Correlations between personality and brain structure: A crucial role of gender.

Previous studies have shown that males and females differ in personality and gender differences have also been reported in brain structure. However, effects of gender on this 'personality-brain' relationship are yet unknown. We therefore investigated if the neural correlates of personality differ between males and females. Whole brain voxel-based morphometry was used to investigate the influence of gender on associations between NEO FFI personality traits and gray matter volume (GMV) in a matched sample of 182 males and 182 females. In order to assess associations independent of and dependent on gender, personality-GMV relationships were tested across the entire sample and separately for males and females. There were no significant correlations between any personality scale and GMV in the analyses across the entire sample. In contrast, significant associations with GMV were detected for neuroticism, extraversion, and conscientiousness only in males. Interestingly, GMV in left precuneus/parieto-occipital sulcus correlated with all 3 traits. Thus, our results indicate that brain structure-personality relationships are highly dependent on gender, which might be attributable to hormonal interplays or differences in brain organization between males and females. Our results thus provide possible neural substrates of personality-behavior relationships and underline the important role of gender in these associations

Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict

A model based approach to how participants learn to choose between two symbols associated with a conflict. One symbol provides high-money for self but also high-shocks to another, the other low-money for self but also low-shocks for another

Sex Classification by Resting State Brain Connectivity

A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone

Predicting personality from network-based resting-state functional connectivity

Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC–personality relations should not be considered independently of gender

In vitro activity of plant extracts against biofilm-producing food-related bacteria

The identification of effective antimicrobial agents also active on biofilms is a topic of crucial importance in food and industrial environment. For that purpose methanol extracts of Turkish plants, Ficus carica L., Juglans regia L., Olea europaea L., Punica granatum L. and Rhus coriaria L., were investigated. Among the extracts, P. granatum L. and R. coriaria L. showed the best antibacterial activity with minimum inhibitory concentrations (MIC) of 78–625 μg/ml for Listeria monocytogenes and Staphylococcus aureus and 312–1250 μg/ml for Escherichia coli and Pseudomonas aeruginosa. SubMICs produced a significant biofilm inhibition equal to 80–60% for L. monocytogenes and 90–80% for S. aureus. The extracts showed also the highest polyphenol content and the strongest antioxidant activity. Bioassay-guided and HPLC procedures demonstrated the presence of apigenin 4′-O-β-glucoside in P. granatum L. and myricetrin and quercitrin in R. coriaria L. Antigenotoxicity of plant extracts was also observed The present findings promote the value-adding of P. granatum L. and R. coriaria L. leaves as natural antimicrobial/antioxidant agents for control of food-related bacterial biofilms

Age differences in predicting working memory performance from network-based functional connectivity

Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity