Neural representations of the multidimensional self in the cortical midline structures

Neural correlates of individual differences in moral identity and its positive moral function

Moral identity is an important moral variable which has positive moral functions, such as contributing to prosocial behaviours, reducing antisocial behaviours, and resisting the risk factors of antisocial behaviours. However, little is known about the neural correlates of moral identity and the neural basis of the effect of moral identity on the risk factors of antisocial behaviours, including moral disengagement. In this study, we explored these issues in 142 college students by estimating the regional homogeneity (ReHo) through resting-state functional magnetic resonance imaging (fMRI). The whole-brain correlation analyses found that higher internalized moral identity was correlated with higher ReHo in the precuneus. Furthermore, the ReHo in the precuneus was negatively correlated with moral disengagement, suggesting positive moral functions of the neural mechanisms of moral identity. These findings deepen our understanding of individual differences in moral identity and provide inspiration for the education of moral identity and the intervention for moral disengagement from the perspective of the brain.

Intracranial Recordings of the Human Orbitofrontal Cortical Activity during Self-Referential Episodic and Valenced Self-Judgments

We recorded directly from the orbital (oPFC) and ventromedial (vmPFC) subregions of the orbitofrontal cortex (OFC) in 22 (9 female, 13 male) epilepsy patients undergoing intracranial electroencephalography (iEEG) monitoring during an experimental task in which the participants judged the accuracy of self-referential autobiographical statements as well as valenced self-judgments (SJs). We found significantly increased high-frequency activity (HFA) in ∼13% of oPFC sites (10/18 subjects) and 16% of vmPFC sites (4/12 subjects) during both of these self-referential thought processes, with the HFA power being modulated by the content of self-referential stimuli. The location of these activated sites corresponded with the location of fMRI-identified limbic network. Furthermore, the onset of HFA in the vmPFC was significantly earlier than that in the oPFC in all patients with simultaneous recordings in both regions. In 11 patients with available depression scores from comprehensive neuropsychological assessments, we documented diminished HFA in the OFC during positive SJ trials among individuals with higher depression scores; responses during negative SJ trials were not related to the patients' depression scores. Our findings provide new temporal and anatomical information about the mode of engagement in two important subregions of the OFC during autobiographical memory and SJ conditions. Our findings from the OFC support the hypothesis that diminished brain activity during positive self-evaluations, rather than heightened activity during negative self-evaluations, plays a key role in the pathophysiology of depression.

Representational similarity analysis of self- versus other-processing: Effect of trait aggressiveness

In this study, using the self/other adjective judgment task, we aimed to explore how people perceive themselves in comparison to various other people, including friends, strangers, and those they dislike. Next, using representational similarity analysis, we sought to elucidate how these perceptual similarities and differences are represented in brain activity and how aggressiveness is related to these representations. Behavioral ratings show that, on average, people tend to consider themselves more like their friends than neutral strangers, and least like people they dislike. This pattern of similarity is positively correlated with neural representation in social and cognitive circuits of the brain and negatively correlated with neural representation in emotional centers that may represent emotional arousal associated with various social objects. Aggressiveness seems to predispose a person to a pattern of behavior that is the opposite of the average pattern, that is, a tendency to think of oneself as less like one's friends and more like one's enemies. This corresponds to an increase in the similarity of the behavioral representation with the representation in the emotional centers and a decrease in its similarity with the representation in the social and cognitive centers. This can be seen as evidence that in individuals prone to aggression, behavior in the social environment may depend to a greater extent on the representation of social objects in the emotional rather than social and cognitive brain circuits.

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality

INTRODUCTION

Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear.

METHODS

We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset.

RESULTS

SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control.

CONCLUSION

Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

Re-cognizing the new self: The neurocognitive plasticity of self-processing following facial transplantation

The face is a defining feature of our individuality, crucial for our social interactions. But what happens when the face connected to the self is radically altered or replaced? We address the plasticity of self-face recognition in the context of facial transplantation. While the acquisition of a new face following facial transplantation is a medical fact, the experience of a new identity is an unexplored psychological outcome. We traced the changes in self-face recognition before and after facial transplantation to understand if and how the transplanted face gradually comes to be perceived and recognized as the recipient's own new face. Neurobehavioral evidence documents a strong representation of the pre-injury appearance pre-operatively, while following the transplantation, the recipient incorporates the new face into his self-identity. The acquisition of this new facial identity is supported by neural activity in medial frontal regions that are considered to integrate psychological and perceptual aspects of the self.

