Extended Data Fig. 2: Validation of Generative Causal Explainer (GCE). | Nature

Extended Data Fig. 2: Validation of Generative Causal Explainer (GCE).

From: Cingulate dynamics track depression recovery with deep brain stimulation

Extended Data Fig. 2

a, Information flow from low-dimensional latent space components to classifier prediction indicates that classifier prediction is affected by the discriminative component and not by the non discriminative components. b, Classifier performance in leave-one-participant-out cross-validation for different datasets. Reconstructed data refers to data reconstructed from GCE using all components. Performance of the classifier in datasets reconstructed by randomizing discriminative and non-discriminative components is shown in magenta and cyan bars. Randomizing the discriminative component of the held-out dataset affected the classifier performance significantly, indicating that the association between data and classifier prediction is impaired, which in turn confirmed that the GCE did not overfit to the training dataset. Grey stars denote AUROC for each fold of the cross-validation. Error bars indicate standard deviation (n = 5 cross-validation fold). c, Receiver operating characteristic curve for neural network classifier trained on the reconstructed data to distinguish ‘sick’ versus ‘stable response’ state.

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