Neuroanatomical Correlates of Social Intelligence Measured by the Guilford Test

Neurocognitive Latent Space Regularization for Multi-Label Diagnosis from MRI

Interpretability is essential to MRI brain studies relying on deep learning models for neuroscientific discovery. One way to facilitate the interpretability of a deep learning model is to ensure the samples are arranged in the model's latent space with respect to clinically meaningful variables. To achieve this in the context of cross-sectional brain MRI studies, we regularize the latent space of a multi-label classifier via pairwise disentanglement, so that the difference between the representation of two brain MRIs along the disentangled direction in the latent space is similar to the difference in their neuropsychological test scores. We apply our technique to classify brain MRIs of 156 controls, 165 cases diagnosed with mild cognitive impairment (MCI), 166 diagnosed with human immunodeficiency virus (HIV)-associated cognitive disorder (HAND), and 32 individuals diagnosed with HIV without HAND. The latent space is disentangled with respect to the neuropsychological z-score (NPZ), which is negatively correlated with the severity of cognitive impairment (i.e., low scores for those diagnosed with MCI or HAND). Based on cross-validation, the proposed model achieves statistically significantly higher balanced accuracy than the same model without disentanglement. Furthermore, the difference between representations along the disentangled direction significantly correlates with the difference in NPZ. Finally, the brain regions guiding the classification process aligned with the neuroscientific literature.

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

Social intelligence mediates the protective role of resting-state brain activity in the social cognition network against social anxiety

Background

Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety.

Objective

This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety.

Methods

Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety.

Results

Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus. Further, the left SFG fALFF was positively correlated with social anxiety; brain-personality-symptom analysis revealed that this relationship was mediated by social intelligence.

Conclusion

These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity → higher social intelligence → lower social anxiety. These may have implication for developing neurobehavioral interventions to mitigate social anxiety.

Dark triad personality traits are associated with decreased grey matter volumes in 'social brain' structures

Introduction

Personality traits and the degree of their prominence determine various aspects of social interactions. Some of the most socially relevant traits constitute the Dark Triad - narcissism, psychopathy, and Machiavellianism - associated with antisocial behaviour, disregard for moral norms, and a tendency to manipulation. Sufficient data point at the existence of Dark Triad 'profiles' distinguished by trait prominence. Currently, neuroimaging studies have mainly concentrated on the neuroanatomy of individual dark traits, while the Dark Triad profile structure has been mostly overlooked.

Methods

We performed a clustering analysis of the Dirty Dozen Dark Triad questionnaire scores of 129 healthy subjects using the k-means method. The variance ratio criterion (VRC) was used to determine the optimal number of clusters for the current data. The two-sample t-test within the framework of voxel-based morphometry (VBM) was performed to test the hypothesised differences in grey matter volume (GMV) for the obtained groups.

Results

Clustering analysis revealed 2 groups of subjects, both with low-to-mid and mid-to-high levels of Dark Triad traits prominence. A further VBM analysis of these groups showed that a higher level of Dark Triad traits may manifest itself in decreased grey matter volumes in the areas related to emotional regulation (the dorsolateral prefrontal cortex, the cingulate cortex), as well as those included in the reward system (the ventral striatum, the orbitofrontal cortex).

Discussion

The obtained results shed light on the neurobiological basis underlying social interactions associated with the Dark Triad and its profiles.

Anodal tDCS over TPJ reduces bidding in Tullock contest: Implications for social decision-making