Decreased hemodynamic response to fearful faces relative to neutral faces in the medial frontal cortex of first-episode drug-naïve major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a disabling disease with impaired recognition of emotional facial expressions. However, the evidence is heterogeneous, regarding the mechanism of emotional processing in MDD. Focusing on patients with first-episode drug-naïve MDD, we used functional near-infrared spectroscopy (fNIRS) to investigate whether MDD have characteristic patterns in cerebral activation under facial emotion recognition task (FERT).

METHODS

Thirty-five patients with first-episode drug-naïve MDD and 39 healthy controls (HCs) underwent fNIRS measure to evaluate cerebral hemodynamic response in the frontal and temporal cortex during FERT. The 17-item Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale and Inventory of Depressive Symptomatology-Self Report were applied to assess the symptoms of the patients. Cognitive functions were assessed using THINC-integrated tool.

RESULTS

Hypoactivation in the medial frontal was observed in patients with MDD during recognition of fearful faces relative to neutral faces (F-N faces). Specifically, we found more right lateralized activation in the medial frontal cortex among patients with MDD compared to HCs. Further, the medial frontal activation under the condition of F-N faces was positively correlated to scores of digit symbol substitution test, and negatively relative to severity of depressive symptoms in MDD group.

LIMITATIONS

Our study is cross-sectional designed, and has a relatively small sample size.

CONCLUSIONS

We found abnormal patterns in the medial frontal activation of patients with first-episode drug-naïve MDD in recognition of F-N faces, which correlates with performance in cognitive function and depressive symptoms.

Disrupted network switching in euthymic bipolar disorder: Working memory and self-referential paradigms

BACKGROUND

Patients with bipolar disorder (BD) frequently suffer from neurocognitive deficits that can persist during periods of clinical stability. Specifically, impairments in executive functioning such as working memory and in self-processing have been identified as the main components of the neurocognitive profile observed in euthymic BD patients. The study of the neurobiological correlates of these state-independent alterations may be a prerequisite to develop reliable biomarkers in BD.

METHODS

A sample of 27 euthymic BD patients and 25 healthy participants (HC) completed working memory and self-referential functional Magnetic Resonance Imaging (fMRI) tasks. Activation maps obtained for each group and contrast images (i.e., 2-back > 1-back/self > control) were used for comparisons between patients and HC.

RESULTS

Euthymic BD patients, in comparison to HC, showed a higher ventromedial prefrontal cortex activation during working memory, a result driven by the lack of deactivation in BD patients. In addition, euthymic BD patients displayed a greater dorsomedial and dorsolateral prefrontal cortex activation during self-reference processing.

LIMITATIONS

Pharmacotherapy was described but not included as a confounder in our models. Sample size was modest.

CONCLUSION

Our findings revealed a lack of deactivation in the anterior default mode network (aDMN) during a working memory task, a finding consistent with prior research in BD patients, but also a higher activation in frontal regions within the central executive network (CEN) during self-processing. These results suggest that an imbalance of neural network dynamics underlying external/internal oriented cognition (the CEN and the aDMN, respectively) may be one of the first reliable biomarkers in euthymic bipolar patients.

Learning whom to cooperate with: neurocomputational mechanisms for choosing cooperative partners

Cooperation is fundamental for survival and a functioning society. With substantial individual variability in cooperativeness, we must learn whom to cooperate with, and often make these decisions on behalf of others. Understanding how people learn about the cooperativeness of others, and the neurocomputational mechanisms supporting this learning, is therefore essential. During functional magnetic resonance imaging scanning, participants completed a novel cooperation-partner-choice task where they learned to choose between cooperative and uncooperative partners through trial-and-error both for themselves and vicariously for another person. Interestingly, when choosing for themselves, participants made faster and more exploitative choices than when choosing for another person. Activity in the ventral striatum preferentially responded to prediction errors (PEs) during self-learning, whereas activity in the perigenual anterior cingulate cortex (ACC) signaled both personal and vicarious PEs. Multivariate pattern analyses showed distinct coding of personal and vicarious choice-making and outcome processing in the temporoparietal junction (TPJ), dorsal ACC, and striatum. Moreover, in right TPJ the activity pattern that differentiated self and other outcomes was associated with individual differences in exploitation tendency. We reveal neurocomputational mechanisms supporting cooperative learning and show that this learning is reflected in trial-by-trial univariate signals and multivariate patterns that can distinguish personal and vicarious choices.

