Self-control and interoception: Linking the neural substrates of craving regulation and the prediction of aversive interoceptive states induced by inspiratory breathing restriction.

Following the interoceptive inference framework, we set out to replicate our previously reported association of self-control and interoceptive prediction and strived to investigate the neural underpinnings subserving the relationship between self-control and aversive interoceptive predictive models. To this end, we used fMRI and a within-subject design including an inspiratory breathing-load task to examine the prediction of aversive interoceptive perturbation and a craving-regulation for palatable foods task to measure self-control. In this current study, we could successfully replicate previous effects with an independent sample (n â€‹= â€‹39) and observed that individuals who 'over-estimated' their upcoming interoceptive state with respect to experienced dyspnea (i.e., anticipated versus experienced) were more effective in the down-regulation of craving using negative future-thinking strategies. These individuals, again, obtained higher scores on a measure of trait self-control, i.e. self-regulation to achieve long-term goals. On a neural level, we found evidence that the anterior insula (AI) and the presupplementary motor area (preSMA), which were recruited in both tasks, partly accounted for these effects. Specifically, levels of AI activation during the anticipation of the aversive interoceptive state (breathing restriction) were associated with self-controlled behavior in the craving task, whereas levels of interoceptive prediction during the breathing task were conversely associated with activation in preSMA during the down-regulation of craving, whose anticipatory activity was correlated with self-control success. Moreover, during the self-control task, levels of interoceptive prediction were associated with connectivity in a spatially distributed network including among other areas the insula and regions of cognitive control, while during the interoceptive prediction task, levels of self-control were associated with connectivity in a spatially distributed network including among other regions the insula and preSMA. In sum, these findings consolidate the notion that self-control is directly linked to interoceptive inference and highlight the contribution of AI and preSMA as candidate regions underlying this relationship possibly creating processing advantages in self-control situations referring to the prediction of future internal states.

The genetic relationships between brain structure and schizophrenia.

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

Sexually divergent development of depression-related brain networks during healthy human adolescence.

Sexual differences in human brain development could be relevant to sex differences in the incidence of depression during adolescence. We tested for sex differences in parameters of normative brain network development using fMRI data on N = 298 healthy adolescents, aged 14 to 26 years, each scanned one to three times. Sexually divergent development of functional connectivity was located in the default mode network, limbic cortex, and subcortical nuclei. Females had a more "disruptive" pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of sexually divergent brain network development was robustly colocated with i prior loci of reward-related brain activation ii a map of functional dysconnectivity in major depressive disorder (MDD), and iii an adult brain gene transcriptional pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. We found normative sexual divergence in adolescent development of a cortico-subcortical brain functional network that is relevant to depression.

Psychological Network Analysis of General Self-Efficacy in High vs. Low Resilient Functioning Healthy Adults.

Resilience to stress has gained increasing interest by researchers from the field of mental health and illness and some recent studies have investigated resilience from a network perspective. General self-efficacy constitutes an important resilience factor. High levels of self-efficacy have shown to promote resilience by serving as a stress buffer. However, little is known about the role of network connectivity of self-efficacy in the context of stress resilience. The present study aims at filling this gap by using psychological network analysis to study self-efficacy and resilience. Based on individual resilient functioning scores, we divided a sample of 875 mentally healthy adults into a high and low resilient functioning group. To compute these scores, we applied a novel approach based on Partial Least Squares Regression on self-reported stress and mental health measures. Separately for both groups, we then estimated regularized partial correlation networks of a ten-item self-efficacy questionnaire. We compared three different global connectivity measures-strength, expected influence, and shortest path length-as well as absolute levels of self-efficacy between the groups. Our results supported our hypothesis that stronger network connectivity of self-efficacy would be present in the highly resilient functioning group compared to the low resilient functioning group. In addition, the former showed higher absolute levels of general self-efficacy. Future research could consider using partial least squares regression to quantify resilient functioning to stress and to study the association between network connectivity and resilient functioning in other resilience factors.