Decoding Unattended Fearful Faces with Whole-Brain Correlations: An Approach to Identify Condition-Dependent Large-Scale Functional Connectivity

Abstract

Processing of unattended threat-related stimuli, such as fearful faces, has been previously examined using group functional magnetic resonance (fMRI) approaches. However, the identification of features of brain activity containing sufficient information to decode, or “brain-read”, unattended (implicit) fear perception remains an active research goal. Here we test the hypothesis that patterns of large-scale functional connectivity (FC) decode the emotional expression of implicitly perceived faces within single individuals using training data from separate subjects. fMRI and a blocked design were used to acquire BOLD signals during implicit (task-unrelated) presentation of fearful and neutral faces. A pattern classifier (linear kernel Support Vector Machine, or SVM) with linear filter feature selection used pair-wise FC as features to predict the emotional expression of implicitly presented faces. We plotted classification accuracy vs. number of top N selected features and observed that significantly higher than chance accuracies (between 90–100%) were achieved with 15–40 features. During fearful face presentation, the most informative and positively modulated FC was between angular gyrus and hippocampus, while the greatest overall contributing region was the thalamus, with positively modulated connections to bilateral middle temporal gyrus and insula. Other FCs that predicted fear included superior-occipital and parietal regions, cerebellum and prefrontal cortex. By comparison, patterns of spatial activity (as opposed to interactivity) were relatively uninformative in decoding implicit fear. These findings indicate that whole-brain patterns of interactivity are a sensitive and informative signature of unattended fearful emotion processing. At the same time, we demonstrate and propose a sensitive and exploratory approach for the identification of large-scale, condition-dependent FC. In contrast to model-based, group approaches, the current approach does not discount the multivariate, joint responses of multiple functional connections and is not hampered by signal loss and the need for multiple comparisons correction.

Reduced Anterior Temporal and Hippocampal Functional Connectivity During Face Processing Discriminates Individuals with Social Anxiety Disorder from Healthy Controls and Panic Disorder, and Increases Following Treatment

“…In the current work, a novel and exploratory approach based on multivariate pattern analysis of large-scale, condition-dependent FC (Pantazatos et al, 2012b) was used to identify FC that discriminated individual subjects with SAD. FC features that discriminated SAD from HCs in the primary sample also discriminated SAD from HCs and from subjects with the closely related diagnosis of PD with significant sensitivity and specificity.…”

“…Subjects performed a previously described task from our group (Etkin et al, 2004;Pantazatos et al, 2012b), which consists of color identification of Ekman fearful, neutral, masked fearful, and masked neutral faces (F, N, MF, and MN, respectively) within a blocked paradigm (four 20 s blocks for each condition, 15 s baseline between each block). See Supplementary Methods for further details regarding the task paradigm and stimuli.…”

“…Before learning, the effects of age and gender were regressed out from the features using a general linear model, and features were z-scored. Classification, performance assessment, and confidence interval estimation followed previously described procedures (Pantazatos et al, 2012a(Pantazatos et al, , 2012b, and are further described in Supplementary Methods.…”

“…A novel approach based on multivariate machine learning-based pattern analysis of large-scale, condition-dependent functional connectivity (FC) recently demonstrated increased sensitivity of patterns of interactivity (ie, pairwise FC from hundreds of nodes) relative to patterns of activity (ie, beta or contrast activation maps) in predicting subliminal and unattended viewing of fearful vs neutral faces in healthy subjects: functional connections that discriminated supraliminal fearful from neutral faces included amygdala, fusiform, thalamus, superior temporal sulcus, superior occipital cortex, hippocampus, angular gyrus, and cerebellum (Pantazatos et al, 2012b) whereas FC that discriminated subliminal fear from neutral faces included middle temporal gyrus, angular gyrus, cerebellum, superior frontal gyrus, and amygdala (Pantazatos et al, 2012a). On the basis of this and previous evidence that SAD subjects exhibit anomalies in the cognitive-emotional processing of emotional and ambiguous social stimuli, it was hypothesized that patterns of interactivity during fearful and neutral face processing would be sensitive in discriminating whether an individual subject has SAD.…”

Individual Differences in Attentional Bias Associated with Cocaine Dependence Are Related to Varying Engagement of Neural Processing Networks

“…These findings support the contention that attentional bias and its associated neural correlates represent neurocognitive markers of drug dependence and individual risk for relapse. We sought to further characterize the neural information processing related to individual variation in attentional bias in cocaine-dependent adults at the level of the involved neural processing networks as this brain state representation has increased power to describe individual differences in brain-behavior relationships relative to more traditional univariate, voxel-wise analyses (Congdon et al, 2010;Pantazatos et al, 2012).…”