Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity
Figure 5
Classification results using beta estimates as features.
(A) Feature selection, cross-validation and SVM learning were performed exactly the same as for FC, but over the range of 1 to 4000 ranked features (voxels). Accuracies for F vs. N classification reached 66–76% with ∼500–2500 features, with maximum accuracy (76%, p = 0.0044, uncorrected) at ∼1,900 features. (B) The most informative voxels with positive SVM weights (F>N, yellow) included fusiform gyrus (−28,−20,−12), cerebellum (−28, −20), amygdala (−20), insula (−12), orbital and ventrolateral prefrontal cortex (−20, −12, −4), midbrain (−12), parahippocampal gyrus (−12), middle temporal gyrus and superior temporal sulcus (−12,−4,4), thalamus/pulvinar (4), dorsolateral prefrontal/opercular cortex (12,20,28), dorsomedial prefrontal cortex (20,28), and superior occipital cortex (20,28) and inferior parietal lobe (36). Informative voxels with negative SVM weights (N>F, blue) included temporal-occipital cortex (−20), subgenual anterior cingulate (−12,−4), striatum (−4,4), lingual gyrus (4,12), precuneus (20) and dorsolateral prefrontal cortex (28,36). (B). Brain images are displayed using Neurological convention (i.e. L = R), and top left number in each panel represents the MNI coordinate (z) of depicted axial slice.