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Anxiety is a debilitating symptom of many psychiatric disorders including generalized anxiety disorder, mood disorders, schizophrenia, and autism. Anxiety involves changes in both central and peripheral biology, yet extant functional imaging studies have focused exclusively on the brain. Here we show, using functional brain and cardiac imaging in sequential brain and cardiac magnetic resonance imaging (MRI) sessions in response to cues that predict either threat (a possible shock) or safety (no possibility of shock), that MR signal change in the amygdala and the prefrontal and insula cortices predicts cardiac contractility to the threat of shock. Participants with greater MR signal change in these regions show increased cardiac contractility to the threat versus safety condition, a measure of the sympathetic nervous system contribution to the myocardium. These findings demonstrate robust neural-cardiac coupling during induced anxiety and indicate that individuals with greater activation in brain regions identified with aversive emotion show larger magnitude cardiac contractility increases to threat.
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Muscle electrical activity, or "electromyogenic" (EMG) artifact, poses a serious threat to the validity of electroencephalography (EEG) investigations in the frequency domain. EMG is sensitive to a variety of psychological processes and can mask genuine effects or masquerade as legitimate neurogenic effects across the scalp in frequencies at least as low as the alpha band (8-13 Hz). Although several techniques for correcting myogenic activity have been described, most are subjected to only limited validation attempts. Attempts to gauge the impact of EMG correction on intracerebral source models (source "localization" analyses) are rarer still. Accordingly, we assessed the sensitivity and specificity of one prominent correction tool, independent component analysis (ICA), on the scalp and in the source-space using high-resolution EEG. Data were collected from 17 participants while neurogenic and myogenic activity was independently varied. Several protocols for classifying and discarding components classified as myogenic and non-myogenic artifact (e.g., ocular) were systematically assessed, leading to the exclusion of one-third to as much as three-quarters of the variance in the EEG. Some, but not all, of these protocols showed adequate performance on the scalp. Indeed, performance was superior to previously validated regression-based techniques. Nevertheless, ICA-based EMG correction exhibited low validity in the intracerebral source-space, likely owing to incomplete separation of neurogenic from myogenic sources. Taken with prior work, this indicates that EMG artifact can substantially distort estimates of intracerebral spectral activity. Neither regression- nor ICA-based EMG correction techniques provide complete safeguards against such distortions. In light of these results, several practical suggestions and recommendations are made for intelligently using ICA to minimize EMG and other common artifacts.
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