Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence

Vicarious punishment of moral violations in naturalistic drama narratives predicts cortical synchronization

Punishment of moral norm violators is instrumental for human cooperation. Yet, social and affective neuroscience research has primarily focused on second- and third-party norm enforcement, neglecting the neural architecture underlying observed (vicarious) punishment of moral wrongdoers. We used naturalistic television drama as a sampling space for observing outcomes of morally-relevant behaviors to assess how individuals cognitively process dynamically evolving moral actions and their consequences. Drawing on Affective Disposition Theory, we derived hypotheses linking character morality with viewers' neural processing of characters' rewards and punishments. We used functional magnetic resonance imaging (fMRI) to examine neural responses of 28 female participants while free-viewing 15 short story summary video clips of episodes from a popular US television soap opera. Each summary included a complete narrative structure, fully crossing main character behaviors (moral/immoral) and the consequences (reward/punishment) characters faced for their actions. Narrative engagement was examined via intersubject correlation and representational similarity analysis. Highest cortical synchronization in 9 specifically selected regions previously implicated in processing moral information was observed when characters who act immorally are punished for their actions with participants' empathy as an important moderator. The results advance our understanding of the moral brain and the role of normative considerations and character outcomes in viewers' engagement with popular narratives.

The science of justice: The neuropsychology of social punishment

The social punishment (SP) of norm violations has received much attention across multiple disciplines. However, current models of SP fail to consider the role of motivational processes, and none can explain the observed behavioral and neuropsychological differences between the two recognized forms of SP: second-party punishment (2PP) and third-party punishment (3PP). After reviewing the literature giving rise to the current models of SP, we propose a unified model of SP which integrates general psychological descriptions of decision-making as a confluence of affect, cognition, and motivation, with evidence that SP is driven by two main factors: the amount of harm (assessed primarily in the salience network) and the norm violator's intention (assessed primarily in the default-mode and central-executive networks). We posit that motivational differences between 2PP and 3PP, articulated in mesocorticolimbic pathways, impact final SP by differentially impacting the assessments of harm and intention done in these domain-general large-scale networks. This new model will lead to a better understanding of SP, which might even improve forensic, procedural, and substantive legal practices.

Altruistic preferences of pre-service teachers: The mediating role of empathic concern and the moderating role of self-control

Empathy and altruistic behavior are more crucial abilities for pre-service teachers to possess when compared with other study fields. The relationship between empathy and altruistic behavior in Chinese pre-service teachers and their underlying mechanisms, however, has received relatively little attention in the literature. Therefore, the goal of the current study was to examine the links between study fields (i.e., pre-service teachers whose study field is pedagogy and non-pre-service teachers whose study field is non-pedagogy), self-control, emotional empathy (i.e., empathic concern), and altruistic preferences among undergraduates and graduates in five Chinese universities (the age range of participants is 18-20 years; 58.4% women) with the Interpersonal Reactivity Index-C Questionnaire, the Self-Control Scale, and the Chinese Self-Report Altruism Scale tests. The results showed a significant difference between pre-service and non-pre-service teachers in empathic concern and self-control. Furthermore, empathic concern and altruistic behavior tendency of pre-service teachers were significantly higher than those of non-pre-service teachers. Moreover, mediation analyses indicated that empathic concern partially mediated the relationship between study fields and altruistic tendency. Moderated mediation analysis further revealed that self-control buffered the relation between empathic concern and altruistic behavior tendency. These results demonstrate that altruistic tendency of pre-service teachers is influenced by empathic concern and self-control.

Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment

Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.

Wired to Punish? Electroencephalographic Study of the Resting-state Neuronal Oscillations Underlying Third-party Punishment

For over a decade, neuroimaging and brain stimulation studies have investigated neural mechanisms of third-party punishment, a key instrument for social norms enforcement. However, the neural dynamics underlying these mechanisms are still unclear. Previous electroencephalographic studies on third-party punishment have shown that inter-brain connectivity is linked to punishment behavior. However, no clear evidence was provided regarding whether the effect of inter-brain connectivity on third-party punishment is mediated by local neuronal states. In this study, we further investigate whether resting-state neuronal activity in the alpha frequency range can predict individual differences in third-party punishment. More specifically, we show that the global resting-state connectivity between the right dorsolateral prefrontal and right temporo-parietal regions is negatively correlated with the level of third-party punishment. Additionally, individuals with stronger local resting-state long-range temporal correlations in the right temporo-parietal cortices demonstrated a lower level of third-party punishment. Thus, our results further support the idea that global and local neuronal dynamics can contribute to individual differences in third-party punishment.

