{"id":"https://openalex.org/W7220474424","doi":"https://doi.org/10.64898/2026.09.29.755278","title":"An information-borrowing Bayesian method for inferring high-dimensional population-level brain connectivity from small samples","display_name":"An information-borrowing Bayesian method for inferring high-dimensional population-level brain connectivity from small samples","publication_year":2026,"publication_date":"2026-10-05","ids":{"openalex":"https://openalex.org/W7220474424","doi":"https://doi.org/10.64898/2026.09.29.755278"},"language":"en","primary_location":{"id":"doi:10.64898/2026.09.29.755278","doi":"https://doi.org/10.64898/2026.09.29.755278","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.09.29.755278","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/05/2026.09.29.755278.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/05/2026.09.29.755278.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104345555","display_name":"Zhibin Pu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhibin Pu","raw_affiliation_strings":["Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019184087","display_name":"Chenhao Zeng","orcid":"https://orcid.org/0000-0002-1714-4962","observed_orcids":["https://orcid.org/0000-0002-1714-4962"]},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenhao Zeng","raw_affiliation_strings":["Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083832514","display_name":"Shufei Ge","orcid":"https://orcid.org/0000-0002-9395-3884","observed_orcids":["https://orcid.org/0000-0002-9395-3884"]},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shufei Ge","raw_affiliation_strings":["Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5083832514"],"corresponding_institution_ids":["https://openalex.org/I30809798"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.5189999938011169,"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":0.5189999938011169,"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/T10136","display_name":"Statistical Methods and Inference","score":0.4462999999523163,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.0032999999821186066,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/brain-connectivity","display_name":"brain connectivity","score":0.6650000214576721},{"id":"https://openalex.org/keywords/high-dimensional-covariance-estimation","display_name":"high-dimensional covariance estimation","score":0.8830000162124634},{"id":"https://openalex.org/keywords/information-borrowing","display_name":"information borrowing","score":0.7609999775886536},{"id":"https://openalex.org/keywords/bayesian-shrinkage","display_name":"Bayesian shrinkage","score":0.734000027179718},{"id":"https://openalex.org/keywords/horseshoe-prior","display_name":"horseshoe prior","score":0.8629999756813049},{"id":"https://openalex.org/keywords/hamiltonian-monte-carlo","display_name":"Hamiltonian Monte Carlo","score":0.9369999766349792},{"id":"https://openalex.org/keywords/ultrahigh-dimensional-data","display_name":"ultra-high dimensional data","score":0.6880000233650208},{"id":"https://openalex.org/keywords/cortical-thickness","display_name":"cortical thickness","score":0.7409999966621399},{"id":"https://openalex.org/keywords/attention-deficit-hyperactivity-disorder","display_name":"attention deficit hyperactivity disorder","score":0.8360000252723694},{"id":"https://openalex.org/keywords/resting-state-fmri","display_name":"resting-state fMRI","score":0.984000027179718},{"id":"https://openalex.org/keywords/default-mode-network","display_name":"default mode network","score":0.7580000162124634},{"id":"https://openalex.org/keywords/sensorimotor-network","display_name":"sensorimotor network","score":0.9179999828338623},{"id":"https://openalex.org/keywords/group-differences","display_name":"group differences","score":0.3930000066757202}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6818000078201294},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5861999988555908},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5771999955177307},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5702999830245972},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5382000207901001},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4918999969959259},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.447299987077713},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4424000084400177},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4374000132083893},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4174000024795532},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.4020000100135803},