Biology subjects

Dixon, M. L.

Publications and source records attributed to Dixon, M. L..

4 recordsLinked to original sources

bioRxiv2020

Neurocognitive Heterogeneity in Social Anxiety Disorder: The Role of Self-Referential Processing and Childhood Maltreatment

Social anxiety disorder (SAD) is characterized by negative self-beliefs and altered brain activation in the default mode network (DMN). However, the extent to which there is neurocognitive heterogeneity in SAD remains unclear. We had two independent samples of patients perform a self-referential encoding task, and complete self-reports of childhood maltreatment, subjective well-being, and emotion regulation. In the replication sample, we also measured DMN activation using functional magnetic resonance imaging. K-means clustering revealed two distinct sub-groups of SAD patients in the discovery sample. Cluster 1 demonstrated higher levels of negative and lower levels of positive self-referential trait endorsement, and significantly higher levels of childhood emotional maltreatment, lower subjective well-being, and altered emotion regulation strategy use. A similar pattern was observed in the replication sample, which further demonstrated higher DMN activation during negative trait judgments in cluster 1. These findings reveal neurocognitive heterogeneity in SAD and its relationship to emotional maltreatment.

neuroscience↗

bioRxiv2020

Brain Network Organization During Mindful Acceptance of Emotions

Individuals use various strategies to cope with challenging emotions such as anxiety. Mindful acceptance involves broadening attentional scope and fully experiencing present moment sensory feelings (whether pleasant or unpleasant) without judgment or elaboration. In contrast, narrative-evaluation involves focusing on a narrow band of sensory experience and generating an elaborate narrative about the meaning and desirability of ones emotional feelings. The current study examined brain network organization during these strategies using graph theoretic analyses. We used a naturalistic task paradigm in which participants reflected on an anxiety-provoking issue from their personal lives and adopted each strategy in different blocks. Compared to narrative-evaluation, mindful acceptance was associated with: (i) increased global network connectivity; (ii) greater integration of interoceptive regions (mid and posterior insula) into large-scale networks; (iii) reorganization of motivational circuits including a shift in the striatums network assignment from the default network to the salience network; and (iv) a shift from default network to frontoparietal control network (FPCN) regions as central hubs that coordinate information flow. Functional connectivity patterns within the left FPCN were associated with acceptance reports. Thus, broadening attentional scope during mindful acceptance is supported by a more globally interconnected neural landscape, as well as greater information flow through FPCN regions that underlie metacognitive awareness and cognitive control.

neuroscience↗

bioRxiv2017

Heterogeneity Within the Frontoparietal Control Network and its Relationship to the Default and Dorsal Attention Networks

The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, in order to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC the default network (DN) and the dorsal attentional network (DAN). This 2-fold FPCN differentiation was observed across four independent data sets, across 9 different conditions (rest and 8 tasks), as well as in meta-analytic co-activation patterns. The extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams.

neuroscience↗

bioRxiv2017

The Neural Basis of Motivational Influences on Cognitive Control: An ALE Meta-Analysis

Cognitive control mechanisms support the deliberate regulation of thought and behavior based on current goals. Recent work suggests that motivational incentives improve cognitive control, and has begun to elucidate the brain regions that may support this effect. Here, we conducted a quantitative meta-analysis of neuroimaging studies of motivated cognitive control using activation likelihood estimation (ALE) and Neurosynth in order to delineate the brain regions that are consistently activated across studies. The analysis included functional neuroimaging studies that investigated changes in brain activation during cognitive control tasks when reward incentives were present versus absent. The ALE analysis revealed consistent recruitment in regions associated with the frontoparietal control network including the inferior frontal sulcus (IFS) and intraparietal sulcus (IPS), as well as consistent recruitment in regions associated with the salience network including the anterior insula and anterior mid-cingulate cortex (aMCC). A large-scale exploratory meta-analysis using Neurosynth replicated the ALE results, and also identified the caudate nucleus, nucleus accumbens, medial thalamus, inferior frontal junction/premotor cortex (IFJ/PMC), and hippocampus. Finally, we conducted separate ALE analyses to compare recruitment during cue and target periods, which tap into proactive engagement of rule-outcome associations, and the mobilization of appropriate viscero-motor states to execute a response, respectively. We found that largely distinct sets of brain regions are recruited during cue and target periods. Altogether, these findings suggest that flexible interactions between frontoparietal, salience, and dopaminergic midbrain-striatal networks may allow control demands to be precisely tailored based on expected value.

neuroscience↗