000908663 001__ 908663
000908663 005__ 20220721190605.0
000908663 037__ $$aFZJ-2022-02752
000908663 041__ $$aEnglish
000908663 1001_ $$0P:(DE-Juel1)164828$$aLi, Jingwei$$b0$$eCorresponding author
000908663 1112_ $$aOrganization for Human Brain Mapping$$cGlasgow, Scotland$$d2022-06-19 - 2022-06-23$$wUK
000908663 245__ $$aCross-ethnicity/race generalization failure of RSFC-based behavioral prediction and potential downstream consequences
000908663 260__ $$c2022
000908663 3367_ $$033$$2EndNote$$aConference Paper
000908663 3367_ $$2DataCite$$aOther
000908663 3367_ $$2BibTeX$$aINPROCEEDINGS
000908663 3367_ $$2DRIVER$$aconferenceObject
000908663 3367_ $$2ORCID$$aLECTURE_SPEECH
000908663 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1658395380_12172$$xAfter Call
000908663 520__ $$aAlgorithmic biases that favor majority populations pose a key challenge to the application of machinelearning for precision medicine. In neuroimaging, there is growing interest in the prediction ofbehavioral phenotypes based on resting-state functional connectivity (RSFC). In that context, predictivemodels are typically built by capitalizing on large cohorts with mixed ethnic groups, in which theproportions of certain groups, e.g. African Americans (AA), are limited. Here, we investigated cross-ethnicity/race generalizability of the current, field-standard behavioral prediction approach using twolarge-scale public datasets from the United States. Specifically, we observed larger prediction errors inAA than white Americans (WA) for most behavioral measures using both the Human ConnectomeProject (HCP) and the Adolescent Brain Cognitive Development (ABCD) data. This prediction biastowards WA corresponded to more WA-like brain-behavior association patterns learned by the models.Looking into the direction of prediction errors, concerns can be raised if the machine-learning predictionresults would be uncritically used, in particular for the diagnosis of mental disorders. For example, socialsupport measures were more overpredicted for AA than WA, whereas social distress measures such asPerceived Rejection were more underpredicted for AA than WA.Furthermore, African pre-adolescent participants suffered from more overpredicted social problems,rule-breaking and aggressive behaviors compared to white participants. Effects of the trainingpopulations were also studied by comparing predictive models trained specifically on AA, specifically onWA, or on a mixture of AA and WA with equal sizes. Although specific training on AA slightly helped toreduce the biases against AA, most behavioral measures still exhibited larger prediction errors in AAthan WA. Other possible sources of the biases such as neuroimaging preprocessing (e.g., brain templatesand functional atlases) and the design of behavioral measures need to be examined in the future.
000908663 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000908663 8564_ $$uhttps://www.humanbrainmapping.org/files/2022/Annual%20Meeting%20Symposia/1017_Machine%5B1%5D.pdf
000908663 909CO $$ooai:juser.fz-juelich.de:908663$$pVDB
000908663 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164828$$aForschungszentrum Jülich$$b0$$kFZJ
000908663 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000908663 9141_ $$y2022
000908663 920__ $$lyes
000908663 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000908663 980__ $$aconf
000908663 980__ $$aVDB
000908663 980__ $$aI:(DE-Juel1)INM-7-20090406
000908663 980__ $$aUNRESTRICTED