Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males.

Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states ("willingness to trust") as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as 'unjustified trust' in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects.

Clinically Anxious Individuals Show Disrupted Feedback between Inferior Frontal Gyrus and Prefrontal-Limbic Control Circuit.

UNLABELLED: Clinical anxiety is associated with generalization of conditioned fear, in which innocuous stimuli elicit alarm. Using Pavlovian fear conditioning (electric shock), we quantify generalization as the degree to which subjects' neurobiological responses track perceptual similarity gradients to a conditioned stimulus. Previous studies show that the ventromedial prefrontal cortex (vmPFC) inversely and ventral tegmental area directly track the gradient of perceptual similarity to the conditioned stimulus in healthy individuals, whereas clinically anxious individuals fail to discriminate. Here, we extend this work by identifying specific functional roles within the prefrontal-limbic circuit. We analyzed fMRI time-series acquired from 57 human subjects during a fear generalization task using entropic measures of circuit-wide regulation and feedback (power spectrum scale invariance/autocorrelation), in combination with structural (diffusion MRI-probabilistic tractography) and functional (stochastic dynamic causal modeling) measures of prefrontal-limbic connectivity within the circuit. Group comparison and correlations with anxiety severity across 57 subjects revealed dysregulatory dynamic signatures within the inferior frontal gyrus (IFG), which our prior work has linked to impaired feedback within the circuit. Bayesian model selection then identified a fully connected prefrontal-limbic model comprising the IFG, vmPFC, and amygdala. Dysregulatory IFG dynamics were associated with weaker reciprocal excitatory connectivity between the IFG and the vmPFC. The vmPFC exhibited inhibitory influence on the amygdala. Our current results, combined with our previous work across a threat-perception spectrum of 137 subjects and a meta-analysis of 366 fMRI studies, dissociate distinct roles for three prefrontal-limbic regions, wherein the IFG provides evaluation of stimulus meaning, which then informs the vmPFC in inhibiting the amygdala. SIGNIFICANCE STATEMENT: Affective neuroscience has generally treated prefrontal regions (orbitofrontal cortex, dorsolateral prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex) equivalently as inhibitory components of the prefrontal-limbic system. Yet research across the anxiety spectrum suggests that the inferior frontal gyrus may have a more complex role in emotion regulation, as this region shows abnormal function in disorders of both hyperarousal and hypoarousal. Using entropic measures of circuit-wide regulation and feedback, in combination with measures of structural and functional connectivity, we dissociate distinct roles for three prefrontal-limbic regions, wherein the inferior frontal gyrus provides evaluation of stimulus meaning, which then informs the ventromedial prefrontal cortex in inhibiting the amygdala. This reconfiguration coheres with studies of conceptual disambiguation also implicating the inferior frontal gyrus.

Using network dynamic fMRI for detection of epileptogenic foci.

BACKGROUND: Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. METHODS: In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients' cognitive performance using a delayed verbal memory recall task. RESULTS: ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal - posterior cingulate cortex connectivity). CONCLUSIONS: Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.

Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks.

Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS-and not tSNR-is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network.