Fig. 2: fMRI training and testing the deep learning model in different datasets. | Nature Medicine

Fig. 2: fMRI training and testing the deep learning model in different datasets.

From: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Fig. 2

a, Ordinary least squares (OLS) regression comparing chronological age versus predicted age with the feature importance list for training (n = 1,155) and testing (n = 289) in the whole sample (P < 1 × 10−15). b, Regression comparing chronological age versus predicted age with the feature importance list for training (n = 773) and testing (n = 194) in the non-LAC dataset (P < 1 × 10−15). c, Regression comparing chronological age versus predicted age with the feature importance list for training (n = 381) and testing (n = 91) in the LAC dataset (P = 4.91 × 10−7). For a, b and c, the bars show the brain region feature importance list in descending order, with ring plots and glass brain representations of the most important network-edge connections. Feature importance (top 10) data are presented as mean values and 99% CI. The values for the features (mean, left limit, right limit) are: feature 1 = (0.975, 0.952, 0.999), feature 2 = (0.735, 0.715, 0.756), feature 3 = (0.627, 0.597, 0.656), feature 4 = (0.470, 0.449, 0.490), feature 5 = (0.375, 0.353, 0.397), feature 6 = (0.314, 0.285, 0.342), feature 7 = (0.239, 0.217, 0.262), feature 8 = (0.198, 0.169, 0.228), feature 9 = (0.161, 0.128, 0.193), feature 10 = (0.119, 0.093, 0.145) (a); feature 1 = (0.968, 0.937, 0.999), feature 2 = (0.736, 0.707, 0.764), feature 3 = (0.541, 0.518, 0.565), feature 4 = (0.434, 0.403, 0.464), feature 5 = (0.315, 0.290, 0.339), feature 6 = (0.253, 0.220, 0.286), feature 7 = (0.177, 0.156, 0.197), feature 8 = (0.140, 0.114, 0.166), feature 9 = (0.111, 0.078, 0.144), feature 10 = (0.079, 0.053, 0.106) (b); and feature 1 = (0.971, 0.944, 0.999), feature 2 = (0.847, 0.816, 0.878), feature 3 = (0.698, 0.667, 0.730), feature 4 = (0.533, 0.512, 0.555), feature 5 = (0.458, 0.430, 0.487), feature 6 = (0.371, 0.344, 0.399), feature 7 = (0.298, 0.272, 0.325), feature 8 = (0.242, 0.216, 0.269), feature 9 = (0.198, 0.169, 0.227), feature 10 = (0.163, 0.130, 0.196) (c). d, Histogram of the prediction error when training in non-LAC dataset (n = 967) and testing in LAC dataset (n = 477). e, Violin plot of the distribution and statistical comparison of training and testing with different regions using a two-sided permutation test without multiple comparisons (5,000 algorithm iterations) with a result of P < 1 × 10−15. Mean, first quartile (q1), third quartile (q3), whisker low, whisker high, minima and maxima values for violin plots are: LAC/non-LAC (−2.52, −7.74, 3.31, −22.52, 17.33, −22.52, 17.33); non-LAC/LAC (5.60, 0.85, 12.14, −12.82, 27.75, −12.82, 27.75). f, Violin plot of the distribution and statistical comparison of testing the models on females (n = 261) and males (n = 216) in LAC using a permutation test (5,000 iterations) with a result of P = 0.042. Mean, q1, q3, whisker low, whisker high, minima and maxima values for violin plots are: male (3.66, −1.83, 9.45, −12.49, 16.32, −12.49, 16.32); and female (6.93, 2.21, 12.78, −12.82, 27.75, −12.82, 27.75). ROI, region of interest. This figure was partially created with BioRender.com (fMRI device).

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