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3804999887943268},{"id":"https://openalex.org/C62438384","wikidata":"https://www.wikidata.org/wiki/Q331350","display_name":"Simplex","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.35040000081062317},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.33730000257492065},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3321000039577484},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.2833999991416931},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.2565999925136566},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.25540000200271606},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.64898/2026.09.29.755278","doi":"https://doi.org/10.64898/2026.09.29.755278","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.09.29.755278","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/05/2026.09.29.755278.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.64898/2026.09.29.755278","doi":"https://doi.org/10.64898/2026.09.29.755278","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.09.29.755278","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/05/2026.09.29.755278.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G7261268585","display_name":null,"funder_award_id":"12401383","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327675","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220474424.pdf","grobid_xml":"https://content.openalex.org/works/W7220474424.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W2013223495","https://openalex.org/W2572702041"],"related_works":["https://openalex.org/W7165877949","https://openalex.org/W4400446852","https://openalex.org/W2737175082","https://openalex.org/W2921455576","https://openalex.org/W3200776430","https://openalex.org/W2891402244","https://openalex.org/W3005515924","https://openalex.org/W3029341825","https://openalex.org/W2773296233","https://openalex.org/W4394775156"],"abstract_inverted_index":{"Abstract":[0],"Estimating":[1],"population-level":[2],"dependence":[3],"between":[4],"brain":[5,33],"regions":[6,13],"is":[7,62,91],"challenging":[8],"when":[9],"the":[10,16,73,92,102,111,159,185],"number":[11,17,74,93],"of":[12,18,75,94,104,119],"greatly":[14],"exceeds":[15],"individuals.":[19],"We":[20],"introduce":[21],"an":[22,44],"information-borrowing":[23],"generalized":[24],"Bayesian":[25],"method":[26],"for":[27,132],"high-dimensional":[28],"covariance":[29,55,100,133],"estimation":[30,134,156],"from":[31,41,78],"region-level":[32],"measurements.":[34],"A":[35],"divide-and-conquer":[36],"parameterization":[37],"separates":[38],"marginal":[39],"variances":[40],"correlation":[42,47],"calibrations:":[43],"empirical":[45],"pilot":[46,120,126],"matrix\u2013by":[48],"default,":[49],"obtained":[50],"by":[51],"standardizing":[52],"a":[53,58,98,105,142,147,172],"sample":[54],"matrix":[56],"(SCM)\u2013provides":[57],"reference":[59],"structure":[60],"that":[61],"adaptively":[63],"calibrated":[64],"through":[65,101],"region-specific":[66],"parameters":[67,77,113],"shared":[68],"across":[69],"pairwise":[70],"correlations,":[71],"reducing":[72],"free":[76],"O":[79,85],"(":[80,86,89],"d":[81,87,90],"2":[82],")":[83,88],"to":[84,171],"regions)":[95],"while":[96,135],"guaranteeing":[97],"positive-definite":[99],"inclusion":[103],"ridge":[106],"term.":[107],"Horseshoe":[108],"priors":[109],"on":[110,125],"calibration":[112],"enable":[114],"selective":[115],"borrowing":[116],"and":[117,146,162,175,188],"shrinkage":[118],"information.":[121],"Under":[122],"suitable":[123],"conditions":[124],"accuracy,":[127],"we":[128],"establish":[129],"theoretical":[130],"guarantees":[131],"retaining":[136],"scalability":[137],"in":[138,184],"ultra-high-dimensional":[139],"settings":[140],"via":[141],"low-rank":[143],"likelihood":[144],"representation":[145],"Woodbury-accelerated":[148],"Hamiltonian":[149],"Monte":[150],"Carlo":[151],"algorithm.":[152],"Simulations":[153],"showed":[154],"lower":[155],"error":[157],"than":[158],"SCM,":[160],"bagging":[161],"iteratively":[163],"refined":[164],"principal":[165],"orthogonal":[166],"complement":[167],"thresholding":[168],"(POETRY).":[169],"Applications":[170],"semi-synthetic":[173],"cortical-thickness":[174],"ADHD":[176],"resting-state":[177],"fMRI":[178],"data":[179],"revealed":[180],"significant":[181],"group":[182],"differences":[183],"default-mode\u2013visual":[186],"network":[187,191],"somatomotor":[189],"network\u2013limbic":[190],"blocks":[192],"after":[193],"Benjamini\u2013Hochberg":[194],"correction.":[195]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-10-06T00:00:00"}
