4.7 Article

Combining multiple connectomes improves predictive modeling of phenotypic measures

期刊

NEUROIMAGE
卷 201, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2019.116038

关键词

Machine learning; Neural networks; Elastic net; Lasso; fMRI; Functional connectivity

资金

  1. NIH Institutes and Centers [1U54MH091657]
  2. NIH Blueprint for Neuroscience Research
  3. McDonnell Center for Systems Neuroscience at Washington University
  4. NIH [RC2MH089983, C2MH089924, R01 MH111424, P40 MH115716, R24 MH114805, T32GM007205]

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Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks-the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort, we validate and compare our framework against performing connectome-based predictive modeling (CPM) on each task connectome independently, CPM on a general functional connectivity matrix created by averaging together all task connectomes for an individual, and CPM with a naive extension to multiple connectomes where each edge for each task is selected independently. Our framework exhibits superior performance in prediction compared with the other competing methods. We found that different tasks contribute differentially to the final predictive model, suggesting that the battery of tasks used in prediction is an important consideration. This work makes two major contributions: First, two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated; Second, we show that these models outperform a previously validated single connectome-based predictive model approach.

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