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Compassion is closely associated with prosocial behavior. Although there is growing interest in developing strategies that cultivate compassion, most available strategies rely on effortful reflective processes. Furthermore, few studies have investigated neurocognitive mechanisms underlying compassion-dependent improvement of prosocial responses. We devised a novel implicit compassion promotion task that operates based on association learning and examined its prosocial effects in two independent experiments. In Experiment 1, healthy adults were assigned to either the compassion or control group. For the intervention task, the compassion group completed word fragments that were consistently related to compassionate responses toward others; in contrast, the control group completed word fragments related to emotionally neutral responses toward others. Following the intervention task, we measured attentional biases to fearful, sad, and happy faces. Prosocial responses were assessed using two measures of helping: the pen-drop test and the helping intentions rating test. In Experiment 2, independent groups of healthy adults completed the same intervention tasks used in Experiment 1. Inside a functional MRI scanner, participants rated empathic care and distress based on either distressful or neutral video clips. Outside the scanner, we assessed the degree of helping intentions toward the victims depicted in the distressful clips. The results of Experiment 1 showed that the compassion promotion task reduced attentional vigilance to fearful faces, which in turn mediated a compassion promotion task-dependent increase in helping intentions. In Experiment 2, relative to the control group, the compassion group showed reduced empathic distress and increased activity in the medial orbitofrontal cortex in response to others’ suffering. Furthermore, increased functional connectivity of the medial orbitofrontal and inferior parietal cortex, predicted by reduced empathic distress, explained the increase in helping intentions. These results suggest the potential of implicit compassion promotion intervention to modulate compassion-related and prosocial responses as well as highlight the brain activation and connectivity related to these responses, contributing to our understanding of the neurocognitive mechanisms underlying compassion-dependent prosocial improvement.

In this functional neuroimaging study, we investigated neural activations during the process of learning to gain monetary rewards and to avoid monetary loss, and how these activations are modulated by individual differences in reward and punishment sensitivity. Healthy young volunteers performed a reinforcement learning task where they chose one of two fractal stimuli associated with monetary gain (reward trials) or avoidance of monetary loss (avoidance trials). Trait sensitivity to reward and punishment was assessed using the behavioral inhibition/activation scales (BIS/BAS). Functional neuroimaging results showed activation of the striatum during the anticipation and reception periods of reward trials. During avoidance trials, activation of the dorsal striatum and prefrontal regions was found. As expected, individual differences in reward sensitivity were positively associated with activation in the left and right ventral striatum during reward reception. Individual differences in sensitivity to punishment were negatively associated with activation in the left dorsal striatum during avoidance anticipation and also with activation in the right lateral orbitofrontal cortex during receiving monetary loss. These results suggest that learning to attain reward and learning to avoid loss are dependent on separable sets of neural regions whose activity is modulated by trait sensitivity to reward or punishment.

Understanding the fundamental alterations in brain functioning that lead to psychotic disorders remains a major challenge in clinical neuroscience. In particular, it is unknown whether any state-independent biomarkers can potentially predict the onset of psychosis and distinguish patients from healthy controls, regardless of paradigm. Here, using multi-paradigm fMRI data from the North American Prodrome Longitudinal Study consortium, we show that individuals at clinical high risk for psychosis display an intrinsic “trait-like” abnormality in brain architecture characterized as increased connectivity in the cerebello–thalamo–cortical circuitry, a pattern that is significantly more pronounced among converters compared with non-converters. This alteration is significantly correlated with disorganization symptoms and predictive of time to conversion to psychosis. Moreover, using an independent clinical sample, we demonstrate that this hyperconnectivity pattern is reliably detected and specifically present in patients with schizophrenia. These findings implicate cerebello–thalamo–cortical hyperconnectivity as a robust state-independent neural signature for psychosis prediction and characterization. Brain function alterations in schizophrenia and other psychotic disorders remain poorly understood. Here, the authors discover that increased neural connectivity in the cerebello-thalamo-cortical circuitry predicts psychosis in those at high risk, and is present in people with schizophrenia.

Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60–80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07–0.17), but increased with increasing scan duration (ICC=0.21–0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level. •fcMRI (seed, matrix, ICD) is stable across 8 sites in a Traveling Subjects dataset.•No major site, scanner manufacturer, or day-of-scan effects were found (GLM).•No outlier sites were found (leave-one-site-out analysis of variance).•Reliability substantially improves when averaging data over multiple days.•Data can be combined across sites to increase power without impacting reliability.

Previous neuroimaging studies have identified several brain regions associated with regulating emotional responses. Different kinds of emotional stimuli, however, may recruit different regulatory processes and, in turn, recruit different regions. We compared emotion regulation for two types of negative emotional stimuli: those involving moral violations (moral stimuli), and those not involving moral violations (non-moral stimuli). In addition, we investigated whether activation in medial prefrontal cortex (MPFC), a region implicated previously in specifically moral processing, may instead reflect greater social and emotional content. Ten female subjects were scanned using fMRI while they passively viewed or were instructed to decrease emotional reactions to moral and non-moral pictures closely matched on social and emotional content. Passive viewing of both picture types elicited similar activations in areas related to the processing of social and emotional content, including MPFC and amygdala. During regulation, different patterns of activation in these regions were observed for moral vs. non-moral pictures. These results suggest that the neural correlates of regulating emotional reactions are modulated by the emotional content of stimuli, such as moral violations. In addition, the current findings suggest that some brain regions previously implicated in moral processing reflect the processing of greater social and emotional content in moral stimuli.