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning

Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.

Neural similarity between mentalizing and live social interaction during the transition to adolescence

Social interactions are essential for human development, yet little neuroimaging research has examined their underlying neurocognitive mechanisms using socially interactive paradigms during childhood and adolescence. Recent neuroimaging research has revealed activity in the mentalizing network when children engage with a live social partner, even when mentalizing is not required. While this finding suggests that social‐interactive contexts may spontaneously engage mentalizing, it is not a direct test of how similarly the brain responds to these two contexts. The current study used representational similarity analysis on data from 8‐ to 14‐year‐olds who made mental and nonmental judgments about an abstract character and a live interaction partner during fMRI. A within‐subject, 2 (Mental/Nonmental) × 2 (Peer/Character) design enabled us to examine response pattern similarity between conditions, and estimate fit to three conceptual models of how the two contexts relate: (1) social interaction and mentalizing about an abstract character are represented similarly; (2) interactive peers and abstract characters are represented differently regardless of the evaluation type; and (3) mental and nonmental states are represented dissimilarly regardless of target. We found that the temporal poles represent mentalizing and peer interactions similarly (Model 1), suggesting a neurocognitive link between the two in these regions. Much of the rest of the social brain exhibits different representations of interactive peers and abstract characters (Model 2). Our findings highlight the importance of studying social‐cognitive processes using interactive approaches, and the utility of pattern‐based analyses for understanding how social‐cognitive processes relate to each other.

Disrupted fronto-parietal network and default-mode network gamma interactions distinguishing suicidal ideation and suicide attempt in depression

BACKGROUND

Precise suicide risk evaluation struggled in Major depressive disorder (MDD), especially for patients with only suicidal-ideation (SI) but without suicide attempt (SA). MDD patients have deficits in negative emotion processing, which is associated with the generation of SI and SA. Given the critical role of gamma oscillations in negative emotion processing, we hypothesize that the transition from SI to SA in MDD could be characterized by abnormal gamma interactions.

METHODS

We recruited 162 participants containing 106 MDD patients and 56 healthy controls (HCs). Participants performed facial recognition tasks while magnetoencephalography data were recorded. Time-frequency-representation (TFR) analysis was conducted to identify the dominant spectra differences between MDD and HCs, and then source analysis was applied to localize the region of interests. Furthermore, frequency-specific functional connectivity network were constructed and a semi-supervised clustering algorithm was utilized to predict potential suicide risk.

RESULTS

Gamma (50-70 Hz) power was found significantly increased in MDD, mainly residing in regions from fronto-parietal-control-network (FPN), visual-network (VN), default-mode-network (DMN) and salience-network (SN). Based on impaired gamma functional connectivity network between well-established SA group and non-SI group, semi-supervised algorithm clustered patients with only SI into two groups with different suicide risks. Moreover, Inter-network gamma connectivity between FPN and DMN significantly negatively correlated with suicide risk and not confounded by depression severity.

CONCLUSION

Inter-network gamma connectivity with FPN and DMN might be the key neuropathological interactions underling the progression from SI to SA. By applying semi-supervised clustering to electrophysiological data, it is possible to predict individual suicide risk.

Neural Correlates of Academic Self-concept and the Association with Academic Achievement in Older Children

Academic self-concept, which can be defined as one's beliefs about their academic ability, plays an important role in students' future academic achievement. Here, we examined the neuroanatomical substrates underlying academic self-concept in 92 school-aged children (9.90 ± 0.85 years, 41 girls) using voxel-based morphometry of images obtained by structural magnetic resonance imaging. Our results revealed a significant positive correlation between academic self-concept and achievement 1 year after assessment. Whole-brain regression analyses found that gray matter volume in the right dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) was negatively associated with academic self-concept. Region of interest analyses further showed that regional gray matter volume in the right DLPFC could significantly predict achievement 1 year after assessment. Notably, mediation analyses suggested that regional gray matter volume in the right DLPFC mediated the effect of academic self-concept on students' future academic achievement.

