Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders.
The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at ( ).
The objective of this study was to evaluate the efficacy and safety of atomoxetine in children and adolescents.We searched for studies published between 1985 and 2006 through Medline, PubMed, PsychInfo and Cochrane Central Register of Controlled Trials (CENTRAL 2006 Issue 3) using keywords related to atomoxetine and attention-deficit/hyperactivity disorder (ADHD) and scanned though reference lists. We included nine randomized placebo-controlled trials (atomoxetine:placebo = 1,150:678).Atomoxetine was superior (p < 0.01) to placebo in reducing ADHD symptoms across different scales (Attention-Deficit/Hyperactivity Disorder Rating Scale-IV, Conners’ Parent and Teacher Rating Scales-Revised:Short Form, Clinical Global Impression-Severity) rated by different raters (parent, teacher, clinician). The number-needed-to-treat (NNTs) for treatment response and relapse prevention were 3.43 (95% CI, 2.79–4.45) and 10.30 (95% CI, 5.89–40.62), respectively. High baseline ADHD symptoms (p = 0.02) was associated with greater reduction in ADHD symptoms, whereas male gender (p = 0.02), comorbid oppositional defiant disorder (ODD) status (p = 0.01) and ADHD hyperactive/impulsive subtype (p = 0.01) were associated with smaller reductions. The commonest adverse events were gastrointestinal [appetite decrease, number-needed-to-harm (NNH) = 8.81; abdominal pain, NNH = 22.48; vomiting, NNH = 29.96; dyspepsia, NNH = 49.38] and sleep related (somnolence, NNH = 19.41). Young age (p = 0.03) and high baseline hyperactive/impulsive symptoms (p < 0.01) were associated with more adverse events, whereas ADHD inattentive subtype (p = 0.04) was associated with less adverse events. Quality of life using Child Health Questionnaire (CHQ) improved (p < 0.01) with atomoxetine treatment. Both ADHD and ODD symptoms (p < 0.01) were reduced in comorbid ADHD+ODD, and ODD status was not associated with more adverse events. Efficacy and side effects were not altered by comorbid general anxiety disorder or major depression.Atomoxetine is efficacious in reducing ADHD symptoms. It may have a role in treating comorbid ODD or depression, and probably in comorbid anxiety.
Parametric voxelwise analysis is a commonly used tool in neuroimaging, as it allows for identification of regions of effects in the absence of a strong a-priori regional hypothesis by comparing each voxel of the brain independently. Due to the inherent imprecision of single voxel measurements, spatial smoothing is performed to increase the signal-to-noise ratio of single-voxel estimates. In addition, smoothing compensates for imprecisions in anatomical registration, and allows for the use of cluster-based statistical thresholding. Smoothing has traditionally been applied in three dimensions, without taking the tissue types of surrounding voxels into account. This procedure may be suitable for subcortical structures, but is problematic for cortical regions for which grey matter often constitutes only a small proportion of the smoothed signal. New methods have been developed for cortical analysis in which voxels are sampled to a surface, and smoothing is restricted to neighbouring regions along the cortical grey matter in two dimensions. This procedure has recently been shown to decrease intersubject variability and bias of PET data. The aim of this study was to compare the variability, bias and test-retest reliability of volumetric and surface-based methods as they are applied in practice. Fifteen healthy young males were each measured twice using the dopamine D1 receptor radioligand [ C]SCH-23390, and analyses were performed at the level of individual voxels and vertices within the cortex. We found that surface-based methods yielded higher BP values, lower coefficient of variation, less bias, better reliability and more precise estimates of parametric binding. All in all, these results suggest that surface-based methods exhibit superior performance to volumetric approaches for voxelwise analysis of PET data, and we advocate for their use when a ROI-based analysis is not appropriate.
Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference of ERPs and a spatial which cancels non task-related brain activity.
Highlights ► In florid stage significantly impaired sagittal plane kinematics on involved and non-involved side. ► Significantly reduced power generation and absorption mainly at the level of involved hip. ► In final stage impaired global hip function in 46, 2% of patients.
We present the results of the Gravitational LEnsing Accuracy Testing 2008 (GREAT08) Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to stimulate new ideas by presenting the problem to researchers outside the shear measurement community. Six GREAT08 Team methods were presented at the launch of the Challenge and five additional groups submitted results during the 6-month competition. Participants analyzed 30 million simulated galaxies with a range in signal-to-noise ratio, point spread function ellipticity, galaxy size and galaxy type. The large quantity of simulations allowed shear measurement methods to be assessed at a level of accuracy suitable for currently planned future cosmic shear observations for the first time. Different methods perform well in different parts of simulation parameter space and come close to the target level of accuracy in several of these. A number of fresh ideas have emerged as a result of the Challenge including a re-examination of the process of combining information from different galaxies, which reduces the dependence on realistic galaxy modelling. The image simulations will become increasingly sophisticated in future GREAT Challenges, meanwhile the GREAT08 simulations remain as a benchmark for additional developments in shear measurement algorithms.
The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1%–70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1%–19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries. ► We analyzed 1484 rest datasets in SPM8 and used a significance threshold of 5%. ► Significant activity was found in 1%–70% of the datasets. ► The whitening used in SPM fails to model the spectra of the residuals. ► The whitening especially fails for short repetition times. ► A better model of the temporal correlations in fMRI rest data is needed.
Objective To report the clinical features of 20 pediatric patients with anti- N -methyl-D-aspartate receptor (NMDAR) encephalitis. Study design Review of clinical data, long-term follow-up, and immunologic studies performed in a single center in Spain in the last 4 years. Results The median age of the patients was 13 years (range, 8 months-18 years), 70% were female. In 12 patients (60%), the initial symptoms were neurologic, usually dyskinesias or seizures, and in the other 40% psychiatric. One month into the disease, all patients had involuntary movements and alterations of behavior and speech. All patients received steroids, intravenous immunoglobulin or plasma exchange, and 7 rituximab or cyclophosphamide. With a median follow up of 17.5 months, 85% had substantial recovery, 10% moderate or severe deficits, and 1 died. Three patients had previous episodes compatible with anti-NMDAR encephalitis, 2 of them with additional relapses after the diagnosis of the disorder. Ovarian teratoma was identified in 2 patients, 1 at onset of encephalitis and the other 1 year later. Two novel observations (1 patient each) include, the identification of an electroencephalographic pattern (“extreme delta brush”) considered characteristic of this disorder, and the development of anti-NMDAR encephalitis as post herpes simplex encephalitis choreoathetosis. Conclusions The initial symptoms of pediatric anti-NMDAR encephalitis vary from those of the adults (more neurologic and less psychiatric in children), the development of a mono-symptomatic illness is extremely rare (except in relapses), and most patients respond to treatment. Our study suggests a link between post herpes simplex encephalitis choreoathetosis and anti-NMDAR encephalitis.