Fig. 2: Anomaly scores for the CNV dataset.
From: Detecting microstructural deviations in individuals with deep diffusion MRI tractometry
a, The autoencoder (AE) network and PCA approaches provided better discriminating power in terms of sensitivity/specificity tradeoffs compared with traditional linear univariate approaches (*P = 5.69 × 10–40 and P = 1.92 × 10–33, Bonferroni corrected with α = 0.01, two-tailed t-tests for AE–z and PCA–z, respectively). The average AUC over 100 iterations (bottom) for the RISH0 feature (top) is displayed (data are presented as mean values ± s.d.). b, The RISH0 features show higher reconstruction error for the CNV (orange box plot) compared with the typically developing patients (purple box plot) with a precision-recall AUC of 0.45 (center line, median; box limits, upper and lower quartiles; whiskers, 1.5 interquartile range; n = 90 healthy participants and n = 8 CNVs). In comparison, a random classifier would score 0.08. The box shows the quartiles of the dataset whereas the whiskers extend to show the rest of the distribution. c, From a group perspective, anomaly rates were mostly observed in the ILF (color map: RISH0, lateral view), optic radiations and SLF. OR, optic radiations; UF, uncinate fasciculus; AF, arcuate fasciculus; IFO, inferior fronto-occipital fasciculus; CC, corpus callosum; Cg, cingulum; ATR, anterior thalamic radiation, R, right brain hemisphere; L, left brain hemisphere.
