Multilevel Kernel Density Analysis Research Articles

Hypofunctional connectivity between the posterior cingulate cortex and ventromedial prefrontal cortex in autism: Evidence from coordinate-based imaging meta-analysis

BackgroundUnderconnectivity in the posterior cingulate cortex (PCC) may be associated with a weakened ability to interpret social signals in autism spectrum disorder (ASD) and result in cognitive inflexibility – a hallmark feature of ASD. However, previous neuroimaging studies using resting-state functional magnetic resonance imaging in ASD reported inconsistent findings on functional connectivity of the PCC. This study investigated the aberrant resting-state functional connectivity of the PCC in ASD using multilevel kernel density analysis. MethodsOnline databases (MEDLINE/PubMed) were searched for PCC-based functional connectivity in ASD. Ten studies (501 subjects; 161 reported foci) met the inclusion criteria of this meta-analysis. ResultsWe found one consistent and strong abnormal functional connectivity of ASD during the resting state, which was the hypoconnectivity between the PCC and ventromedial prefrontal cortex (VMPFC). Importantly, the Jackknife sensitivity analysis revealed that the VMPFC cluster was stably hypoconnected with the PCC in ASD (maximum spatial overlap rate: 100%). ConclusionsThe reduced PCC–VMPFC functional coupling may provide an early insight into the effects of ASD on multiple dimensions of functioning, including higher-order cognitive and complex social functions.

Functional neural correlates of psychopathy: a meta-analysis of MRI data

Neuroimaging studies over the last two decades have begun to specify the neurobiological correlates of psychopathy, a personality disorder that is strongly related to criminal offending and recidivism. Despite the accumulation of neuroimaging studies of psychopathy, a clear and comprehensive picture of the disorder’s neural correlates has yet to emerge. The current study is a meta-analysis of functional MRI studies of psychopathy. Multilevel kernel density analysis was used to identify consistent findings across 25 studies (460 foci) of task-related brain activity. Psychopathy was associated with increased task-related activity predominantly in midline cortical regions overlapping with the default mode network (dorsomedial prefrontal cortex, posterior cingulate, and precuneus) as well as medial temporal lobe (including amygdala). Psychopathy was related to decreased task-related activity in a region of the dorsal anterior cingulate cortex overlapping with the salience network. These findings challenge predominant theories of amygdala hypoactivity and highlight the potential role of hyperactivity in medial default mode network regions and hypoactivity in a key node of the salience network during task performance in psychopathy.

Brain Differences Associated with Self-Injurious Thoughts and Behaviors: A Meta-Analysis of Neuroimaging Studies

This meta-analysis aims to evaluate whether the extant literature justifies any definitive conclusions about whether and how SITBs may be associated with brain differences. A total of 77 papers (N = 4,903) published through January 1, 2019 that compared individuals with and without SITBs were included, resulting in 882 coordinates. A pooled meta-analysis assessing for general risk for SITBs indicated a lack of convergence on structural differences. When all types of control groups were considered, functional differences in the left posterior cingulate cortex (PCC), right amygdala, left hippocampus, and right thalamus were significant using multi-level kernel density analysis (pcorrected < 0.05) but nonsignificant using activation-likelihood estimation. These results suggest that a propensity for internally-oriented, emotional processing coupled with under-active pain processing could potentially underlie SITBs, but additional research is needed to test this possibility. Separate analyses for types of SITBs suggested that the brain differences associated with deliberate self-harm were consistent with the overall findings. Checkered moderator effects were detected. Overall, the meta-analytic evidence was not robust. More studies are needed to reach definitive conclusions about whether SITBs are associated with brain differences.

Meta-analysis and review of functional neuroimaging differences underlying adolescent vulnerability to substance use

