Dynamic Causal Modeling of the Prefrontal/Amygdala Network During Processing of Emotional Faces

Facing emotions: real-time fMRI-based neurofeedback using dynamic emotional faces to modulate amygdala activity

Introduction

Maladaptive functioning of the amygdala has been associated with impaired emotion regulation in affective disorders. Recent advances in real-time fMRI neurofeedback have successfully demonstrated the modulation of amygdala activity in healthy and psychiatric populations. In contrast to an abstract feedback representation applied in standard neurofeedback designs, we proposed a novel neurofeedback paradigm using naturalistic stimuli like human emotional faces as the feedback display where change in the facial expression intensity (from neutral to happy or from fearful to neutral) was coupled with the participant's ongoing bilateral amygdala activity.

Methods

The feasibility of this experimental approach was tested on 64 healthy participants who completed a single training session with four neurofeedback runs. Participants were assigned to one of the four experimental groups (n = 16 per group), i.e., happy-up, happy-down, fear-up, fear-down. Depending on the group assignment, they were either instructed to "try to make the face happier" by upregulating (happy-up) or downregulating (happy-down) the amygdala or to "try to make the face less fearful" by upregulating (fear-up) or downregulating (fear-down) the amygdala feedback signal.

Results

Linear mixed effect analyses revealed significant amygdala activity changes in the fear condition, specifically in the fear-down group with significant amygdala downregulation in the last two neurofeedback runs as compared to the first run. The happy-up and happy-down groups did not show significant amygdala activity changes over four runs. We did not observe significant improvement in the questionnaire scores and subsequent behavior. Furthermore, task-dependent effective connectivity changes between the amygdala, fusiform face area (FFA), and the medial orbitofrontal cortex (mOFC) were examined using dynamic causal modeling. The effective connectivity between FFA and the amygdala was significantly increased in the happy-up group (facilitatory effect) and decreased in the fear-down group. Notably, the amygdala was downregulated through an inhibitory mechanism mediated by mOFC during the first training run.

Discussion

In this feasibility study, we intended to address key neurofeedback processes like naturalistic facial stimuli, participant engagement in the task, bidirectional regulation, task congruence, and their influence on learning success. It demonstrated that such a versatile emotional face feedback paradigm can be tailored to target biased emotion processing in affective disorders.

Reactivity of the ventromedial prefrontal cortex, but not the amygdala, to negative emotion faces predicts greed personality trait

This study explored whether amygdala reactivity predicted the greed personality trait (GPT) using both task-based and resting-state functional connectivity analyses (ntotal = 452). In Cohort 1 (n = 83), task-based functional magnetic resonance imaging (t-fMRI) results from a region-of-interest (ROI) analysis revealed no direct correlation between amygdala reactivity to fearful and angry faces and GPT. Instead, whole-brain analyses revealed GPT to robustly negatively vary with activations in the right ventromedial prefrontal cortex (vmPFC), supramarginal gyrus, and angular gyrus in the contrast of fearful + angry faces > shapes. Moreover, task-based psychophysiological interaction (PPI) analyses showed that the high GPT group showed weaker functional connectivity of the vmPFC seed with a top-down control network and visual pathways when processing fearful or angry faces compared to their lower GPT counterparts. In Cohort 2, resting-state functional connectivity (rs-FC) analyses indicated stronger connectivity between the vmPFC seed and the top-down control network and visual pathways in individuals with higher GPT. Comparing the two cohorts, bilateral amygdala seeds showed weaker associations with the top-down control network in the high group via PPI analyses in Cohort 1. Yet, they exhibited distinct rs-FC patterns in Cohort 2 (e.g., positive associations of GPT with the left amygdala-top-down network FC but negative associations with the right amygdala-visual pathway FC). The study underscores the role of the vmPFC and its functional connectivity in understanding GPT, rather than amygdala reactivity.

Leveraging Julia's automated differentiation and symbolic computation to increase spectral DCM flexibility and speed

Using neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language package designed to i) compose complex dynamical models in a simple and modular way with ModelingToolkit.jl, ii) implement parameter fitting based on spectral dynamic causal modeling (sDCM) using the Laplace approximation, analogous to MATLAB implementation in SPM12, and iii) leverage Julia's unique strengths to increase accuracy and speed by employing Automatic Differentiation during the fitting procedure. To illustrate the utility of our flexible modular approach, we provide a method to improve correction for fMRI scanner field strengths (1.5T, 3T, 7T) when fitting models to real data.

