Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295906315> ?p ?o ?g. }
- W4295906315 endingPage "4025" @default.
- W4295906315 startingPage "4013" @default.
- W4295906315 abstract "Abstract Background Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors. Objective Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants. Methods In both brain assessments, we used penalized logistic regression models and nonparametric permutation. Results We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings. Conclusions These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain." @default.
- W4295906315 created "2022-09-16" @default.
- W4295906315 creator A5022464626 @default.
- W4295906315 creator A5023785316 @default.
- W4295906315 creator A5032788727 @default.
- W4295906315 creator A5034034296 @default.
- W4295906315 creator A5051587217 @default.
- W4295906315 creator A5060641903 @default.
- W4295906315 creator A5072588247 @default.
- W4295906315 creator A5084051019 @default.
- W4295906315 date "2022-09-14" @default.
- W4295906315 modified "2023-09-25" @default.
- W4295906315 title "Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity" @default.
- W4295906315 cites W1687955123 @default.
- W4295906315 cites W179251563 @default.
- W4295906315 cites W1947482578 @default.
- W4295906315 cites W1964866242 @default.
- W4295906315 cites W1965445933 @default.
- W4295906315 cites W1968206530 @default.
- W4295906315 cites W1987734440 @default.
- W4295906315 cites W1987763849 @default.
- W4295906315 cites W1992065488 @default.
- W4295906315 cites W1998502545 @default.
- W4295906315 cites W2000455933 @default.
- W4295906315 cites W2002870762 @default.
- W4295906315 cites W2007318901 @default.
- W4295906315 cites W2016899355 @default.
- W4295906315 cites W2035614594 @default.
- W4295906315 cites W2036245134 @default.
- W4295906315 cites W2038045764 @default.
- W4295906315 cites W2043789426 @default.
- W4295906315 cites W2045830310 @default.
- W4295906315 cites W2047751255 @default.
- W4295906315 cites W2050489747 @default.
- W4295906315 cites W2052644075 @default.
- W4295906315 cites W2055602145 @default.
- W4295906315 cites W2060232149 @default.
- W4295906315 cites W2064098293 @default.
- W4295906315 cites W2071881327 @default.
- W4295906315 cites W2077961641 @default.
- W4295906315 cites W2078176812 @default.
- W4295906315 cites W2082714165 @default.
- W4295906315 cites W2084387589 @default.
- W4295906315 cites W2087176235 @default.
- W4295906315 cites W2093617721 @default.
- W4295906315 cites W2103466378 @default.
- W4295906315 cites W2111902267 @default.
- W4295906315 cites W2118155022 @default.
- W4295906315 cites W2122550309 @default.
- W4295906315 cites W2130961842 @default.
- W4295906315 cites W2131248082 @default.
- W4295906315 cites W2133125780 @default.
- W4295906315 cites W2138790588 @default.
- W4295906315 cites W2145330142 @default.
- W4295906315 cites W2148272889 @default.
- W4295906315 cites W2148726987 @default.
- W4295906315 cites W2257837858 @default.
- W4295906315 cites W2344523805 @default.
- W4295906315 cites W2584622303 @default.
- W4295906315 cites W2590328111 @default.
- W4295906315 cites W2604434957 @default.
- W4295906315 cites W2610021178 @default.
- W4295906315 cites W2771290777 @default.
- W4295906315 cites W2771873354 @default.
- W4295906315 cites W2883171159 @default.
- W4295906315 cites W2886589619 @default.
- W4295906315 cites W2901568506 @default.
- W4295906315 cites W2951617899 @default.
- W4295906315 cites W2958910770 @default.
- W4295906315 cites W2960722855 @default.
- W4295906315 cites W2970816531 @default.
- W4295906315 cites W3003899665 @default.
- W4295906315 cites W3092538788 @default.
- W4295906315 cites W3095360711 @default.
- W4295906315 cites W3133803551 @default.
- W4295906315 cites W3151011896 @default.
- W4295906315 cites W3157114013 @default.
- W4295906315 cites W4211111853 @default.
- W4295906315 cites W4225141725 @default.
- W4295906315 doi "https://doi.org/10.1093/cercor/bhac323" @default.
- W4295906315 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36104854" @default.
- W4295906315 hasPublicationYear "2022" @default.
- W4295906315 type Work @default.
- W4295906315 citedByCount "0" @default.
- W4295906315 crossrefType "journal-article" @default.
- W4295906315 hasAuthorship W4295906315A5022464626 @default.
- W4295906315 hasAuthorship W4295906315A5023785316 @default.
- W4295906315 hasAuthorship W4295906315A5032788727 @default.
- W4295906315 hasAuthorship W4295906315A5034034296 @default.
- W4295906315 hasAuthorship W4295906315A5051587217 @default.
- W4295906315 hasAuthorship W4295906315A5060641903 @default.
- W4295906315 hasAuthorship W4295906315A5072588247 @default.
- W4295906315 hasAuthorship W4295906315A5084051019 @default.
- W4295906315 hasConcept C106934330 @default.
- W4295906315 hasConcept C126838900 @default.
- W4295906315 hasConcept C143409427 @default.
- W4295906315 hasConcept C145940234 @default.
- W4295906315 hasConcept C154945302 @default.