Overlapping and distinct neural correlates of self-evaluations and self-regulation from the perspective of self and others

Prior research has implicated the medial and lateral prefrontal cortex (PFC) in processing evaluations from the perspective of self (self-traits) and evaluations from others (peer feedback), suggesting that these areas form a neural substrate that serves an intertwined function in monitoring self in relation to others. To test this possibility, we examined neural activation overlap in medial and lateral PFC after processing self- and other-informed evaluations. Young adults (age range 18-30-yrs, n = 40) performed two fMRI tasks. The self-concept task involved rating whether positive and negative traits described themselves. The Social Network Aggression Task involved processing positive, neutral or negative feedback from others, with the possibility to retaliate by blasting a loud noise following feedback. The results show that rating positive self traits and receiving positive peer feedback was associated with increased activity in an overlapping region in medial PFC. There were no significant correlations on a behavioral level and medial PFC activity for self-versus-other evaluations. The study further replicated the finding from previous research showing that higher activity in dorsolateral PFC (dlPFC) when receiving negative social feedback was associated with reduced noise blast aggression. Finally, during retaliatory responses after receiving positive feedback, participants showed increased activity in the dlPFC. Together these findings suggest that medial PFC is more strongly involved in protecting positive self-views from both internal (self traits) and external (peer feedback) points of view, whereas dlPFC is more strongly involved in regulating retaliatory responses following social rejection, and actively inhibiting aggressive behavior after receiving positive peer feedback.

A large-scale structural and functional connectome of social mentalizing

Humans have a remarkable ability to infer the mind of others. This mentalizing skill relies on a distributed network of brain regions but how these regions connect and interact is not well understood. Here we leveraged large-scale multimodal neuroimaging data to elucidate the brain-wide organization and mechanisms of mentalizing processing. Key connectomic features of the mentalizing network (MTN) have been delineated in exquisite detail. We found the structural architecture of MTN is organized by two parallel subsystems and constructed redundantly by local and long-range white matter fibers. We uncovered an intrinsic functional architecture that is synchronized according to the degree of mentalizing, and its hierarchy reflects the inherent information integration order. We also examined the correspondence between the structural and functional connectivity in the network and revealed their differences in network topology, individual variance, spatial specificity, and functional specificity. Finally, we scrutinized the connectome resemblance between the default mode network and MTN and elaborated their inherent differences in dynamic patterns, laterality, and homogeneity. Overall, our study demonstrates that mentalizing processing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, which make it distinguishable from the default mode network and other vicinity brain networks supporting autobiographical memory, semantic memory, self-referential, moral reasoning, and mental time travel.

Charity preferences and perceived impact moderate charitable giving and associated neural response

Charitable giving depends on individuals' abilities to make altruistic decisions. Previous studies suggest that altruism involves recruitment of neural resources in regions including social processing, reward/reinforcement learning, emotional response, and cognition. Despite evolutionary and social benefits to altruism, we know that humans do not always engage in altruistic behavior, like charitable giving. Understanding the underlying processes leading to decisions to donate is vital to improve prosocial community engagement. The present study examined how characteristics of the charitable giving opportunity influence an individual's decision to give and the neural engagement underlying these features. Twenty-nine participants subjectively rated ten charities on their value, effectiveness, and the subject's personal chance of donating. Participants then completed an fMRI task requiring them to decide to donate to certain charities given the probability of the donation helping, their personal preference for the charity, and whether the donation came at cost to themselves. There was a significant reduction in donating when the probability of helping was low versus high, and subjects were significantly less likely to donate to their lowest-rated charities. Further, probability of a donation being helpful and how much the subject favored a charity moderated PCC and left IFG engagement. Interestingly, reward neurocircuitry did not demonstrate similar sensitivity to these variations. These results may suggest individuals engage motivated reasoning to justify failure to donate, while donations are driven by emotion mentalizing that focuses on the welfare of others. This may provide valuable insight into how to engage individuals in altruistic giving.

Neural processing of personal, relational, and collective self-worth reflected individual differences of self-esteem

INTRODUCTION

Self-esteem stems from an individual's attributes (PSE), relationships with important others (RSE), and collective membership (CSE). Our study aimed to identify neurological indicators in the processing of personal, relational, and collective self-worth, and to investigate whether these neural indicators could reflect individual differences of self-esteem.

METHODS

Fifty students underwent the evaluation of personal, relational, and collective self-worth using a self-referential paradigm while brain activities were recorded using functional-magnetic-resonance-imaging. Meanwhile, their PSE, RSE, and CSE were measured through questionnaires.

