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| Hauptseite > Publikationsdatenbank > Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI > print |
| Hauptseite > Publikationsdatenbank > Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI > print |
| 001 | 915908 | ||
| 005 | 20230123110758.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2022.119636 |2 doi |
| 024 | 7 | _ | |a 1053-8119 |2 ISSN |
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| 100 | 1 | _ | |a Ooi, Leon Qi Rong |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI |
| 260 | _ | _ | |a Orlando, Fla. |c 2022 |b Academic Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC. |
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| 700 | 1 | _ | |a Chen, Jianzhong |0 P:(DE-HGF)0 |b 1 |
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| 700 | 1 | _ | |a Kong, Ru |0 P:(DE-HGF)0 |b 3 |
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| 700 | 1 | _ | |a Holmes, Avram J |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 9 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.neuroimage.2022.119636 |g Vol. 263, p. 119636 - |0 PERI:(DE-600)1471418-8 |p 119636 - |t NeuroImage |v 263 |y 2022 |x 1053-8119 |
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