001048963 001__ 1048963
001048963 005__ 20251209202151.0
001048963 0247_ $$2doi$$a10.21203/rs.3.rs-7721822/v1
001048963 037__ $$aFZJ-2025-05060
001048963 1001_ $$0P:(DE-Juel1)177058$$aWu, Jianxiao$$b0$$eCorresponding author
001048963 245__ $$aMultimodal neuroimaging data boosts the prediction of multifaceted cognition
001048963 260__ $$c2025
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001048963 3367_ $$2ORCID$$aWORKING_PAPER
001048963 3367_ $$028$$2EndNote$$aElectronic Article
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001048963 3367_ $$2BibTeX$$aARTICLE
001048963 3367_ $$2DataCite$$aOutput Types/Working Paper
001048963 520__ $$aRelating individual brain patterns to behavioural phenotypes through predictive modelling has been increasingly popular. Several recent studies have focused on the fundamental challenge of improving behavioural prediction based on individual brain patterns, by integrating information from multimodal neuroimaging data. However, the benefit of multimodal integration in brain-based behaviour prediction remains debated due to inconsistent findings. This issue raises the need of a systematic and extensive evaluation. Here, we investigated the necessity and benefit of multimodal integration in 3 large datasets covering different age ranges, using 25 to 33 feature types from different imaging modalities, and 21 behavioural measures from different domains. By setting up multiple predictive models corresponding to increasing levels of multimodal integration, we demonstrated that prediction performance saturates after integrating a few types of features. In general, our analyses revealed that multifaceted cognitive scores tend to require higher levels of multimodal integration, while other predictions may depend on single feature types. In most cases, multimodal integration can remain focused on functional features, especially in young adults. However, predictions in aging can also require structural and diffusion features. Along the same line, while model-free rest and task functional connectivity may provide relevant brain phenotype for behavioural prediction in most applications, in aging, effective connectivity appears relevant too. Thus, our study demonstrates that alternatives to model-free functional connectivity and, more generally, to functional imaging features should be considered for predictive modelling of behaviour, especially in aging populations where understanding interindividual variability in remain as a key challenge.
001048963 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001048963 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001048963 588__ $$aDataset connected to CrossRef
001048963 7001_ $$0P:(DE-Juel1)164828$$aLi, Jingwei$$b1
001048963 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b2
001048963 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b3
001048963 7001_ $$0P:(DE-HGF)0$$aYeo, B. T. Thomas$$b4
001048963 7001_ $$0P:(DE-Juel1)161225$$aGenon, Sarah$$b5$$eEditor
001048963 773__ $$a10.21203/rs.3.rs-7721822/v1
001048963 8564_ $$uhttps://www.researchsquare.com/article/rs-7721822/v1
001048963 909CO $$ooai:juser.fz-juelich.de:1048963$$pVDB
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001048963 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b3
001048963 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161225$$aForschungszentrum Jülich$$b5$$kFZJ
001048963 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001048963 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)POF4-5253$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
001048963 9141_ $$y2025
001048963 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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