Adolescence is increasingly viewed as a sensitive period in the development of substance use disorders (SUDs). Neurodevelopmental ‘dual-risk’ theories suggest adolescent vulnerability to problematic substance use is driven by an overactive reward drive mediated by the striatum, and poor cognitive control mediated by the prefrontal cortex. To this end, there has been a growing number of neuroimaging studies examining cognitive and affective neural systems during adolescence for markers of vulnerability to problematic substance use. Here, we perform a coordinate-based meta-analysis on this emerging literature. Twenty-two task-based voxelwise fMRI studies with activation differences associated with substance use vulnerability, representative of approximately 1092 subjects, were identified through a systematic literature search (PubMed, Scopus) and coordinates of activation differences (N ​= ​190) were extracted. Adolescents were defined as ‘at-risk’ for problematic substance use based on a family history of SUD or through prospective prediction of substance use initiation or escalation. Multilevel kernel density analysis was used to identify the most consistent brain regions associated with adolescent substance use vulnerability. Across the included studies, substance use vulnerability was most reliably associated with activation differences in the striatum, where at-risk adolescents had hyper-activation in the dorsal subdivision (putamen). Follow-up analyses suggested striatal differences were driven by tasks sharing a motivational and/or reward component (e.g., monetary incentive) and common across subgroups of substance use risk (family history and prospective prediction studies). Analyses examining the role of psychiatric comorbidity revealed striatal activation differences were significantly more common in samples whose definition of substance use risk included cooccurring externalizing psychopathology. Furthermore, substance use risk meta-analytic results were no longer significant when excluding these studies, although this may reflect limitations in statistical power. No significant activation differences were observed in prefrontal cortex in any analysis. These results suggest striatal dysfunction, rather than prefrontal, may be a more primary neural feature of adolescent vulnerability to problematic substance use, possibly through a dimension of individual variability shared with externalizing psychopathology. However, our systematic literature search confirms this is still an emerging field. More studies, increased data sharing, and further quantitative integration are necessary for a comprehensive understanding of the neuroimaging markers of adolescent substance use risk.

Building the bodily self-awareness: Evidence for the convergence between interoceptive and exteroceptive information in a multilevel kernel density analysis study.

Exteroceptive and interoceptive signals shape and sustain the bodily self‐awareness. The existence of a set of brain areas, supporting the integration of information coming from the inside and the outside of the body in building the sense of bodily self‐awareness has been postulated, yet the evidence remains limited, a matter of discussion never assessed quantitatively. With the aim of unrevealing where in the brain interoceptive and exteroceptive signals may converge, we performed a meta‐analysis on imaging studies of the sense of body ownership, modulated by external visuotactile stimulation, and studies on interoception, which involves the self‐awareness for internal bodily sensations. Using a multilevel kernel density analysis, we found that processing of stimuli of the two domains converges primarily in the supramarginal gyrus bilaterally. Furthermore, we found a right‐lateralized set of areas, including the precentral and postcentral, and superior temporal gyri. We discuss these results and propose this set of areas as ideal candidates to match multiple body‐related signals contributing to the creation of a multidimensional representation of the bodily self.

The inhibition of automatic imitation: A meta-analysis and synthesis of fMRI studies

Humans copy other people without their conscious awareness, a behaviour known as automatic imitation. Although automatic imitation forms a key part of daily social interactions, we do not copy other people indiscriminately. Instead, we control imitative tendencies by prioritising some actions and inhibiting others. To date, neuroimaging studies investigating the control of automatic imitation have produced inconsistent findings. Some studies suggest that imitation control relies on a domain-specific neural circuit related to social cognition (the theory-of-mind network). In contrast, other studies show engagement of a domain-general neural circuit that is engaged during a diverse range of cognitive control tasks (the multiple demand network). Given the inconsistency of prior findings, in the current paper we avoided problems associated with interpreting individual studies by performing a meta-analysis. To do so, we used a multi-level kernel density analysis to quantitatively identify consistent patterns of activation across functional magnetic resonance imaging studies investigating the control of imitation. Our results show clear and consistent evidence across studies that the control of automatic imitation is guided by brain regions in the multiple demand network including dorsolateral frontoparietal cortex. In contrast, there was only limited evidence that regions in the theory of mind network were engaged. Indeed, medial prefrontal cortex showed no consistent engagement and right temporoparietal junction engagement may reflect spatial rather than imitative control. As such, the current meta-analysis reinforces the role of domain-general control mechanisms and provides limited evidence in support of the role of domain-specific processes in regulating imitative tendencies. Consequently, neurocognitive models of imitation need updating to place more emphasis on domain-general control mechanisms, as well as to consider more complex organisational structures of control, which may involve contributions from multiple cognitive systems.

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.

Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI.

The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results.

Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis

An influential hypothesis from the last decade proposed that regions within the right inferior frontal cortex of the human brain were dedicated to supporting response inhibition. There is growing evidence, however, to support an alternative model, which proposes that neural areas associated with specific inhibitory control tasks co-exist as common network mechanisms, supporting diverse cognitive processes. This meta-analysis of 225 studies comprising 323 experiments examined the common and distinct neural correlates of cognitive processes for response inhibition, namely interference resolution, action withholding, and action cancellation. Activation coordinates for each subcategory were extracted using multilevel kernel density analysis (MKDA). The extracted activity patterns were then mapped onto the brain functional network atlas to derive the common (i.e., process-general) and distinct (i.e., domain-oriented) neural network correlates of these processes. Independent of the task types, activation of the right hemispheric regions (inferior frontal gyrus, insula, median cingulate, and paracingulate gyri) and superior parietal gyrus was common across the cognitive processes studied. Mapping the activation patterns to a brain functional network atlas revealed that the fronto-parietal and ventral attention networks were the core neural systems that were commonly engaged in different processes of response inhibition. Subtraction analyses elucidated the distinct neural substrates of interference resolution, action withholding, and action cancellation, revealing stronger activation in the ventral attention network for interference resolution than action inhibition. On the other hand, action withholding/cancellation primarily engaged the fronto-striatal circuit. Overall, our results suggest that response inhibition is a multidimensional cognitive process involving multiple neural regions and networks for coordinating optimal performance. This finding has significant implications for the understanding and assessment of response inhibition.

Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity.

Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

Convergence of interoception, emotion, and social cognition: A twofold fMRI meta-analysis and lesion approach

Guided by indirect evidence, recent approaches propose a tripartite crosstalk among interoceptive signaling, emotional regulation, and low-level social cognition. Here we examined the neurocognitive convergence of such domains. First, we performed three meta-analyses of functional magnetic resonance imaging studies to identify which areas are consistently coactivated by these three systems. Multi-level Kernel Density Analysis (MKDA) revealed major overlaps in the right anterior insular and frontotemporal regions (viz., the orbitofrontal and inferior frontal gyri, the amygdala, and mid temporal lobe/subcortical structures). Second, we explored such domains in patients with fronto-insulo-temporal damage. Relative to controls, the patients showed behavioral impairments of interoception, emotional processing, and social cognition, with preservation of other cognitive functions. Convergent results from both studies offer direct support for a model of insular-frontotemporal regions integrating interoception, emotion, and social cognition.

The impact of oxytocin administration on brain activity: a systematic review and meta-analysis protocol.

BackgroundConverging evidence demonstrates the important role of the neuropeptide hormone oxytocin (OT) in human behaviour and cognition. Intranasal OT administration has been shown to improve several aspects of social communication, such as the theory of mind performance and gaze to the eye region, and reduce anxiety and related negative cognitive appraisals. While this early research has demonstrated the potential for intranasal OT to treat psychiatric illnesses characterized by social impairments, the neurobiological mechanisms are not well known. Researchers have used functional magnetic resonance imaging (fMRI) to examine the neural correlates of OT response; however, results have been variable and moderating factors are poorly understood. The aim of this meta-analysis is to synthesize data examining the impact of intranasal OT administration on neural activity.Methods/designStudies that report fMRI data after intranasal OT administration will be identified. PubMed, Embase, PsycINFO, and Google Scholar databases will be searched as well as the citation lists of retrieved articles. Eligible articles written in English from 2005 onwards will be included in the meta-analysis, and corresponding authors of these papers will be invited to contribute t-maps. Data will be collected from eligible studies for synthesis using Seed-based d Mapping (SDM) or Multi-Level Kernel Density Analysis (MKDA), depending on the number of usable t-maps received. Additionally, publication bias and risk of bias will be assessed.DiscussionThis systematic review and meta-analysis will be the first pre-registered synthesis of data to identify the neural correlates of OT nasal spray response. The identification of brain regions underlying OT’s observed effects will help guide future research and better identify treatment targets.Systematic review registrationPROSPERO CRD42016038781 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0386-2) contains supplementary material, which is available to authorized users.

Neural correlates of heart-focused interoception: a functional magnetic resonance imaging meta-analysis.

Interoception is the ability to perceive one's internal body state including visceral sensations. Heart-focused interoception has received particular attention, in part due to a readily available task for behavioural assessment, but also due to accumulating evidence for a significant role in emotional experience, decision-making and clinical disorders such as anxiety and depression. Improved understanding of the underlying neural correlates is important to promote development of anatomical-functional models and suitable intervention strategies. In the present meta-analysis, nine studies reporting neural activity associated with interoceptive attentiveness (i.e. focused attention to a particular interoceptive signal for a given time interval) to one's heartbeat were submitted to a multilevel kernel density analysis. The findings corroborated an extended network associated with heart-focused interoceptive attentiveness including the posterior right and left insula, right claustrum, precentral gyrus and medial frontal gyrus. Right-hemispheric dominance emphasizes non-verbal information processing with the posterior insula presumably serving as the major gateway for cardioception. Prefrontal neural activity may reflect both top-down attention deployment and processing of feed-forward cardioceptive information, possibly orchestrated via the claustrum.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.