RESULTS

Conjunction analysis found self-worth processing recruited the precuneus, posterior cingulate cortex, and posterior insula. Multivariate pattern analysis showed compared to relational and collective self-worth, personal self-worth processing was distinguished by cortical-midline-structures and affective-related regions, including caudate and putamen, and that these neural patterns could reflect individual differences of PSE. Compared to personal self-worth, relational self-worth was distinguished by the neural activity of temporoparietal-junction, and this neural pattern reflected individual differences of RSE. Compared to relational self-worth, collective self-worth was distinguished by neural activity of the anterior insula, and this neural pattern reflected individual differences of CSE.

DISCUSSION

These results suggested the neurological indicators of self-worth can be recognized as an alternative way to reflect individual differences of self-esteem.

Functional Brain Connectivity During Narrative Processing Relates to Transportation and Story Influence

Engaging with narratives involves a complex array of cognitive and affective processes. These processes make stories persuasive in ways that standard arguments are not, though the underlying reasons for this remain unclear. Transportation theory proposes a potential explanation for this: narratives are processed in a way which makes individuals feel immersed in the world of a story, which in turn leads people to resonate emotionally with the events of the story. Recent fMRI studies have shown that the posterior medial cortex (PMC) and anterior insula (AI) play important roles in understanding the meaning of stories and experiencing the feelings they produce. In this study, we aimed to explore the AI’s and PMC’s role in narrative processing by measuring their functional connectivity with the rest of the brain during story listening, and how connectivity changes as a function of narrative transportation and the persuasiveness of the story. We analyzed data from 36 right-handed subjects who listened to two stories, obtained from podcasts, inside the fMRI scanner. After the scan, subjects were asked a series of questions, including a measure of how transported into the story they felt, how likely they would be to donate to causes related to the messages of the stories. We used searchlight multivariate pattern analysis (MVPA) to classify functional connectivity maps using seeds in both the AI and PMC and to compare these maps between participants who differed in transportation and prosocial intention. We found that connectivity to various regions successfully distinguished between high and low ratings on each of these behavioral measures with accuracies over 75%. However, only one pattern of connectivity was consistent across both stories: PMC-inferior frontal gyrus connectivity successfully distinguished high and low ratings of narrative transportation in both stories. All other findings were not consistent across stories. Instead, we found that patterns of connectivity may relate more to the specific content of the story rather than to a universal way in which narratives are processed.

Symphony of Well-Being: Harmony Between Neural Variability and Self-Construal

Both neural activities and psychological processes vary over time. Individuals with interdependent self-construal tend to define themselves and adjust their behaviors to social contexts and others. The current research tested the hypothesis that the coordination between interdependent self-construal and neural variability could predict life satisfaction changes in university freshmen. We integrated resting-state functional magnetic resonance imaging scanning and self-construal assessment to estimate self-dependent neural variability (SDNV). In the whole-brain prediction, SDNV successfully predicted individuals’ life satisfaction changes over 2 years. Interdependent individuals with higher neural variability and independent individuals with lower neural variability became more satisfied with their lives. In the network-based prediction, the predictive effects were significant in the default mode, frontoparietal control, visual and salience networks. The important nodes that contributed to the predictive models were more related to psychological constructs associated with the social and self-oriented functions. The current research sheds light on the neural and psychological mechanisms of the subjective well-being of individuals from a dynamic perspective.

Multivariable pattern classification differentiates relational self-esteem from personal self-esteem

Relational self-esteem (RSE) refers to one’s sense of self-worth based on the relationship with significant others, such as family and best friends. Although previous neuroimaging research has investigated the neural processes of RSE, it is less clear how RSE is represented in multivariable neural patterns. Being able to identify a stable RSE signature could contribute to knowledge about relational self-worth. Here, using multivariate pattern classification to differentiate RSE from personal self-esteem (PSE), which pertains to self-worth derived from personal attributes, we obtained a stable diagnostic signature of RSE relative to PSE. We found that multivariable neural activities in the superior/middle temporal gyrus, precuneus, posterior cingulate cortex, dorsal medial Prefrontal Cortex (dmPFC) and temporo-parietal junction were responsible for diagnosis of RSE, suggesting that the evaluation of RSE involves the retrieval of relational episodic memory, perspective-taking and value calculation. Further, these diagnostic neural signatures were able to sensitively decode neural activities related to RSE in another independent test sample, indicating the reliability of the brain state represented. By providing a reliable multivariate brain pattern for RSE relative to PSE, our results informed more cognitively prominent processing of RSE than that of PSE and enriched our knowledge about how relational self-worth is generated in the brain.