Contests, as economic, political, and social interactions, can stimulate high levels of effort, but they can also lead to inefficient expenditure of effort (overbidding), resulting in the wastage of social resources. Prior studies have indicated that the temporoparietal junction (TPJ) is associated with overbidding and speculating on the intentions of others during contests. This study aimed to investigate the neural mechanisms of the TPJ in overbidding and to examine changes in bidding behavior after modulating TPJ activity using transcranial direct current stimulation (tDCS). The experiment randomly allocated participants into three groups, each receiving either anodal stimulation of the LTPJ/RTPJ or sham stimulation. Following the stimulation, the participants engaged in the Tullock rent-seeking game. Our results revealed that participants who received anodal stimulation of the LTPJ and RTPJ significantly reduced their bids compared to the sham group, possibly due to enhanced accuracy in guessing others' strategies or enhanced altruistic preferences. Moreover, our findings suggest that while both the LTPJ and RTPJ are associated with overbidding behavior, anodal tDCS targeting the RTPJ is more effective than stimulation of the LTPJ in decreasing overbidding. The aforementioned revelations offer proof of the neural mechanisms of the TPJ in overbidding and provide fresh substantiation for the neural mechanisms of social behavior.

Hemispheric Cortical, Cerebellar and Caudate Atrophy Associated to Cognitive Impairment in Metropolitan Mexico City Young Adults Exposed to Fine Particulate Matter Air Pollution

Exposures to fine particulate matter PM_2.5 are associated with Alzheimer's, Parkinson's (AD, PD) and TDP-43 pathology in young Metropolitan Mexico City (MMC) residents. High-resolution structural T1-weighted brain MRI and/or Montreal Cognitive Assessment (MoCA) data were examined in 302 volunteers age 32.7 ± 6.0 years old. We used multivariate linear regressions to examine cortical surface area and thickness, subcortical and cerebellar volumes and MoCA in ≤30 vs. ≥31 years old. MMC residents were exposed to PM_2.5 ~ 30.9 µg/m³. Robust hemispheric differences in frontal and temporal lobes, caudate and cerebellar gray and white matter and strong associations between MoCA total and index scores and caudate bilateral volumes, frontotemporal and cerebellar volumetric changes were documented. MoCA LIS scores are affected early and low pollution controls ≥ 31 years old have higher MoCA vs. MMC counterparts (p ≤ 0.0001). Residency in MMC is associated with cognitive impairment and overlapping targeted patterns of brain atrophy described for AD, PD and Fronto-Temporal Dementia (FTD). MMC children and young adult longitudinal studies are urgently needed to define brain development impact, cognitive impairment and brain atrophy related to air pollution. Identification of early AD, PD and FTD biomarkers and reductions on PM2.5 emissions, including poorly regulated heavy-duty diesel vehicles, should be prioritized to protect 21.8 million highly exposed MMC urbanites.

The Interaction Between Caudate Nucleus and Regions Within the Theory of Mind Network as a Neural Basis for Social Intelligence

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.

Functional Interactions Between Neural Substrates of Socio-cognitive Mechanisms Involved in Simple Deception and Manipulative Truth

Introduction: Deceptive intentions may be realized by imparting false (simple deception) or true (manipulative truth) information. Both forms of deception require inferring others' thoughts and are underpinned by the theory of mind (TOM) neural system. Manipulative truth is thought to more strongly recruit these processes. However, the organization of functional interactions underlying simple deception and manipulative truth remains unclear. Materials and Methods: We performed psychophysiological interaction analysis for a key node in the TOM system, the right temporoparietal junction (rTPJ), using functional MRI data obtained from 23 volunteers (14 men and 9 women, age range 18-45 years) during the sender-receiver game. During the game, participants sent true, simple deceptive, or manipulative truthful messages to another player according to their own choice. A Bayesian approach to statistics was employed to perform statistical inference and define voxels with significant changes in functional interactions. Results: We observed functional interactions between nodes of the TOM system (bilateral TPJ, left precuneus, left dorsomedial prefrontal cortex, and right superior temporal sulcus) characterizing both forms of deception. We identified an increment in functional interactions of the rTPJ with the left TPJ (lTPJ) and right precuneus associated with manipulative truth. Furthermore, we demonstrated that a higher rate of manipulative truthful actions was associated with weaker functional interactions between the rTPJ and lTPJ, left precuneus, and left dorsomedial prefrontal cortex. Discussion: Compared with simple deception, manipulative truth is associated with a higher demand for socio-cognitive processes that contributes to the cognitive load of this form of deception.