The prefrontal cortex is believed to be responsible for execution of deceptive behavior and its involvement is associated with greater cognitive efforts. It is also generally assumed that deception is associated with the inhibition of default honest actions. However, the precise neurophysiological mechanisms underlying this process remain largely unknown. The present study was aimed to use functional magnetic resonance imaging to reveal the underlying functional integration within the prefrontal cortex during the task which requires that subjects to deliberately mislead an opponent through the sequential execution of deceptive and honest claims. To address this issue, we performed psychophysiological interaction (PPI) analysis, which allows for statistical assessment of changes in functional relationships between active brain areas in changing psychological contexts. As a result the whole brain PPI-analysis established that both manipulative honest and deceptive claiming were associated with an increase in connectivity between the left middle frontal gyrus and right temporo-parietal junction (rTPJ). Taking into account the role played by rTPJ in processes associated with the theory of mind the revealed data can reflect possible influence of socio-cognitive context on the process of selecting manipulative claiming regardless their honest or deceptive nature. Direct comparison between deceptive and honest claims revealed pattern enhancement of coupling between the left middle frontal gyrus and the left inferior frontal gyrus. This finding provided evidence that the execution of deception relies to a greater extent on higher-order hierarchically-organized brain mechanisms of executive control required to select between two competing deceptive or honest task sets.

Social interactions are a crucial aspect of human behaviour. Numerous neurophysiological studies have focused on socio-cognitive processes associated with the so-called theory of mind—the ability to attribute mental states to oneself and others. Theory of mind is closely related to social intelligence defined as a set of abilities that facilitate effective social interactions. Social intelligence encompasses multiple theory of mind components and can be measured by the Four Factor Test of Social Intelligence (the Guilford-Sullivan test). However, it is unclear whether the differences in social intelligence are reflected in structural brain differences. During the experiment, 48 healthy right-handed individuals completed the Guilford-Sullivan test. T1-weighted structural MRI images were obtained for all participants. Voxel-based morphometry analysis was performed to reveal grey matter volume differences between the two groups (24 subjects in each)—with high social intelligence scores and with low social intelligence scores, respectively. Participants with high social intelligence scores had larger grey matter volumes of the bilateral caudate. The obtained results suggest the caudate nucleus involvement in the neural system of socio-cognitive processes, reflected by its structural characteristics.

The organization of socio-cognitive processes is a multifaceted problem for which many sophisticated concepts have been proposed. One of these concepts is social intelligence (SI), i.e., the set of abilities that allow successful interaction with other people. The theory of mind (ToM) human brain network is a good candidate for the neural substrate underlying SI since it is involved in inferring the mental states of others and ourselves and predicting or explaining others’ actions. However, the relationship of ToM to SI remains poorly explored. Our recent research revealed an association between the gray matter volume of the caudate nucleus and the degree of SI as measured by the Guilford-Sullivan test. It led us to question whether this structural peculiarity is reflected in changes to the integration of the caudate with other areas of the brain associated with socio-cognitive processes, including the ToM system. We conducted seed-based functional connectivity (FC) analysis of resting-state fMRI data for 42 subjects with the caudate as a region of interest. We found that the scores of the Guilford-Sullivan test were positively correlated with the FC between seeds in the right caudate head and two clusters located within the right superior temporal gyrus and bilateral precuneus. Both regions are known to be nodes of the ToM network. Thus, the current study demonstrates that the SI level is associated with the degree of functional integration between the ToM network and the caudate nuclei.

The response inhibition is a crucial mechanism of goal-directed behavior, which is conventionally considered as a selective mechanism triggered by particular "inhibitory" stimuli or events. Based on recent research, the alternative model of non-selective response inhibition was proposed by several authors. Accordingly, response inhibition mechanism may nonselectively inhibit all potential response options to execute an appropriate one, and such inhibition may be triggered not only by the presentation of "inhibitory" stimuli but also by the occurrence of any imperative stimuli instructing on the necessity to suppress or implement a prepared action. In previous research, the support towards this notion was mainly based on an absence of significant changes in the BOLD signal or amplitude of event-related potentials related between Go-and NoGo-stimuli when both of them were presented equiprobably. All previous studies in this research domain utilized statistical methods based on frequentist inference that makes impossible the acceptation of the null hypothesis. Therefore, the current research was aimed to reveal direct proof of the similarity of neuronal activity level between Goand NoGo-trials in the brain areas associated with response inhibition utilizing Bayesian analysis of fMRI data. Twenty healthy, right-handed volunteer subjects (16 women), aged (mean ± SD):23.9 ± 4.6, participated in the present fMRI performing paired stimulus modification of the Go/NoGo with an equiprobable presentation of GO and NoGo stimuli. To reveal brain areas demonstrating similar levels in the BOLD signal for both Go and NoGo trials Bayesian grouplevel analysis was combined with a meta-analysis of fMRI studies using equal probability Go/NoGo tasks comparable to the present study. As a result, we observed the overlap between brain areas previously associated with response inhibition and brain areas demonstrating practical equivalence of neuronal activity located in the right DLPFC, IPL, PMC, and left IFG (AIFO). Therefore obtained results favor the existence of non-selective response inhibition, which can act as a non-selective mechanism of action restraint in the settings of context uncertainty, is modeled by the equal probability of Go and NoGo-trials.Key words: non-selective response inhibition, functional MRI, Bayesian inference, accepting the null hypothesis, practical equivalence, Go/NoGo task Highlights• Non-selective response inhibition hypothesis was assessed by Go/NoGo task • Bayesian analysis of fMRI data was combined with a meta-analysis of fMRI studies• Nodes of response-inhibition system were equally involved in Go-and NoGo-trials• Evidence for non-selective response inhibition in uncertainty context was found

Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.