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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.
Instrumental phonetic techniques illustrate the analyses behind the interpretation of laryngeal articulator function and laryngeal sounds. High-speed laryngoscopy demonstrates aryepiglottic trilling. Cineradiography demonstrates where and how epiglottal stop and voiceless and voiced aryepiglottic trilling are generated. Simultaneous laryngoscopy and laryngeal ultrasound gauge the vertical displacement of the larynx during laryngeally constricted articulations compared to opening manoeuvres. MRI provides insight into the effects of lower-vocal-tract configurations on changes in vowel quality. Computational modelling shows how algorithms that account for voicing can be adapted to explain the mechanics of complex laryngeal vibrations. Vocal-ventricular fold coupling (VVFC) occurs as a vertical compression effect in stopping airflow and in constricted phonation types (creaky voice, harsh voice) and is modelled to illustrate the relationships and actions among laryngeal structures. Analyses, data capture, explanations of the algorithms, and videos of the working models are incorporated in the online companion materials, including articulatory simulations by the laryngeal component of the ‘ArtiSynth’ model.
In patients after atrioventricular septal defect correction, altered geometry leads to a changed position and subsequent flow over the left ventricular outflow tract. We hypothesised that this altered flow may influence haemodynamics in the ascending aorta.
In total, 30 patients after atrioventricular septal defect correction (age 27.6 ± 12.8 years) and 28 healthy volunteers (age 24.8 ± 13.7 years) underwent 4D flow cardiovascular magnetic resonance. Left ventricular ejection fraction and mean and peak wall shear stress calculated at ascending aortic peak systole were obtained from cardiovascular magnetic resonance. Left ventricular outflow tract data including velocity and diameter were obtained from echocardiography.
Patients showed a higher mean (911 ± 173 versus 703 ± 154 mPa, p = 0.001) and peak ascending aortic wall shear stress (1264 ± 302 versus 1009 ± 240 mPa, p = 0.001) compared to healthy volunteers. Increased blood flow velocities over the left ventricular outflow tract (1.49 ± 0.30 m/s versus 1.22 ± 0.20 m/s, p < 0.001) correlated well with mean and peak ascending aortic wall shear stress (r = 0.67, p < 0.001 and r = 0.77, p < 0.001).
After atrioventricular septal defect correction, increased wall shear stress was observed, which correlated to velocities over the left ventricular outflow tract. These findings imply that altered outflow tract geometry contributes to changed aortic haemodynamics.
Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.
We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.
Two genes (CSMD1, p = 5.32×10−6; CNTNAP5, p = 1.32×10−6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10−5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10−6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10−4) and Alzheimer Disease Up (FDR q = 6.12×10−4).
Both rare variants analysis and imaging–genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.
An obsessive-compulsive disorder (OCD) subtype has been associated with streptococcal infections and is called pediatric autoimmune neuropsychiatric disorders associated with streptococci (PANDAS). The neuroanatomical characterization of subjects with this disorder is crucial for the better understanding of its pathophysiology; also, evaluation of these features as classifiers between patients and controls is relevant to determine potential biomarkers and useful in clinical diagnosis. This was the first multivariate pattern analysis (MVPA) study on an early-onset OCD subtype.
Fourteen pediatric patients with PANDAS were paired with 14 healthy subjects and were scanned to obtain structural magnetic resonance images (MRI). We identified neuroanatomical differences between subjects with PANDAS and healthy controls using voxel-based morphometry, diffusion tensor imaging (DTI), and surface analysis. We investigated the usefulness of these neuroanatomical differences to classify patients with PANDAS using MVPA.
The pattern for the gray and white matter was significantly different between subjects with PANDAS and controls. Alterations emerged in the cortex, subcortex, and cerebellum. There were no significant group differences in DTI measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity) or cortical features (thickness, sulci, volume, curvature, and gyrification). The overall accuracy of 75% was achieved using the gray matter features to classify patients with PANDAS and healthy controls.
The results of this integrative study allow a better understanding of the neural substrates in this OCD subtype, suggesting that the anatomical gray matter characteristics could have an immune origin that might be helpful in patient classification.
Individuals with bipolar disorder (BD) have a higher prevalence of obesity and metabolic syndrome (MetS) compared with the general population. Obesity and MetS are associated with cognitive deficits and brain imaging abnormalities in the general population. Obesity and components of MetS might potentially associate with neuroimaging and neurocognitive findings in BD.
A literature search of studies investigating the association between obesity (and other components of MetS) and neurocognitive and neuroimaging findings in BD was conducted. In addition to a systematic review, a random-effects meta-analysis was conducted when sufficient data were available.
Twenty-three studies were included in the current systematic review. Overweight/obese patients were significantly associated with impaired neurocognition compared normal weight individuals with BD (d = 0.37). The most robust association between obesity and cognitive deficits in BD was observed in the cognitive subdomain of executive functions (d = 0.61). There was also evidence for a significant relationship between cognitive impairment in BD and other components of MetS including hypertension, dyslipidemia, and diabetes. Overweight/obese individuals with BD had more pronounced brain imaging abnormalities than normal weight individuals with BD.
Obesity and related cardiovascular risk factors significantly are associated with more severe cognitive and brain imaging abnormalities in BD. Medical co-morbidities can potentially contribute to functional decline observed in some patients throughout the course of BD.
Psychopathy is a personality disorder associated with severe emotional and interpersonal consequences and persistent antisocial behavior. Neurobiological models of psychopathy emphasize impairments in emotional processing, attention, and integration of information across large-scale neural networks in the brain. One of the largest integrative hubs in the brain is the corpus callosum (CC) – a large white matter structure that connects the two cerebral hemispheres.
