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| Hauptseite > Publikationsdatenbank > Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion > print |
| Hauptseite > Publikationsdatenbank > Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion > print |
| 001 | 848149 | ||
| 005 | 20210129234004.0 | ||
| 024 | 7 | _ | |a 10.1093/cercor/bhy123 |2 doi |
| 024 | 7 | _ | |a 1047-3211 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2018-03421 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Kong, Ru |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion |
| 260 | _ | _ | |a Oxford |c 2019 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1557994903_1843 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior. |
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| 700 | 1 | _ | |a Li, Jingwei |0 P:(DE-HGF)0 |b 1 |
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| 700 | 1 | _ | |a Sabuncu, Mert R |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Liu, Hesheng |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Schaefer, Alexander |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Sun, Nanbo |0 P:(DE-HGF)0 |b 6 |
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| 700 | 1 | _ | |a Yeo, B T Thomas |0 0000-0002-0119-3276 |b 10 |e Corresponding author |
| 773 | _ | _ | |a 10.1093/cercor/bhy123 |0 PERI:(DE-600)1483485-6 |n 6 |p 2533–2551 |t Cerebral cortex |v 29 |y 2019 |x 1460-2199 |
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