Meta-analysis of Functional Neuroimaging of Major Depressive Disorder in Youth.

Despite its high prevalence and morbidity, the underlying neural basis of major depressive disorder (MDD) in youth is not well understood. To identify in youth diagnosed as having MDD the most reliable neural abnormalities reported in existing functional neuroimaging studies and characterize their relations with specific psychological dysfunctions. Searches were conducted in PubMed and Web of Science to identify relevant studies published from November 2006 through February 2015. The current analysis took place from August 21, 2014, to March 28, 2015. We retained articles that conducted a comparison of youth aged 4 to 24 years diagnosed as having MDD and age-matched healthy controls using task-based functional magnetic resonance imaging and a voxelwise whole-brain approach. We extracted coordinates of brain regions exhibiting differential activity in youth with MDD compared with healthy control participants. Multilevel kernel density analysis was used to examine voxelwise between-group differences throughout the whole brain. Correction for multiple comparisons was performed by computing null hypothesis distributions from 10 000 Monte Carlo simulations and calculating the cluster size necessary to obtain the familywise error rate control at P < .05. Abnormal levels of activation in youth diagnosed as having MDD compared with control participants during a variety of affective processing and executive functioning tasks. Compared with age-matched healthy control participants (n = 274), youth with MDD (n = 246) showed reliable patterns of abnormal activation, including the following task-general and task-specific effects: hyperactivation in subgenual anterior cingulate cortex (P < .05) and ventrolateral prefrontal cortex (P < .05) and hypoactivation in caudate (P < .01) across aggregated tasks; hyperactivation in thalamus (P < .03) and parahippocampal gyrus (P < .003) during affective processing tasks; hypoactivation in cuneus (P < .001), dorsal cingulate cortex (P < .05), and dorsal anterior insula (P < .05) during executive functioning tasks; hypoactivity in posterior insula (P < .005) during positive valence tasks; and hyperactivity in dorsolateral prefrontal cortex (P < .001) and superior temporal cortex (P < .003) during negative valence tasks. Altered activations in several distributed brain networks may help explain the following seemingly disparate symptoms of MDD in youth: hypervigilance toward emotional stimuli from the overactivation of central hubs in the subgenual anterior cingulate cortex and thalamus that lead to a cascade of other symptoms; ineffective emotion regulation despite increased activation of the dorsolateral prefrontal cortex and ventrolateral prefrontal cortex during affective processing, which may reverse across development or the clinical course; maladaptive rumination and poor executive control from difficulties shifting from default mode network activity to task-positive network activity during cognitively demanding tasks; and anhedonia from hypoactivation of the cuneus and posterior insula during reward processing.

Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. To investigate network dysfunction in MDD through a meta-analysis of rsFC studies. Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.

A novel meta-analytic approach: Mining frequent co-activation patterns in neuroimaging databases

In recent years, coordinate-based meta-analyses have become a powerful and widely used tool to study co-activity across neuroimaging experiments, a development that was supported by the emergence of large-scale neuroimaging databases like BrainMap. However, the evaluation of co-activation patterns is constrained by the fact that previous coordinate-based meta-analysis techniques like Activation Likelihood Estimation (ALE) and Multilevel Kernel Density Analysis (MKDA) reveal all brain regions that show convergent activity within a dataset without taking into account actual within-experiment co-occurrence patterns. To overcome this issue we here propose a novel meta-analytic approach named PaMiNI that utilizes a combination of two well-established data-mining techniques, Gaussian mixture modeling and the Apriori algorithm. By this, PaMiNI enables a data-driven detection of frequent co-activation patterns within neuroimaging datasets. The feasibility of the method is demonstrated by means of several analyses on simulated data as well as a real application. The analyses of the simulated data show that PaMiNI identifies the brain regions underlying the simulated activation foci and perfectly separates the co-activation patterns of the experiments in the simulations. Furthermore, PaMiNI still yields good results when activation foci of distinct brain regions become closer together or if they are non-Gaussian distributed. For the further evaluation, a real dataset on working memory experiments is used, which was previously examined in an ALE meta-analysis and hence allows a cross-validation of both methods. In this latter analysis, PaMiNI revealed a fronto-parietal “core” network of working memory and furthermore indicates a left-lateralization in this network. Finally, to encourage a widespread usage of this new method, the PaMiNI approach was implemented into a publicly available software system.