The emerging neuroscience of social punishment: Meta-analytic evidence

Social punishment (SOP)-third-party punishment (TPP) and second-party punishment (SPP)-sanctions norm-deviant behavior. The hierarchical punishment model (HPM) posits that TPP is an extension of SPP and both recruit common processes engaging large-scale domain-general brain networks. Here, we provided meta-analytic evidence to the HPM by combining the activation likelihood estimation approach with connectivity analyses and hierarchical clustering analyses. Although both forms of SOP engaged the dorsolateral prefrontal cortex and bilateral anterior insula (AI), a functional differentiation also emerged with TPP preferentially engaging social cognitive regions (temporoparietal junction) and SPP affective regions (AI). Further, although both TPP and SPP recruit domain-general networks (salience, default-mode, and central-executive networks), some specificity in network organization was observed. By revealing differences and commonalities of the neural networks consistently activated by different types of SOP, our findings contribute to a better understanding of the neuropsychological mechanisms of social punishment behavior--one of the most peculiar human behaviors.

Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting

People punish transgressors with different intensity depending if they are members of their group or not. We explore this in a cross-sectional analytical study with paired samples in children with developmental disorders who watched two videos and expressed their opinion. In Video-1, a football-player from the participant's country scores a goal with his hand. In Video-2, a player from another country does the same against the country of the participant. Each subject watched the two videos and their answers were compared. The autism spectrum disorder (ASD) group showed negative feelings in Video 1 (M = - .1; CI 95% - .51 to .31); and in Video 2 (M = - .43; CI 95% .77 to - .09; t(8) = 1.64, p = .13), but the attention deficit hyperactivity disorder, learning disabilities, intellectual disability groups showed positive opinion in Video-1 and negative in Video-2. This suggests that children with ASD respect rules regardless of whether those who break them belong or not to their own group, possibly due to lower degrees of empathy.

Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks

OBJECTIVE

It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.

METHODS

Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.

RESULTS

MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.

CONCLUSION

Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

Hippocampal connectivity in the aftermath of acute social stress

The hippocampus is a core brain region that responds to stress. Previous studies have found a dysconnectivity between hippocampus and other brain regions under acute and chronic stress. However, whether and how acute social stress influences the directed connectivity patterns from and to the hippocampus remains unclear. In this study, using a within-subject design and Granger causal analysis (GCA), we investigated the alterations of resting state effective connectivity from and to hippocampal subregions after an acute social stressor (the Trier Social Stress Test). Participants were engaged in stress and control conditions spaced approximately one month apart. Our findings showed that stress altered the information flows in the thalamus-hippocampus-insula/midbrain circuit. The changes in this circuit could also predict with high accuracy the stress and control conditions at the subject level. These hippocampus-related brain networks have been documented to be involved in emotional information processing and storage, as well as habitual responses. We speculate that alterations of the effective connectivity between these brain regions may be associated with the registering and encoding of threatening stimuli under stress. Our investigation of hippocampal functional connectivity at a subregional level may help elucidate the functional neurobiology of stress-related psychiatric disorders.

Brain responses to social punishment: a meta-analysis

Many studies suggest that social punishment is beneficial for cooperation and consequently maintaining the social norms in society. Neuroimaging and brain stimulation studies show that the brain regions which respond to violations of social norms, the understanding of the mind of others and the executive functions, are involved during social punishment. Despite the rising number of studies on social punishment, the concordant map of activations - the set of key regions responsible for the general brain response to social punishment - is still unknown. By using coordinate-based fMRI meta-analysis, the present study examined the concordant map of neural activations associated with various social punishment tasks. A total of 17 articles with 18 contrasts including 383 participants, equalling 191 foci were included in activation likelihood estimation (ALE) analysis. The majority of the studies (61%) employed the widely used neuroeconomic paradigms, such as fairness-related norm tasks (Ultimatum Game, third-party punishment game), while the remaining tasks reported criminal scenarios evaluation and social rejection tasks. The analysis revealed concordant activation in the bilateral claustrum, right interior frontal and left superior frontal gyri. This study provides an integrative view on brain responses to social punishment.

Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma

Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R ² = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.

Soft on crime: Patients with ventromedial prefrontal cortex damage allocate reduced third-party punishment to violent criminals

The human impulse to punish those who have unjustly harmed others (i.e., third-party punishment) is critical for stable, cooperative societies. Punishment selection is influenced by both harm outcome and the intent of the moral agent (i.e., the offender's knowledge of wrongdoing and desire that the prohibited consequence occur). We allocate severe punishments to those who commit violent crimes and milder punishments to those who commit non-violent crimes; and we allocate severe punishments to criminals who have malicious intent and milder punishments to criminals who lack malicious intent. Prior research has indicated that aversive, emotional responses of third-party judges may influence punishment allocation, as increased negative emotion correlates with more punitive punishments. Here, we show that patients with damage to the ventromedial prefrontal cortex (vmPFC; a region necessary for the normal generation of emotion), compared to other neurological patients and healthy adult participants, allocate more lenient third-party punishment to criminals who commit emotionally-evocative, violent crimes. By contrast, patients with vmPFC damage did not differ from comparison participants on punishment allocation for non-emotional, non-violent crimes. These results demonstrate the necessity of the vmPFC for the integration of emotion into third-party punishment decisions, and indicate that negative emotion influences third-party punishment allocation particularly for scenarios involving physical harm to another.

Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

When morality opposes the law: An fMRI investigation into punishment judgments for crimes with good intentions

In judicial practice, morally right but legally wrong instances usually pose significant challenges for legal decision makers. To examine the cognitive and neural foundations of legal judgments in criminal cases involving apparent moral conflicts, we scanned 30 female participants during punishment judgments for crimes committed with good intentions. The behavioral results confirmed that moral acceptability was significantly correlated with the punishment ratings only in the good-intentioned crimes. The fMRI data mainly revealed that the right temporoparietal junction (rTPJ) plays special roles in processing criminal offenders' state of mind and that the right dorsal lateral prefrontal cortex (rDLPFC) plays roles in resolving moral conflicts involved in legal judgments. Specifically, we found that compared to the bad-intentioned scenarios, the good-intentioned scenarios evoked greater activities during the postreading stage in the brain area of the rTPJ and that a signal increase in the rTPJ was associated with more lenient penalty judgments in the good-intentioned scenarios. Furthermore, reading crime scenarios with good intentions elicited stronger activation in the rdlPFC, which showed enhanced functional connectivity with the medial prefrontal cortex (mPFC). Overall, our study sheds some light on the neurocognitive underpinnings of legal judgments in special criminal cases and enhances our understanding of the relationship between legal and moral judgments.

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

Network physiology of 'fight or flight' response in facial superficial blood vessels

OBJECTIVE

We introduced a novel framework to identify the dynamic pattern of blood flow changes in the cutaneous superficial blood vessels of the face for 'fight or flight' responses through facial thermal imaging.

APPROACH

For this purpose, a thermal dataset was collected from 41 subjects in a mock crime scenario. Five facial areas including periorbital, forehead, perinasal, cheek and chin were selected on the face. Due to the cause and effect movement of blood in the facial cutaneous vasculature, the effective connectivity approach and graph analysis were used to extract causality features. The effective connectivity was quantified using a modified version of the multivariate Granger causality (GC) method among each pair of facial region of interests.

MAIN RESULTS

Validation was performed using statistical analysis, and the results demonstrated that the proposed method was statistically significant in detecting the physiological pattern of deceptive anxiety on the face. Moreover, the obtained graph is visualized by different schemes to show these interactions more effectively. We used machine learning techniques to classify our data based on the GC values, which result in a greater than 87% accuracy rate in discriminating between deceptive and truthful subjects.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

Zinc Nanoparticles Enhance Brain Connectivity in the Canine Olfactory Network: Evidence From an fMRI Study in Unrestrained Awake Dogs

Prior functional Magnetic Resonance Imaging (fMRI) studies have indicated increased neural activation when zinc nanoparticles are added to odorants in canines. Here we demonstrate that zinc nanoparticles up-regulate directional brain connectivity in parts of the canine olfactory network. This provides an explanation for previously reported enhancement in the odor detection capability of the dogs in the presence of zinc nanoparticles. In this study, we obtained fMRI data from awake and unrestrained dogs while they were being exposed to odorants with and without zinc nanoparticles, zinc nanoparticles suspended in water vapor, as well as just water vapor alone. We obtained directional connectivity between the brain regions of the olfactory network that were significantly stronger for the condition of odorant + zinc nanoparticles compared to just odorants, water vapor + zinc nanoparticles and water vapor alone. We observed significant strengthening of the paths of the canine olfactory network in the presence of zinc nanoparticles. This result indicates that zinc nanoparticles could potentially be used to increase canine detection capabilities in the environments of very low concentrations of the odorants, which would have otherwise been undetected.

Multiple-Brain Connectivity During Third Party Punishment: an EEG Hyperscanning Study

Compassion is a particular form of empathic reaction to harm that befalls others and is accompanied by a desire to alleviate their suffering. This altruistic behavior is often manifested through altruistic punishment, wherein individuals penalize a deprecated human’s actions, even if they are directed toward strangers. By adopting a dual approach, we provide empirical evidence that compassion is a multifaceted prosocial behavior and can predict altruistic punishment. In particular, in this multiple-brain connectivity study in an EEG hyperscanning setting, compassion was examined during real-time social interactions in a third-party punishment (TPP) experiment. We observed that specific connectivity patterns were linked to behavioral and psychological intra- and interpersonal factors. Thus, our results suggest that an ecological approach based on simultaneous dual-scanning and multiple-brain connectivity is suitable for analyzing complex social phenomena.

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.

Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD.

Neuroanatomical correlates of forgiving unintentional harms

Mature moral judgments rely on the consideration of a perpetrator’s mental state as well as harmfulness of the outcomes produced. Prior work has focused primarily on the functional correlates of how intent information is neurally represented for moral judgments, but few studies have investigated whether individual differences in neuroanatomy can also explain variation in moral judgments. In the current study, we conducted voxel-based morphometry analyses to address this question. We found that local grey matter volume in the left anterior superior temporal sulcus, a region in the functionally defined theory of mind or mentalizing network, was associated with the degree to which participants relied on information about innocent intentions to forgive accidental harms. Our findings provide further support for the key role of mentalizing in the forgiveness of accidental harms and contribute preliminary evidence for the neuroanatomical basis of individual differences in moral judgments.