The current study examines CC volume, measured via Freesurfer parcellation, in a large sample (n = 495) of incarcerated men who were assessed for psychopathic traits using the Hare Psychopathy Checklist-Revised (PCL-R).
Psychopathy was associated with reduced volume across all five sub-regions of the CC. These relationships were primarily driven by the affective/interpersonal elements of psychopathy (PCL-R Factor 1), as no significant associations were found between the CC and the lifestyle/antisocial traits of psychopathy. The observed effects were not attributable to differences in substance use severity, age, IQ, or total brain volume.
These findings align with suggestions that core psychopathic traits may be fostered by reduced integrative capacity across large-scale networks in the brain.
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.
Evidence of cerebral degeneration is not apparent on routine brain MRI in amyotrophic lateral sclerosis (ALS). Texture analysis can detect change in images based on the statistical properties of voxel intensities. Our objective was to test the utility of texture analysis in detecting cerebral degeneration in ALS. A secondary objective was to determine whether the performance of texture analysis is dependent on image resolution.
High-resolution (0.5×0.5 mm2 in-plane) coronal T2-weighted MRI of the brain were acquired from 12 patients with ALS and 19 healthy controls on a 4.7 Tesla MRI system. Image data sets at lower resolutions were created by down-sampling to 1×1, 2×2, 3×3, and 4×4 mm2. Texture features were extracted from a slice encompassing the corticospinal tract at the different resolutions and tested for their discriminatory power and correlations with clinical measures. Subjects were also classified by visual assessment by expert reviewers.
Texture features were different between ALS patients and healthy controls at 1×1, 2×2, and 3×3 mm2 resolutions. Texture features correlated with measures of upper motor neuron function and disability. Optimal classification performance was achieved when best-performing texture features were combined with visual assessment at 2×2 mm2 resolution (0.851 area under the curve, 83% sensitivity, 79% specificity).
Texture analysis can detect subtle abnormalities in MRI of ALS patients. The clinical yield of the method is dependent on image resolution. Texture analysis holds promise as a potential source of neuroimaging biomarkers in ALS.
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.
In addition to neurocognitive studies, neuroimaging techniques provide a unique opportunity to study brain characteristics. Structural imaging studies clearly demonstrate volumetric differences in particular brain areas between individuals with a history of nonfatal suicidal behavior and those without such a history. Functional imaging studies show a reduced prefrontal perfusion or metabolism and a blunted increase in activation when challenged in the brains of individuals with a history of suicide attempts. Moreover, impairment of the prefrontal serotonergic system in association with suicidal behavior is demonstrated in a number of studies. Recent structural and functional imaging studies show changes in cortical and subcortical areas and their connections in association with suicidal behavior and risk factors such as mental pain, hopelessness, and impulsivity. The global picture that emerges from these studies reflects the involvement of a particular circuit in the development of suicidal behavior, the so-called frontothalamic network.
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.
Ferumoxytol, an “off-label” contrast agent, allows for better cardiac MRI quality as compared with gadolinium-based contrast agents. However, hypotension has been reported with the use of ferumoxytol for indications other than cardiac MRI. The purpose of our investigation was to evaluate the safety of ferumoxytol in children undergoing general anaesthesia for cardiac MRI.
Medical records of children undergoing general anaesthesia for cardiac MRI were reviewed. Baseline demographic and medical characteristics, as well as imaging and anaesthetic duration and technique, were collected. The incidence of hypotension or other adverse events’, need for vasoactive support, or airway intervention throughout the anaesthetic, was recorded.
A total of 95 patients were identified, 61 received ferumoxytol and 34 received gadolinium. There were no significant differences between groups with respect to age, weight, or baseline blood pressure. The incidence of low blood pressure – systolic or mean – after contrast administration did not differ between groups, and there was no difference in sustained hypotension or use of vasopressors between groups. One patient who received ferumoxytol had possible anaphylaxis. The image acquisition time (45 versus 68 min, p=0.002) and anaesthesia duration (100 versus 132 min, p=0.02) were shorter in the ferumoxytol group.
Transient low blood pressure was common in children undergoing cardiac MRI with anaesthesia, but the incidence of hypotension did not differ between ferumoxytol and gadolinium groups. The use of ferumoxytol was associated with significantly shorter scan time and anaesthesia duration, as well as a decreased need for airway intervention.
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.
Although the study of the neuroanatomical correlates of generalized anxiety disorder (GAD) is gaining increasing interest, up to now the cortical anatomy of GAD patients has been poorly investigated and still no data on cortical gyrification are available. The aim of the present study is to quantitatively examine the cortical morphology in patients with GAD compared with healthy controls (HC) using magnetic resonance imaging (MRI). To the best of our knowledge, this is the first study analyzing the gyrification patterns in GAD.
A total of 31 GAD patients and 31 HC underwent 3 T structural MRI. For each subject, cortical surface area (CSA), cortical thickness (CT), gray matter volume (GMV), and local gyrification index (LGI) were estimated in 19 regions of interest using the Freesurfer software. These parameters were then compared between the two groups using General Linear Model designs.
Compared with HC, GAD patients showed: (1) reduced CT in right caudal middle frontal gyrus (p < 0.05, Bonferroni corrected), (2) hyper-gyrification in right fusiform, inferior temporal, superior parietal and supramarginal gyri and in left supramarginal and superior frontal gyri (p < 0.05, Bonferroni corrected). No significant alterations in CSA and GMV were observed.
Our findings support the hypothesis of a neuroanatomical basis for GAD, highlighting a possible key role of the right hemisphere. The alterations of CT and gyrification in GAD suggest a neurodevelopmental origin of the disorder. Further studies on GAD are needed to understand the evolution of the cerebral morphology with age and during the clinical course of the illness.
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.