Retrograde Type A Aortic Dissection 48 Hours after TEVAR in a Patient with a Delayed Diagnosis of Vascular Ehlers-Danlos Syndrome.

We report a case of a fatal retrograde Type A aortic dissection following thoracic endovascular aortic repair (TEVAR). The patient was diagnosed with vascular Ehlers-Danlos syndrome (vEDS) only postoperatively, which is a relative contraindication for TEVAR. The patient had no major or minor criteria for vEDS. This case report emphasizes pitfalls of TEVAR in patients with a connective tissue disorder.

Searching for Imaging Biomarkers of Psychotic Dysconnectivity.

BACKGROUND: Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging. METHODS: We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics. RESULTS: Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results. CONCLUSIONS: Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Associations of cannabis use disorder with cognition, brain structure, and brain function in African Americans.

Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.

Beyond Screen Time: A Synergistic Approach to a More Comprehensive Assessment of Family Media Exposure During Early Childhood.

Digital media availability has surged over the past decade. Because of a lack of comprehensive measurement tools, this rapid growth in access to digital media is accompanied by a scarcity of research examining the family media context and sociocognitive outcomes. There is also little cross-cultural research in families with young children. Modern media are mobile, interactive, and often short in duration, making them difficult to remember when caregivers respond to surveys about media use. The Comprehensive Assessment of Family Media Exposure (CAFE) Consortium has developed a novel tool to measure household media use through a web-based questionnaire, time-use diary, and passive-sensing app installed on family mobile devices. The goal of developing a comprehensive assessment of family media exposure was to take into account the contextual factors of media use and improve upon the limitations of existing self-report measures, while creating a consistent, scalable, and cost-effective tool. The CAFE tool captures the content and context of early media exposure and addresses the limitations of prior media measurement approaches. Preliminary data collected using this measure have been integrated into a shared visualization platform. In this perspective article, we take a tools-of-the-trade approach (Oakes, 2010) to describe four challenges associated with measuring household media exposure in families with young children: measuring attitudes and practices; capturing content and context; measuring short bursts of mobile device usage; and integrating data to capture the complexity of household media usage. We illustrate how each of these challenges can be addressed with preliminary data collected with the CAFE tool and visualized on our dashboard. We conclude with future directions including plans to test reliability, validity, and generalizability of these measures.

Young Children's Use of Smartphones and Tablets.

BACKGROUND AND OBJECTIVES: Child mobile device use is increasingly prevalent, but research is limited by parent-report survey methods that may not capture the complex ways devices are used. We aimed to implement mobile device sampling, a set of novel methods for objectively measuring child mobile device use. METHODS: We recruited 346 English-speaking parents and guardians of children aged 3 to 5 years to take part in a prospective cohort study of child media use. All interactions with participants were through e-mail, online surveys, and mobile device sampling; we used a passive-sensing application (Chronicle) in Android devices and screenshots of the battery feature in iOS devices. Baseline data were analyzed to describe usage behaviors and compare sampling output with parent-reported duration of use. RESULTS: The sample comprised 126 Android users (35 tablets, 91 smartphones) and 220 iOS users (143 tablets, 77 smartphones); 35.0% of children had their own device. The most commonly used applications were YouTube, YouTube Kids, Internet browser, quick search or Siri, and streaming video services. Average daily usage among the 121 children with their own device was 115.3 minutes/day (SD 115.1; range 0.20-632.5) and was similar between Android and iOS devices. Compared with mobile device sampling output, most parents underestimated (35.7%) or overestimated (34.8%) their child's use. CONCLUSIONS: Mobile device sampling is an unobtrusive and accurate method for assessing mobile device use. Parent-reported duration of mobile device use in young children has low accuracy, and use of objective measures is needed in future research.

fMRIPrep: a robust preprocessing pipeline for functional MRI.

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

Preprocessed Consortium for Neuropsychiatric Phenomics dataset.

Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI.

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null.

Scanning the horizon: towards transparent and reproducible neuroimaging research.

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Effects of thresholding on correlation-based image similarity metrics.

The computation of image similarity is important for a wide range of analyses in neuroimaging, from decoding to meta-analysis. In many cases the images being compared have empty voxels, but the effects of such empty voxels on image similarity metrics are poorly understood. We present a detailed investigation of the influence of different degrees of image thresholding on the outcome of pairwise image comparison. Given a pair of brain maps for which one of the maps is thresholded, we show that an analysis using the intersection of non-zero voxels across images at a threshold of Z = ±1.0 maximizes accuracy for retrieval of a list of maps of the same contrast, and thresholding up to Z = ±2.0 can increase accuracy as compared to comparison using unthresholded maps. Finally, maps can be thresholded up to to Z = ±3.0 (corresponding to 25% of voxels non-empty within a standard brain mask) and still maintain a lower bound of 90% accuracy. Our results suggest that a small degree of thresholding may improve the accuracy of image similarity computations, and that robust meta-analytic image similarity comparisons can be obtained using thresholded images.

