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| 001 | 1023459 | ||
| 005 | 20250204113806.0 | ||
| 024 | 7 | _ | |a 10.1142/S0129065724500187 |2 doi |
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| 100 | 1 | _ | |a Jiang, Lin |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Multimodal Covariance Network Reflects Individual Cognitive Flexibility |
| 260 | _ | _ | |a Singapore [u.a.] |c 2024 |b World Scientific Publ. Co. |
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| 520 | _ | _ | |a Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.Keywords: Cognitive flexibility; EEG-MRI; multimodal covariance network; response prediction; trail-making test. |
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| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 1 |
| 700 | 1 | _ | |a Genon, Sarah |0 P:(DE-Juel1)161225 |b 2 |
| 700 | 1 | _ | |a Wang, Guangying |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Yi, Chanlin |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a He, Runyang |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Huang, Xunan |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Yao, Dezhong |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Dong, Debo |0 P:(DE-Juel1)178872 |b 8 |
| 700 | 1 | _ | |a Li, Fali |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Xu, Peng |0 P:(DE-HGF)0 |b 10 |e Corresponding author |
| 773 | _ | _ | |a 10.1142/S0129065724500187 |g p. 2450018 |0 PERI:(DE-600)1498197-X |n 4 |p 2450018 |t International journal of neural systems |v 34 |y 2024 |x 0129-0657 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1023459/files/jiang-et-al-2024-multimodal-covariance-network-reflects-individual-cognitive-flexibility.pdf |
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