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001040294 1001_ $$0P:(DE-Juel1)184969$$aLi, Xuan$$b0$$eCorresponding author
001040294 245__ $$aStimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions
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001040294 500__ $$aFunding: This work was supported by European Union's Horizon 2020 Research and Innovation Programme (945539; HBP SGA3) and Deutsche Forschungsgemeinschaft (491111487).
001040294 520__ $$aWhile the use of naturalistic stimuli such as movie clips for understanding individual differences and brain–behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning to predict individual traits (phenotypes) from brain activity evoked during various movie clips, we show that different movie stimuli can result in distinct prediction performances. In brain regions related to lower-level processing of the stimulus, prediction to a certain degree benefits from stronger synchronisation of brain activity across subjects. By contrast, better predictions in frontoparietal brain regions are mainly associated with larger inter-subject variability. Furthermore, we demonstrate that while movie clips with rich social content in general achieve better predictions, the importance of specific movie features for prediction highly depends on the phenotype under investigation. Overall, our findings underscore the importance of careful stimulus selection and provide novel insights into stimulus selection for phenotype prediction in naturalistic conditions, opening new avenues for future research.
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001040294 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1$$ufzj
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001040294 773__ $$0PERI:(DE-600)1492703-2$$a10.1002/hbm.70164$$gVol. 46, no. 3, p. e70164$$n3$$pe70164$$tHuman brain mapping$$v46$$x1065-9471$$y2025
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