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| Home > Publications database > Predicting dual-task performance from individualized functional and structural netwo |
| Poster (After Call) | FZJ-2023-03487 |
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2023
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Please use a persistent id in citations: doi:10.34734/FZJ-2023-03487
Abstract: IntroductionDual-tasking, the ability to perform two tasks simultaneously or in close succession, has been associated with increased fronto-parietal activity [1,2]. Furthermore, functional and structural connectivity (FC and SC) display unique features shown to be relevant to cognition and performance; however, prediction studies show overall low accuracies [3]. Accounting for inter-individual variability in macroscopic brain organization may be informative for elucidating such brain-behavior associations at the individual level [4,5]. Here, we aimed to predict dual-task performance from FC and SC in individualized task-specific and whole-brain (WB) networks.MethodsWe obtained 92 individualized discrete and homologous functional regions (Fig. 1A) from 71 healthy adults (33 females; 41 young and 30 older) by implementing an iterative cortical parcellation approach based on functional surface-level connectivity patterns [4,5] in combined resting-state and task-based fMRI data. For each participant, Pearson correlations were computed between the individualized parcels' time series to derive FC estimates. The individualized parcels were projected onto processed diffusion-weighted images and normalized between-parcel streamline counts were calculated to derive individualized SC estimates. Besides deriving WB networks, connectomes were additionally obtained from task-related parcels. These were defined through a group-level general linear model of brain activity elicited in a dual-task paradigm with spatially incongruent manual responses to auditory stimuli [6,7]. The incongruent dual-task condition recruited a large fronto-parietal network [2], which overlapped with 25 out of 92 functional regions (Fig. 1B). For comparison, we also computed FC and SC between non-individualized parcels for both WB and task-specific networks. To predict subject-level reaction time in dual-tasking with incongruent responses (615.21 ± 118.38 ms), we implemented CPM, PLS, KRR and RFR as incorporated in JuLearn [8] with a 5-times, 5-fold cross-validation (CV) approach based on eight separate feature spaces (i.e., FC vs. SC × task-specific vs. WB networks × indiv. vs. non-indiv. networks). Prediction performance was evaluated by the mean absolute percentage error (MAPE), averaged across all models for all folds and repetitions of the CV test set.ResultsThe best prediction of dual-task performance was achieved by the individualized task-specific and WB structural networks (MAPE = 15.13 ± 2.32 %, and MAPE = 15.34 ± 2.27%, respectively), as well as the individualized WB functional network (MAPE = 15.45 ± 3.18 %). This was followed by the non-individualized WB functional network (MAPE = 15.73 ± 3.15 %). Predictability was lowest for the individualized task-specific functional network (MAPE = 16.73 ± 3.00).ConclusionsWhile most brain-behavior prediction studies use group-level brain atlases, our findings indicate a slight improvement in prediction accuracy when accounting for inter-individual variability in the brain's functional organization, despite the overall low predictability and small differences between feature spaces. Our results for predicting dual-task performance align with an improvement in predicting fluid intelligence from individualized functional networks [4]. Counter to expectation, WB functional networks outperformed the dual-task-specific ones, which could be related to the relevance of global brain organizational properties in brain-behavior associations [9,10], whose individual nuances are better captured by the WB functional connectome. Finally, we suggest a replication study with a larger sample and the better predictability from SC (vs. FC) calls for further research integrating multi-modal brain features to explore their unique and combined contribution to cognition [3].
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