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@ARTICLE{Ooi:915908,
      author       = {Ooi, Leon Qi Rong and Chen, Jianzhong and Zhang, Shaoshi
                      and Kong, Ru and Tam, Angela and Li, Jingwei and Dhamala,
                      Elvisha and Zhou, Juan Helen and Holmes, Avram J and Yeo, B.
                      T. Thomas},
      title        = {{C}omparison of individualized behavioral predictions
                      across anatomical, diffusion and functional connectivity
                      {MRI}},
      journal      = {NeuroImage},
      volume       = {263},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2022-05777},
      pages        = {119636 -},
      year         = {2022},
      abstract     = {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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36116616},
      UT           = {WOS:000870701300005},
      doi          = {10.1016/j.neuroimage.2022.119636},
      url          = {https://juser.fz-juelich.de/record/915908},
}