":0.0010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6980999708175659},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6416000127792358},{"id":"https://openalex.org/keywords/connectome","display_name":"Connectome","score":0.5626000165939331},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.446399986743927},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4311000108718872},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42179998755455017},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.41990000009536743},{"id":"https://openalex.org/keywords/human-connectome-project","display_name":"Human Connectome Project","score":0.4074999988079071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516999840736389},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6980999708175659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6527000069618225},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6416000127792358},{"id":"https://openalex.org/C45715564","wikidata":"https://www.wikidata.org/wiki/Q1292103","display_name":"Connectome","level":3,"score":0.5626000165939331},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5034000277519226},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.446399986743927},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C97820695","wikidata":"https://www.wikidata.org/wiki/Q387749","display_name":"Human Connectome Project","level":3,"score":0.4074999988079071},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.2824000120162964},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2547000050544739},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13933","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13933","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.13933","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13933","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Acquisition":[0],"differences":[1],"across":[2],"sites,":[3],"scanners,":[4],"and":[5,64,109,134,154,170,221,231],"protocols":[6],"in":[7,26,215],"dMRI":[8],"introduce":[9,86],"variability":[10,214],"that":[11,21,90,173,194],"complicates":[12],"structural":[13,124],"connectome":[14],"analysis.":[15],"This":[16],"motivates":[17],"deep":[18],"learning":[19,186],"models":[20,61,172],"can":[22],"represent":[23],"high-dimensional":[24],"connectomes":[25,125],"a":[27,54,120,163,195,207],"low-dimensional":[28],"space":[29],"while":[30],"explicitly":[31],"separating":[32],"acquisition-related":[33],"effects":[34,49],"from":[35,127],"biological":[36],"variation.":[37],"Conventional":[38],"dimensionality":[39],"reduction":[40],"methods":[41],"model":[42,104],"all":[43],"variance":[44],"as":[45],"continuous,":[46],"so":[47],"acquisition":[48,213],"often":[50],"get":[51],"absorbed":[52],"into":[53],"continuous":[55,65,110],"latent":[56,111,198],"space.":[57],"Recent":[58],"hybrid":[59,171,196],"latent-space":[60],"combine":[62],"discrete":[63,79,108],"components":[66],"to":[67,76,105,132],"address":[68],"this,":[69],"but":[70],"typically":[71],"require":[72],"manual":[73,93],"capacity":[74],"tuning":[75,94],"ensure":[77],"the":[78,82,103,177,224],"component":[80],"captures":[81],"intended":[83],"variability.":[84],"We":[85,160],"an":[87],"unsupervised":[88,209],"framework":[89],"removes":[91],"this":[92],"by":[95,217],"architecturally":[96],"annealing":[97,182],"encoder":[98],"outputs":[99],"before":[100],"decoding,":[101],"allowing":[102],"adaptively":[106],"balance":[107],"variables":[112],"during":[113],"training.":[114],"To":[115],"evaluate":[116],"it,":[117],"we":[118],"curated":[119],"dataset":[121],"of":[122],"N=7,416":[123],"derived":[126],"dMRI,":[128],"spanning":[129],"ages":[130],"2":[131],"102":[133],"13":[135],"studies":[136],"with":[137,167,200,229],"25":[138],"unique":[139],"acquisition-parameter":[140],"combinations.":[141],"Of":[142],"these,":[143],"5,900":[144],"are":[145],"cognitively":[146],"unimpaired,":[147],"877":[148],"have":[149,156],"mild":[150],"cognitive":[151],"impairment":[152],"(MCI),":[153],"639":[155],"Alzheimer's":[157],"disease":[158],"(AD).":[159],"compare":[161],"against":[162],"standard":[164],"VAE,":[165],"PCA":[166],"k-means":[168],"clustering,":[169],"anneal":[174],"only":[175],"through":[176],"loss":[178],"function.":[179],"Our":[180],"architectural":[181,201],"produces":[183],"stronger":[184],"site":[185],"(ARI=0.53,":[187],"p&lt;0.05)":[188],"than":[189,203],"these":[190],"baselines.":[191],"Results":[192],"show":[193],"continuous-discrete":[197],"space,":[199],"rather":[202],"loss-based":[204],"annealing,":[205],"provides":[206],"useful":[208],"mechanism":[210],"for":[211],"capturing":[212],"dMRI:":[216],"jointly":[218],"modeling":[219],"smooth":[220],"categorical":[222],"structure,":[223],"Joint-VAE":[225],"recovers":[226],"clusters":[227],"aligned":[228],"scanner":[230],"protocol":[232],"differences.":[233]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}
