Objective: Focal cortical dysplasia (FCD) is a common cause of refractory, focal onset epilepsy in children. Interictal, scalp electroencephalograph (EEG) markers have been associated with these pathologies and epilepsy surgery may be an option for some patients. We aim to study how scalp EEG and magnetic resonance imaging (MRI) markers of FCD affect referral of these patients for surgical evaluation. Methods: A single-center, retrospective review of children with focal onset epilepsy. Patients were included if they were between 1 month and 18 years of age, had focal onset seizures, prolonged scalp EEG monitoring, and an MRI conducted after 2 years of age. Statistics were carried out using the chi-squared and student’s t-test, as well as a logistic regression model. Results: Sixty-eight patients were included in the study. Thirty-seven of these patients were referred to a comprehensive pediatric epilepsy program (CPEP) for surgical evaluation, and of these 22% showed FCD EEG markers, 32% FCD MRI markers, and 10% had both. These markers were also present in patients not referred to a CPEP. The MRI markers were significantly associated with CPEP referral, whereas EEG markers were not. Neither marker type was associated with epilepsy surgery. Conclusion: This study found that children with focal onset epilepsy were more likely to be referred for surgical evaluation if they were medically refractory, or were diagnosed with FCD or tumor on MRI. Scalp EEG markers of FCD were not associated with CPEP referral. The online tool CASES may be a useful physician guide for identifying appropriate children for epilepsy surgery referral.

Psychopathy is a personality type characterized by both callous emotional dysfunction and deviant behavior that affects society in the form of actions that harm others. Historically, researchers have been concerned with seeking data and arguments to support a neurobiological foundation of psychopathy. In the past few years, increasing research has begun to reveal brain alterations putatively underlying the enigmatic psychopathic personality. In this review, we describe the brain anatomical and functional features that characterize psychopathy from a synthesis of available neuroimaging research and discuss how such brain anomalies may account for psychopathic behavior. The results are consistent in showing anatomical alterations involving primarily a ventral system connecting the anterior temporal lobe to anterior and ventral frontal areas, and a dorsal system connecting the medial frontal lobe to the posterior cingulate cortex/precuneus complex and, in turn, to medial structures of the temporal lobe. Functional imaging data indicate that relevant emotional flow breakdown may occur in both these brain systems and suggest specific mechanisms via which emotion is anomalously integrated into cognition in psychopathic individuals during moral challenge. Directions for future research are delineated emphasizing, for instance, the relevance of further establishing the contribution of early life stress to a learned blockage of emotional self-exposure, and the potential role of androgenic hormones in the development of cortical anomalies.

Ventricular septal defects – large, surgically closed or small, untreated – have demonstrated lower peak exercise capacity compared with healthy controls. The mechanisms behind these findings are not yet fully understood. Therefore, we evaluated biventricular morphology in adults with a ventricular septal defect using MRI. Adults with either childhood surgically closed or small, untreated ventricular septal defects and healthy controls underwent cine MRI for the evaluation of biventricular volumes and quantitative flow scans for measurement of stroke index. Scans were analysed post hoc in a blinded manner. In total, 20 operated patients (22±2 years) and 20 healthy controls (23±2 years) were included, along with 32 patients with small, unrepaired ventricular septal defects (26±6 years) and 28 controls (27±5 years). Operated patients demonstrated larger right ventricular end-diastolic volume index (103±20 ml/m2) compared with their controls (88±16 ml/m2), p=0.01. Heart rate and right ventricular stroke index did not differ between operated patients and controls. Patients with unrepaired ventricular septal defects revealed larger right ventricular end-diastolic volume index (105±17 ml/m2) compared with their controls (88±13 ml/m2), p<0.01. Furthermore, right ventricular stroke index was higher in unrepaired ventricular septal defects (53±12 ml/minute/m2) compared with controls (46±8 ml/minute/m2), p=0.02, with similar heart rates. Both patient groups’ right ventricles were visually characterised by abundant coarse trabeculation. Positive correlations were demonstrated between right ventricular end-diastolic volume indices and peak exercise capacity in patients. Left ventricle measurements displayed no differences between groups. In conclusion, altered right ventricular morphology was demonstrated in adults 20 years after surgical ventricular septal defect repair and in adults with small, untreated ventricular septal defects.

The Virtual Personalities Model is a motive-based neural network model that provides both a psychological model and a computational implementation that explicates the dynamics and often large within-person variability in behavior that arises over time. At the same time the same model can produce—across many virtual personalities—between-subject variability in behavior that when factor analyzed yields familiar personality structure (e.g., the Big Five). First, we describe our personality model and its implementation as a neural network model. Second, we focus on detailing the neurobiological underpinnings of this model. Third, we examine the learning mechanisms, and their biological substrates, as ways that the model gets “wired up,” discussing Pavlovian and Instrumental conditioning, Pavlovian to Instrumental transfer, and habits. Finally, we describe the dynamics of how initial differences in propensities (e.g., dopamine functioning), wiring differences due to experience, and other factors could operate together to develop and change personality over time, and how this might be empirically examined. Thus, our goal is to contribute to the rising chorus of voices seeking a more precise neurobiologically based science of the complex dynamics underlying personality.

Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 884 young healthy adults in the Human Connectome Project database. We attempted to predict personality traits from the “Big Five,” as assessed with the Neuroticism/Extraversion/Openness Five-Factor Inventory test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness, and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two intersubject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 hr of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; three denoising strategies; two alignment schemes; three models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=.24, R2=.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=.26, R2=.044). Other factors (Extraversion, Neuroticism, Agreeableness, and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the Neuroticism/Extraversion/Openness Five-Factor Inventory factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=.27, R2=.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

A converging literature has revealed the existence of a set of largely consistent, hierarchically organized personality traits, that is broader traits are able to be differentiated into more fine-grained traits, in both humans and chimpanzees. Despite recent work suggesting a neural basis to personality in chimpanzees, little is known with regard to the involvement of limbic structures (i.e., amygdala and hippocampus), which are thought to play important roles in emotion. Using saved maximum likelihood estimated exploratory factor scores (two to five factors) in the context of a series of path analyses, the current study examined associations among personality dimensions across various levels of the personality hierarchy and individual variability of amygdala and hippocampal grey matter (GM) volume in a sample of captive chimpanzees (N=191). Whereas results revealed no association between personality dimensions and amygdala volume, a more nuanced series of associations emerged between hippocampal GM volume and personality dimensions at various levels of the hierarchy. Hippocampal GM volume associated most notably with Alpha (a dimension reflecting a tendency to behave in an undercontrolled and agonistic way) at the most basic two-factor level of the hierarchy; associated positively with Disinhibition at the next level of the hierarchy (“Big Three”); and finally, associated positively with Impulsivity at the most fine-grained level (“five-factor model”) of the hierarchy. Findings underscore the importance of the hippocampus in the neurobiological foundation of personality, with support for its regulatory role of emotion. Further, results suggest the importance of the distinction between structure and function, particularly with regard to the amygdala.

Objectives: There are no current established pathognomonic diagnostic features for uterine leiomyosarcomas in the pre- or perioperative setting. Recent inadvertent upstaging of this rare malignancy during laparoscopic morcellation of a presumed fibroid has prompted widespread debate among clinicians regarding the safety of current surgical techniques for management of fibroids. This study aims to conduct a systematic review investigating significant diagnostic features in magnetic resonance imaging (MRI) of uterine leiomyosarcomas.

Methods: A comprehensive database search was conducted guided by PRISMA recommendations for peer-reviewed publications to November 2017. Parameters available in MRI were compared for reliability and accuracy of diagnosis of leiomyosarcomas. A decision tree algorithm classifier model was constructed to investigate whether T1 and T2 MRI signal intensities are useful indicators.

Results: Nine eligible studies were identified for analysis. There appears to be a significant relationship between histopathological type and T1 and T2 intensity signals (p < .05). A decision tree model analyzing T1 and T2 signal intensity readings supports this trend, with a diagnostic specificity of 77.78 percent for uterine leiomyosarcomas. The apparent diffusion coefficient (ADC) values were not observed to have a significant relationship with tumor pathology (p = .18).

Conclusions: Various studies have investigated pre- and perioperative techniques in differentiating uterine leiomyosarcoma from benign fibroids. Given the rarity of the malignancy and lack of pathognomonic diagnostic parameters, there is difficulty in establishing definitive criteria. A decision tree model is proposed to aid diagnosis based on MRI signal intensities.

Brain imaging techniques, especially those based on magnetic resonance imaging (MRI) and magnetoencephalography (MEG), have been increasingly applied to study multiple large-scale distributed brain networks in healthy people and neurological patients. With regard to neurodegenerative disorders, amyotrophic lateral sclerosis (ALS), clinically characterized by the predominant loss of motor neurons and progressive weakness of voluntary muscles, and frontotemporal lobar degeneration (FTLD), the second most common early-onset dementia, have been proven to share several clinical, neuropathological, genetic, and neuroimaging features. Specifically, overlapping or mildly diverging brain structural and functional connectivity patterns, mostly evaluated by advanced MRI techniques—such as diffusion tensor and resting-state functional MRI (DT–MRI, RS–fMRI)—have been described comparing several ALS and FTLD populations. Moreover, though only pioneering, promising clues on connectivity patterns in the ALS–FTLD continuum may derive from MEG investigations. We will herein overview the current state of knowledge concerning the most advanced neuroimaging findings associated with clinical and genetic patterns of neurodegeneration across the ALS–FTLD continuum, underlying the possibility that network-based approaches may be useful to develop novel biomarkers of disease for adequately designing and monitoring more appropriate treatment strategies.