Self-Related Stimuli Decoding With Auditory and Visual Modalities Using Stereo-Electroencephalography

Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities. The neural activities were classified using spatiotemporal features of high-gamma, beta, and alpha band. Results showed that different names could be decoded using multi-region SEEG signals, and the best classification performance was achieved at high gamma (60–145 Hz) band. In this case, auditory and visual modality-based name classification accuracies were 84.5 ± 8.3 and 79.9 ± 4.6%, respectively. Additionally, some single regions such as the supramarginal gyrus, middle temporal gyrus, and insula could also achieve remarkable accuracies for both modalities, supporting their roles in the processing of self-related information. The average latency of the difference between the two responses in these precise regions was 354 ± 63 and 285 ± 59 ms in the auditory and visual modality, respectively. This study suggested that name recognition was attributed to a distributed brain network, and the subsets with decoding capabilities might be potential implanted regions for awareness detection and cognition evaluation.

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

Intrinsic functional network contributions to the relationship between trait empathy and subjective happiness

Subjective happiness (well-being) is a multi-dimensional construct indexing one's evaluations of everyday emotional experiences and life satisfaction, and has been associated with different aspects of trait empathy. Despite previous research identifying the neural substrates of subjective happiness and empathy, the mechanisms mediating the relationship between the two constructs remain largely unclear. Here, we performed a data-driven, multi-voxel pattern analysis of whole-brain intrinsic functional connectivity to reveal the neural mechanisms of subjective happiness and trait empathy in a sample of young females. Behaviorally, we found that subjective happiness was negatively associated with personal distress (i.e., self-referential experience of others' feelings). Consistent with this inverse relationship, subjective happiness was associated with the dorsolateral prefrontal cortex exhibiting decreased functional connectivity with regions important for the representation of unimodal sensorimotor information (e.g., primary sensory cortices) or multi-modal summaries of brain states (e.g., default mode network) and increased functional connectivity with regions important for the attentional modulation of these representations (e.g., frontoparietal, attention networks). Personal distress was associated with the medial prefrontal cortex exhibiting functional connectivity differences with similar networks--but in the opposite direction. Finally, intrinsic functional connectivity within and between these networks fully mediated the relationship between the two behavioral measures. These results identify an important contribution of the macroscale functional organization of the brain to human well-being, by demonstrating that lower levels of personal distress lead to higher subjective happiness through variation in intrinsic functional connectivity along a neural representation vs. modulation gradient.

Self-Other Representation in the Social Brain Reflects Social Connection

Social connection is critical to well-being, yet how the brain reflects our attachment to other people remains largely unknown. We combined univariate and multivariate brain imaging analyses to assess whether and how the brain organizes representations of others based on how connected they are to our own identity. During an fMRI scan, female and male human participants (N = 43) completed a self- and other-reflection task for 16 targets: the self, five close others, five acquaintances, and five celebrities. In addition, they reported their subjective closeness to each target and their own trait loneliness. We examined neural responses to the self and others in a brain region that has been associated with self-representation (mPFC) and across the whole brain. The structure of self-other representation in the mPFC and across the social brain appeared to cluster targets into three social categories: the self, social network members (including close others and acquaintances), and celebrities. Moreover, both univariate activation in mPFC and multivariate self-other similarity in mPFC and across the social brain increased with subjective self-other closeness ratings. Critically, participants who were less socially connected (i.e., lonelier) showed altered self-other mapping in social brain regions. Most notably, in mPFC, loneliness was associated with reduced representational similarity between the self and others. The social brain apparently maintains information about broad social categories as well as closeness to the self. Moreover, these results point to the possibility that feelings of chronic social disconnection may be mirrored by a "lonelier" neural self-representation.SIGNIFICANCE STATEMENT Social connection is critical to well-being, yet how the brain reflects our attachment to people remains unclear. We found that the social brain stratifies neural representations of people based on our subjective connection to them, separately clustering people who are and are not in our social network. Moreover, the people we feel closest to are represented most closely to ourselves. Finally, lonelier individuals also appeared to have a "lonelier" neural self-representation in the mPFC, as loneliness attenuated the similarity between self and other neural representations in this region. The social brain appears to map our interpersonal ties, and alterations in this map may help explain why lonely individuals endorse statements such as "people are around me but not with me."

Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies

Identifying brain alterations that contribute to suicidal thoughts and behaviors (STBs) are important to develop more targeted and effective strategies to prevent suicide. In the last decade, and especially in the last 5 years, there has been exponential growth in the number of neuroimaging studies reporting structural and functional brain circuitry correlates of STBs. Within this narrative review, we conducted a comprehensive review of neuroimaging studies of STBs published to date and summarize the progress achieved on elucidating neurobiological substrates of STBs, with a focus on converging findings across studies. We review neuroimaging evidence across differing mental disorders for structural, functional, and molecular alterations in association with STBs, which converges particularly in regions of brain systems that subserve emotion and impulse regulation including the ventral prefrontal cortex (VPFC) and dorsal PFC (DPFC), insula and their mesial temporal, striatal and posterior connection sites, as well as in the connections between these brain areas. The reviewed literature suggests that impairments in medial and lateral VPFC regions and their connections may be important in the excessive negative and blunted positive internal states that can stimulate suicidal ideation, and that impairments in a DPFC and inferior frontal gyrus (IFG) system may be important in suicide attempt behaviors. A combination of VPFC and DPFC system disturbances may lead to very high risk circumstances in which suicidal ideation is converted to lethal actions via decreased top-down inhibition of behavior and/or maladaptive, inflexible decision-making and planning. The dorsal anterior cingulate cortex and insula may play important roles in switching between these VPFC and DPFC systems, which may contribute to the transition from suicide thoughts to behaviors. Future neuroimaging research of larger sample sizes, including global efforts, longitudinal designs, and careful consideration of developmental stages, and sex and gender, will facilitate more effectively targeted preventions and interventions to reduce loss of life to suicide.

A Guide to Representational Similarity Analysis for Social Neuroscience

Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.

Direct replication of task-dependent neural activation patterns during sadness introspection in two independent adolescent samples

Functional neuroimaging results need to replicate to inform sound models of human social cognition and its neural correlates. Introspection, the capacity to reflect on one's thoughts and feelings, is one process required for normative social cognition and emotional functioning. Engaging in introspection draws on a network of brain regions including medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), middle temporal gyri (MTG), and temporoparietal junction (TPJ). Maturation of these regions during adolescence mirrors the behavioral advances seen in adolescent social cognition, but the neural correlates of introspection in adolescence need to replicate to confirm their generalizability and role as a possible mechanism. The current study investigated whether reflecting upon one's own feelings of sadness would activate and replicate similar brain regions in two independent samples of adolescents. Participants included 156 adolescents (50% female) from the California Families Project and 119 adolescent girls from the Pittsburgh Girls Study of Emotion. All participants completed the Emotion Regulation Questionnaire (ERQ) and underwent a functional magnetic resonance imaging scan while completing the same facial emotion‐processing task at age 16–17 years. Both samples showed similar whole‐brain activation patterns when engaged in sadness introspection and when judging a nonemotional facial feature. Whole‐brain activation was unrelated to ERQ scores in both samples. Neural responsivity to task manipulations replicated in regions recruited for socio‐emotional (mPFC, PCC, MTG, TPJ) and attention (dorsolateral PFC, precentral gyri, superior occipital gyrus, superior parietal lobule) processing. These findings demonstrate robust replication of neural engagement during sadness introspection in two independent adolescent samples.

Meta-analysis of the moral brain: patterns of neural engagement assessed using multilevel kernel density analysis

The neuroimaging literature in moral cognition has rapidly developed in the last decade with more than 200 publications on the topic. Neuroimaging based models generally agree that limbic regions work with medial prefrontal and temporal regions during moral processing to integrate emotional, social, and cognitive elements into decision-making. However, no quantitative work has been done examining neural response across types of moral cognition tasks. This paper uses Multilevel Kernel Density Analysis (MKDA) to conduct neuroimaging meta-analyses of the moral cognitive literature. MKDA replicated previous findings of the neural correlates of moral cognition: the left amygdala, medial prefrontal cortex, bilateral temporoparietal junction, and posterior cingulate. Random forest algorithms classified neural features as belonging to simple/utilitarian moral dilemmas, explicit/implicit moral tasks, and word/picture moral stimuli tasks; in combination with univariate contrast analyses, these results indicated a distinct pattern of processing for each of the members of these paradigm pairs. Overall, the results emphasize that the task selected for use in a moral cognition neuroimaging study is vital for the elicitation and interpretation of results. It also replicates and re-establishes the neural basis for moral processing, especially important in light of implementation errors in previous meta-analysis.