Multiple testing in fMRI: an empirical case study on the balance between sensitivity, specificity, and stability.

Functional Magnetic Resonance Imaging is a widespread technique in cognitive psychology that allows visualizing brain activation. The data analysis encompasses an enormous number of simultaneous statistical tests. Procedures that either control the familywise error rate or the false discovery rate have been applied to these data. These methods are mostly validated in terms of average sensitivity and specificity. However, procedures are not comparable if requirements on their error rates differ. Moreover, less attention has been given to the instability or variability of results. In a simulation study in the context of imaging, we first compare the Bonferroni and Benjamini-Hochberg procedures. Considering Bonferroni as a way to control the expected number of type I errors enables more lenient thresholding compared to familywise error rate control and a direct comparison between both procedures. We point out that while the same balance is obtained between average sensitivity and specificity, the Benjamini-Hochberg procedure appears less stable. Secondly, we have implemented the procedure of Gordon et al. () (originally proposed for gene selection) that includes stability, measured through bootstrapping, in the decision criterion. Simulations indicate that the method attains the same balance between sensitivity and specificity. It improves the stability of Benjamini-Hochberg but does not outperform Bonferroni, making this computationally heavy bootstrap procedure less appealing. Third, we show how stability of thresholding procedures can be assessed using real data. In a dataset on face recognition, we again find that Bonferroni renders more stable results.

Post-hoc power estimation for topological inference in fMRI.

When analyzing functional MRI data, several thresholding procedures are available to account for the huge number of volume units or features that are tested simultaneously. The main focus of these methods is to prevent an inflation of false positives. However, this comes with a serious decrease in power and leads to a problematic imbalance between type I and type II errors. In this paper, we show how estimating the number of activated peaks or clusters enables one to estimate post-hoc how powerful the selection procedure performs. This procedure can be used in real studies as a diagnostics tool, and raises awareness on how much activation is potentially missed. The method is evaluated and illustrated using simulations and a real data example. Our real data example illustrates the lack of power in current fMRI research.

Alternative-based thresholding with application to presurgical fMRI.

Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive-that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.

Assessing the relationships between health-related quality of life and postoperative length of hospital stay after oesophagectomy for cancer of the oesophagus and the gastro-oesophageal junction.

OBJECTIVES: To evaluate baseline health-related quality of life (HRQL) factors that influence short-term outcome after oesophagectomy for cancer of the oesophagus and gastro-oesophageal junction and the effects of postoperative length of hospital stay on postoperative HRQL, as perceived by the patients themselves. METHODS: Four hundred and fifty-five patients operated on with curative intent between January 2005 and December 2009 were analysed. HRQL scores were obtained by European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (QLQ)-C30 and oesophageal-specific symptoms (OES-18) questionnaires at baseline (=day before surgery) and 3-monthly post-surgery for the first year. RESULTS: There were 372 males and 83 females, with a mean age of 63.1 years. Hospital mortality was 3.7% (17 patients). When analysing postoperative length of stay (LOS), a median of 10 days was found. In a multivariable analysis, using a binary logistic regression model, independent prognosticators for a longer LOS (>10 days) were: medical [hazard ratio, HR, 6.2 (3.62-10.56); P < 0.0001] and surgical [HR 2.79 (1.70-4.59); P < 0.0001] morbidity, readmittance to intensive care unit [HR 33.82 (4.55-251.21); P = 0.001] and poor physical functioning [HR 1.89 (1.14-3.14); P = 0.014]. Postoperatively, patients with early discharge (LOS <10 days) indicated, at 3 and 12 months postoperatively, significant better HRQL scores in the functional scales (physical, emotional, social and role functioning) and in symptoms scales (fatigue, nausea, dyspnoea appetite loss and dry mouth) when compared with LOS >10 days. Return to the level of the reference population scores was achieved at 1 year in the LOS ≤10 days for almost all the scales, but not in the LOS >10 days group. CONCLUSIONS: A better perception of preoperative physical functioning might have a beneficial effect on LOS. Our data, furthermore, suggest that early discharge correlates with improved postoperative HRQL outcomes. A clear decrease of the HRQL is seen at 3 months after the surgery, particularly in the LOS >10 days group. Generally, return to the level of the reference population scores is achieved at 1 year in the LOS ≤10 days, but not in the LOS >10 days group.