Amygdala activation by emotional arousal during memory formation can prioritize events for long-term memory. Building upon our prior demonstration that brief electrical stimulation to the human amygdala reliably improved long-term recognition memory for images of neutral objects without eliciting an emotional response, our study aims to explore and describe individual differences and stimulation-related factors in amygdala-mediated memory modulation. Thirty-one patients undergoing intracranial monitoring for intractable epilepsy were shown neutral object images paired with direct amygdala stimulation during encoding with recognition memory tested immediately and one day later. Adding to our prior sample, we found an overall memory enhancement effect without subjective emotional arousal at the one-day delay, but not at the immediate delay, for previously stimulated objects compared to not stimulated objects. Importantly, we observed a larger variation in performance across this larger sample than our initial sample, including some participants who showed a memory impairment for stimulated objects. Of the explored individual differences, the factor that most accounted for variability in memory modulation was each participant’s pre-operative memory performance. Worse memory performance on standardized neuropsychological tests was associated with a stronger susceptibility to memory modulation in a positive or negative direction. Sex differences and the frequency of interictal epileptiform discharges (IEDs) during testing also accounted for some variance in amygdala-mediated memory modulation. Given the potential and challenges of this memory modulation approach, we discuss additional individual and stimulation factors that we hope will differentiate between memory enhancement and impairment to further optimize the potential of amygdala-mediated memory enhancement for therapeutic interventions.

Direct electrical stimulation of the human brain has been used for numerous clinical and scientific applications. At present, however, little is known about how intracranial stimulation affects activity at the microscale. In this study, we recorded intracranial EEG data from a cohort of patients with medically refractory epilepsy as they completed a visual recognition memory task. During the memory task, brief trains of intracranial theta burst stimulation (TBS) were delivered to the basolateral amygdala (BLA). Using simultaneous microelectrode recordings, we isolated neurons in the hippocampus, amygdala, orbitofrontal cortex, and anterior cingulate cortex and tested whether stimulation enhanced or suppressed firing rates. Additionally, we characterized the properties of modulated neurons, clustered presumed excitatory and inhibitory neurons by waveform morphology, and examined the extent to which modulation affected memory task performance. We observed a subset of neurons (~30%) whose firing rate was modulated by TBS, exhibiting highly heterogeneous responses with respect to onset latency, duration, and direction of effect. Notably, location and baseline activity predicted which neurons were most susceptible to modulation, although the impact of this neuronal modulation on memory remains unclear. These findings advance our limited understanding of how focal electrical fields influence neuronal firing at the single-cell level.

Considerable evidence indicates that the amygdala plays a critical role in negative, aversive human emotions. Although researchers have speculated that the amygdala plays a role in positive emotion, little relevant evidence exists. We examined the neural correlates of positive and negative emotion using positron emission tomography (PET), focusing on the amygdala. Participants viewed positive and negative photographs, as well as interesting and uninteresting neutral photographs, during PET scanning. The left amygdala and ventromedial prefrontal cortex were activated during positive emotion, and bilateral amygdala activation occurred during negative emotion. High-interest, unusual photographs also elicited left amygdala activation, a finding consistent with suggestions that the amygdala is involved in vigilance reactions to associatively ambiguous stimuli. The current results constitute the first neuroimaging evidence for a role of the amygdala in positive emotional reactions elicited by visual stimuli. Although the amygdala appears to play a more extensive role in negative emotion, it is involved in positive emotion as well.

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification. In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects. The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.

Multi-site neuroimaging studies offer an efficient means to study brain functioning in large samples of individuals with rare conditions; however, they present new challenges given that aggregating data across sites introduces additional variability into measures of interest. Assessing the reliability of brain activation across study sites and comparing statistical methods for pooling functional data are critical to ensuring the validity of aggregating data across sites. The current study used two samples of healthy individuals to assess the feasibility and reliability of aggregating multi-site functional magnetic resonance imaging (fMRI) data from a Sternberg-style verbal working memory task. Participants were recruited as part of the North American Prodrome Longitudinal Study (NAPLS), which comprises eight fMRI scanning sites across the United States and Canada. In the first study sample (n=8), one participant from each home site traveled to each of the sites and was scanned while completing the task on two consecutive days. Reliability was examined using generalizability theory. Results indicated that blood oxygen level-dependent (BOLD) signal was reproducible across sites and was highly reliable, or generalizable, across scanning sites and testing days for core working memory ROIs (generalizability ICCs=0.81 for left dorsolateral prefrontal cortex, 0.95 for left superior parietal cortex). In the second study sample (n=154), two statistical methods for aggregating fMRI data across sites for all healthy individuals recruited as control participants in the NAPLS study were compared. Control participants were scanned on one occasion at the site from which they were recruited. Results from the image-based meta-analysis (IBMA) method and mixed effects model with site covariance method both showed robust activation in expected regions (i.e. dorsolateral prefrontal cortex, anterior cingulate cortex, supplementary motor cortex, superior parietal cortex, inferior temporal cortex, cerebellum, thalamus, basal ganglia). Quantification of the similarity of group maps from these methods confirmed a very high (96%) degree of spatial overlap in results. Thus, brain activation during working memory function was reliable across the NAPLS sites and both the IBMA and mixed effects model with site covariance methods appear to be valid approaches for aggregating data across sites. These findings indicate that multi-site functional neuroimaging can offer a reliable means to increase power and generalizability of results when investigating brain function in rare populations and support the multi-site investigation of working memory function in the NAPLS study, in particular. •We examined reliability of fMRI during a working memory task in a multi-site study.•Greater variance due to person than site factors in variance component analysis.•Reliability coefficients showed excellent reliability for person effect in primary ROIs.•Two methods for aggregating data across sites produced similar activation maps.•Implications for design and implementation of multi-site fMRI studies.