Ultra-high field imaging of the amygdala - A narrative review

The amygdala is an evolutionarily conserved core structure in emotion processing and one of the key regions of interest in affective neuroscience. Results of neuroimaging studies focusing on the amygdala are, however, often heterogeneous since it is composed of functionally and neuroanatomically distinct subnuclei. Fortunately, ultra-high-field imaging offers several advances for amygdala research, most importantly more accurate representation of functional and structural properties of subnuclei and their connectivity. Most clinical studies using ultra-high-field imaging focused on major depression, suggesting either overall rightward amygdala atrophy or distinct bilateral patterns of subnuclear atrophy and hypertrophy. Other pathologies are only sparsely covered. Connectivity analyses identified widespread networks for learning and memory, stimulus processing, cognition, and social processes. They provide evidence for distinct roles of the central, basal, and basolateral nucleus, and the extended amygdala in fear and emotion processing. Amid largely sparse and ambiguous evidence, we propose theoretical and methodological considerations that will guide ultra-high-field imaging in comprehensive investigations to help disentangle the ambiguity of the amygdala's function, structure, connectivity, and clinical relevance.

Age-related reduction in trait anxiety: Behavioral and neural evidence of automaticity in negative facial emotion processing

Trait anxiety diminishes with age, which may result from age-related decline in registering salient emotional stimuli and/or enhancement in emotion regulation. We tested the hypotheses in 88 adults 21 to 85 years of age and studied with fMRI of the Hariri task. Age-related decline in stimulus registration would manifest in delayed reaction time (RT) and diminished saliency circuit activity in response to emotional vs. neutral stimuli. Enhanced control of negative emotions would manifest in diminished limbic/emotional circuit and higher prefrontal cortical (PFC) responses to negative emotion. The results showed that anxiety was negatively correlated with age. Age was associated with faster RT and diminished activation of the medial PFC, in the area of the dorsal and rostral anterior cingulate cortex (dACC/rACC) - a hub of the saliency circuit - during matching of negative but not positive vs. neutral emotional faces. A slope test confirmed the differences in the regressions. Further, age was not associated with activation of the PFC in whole-brain regression or in region-of-interest analysis of the dorsolateral PFC, an area identified from meta-analyses of the emotion regulation literature. Together, the findings fail to support either hypothesis; rather, the findings suggest age-related automaticity in processing negative emotions as a potential mechanism of diminished anxiety. Automaticity results in faster RT and diminished anterior cingulate activity in response to negative but not positive emotional stimuli. In support, analyses of psychophysiological interaction demonstrated higher dACC/rACC connectivity with the default mode network, which has been implicated in automaticity in information processing. As age increased, individuals demonstrated faster RT with higher connectivity during matching of negative vs. neutral images. Automaticity in negative emotion processing needs to be investigated as a mechanism of age-related reduction in anxiety.

Improved brain stimulation targeting by optimising image acquisition parameters

Functional connectivity analysis from rs-fMRI data has been used for determining cortical targets in therapeutic applications of non-invasive brain stimulation using transcranial magnetic stimulation (TMS). Reliable connectivity measures are therefore essential for every rs-fMRI-based TMS targeting approach. Here, we examine the effect of echo time (TE) on the reproducibility and spatial variability of resting-state connectivity measures. We acquired multiple runs of single-echo fMRI data with either short (TE = 30 ms) or long (TE = 38 ms) echo time to investigate inter-run spatial reproducibility of a clinically relevant functional connectivity map, i.e., originating from the sgACC. We find that connectivity maps obtained from TE = 38 ms rs-fMRI data are significantly more reliable than those obtained from TE = 30 ms data sets. Our results clearly show that optimizing sequence parameters can be beneficial for ensuring high-reliability resting-state acquisition protocols to be used for TMS targeting. The differences between reliability in connectivity measures for different TEs could inform future clinical research in optimising MR sequences.