While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic “trait” architecture that is independent of any given paradigm. We have previously proposed the use of “cross-paradigm connectivity (CPC)” to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain’s “trait” structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional “trait” networks and offer some methodological implications for future CPC studies.

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, may be implicated in the progression to full psychosis.

Prenatal cocaine exposure (PCE) is associated with arousal dysregulation, and alterations of amygdala activity in response to emotional arousal were previously reported. However, voluntary regulation of emotional affect, enabling appropriate neural response to different streams of stimuli, must also engage prefrontal regions, yet PCE impact on these prefrontal mechanisms has not been investigated. Recent neuroimaging studies have shown the involvement of ventral prefrontal cortex (vPFC) in the modulation of amygdala reactivity and the mediation of effective emotion regulation. Based on these findings, using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the present study compared functional activations of vPFC as well as its structural connectivity with amygdala between groups of PCE and control adolescents. In a working memory task with emotional distracters, the PCE adolescents exhibited less capability of increasing their vPFC activation in response to increased memory load, which corresponded with their less suppressed amygdala activation. Reduced structural connectivity between vPFC and amygdala were also observed from DTI measurement in the PCE group. In addition, correlations between amygdala activation and (i) vPFC activation, as well as (ii) amygdala-vPFC structural connectivity, were observed in the control but not in the PCE group. These data complemented previous findings of PCE impact on amygdala activity and extended our understanding of the neurobiological mechanisms of PCE effect on arousal dysregulation reported in human and animal studies.

There is growing concern about a potential decline in empathy among medical students over time. Despite the importance of empathy toward patients in medicine, it remains unclear the nature of the changes in empathy among medical students. Thus, we systematically investigated affective and cognitive empathy for patients among medical students using neuroscientific approach. Nineteen medical students who completed their fifth-year medical curriculum and 23 age- and sex-matched nonmedical students participated in a functional magnetic resonance imaging study. Inside a brain scanner, all participants read empathy-eliciting scenarios while adopting either the patient or doctor perspective. Brain activation and self-reported ratings during the experience of empathy were obtained. Behavioral results indicated that all participants reported greater emotional negativity and empathic concern in association with the patient perspective condition than with the doctor perspective condition. Functional brain imaging results indicated that neural activity in the posterior superior temporal region implicated in goal-relevant attention reorienting was overall increased under the patient perspective than the doctor perspective condition. Relative to nonmedical students, medical students showed decreased activity in the temporoparietal region implicated in mentalizing under the patient perspective versus doctor perspective condition. Notably, this same region showed increased activity under the doctor versus patient condition in medical students relative to nonmedical students. This study is among the first to investigate the neural mechanisms of empathy among medical students and the current findings point to the cognitive empathy system as the locus of the primary brain differences associated with empathy toward patients.

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.

The fearlessness model posits that psychopathy is underpinned by a deficiency in the capacity to experience fear, predisposing to other features of the condition, such as superficial charm, guiltlessness, callousness, narcissism, and dishonesty. Nevertheless, it is unclear whether fearlessness is irrelevant, necessary, sufficient, or merely contributory to psychopathy. In the present case study, we sought to examine the fearlessness model by studying an extensively investigated female patient-S. M.-who experienced early emerging bilateral calcifications of the amygdala, resulting in a virtual absence of fear. We aimed to replicate findings regarding S. M.'s deficient experience of self-reported fear and examine her levels of triarchic psychopathy dimensions (boldness, meanness, disinhibition). We also examined S. M.'s history of heroic behaviors given conjectures that fearlessness contributes to both heroism and psychopathy. Compared with population-based norms, S. M. reported deficient levels of self-reported fear and self-control, as well as elevated levels of heroism. She did not, however, exhibit elevated levels of the core affective deficits of psychopathy, as reflected in measures of coldheartedness and meanness. These findings suggest that severe fear deficits may be insufficient to yield the full clinical picture of psychopathy, although they do not preclude the possibility that these deficits are necessary.

Prenatal cocaine exposure (PCE) is associated with attention/arousal dysregulation and possible inefficiencies in some cognitive functions. However, the neurobiological bases of these teratogenic effects have not been well characterized. Because activities in the default mode network (DMN) reflect intrinsic brain functions that are closely associated with arousal regulation and cognition, alterations in the DMN could underlie cognitive effects related to PCE. With resting-state and task activation functional magnetic resonance imaging (fMRI), this study investigated the possible PCE related changes in functional brain connectivity and brain activation in the DMN. In the resting state, the PCE group was found to have stronger functional connectivity in the DMN, as compared to the nonexposed controls. During a working memory task with emotional distracters, the PCE group exhibited less deactivation in the DMN and their fMRI signal was more increased by emotional arousal. These data revealed additional neural effects related to PCE, and consistent with previous findings, indicate that PCE may affect behavior and functioning by increasing baseline arousal and altering the excitatory/inhibitory balancing mechanisms involved in cognitive resource allocation.

Self-reported anxiety is associated with various medical procedures, including structural and functional magnetic resonance imaging (MRI). The present study tested the hypothesis that MRI scanning would be associated with elevated cortisol levels in participants with no prior scanning experience. Baseline and post-scan cortisol levels, as well as measures of state and trait anxiety, were obtained from scanner-naive (n = 6) and scanner-experienced (n = 8) research participants. The anxiety scores and cortisol responses of the scanner-naive and scanner-experienced participants were compared. Subjects novel to MRI were no more anxious before the scan than were subjects familiar with the MRI examination, but the scanner-naive subjects manifested heightened post-scan cortisol secretion when compared to their pre-scan level and when compared to the scanner-experienced participants. The results are consistent with the hypothesis that the scanning environment can induce cortisol elevations and are congruent with the well-established effects of acute stressors on activity of the hypothalamic-pituitary-adrenal (HPA) axis. The implications for neuroimaging studies are discussed.

Previous brain imaging work suggests that stroke alters the effective connectivity (the influence neural regions exert upon each other) of motor execution networks. The present study examines the intrinsic effective connectivity of top-down motor control in stroke survivors (n=13) relative to healthy participants (n=12). Stroke survivors exhibited significant deficits in motor function, as assessed by the Fugl-Meyer Motor Assessment. We used structural equation modeling (SEM) of resting-state fMRI data to investigate the relationship between motor deficits and the intrinsic effective connectivity between brain regions involved in motor control and motor execution. An exploratory adaptation of SEM determined the optimal model of motor execution effective connectivity in healthy participants, and confirmatory SEM assessed stroke survivors’ fit to that model. We observed alterations in spontaneous resting-state effective connectivity from fronto-parietal guidance systems to the motor network in stroke survivors. More specifically, diminished connectivity was found in connections from the superior parietal cortex to primary motor cortex and supplementary motor cortex. Furthermore, the paths demonstrated large individual variance in stroke survivors but less variance in healthy participants. These findings suggest that characterizing the deficits in resting-state connectivity of top-down processes in stroke survivors may help optimize cognitive and physical rehabilitation therapies by individually targeting specific neural pathway.

Background: 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. Methodology/Principal Findings: 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. Conclusions/Significance: 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 c ertainty.