Central correlates of placebo effects in nausea differ between men and women.

INTRODUCTION: Despite growing evidence validating placebo effects in nausea, little is known about the underlying cortical mechanisms in women and men. Therefore, the present study examined sex differences and electroencephalography (EEG) characteristics of the placebo effect on nausea. METHODS: On 2 consecutive days, 90 healthy subjects (45 females) were exposed to a nauseating visual stimulus. Nausea was continuously rated on an 11-point numeric rating scale, and 32 EEG channels were recorded. On day 2, subjects were randomly allocated to either placebo treatment or no treatment: the placebo group received sham acupuncture, whereas the control group did not receive any intervention. RESULTS: In contrast to the control group, both sexes in the placebo group showed reduced signs for anticipatory nausea in the EEG, indexed by increased frontal lobe and anterior cingulate activity. Among women, the improvement in perceived nausea in the placebo group was accompanied by decreased activation in the parietal, frontal, and temporal lobes. In contrast, the placebo-related improvement of perceived nausea in men was accompanied by increased activation in the limbic and sublobar (insular) lobes. CONCLUSION: Activation of the parietal lobe in women during the placebo intervention may reflect altered afferent activity from gastric mechanoreceptors during nausea-induced tachyarrhythmia, whereas in men, altered interoceptive signals in the insular cortex might play a role. Thus, the results suggest different cerebral mechanisms underlying the placebo effects in men and women, which could have implications for the treatment of nausea.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions.

BACKGROUND: Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. NEW METHODS: We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. RESULTS: Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn't strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. CONCLUSIONS: The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography.

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.