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. Author manuscript; available in PMC: 2020 May 15.
Published in final edited form as: Neuroimage. 2019 Feb 27;192:145–155. doi: 10.1016/j.neuroimage.2019.02.060

Figure 2. Approach for predicting ELC scores at age 2 from WM connectomes at birth.

Figure 2.

Data were preprocessed to reshape each WM connectivity matrix at birth into a 3,003 dimension connectivity feature vector ((78 × 77)/2 = 3,003 region pairs) using the normalized connectivity values in the upper triangular portion of the symmetric connectome (not including the main diagonal). Classification labels were generated by grouping 75 FT subjects into two categories based on their performance on the 2-year ELC relative to others in our sample: above the median (AM) and below the median (BM). Next, a 10-fold cross-validation approach was used to train and test a two-step prediction pipeline. First, a classification model was trained to identify ELC group (AM, BM), generating classification probability values. Classification probability values were then used to train two separate prediction models for each ELC median score group that directly estimates the ELC score at age 2.