The cortical integration of auditory and visual features is crucial for efficient object recognition. Previous studies have shown that audiovisual (AV) integration is affected by where and when auditory and visual features occur. However, because relatively little is known about the impact of what is integrated, we here investigated the impact of semantic congruency and object familiarity on the neural correlates of AV integration. We used functional magnetic resonance imaging to identify regions involved in the integration of both (congruent and incongruent) familiar animal sounds and images and of arbitrary combinations of unfamiliar artificial sounds and object images. Unfamiliar object images and sounds were integrated in the inferior frontal cortex (IFC), possibly reflecting learning of novel AV associations. Integration of familiar, but semantically incongruent combinations also correlated with IFC activation and additionally involved the posterior superior temporal sulcus (pSTS). For highly familiar semantically congruent AV pairings, we again found AV integration effects in pSTS and additionally in superior temporal gyrus. These findings demonstrate that the neural correlates of objectrelated AV integration reflect both semantic congruency and familiarity of the integrated sounds and images.

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Context-Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD.Objective-To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT).Design-Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli.

We asked whether brain connectomics can predict response to treatment for a neuropsychiatric disorder better than conventional clinical measures. Pre-treatment resting-state brain functional connectivity and diffusion-weighted structural connectivity were measured in 38 patients with social anxiety disorder (SAD) to predict subsequent treatment response to cognitive behavioral therapy (CBT). We used a priori bilateral anatomical amygdala seed-driven resting connectivity and probabilistic tractography of the right inferior longitudinal fasciculus together with a data-driven multivoxel pattern analysis of whole-brain resting-state connectivity before treatment to predict improvement in social anxiety after CBT. Each connectomic measure improved the prediction of individuals' treatment outcomes significantly better than a clinical measure of initial severity, and combining the multimodal connectomics yielded a fivefold improvement in predicting treatment response. Generalization of the findings was supported by leave-one-out cross-validation. After dividing patients into better or worse responders, logistic regression of connectomic predictors and initial severity combined with leave-one-out cross-validation yielded a categorical prediction of clinical improvement with 81% accuracy, 84% sensitivity and 78% specificity. Connectomics of the human brain, measured by widely available imaging methods, may provide brain-based biomarkers (neuromarkers) supporting precision medicine that better guide patients with neuropsychiatric diseases to optimal available treatments, and thus translate basic neuroimaging into medical practice.