A Quantitative Meta-Analysis of Functional Imaging Studies of Social Rejection

Early neuroimaging studies using Cyberball suggested that social rejection activated the pain matrix, as identified in studies of physical pain. However, these early studies were characterized by small sample sizes. Our statistical multi-level kernel density analysis (MKDA) of Cyberball neuroimaging studies with 244 participants fails to support the claim that social rejection operates on the same pain matrix as nociceptive stimuli, questioning whether social pain is more figurative or literal. We also performed an MKDA of the neuroimaging studies of reliving a romantic rejection to test whether the pain matrix was activated if the rejection were more meaningful. Results again failed to support the notion that rejection activates the neural matrix identified in studies of physical pain. Reliving an unwanted rejection by a romantic partner was significantly characterized by activation within and beyond the “Cyberball” brain network, suggesting that the neural correlates of social pain are more complex than previously thought.

A Meta-analysis of Functional Neuroimaging Studies of Self- and Other Judgments Reveals a Spatial Gradient for Mentalizing in Medial Prefrontal Cortex

The distinction between processes used to perceive and understand the self and others has received considerable attention in psychology and neuroscience. Brain findings highlight a role for various regions, in particular the medial PFC (mPFC), in supporting judgments about both the self and others. We performed a meta-analysis of 107 neuroimaging studies of self- and other-related judgments using multilevel kernel density analysis [Kober, H., & Wager, T. D. Meta-analyses of neuroimaging data. Wiley Interdisciplinary Reviews, 1, 293-300, 2010]. We sought to determine what brain regions are reliably involved in each judgment type and, in particular, what the spatial and functional organization of mPFC is with respect to them. Relative to nonmentalizing judgments, both self- and other judgments were associated with activity in mPFC, ranging from ventral to dorsal extents, as well as common activation of the left TPJ and posterior cingulate. A direct comparison between self- and other judgments revealed that ventral mPFC as well as left ventrolateral PFC and left insula were more frequently activated by self-related judgments, whereas dorsal mPFC, in addition to bilateral TPJ and cuneus, was more frequently activated by other-related judgments. Logistic regression analyses revealed that ventral and dorsal mPFC lay at opposite ends of a functional gradient: The z coordinates reported in individual studies predicted whether the study involved self- or other-related judgments, which were associated with increasingly ventral or dorsal portions of mPFC, respectively. These results argue for a distributed rather than localizationist account of mPFC organization and support an emerging view on the functional heterogeneity of mPFC.

The social evaluation of faces: a meta-analysis of functional neuroimaging studies

Neuroscience research on the social evaluation of faces has accumulated over the last decade, yielding divergent results. We used a meta-analytic technique, multi-level kernel density analysis (MKDA), to analyze 29 neuroimaging studies on face evaluation. Across negative face evaluations, we observed the most consistent activations in bilateral amygdala. Across positive face evaluations, we observed the most consistent activations in medial prefrontal cortex, pregenual anterior cingulate cortex (pgACC), medial orbitofrontal cortex (mOFC), left caudate and nucleus accumbens (NAcc). Based on additional analyses comparing linear and non-linear responses, we propose a ventral/dorsal dissociation within the amygdala, wherein separate populations of neurons code for face valence and intensity, respectively. Finally, we argue that some of the differences between studies are attributable to differences in the typicality of face stimuli. Specifically, extremely attractive faces are more likely to elicit responses in NAcc/caudate and mOFC.

Distinction between Externally vs. Internally Guided Decision-Making: Operational Differences, Meta-Analytical Comparisons and Their Theoretical Implications

Most experimental studies of decision-making have specifically examined situations in which a single less-predictable correct answer exists (externally guided decision-making under uncertainty). Along with such externally guided decision-making, there are instances of decision-making in which no correct answer based on external circumstances is available for the subject (internally guided decision-making). Such decisions are usually made in the context of moral decision-making as well as in preference judgment, where the answer depends on the subject’s own, i.e., internal, preferences rather than on external, i.e., circumstantial, criteria. The neuronal and psychological mechanisms that allow guidance of decisions based on more internally oriented criteria in the absence of external ones remain unclear. This study was undertaken to compare decision-making of these two kinds empirically and theoretically. First, we reviewed studies of decision-making to clarify experimental–operational differences between externally guided and internally guided decision-making. Second, using multi-level kernel density analysis, a whole-brain-based quantitative meta-analysis of neuroimaging studies was performed. Our meta-analysis revealed that the neural network used predominantly for internally guided decision-making differs from that for externally guided decision-making under uncertainty. This result suggests that studying only externally guided decision-making under uncertainty is insufficient to account for decision-making processes in the brain. Finally, based on the review and results of the meta-analysis, we discuss the differences and relations between decision-making of these two types in terms of their operational, neuronal, and theoretical characteristics.