A Comprehensive Analysis of the Correlations between Resting-State Oscillations in Multiple-Frequency Bands and Big Five Traits

Neural correlates of neuroticism: A coordinate-based meta-analysis of resting-state functional brain imaging studies

Neuroticism is one of the most robust higher-order personality traits associated with negative emotionality and risk of mental disorders. Many studies have investigated relationships between neuroticism and the brain, but the results have been inconsistent. We conducted a meta-analysis of whole-brain resting-state functional neuroimaging studies to identify the most stable neurofunctional substrates of neuroticism. We found stable significant positive correlations between neuroticism and resting-state brain activity in the left middle temporal gyrus (MTG), left striatum, and right hippocampus. In contrast, resting-state brain activity in the left superior temporal gyrus (STG) and right supramarginal gyrus (SMG) was negatively associated with neuroticism. Additionally, meta-regression analysis revealed brain regions in which sex and age moderated the link of spontaneous activity with neuroticism. This is the first study to provide a comprehensive understanding of resting-state brain activity correlates of neuroticism, and the findings may be useful for the targeting of specific brain regions for interventions to decrease the risks of mental health problems.

Maternal psychological distress associates with alterations in resting-state low-frequency fluctuations and distal functional connectivity of the neonate medial prefrontal cortex

Prenatal stress exposure (PSE) has been observed to exert a programming effect on the developing infant brain, possibly with long-lasting consequences on temperament, cognitive functions and the risk for developing psychiatric disorders. Several prior studies have revealed that PSE associates with alterations in neonate functional connectivity in the prefrontal regions and amygdala. In this study, we explored whether maternal psychological symptoms measured during the 24th gestational week had associations with neonate resting-state network metrics. Twenty-one neonates (nine female) underwent resting-state fMRI scanning (mean gestation-corrected age at scan 26.95 days) to assess fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). The ReHo/fALFF maps were used in multiple regression analysis to investigate whether maternal self-reported anxiety and/or depressive symptoms associate with neonate functional brain features. Maternal psychological distress (composite score of depressive and anxiety symptoms) was positively associated with fALFF in the neonate medial prefrontal cortex (mPFC). Anxiety and depressive symptoms, assessed separately, exhibited similar but weaker associations. Post hoc seed-based connectivity analyses further showed that distal connectivity of mPFC covaried with PSE. No associations were found between neonate ReHo and PSE. These results offer preliminary evidence that PSE may affect functional features of the developing brain during gestation.

Functional connectivity of the central autonomic and default mode networks represent neural correlates and predictors of individual personality

There is solid evidence for the prominent involvement of the central autonomic and default mode systems in shaping personality. However, whether functional connectivity of these systems can represent neural correlates and predictors of individual variation in personality traits is largely unknown. Resting-state functional magnetic resonance imaging data of 215 healthy young adults were used to construct the sympathetic (SN), parasympathetic (PN), and default mode (DMN) networks, with intra- and internetwork functional connectivity measured. Personality factors were assessed using the five-factor model. We examined the associations between personality factors and functional network connectivity, followed by performance of personality prediction based on functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. All personality factors (neuroticism, extraversion, conscientiousness, and agreeableness) other than openness were significantly correlated with intra- and internetwork functional connectivity of the SN, PN, and DMN. Moreover, the CPM models successfully predicted conscientiousness and agreeableness at the individual level using functional network connectivity. Our findings may expand existing knowledge regarding the neural substrates underlying personality.

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Predicting behavior through dynamic modes in resting-state fMRI data

Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0-0.1,...,0.6-0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.

Cortico-striatal-thalamic loop as a neural correlate of neuroticism in the mind-body interface

OBJECTIVE

Although brain structural studies have demonstrated the neural correlates of neuroticism, the outcomes are not easily identified because of the various possible brain regions involved, low statistical power (low number of subjects), and brain structural measures available, such as mean diffusivity (MD), which are more suitable than standard regional measures of grey and white-matter volume (rGMV, rWMV) and fractional anisotropy (FA). We hypothesized that neuroticism neural correlates could be detected by MD and differentially identified using other measures. We aimed to visualize the neural correlates of neuroticism.

METHODS

A voxel-by-voxel regression analysis was performed using the MD, rGMV, rWMV, or FA value as the dependent variable and with neuroticism scores based on the NEO-FFI and its confounding factors as independent variables in 1207 (693 men and 514 women; age, 20.7 ± 1.8, 18-27 years), non-clinical students in a cross-sectional study.

RESULTS

MD in the cortico- (orbitofrontal cortex, anterior cingulate cortex, and posterior insula) striatal- (caudate and putamen) thalamic loop regions, including the right posterior limb of the internal capsule, were positively associated with neuroticism using the threshold-free cluster enhancement method with a family-wise error-corrected threshold of P < 0.0125 (0.05/4, Bonferroni correction for four types of MRI data [MD, rGMV, rWMV, and FA]) at the whole-brain level.

CONCLUSIONS

An increased MD has generally been associated with reduced neural tissues and possibly area function. Accordingly, this finding helps elucidate the mechanism of somatization in neuroticism because the regions related to neuroticism are considered neural correlates of somatoform disorders.

Neural substrates of self- and external-preoccupation: A voxel-based morphometry study

INTRODUCTION

Self- and external-preoccupation have been linked to psychopathological states. The neural substrates underlying self- and external-preoccupation remain unclear. In the present study, we aim to provide insight into the information-processing mechanisms associated with self- and external-preoccupation at the structural level.

METHODS

To investigate the neural substrates of self- and external-preoccupation, we acquired high-resolution T1-weighted structural images and Preoccupation Scale scores from 1,122 young subjects. Associations between regional gray matter volume (rGMV) and Preoccupation Scale subscores for self- and external-preoccupation were estimated using voxel-based morphometry.

RESULTS

Significant positive associations between self-preoccupation and rGMV were observed in widespread brain areas such as the bilateral precuneus and posterior cingulate gyri, structures known to be associated with self-triggered self-reference during rest. Significant negative associations between external-preoccupation and rGMV were observed only in the bilateral cerebellum, regions known to be associated with behavioral addiction, sustained attention, and reward system.

CONCLUSION

Our results reveal distinct neural substrates for self- and external-preoccupation at the structural level.