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High-grade serous ovarian cancer (HGSOC) is responsible for the largest number of ovarian cancer deaths. The frequent therapy-resistant relapses necessitate a better understanding of mechanisms driving therapy resistance. Therefore, we mapped more than a hundred thousand cells of HGSOC patients in different phases of the disease, using single-cell RNA sequencing. Within patients, we compared chemonaive with chemotreated samples. As such, we were able to create a single-cell atlas of different HGSOC lesions and their treatment. This revealed a high intrapatient concordance between spatially distinct metastases. In addition, we found remarkable baseline differences in transcriptomics of ascitic and solid cancer cells, resulting in a different response to chemotherapy. Moreover, we discovered different robust subtypes of cancer-associated fibroblasts (CAF) in all patients. Besides inflammatory CAFs, vascular CAFs, and matrix CAFs, we identified a new CAF subtype that was characterized by high expression of STAR, TSPAN8, and ALDH1A1 and clearly enriched after chemotherapy. Together, tumor heterogeneity in both cancer and stromal cells contributes to therapy resistance in HGSOC and could form the basis of novel therapeutic strategies that differentiate between ascitic and solid disease.Implications:The newly characterized differences between ascitic and solid cancer cells before and after chemotherapy could inform novel treatment strategies for metastatic HGSOC.

Supplementary Figure from Distinct Transcriptional Programs in Ascitic and Solid Cancer Cells Induce Different Responses to Chemotherapy in High-Grade Serous Ovarian Cancer

Supplementary Table from Distinct Transcriptional Programs in Ascitic and Solid Cancer Cells Induce Different Responses to Chemotherapy in High-Grade Serous Ovarian Cancer

Spatial transcriptomics (ST) is an emerging field that aims to profile the transcriptome of a cell while keeping its spatial context. Although the resolution of non-targeted ST technologies has been rapidly improving in recent years, most commercial methods do not yet operate at single-cell resolution. To tackle this issue, computational methods such as deconvolution can be used to infer cell type proportions in each spot by learning cell type-specific expression profiles from reference single-cell RNA-sequencing (scRNA-seq) data. Here, we benchmarked the performance of 11 deconvolution methods using 54 silver standards, 3 gold standards, and one in-depth case study on the liver. The silver standards were generated using our novel simulation engine synthspot, where we used six scRNA-seq datasets to create synthetic spots that followed one of nine different biological tissue patterns. The gold standards were generated using imaging-based ST technologies at single-cell resolution. We evaluated method performance based on the root-mean-squared error, area under the precision-recall curve, and Jensen-Shannon divergence. Our evaluation revealed that method performance significantly decreases in datasets with highly abundant or rare cell types. Moreover, we evaluated the stability of each method when using different reference datasets and found that having sufficient number of genes for each cell type is crucial for good performance. We conclude that while RCTD and cell2location are the top-performing methods, a simple off-the-shelf deconvolution method surprisingly outperforms almost half of the dedicated spatial deconvolution methods. Our freely available Nextflow pipeline allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Billiet et al. identify the post-thymic progeny of the agonist-selected CD10(+) PD-1(+) precursors in humans based on shared characteristics of the T cell receptor repertoire and the transcriptome. This lineage represents a well-defined but heterogeneous, unconventional TCR alpha beta(+) lineage mostly confined within the CD8(+) Helios(+) T cells. In the human thymus, a CD10(+) PD-1(+) TCR alpha beta(+) differentiation pathway diverges from the conventional single positive T cell lineages at the early double-positive stage. Here, we identify the progeny of this unconventional lineage in antigen-inexperienced blood. These unconventional T cells (UTCs) in thymus and blood share a transcriptomic profile, characterized by hallmark transcription factors (i.e., ZNF683 and IKZF2), and a polyclonal TCR repertoire with autoreactive features, exhibiting a bias toward early TCR alpha chain rearrangements. Single-cell RNA sequencing confirms a common developmental trajectory between the thymic and blood UTCs and clearly delineates this unconventional lineage in blood. Besides MME+ recent thymic emigrants, effector-like clusters are identified in this heterogeneous lineage. Expression of Helios and KIR and a decreased CD8 beta expression are characteristics of this lineage. This UTC lineage could be identified in adult blood and intestinal tissues. In summary, our data provide a comprehensive characterization of the polyclonal unconventional lineage in antigen-inexperienced blood and identify the adult progeny.

We are grateful for the opportunity to provide a discussion on this paper. We will first focus on the general context. Next, we will emphasize the novel key ideas proposed by the authors before formulating some open questions.

High-grade serous ovarian cancer (HGSOC) is responsible for the largest number of ovarian cancer deaths. The frequent therapy-resistant relapses necessitate a better understanding of mechanisms driving therapy resistance. Therefore, we mapped more than a hundred thousand cells of HGSOC patients in different phases of the disease, using single-cell RNA sequencing. Within patients, we compared chemonaive with chemotreated samples. As such, we were able to create a single-cell atlas of different HGSOC lesions and their treatment. This revealed a high intrapatient concordance between spatially distinct metastases. In addition, we found remarkable baseline differences in transcriptomics of ascitic and solid cancer cells, resulting in a different response to chemotherapy. Moreover, we discovered different robust subtypes of cancer-associated fibroblasts (CAF) in all patients. Besides inflammatory CAFs, vascular CAFs, and matrix CAFs, we identified a new CAF subtype that was characterized by high expression of STAR, TSPAN8, and ALDH1A1 and clearly enriched after chemotherapy. Together, tumor heterogeneity in both cancer and stromal cells contributes to therapy resistance in HGSOC and could form the basis of novel therapeutic strategies that differentiate between ascitic and solid disease. Implications: The newly characterized differences between ascitic and solid cancer cells before and after chemotherapy could inform novel treatment strategies for metastatic HGSOC.

Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.

In the human thymus, a CD10+ PD-1+ TCRαβ+ differentiation pathway diverges from the conventional single positive T cell lineages at the early double positive stage. These cells are phenotypically and functionally similar to murine unconventional intraepithelial lymphocyte (uIEL) precursors. Here, the progeny of the human uIEL lineage was identified in antigen-inexperienced blood. The uIELs in thymus and peripheral blood share a transcriptomic profile, characterized by hallmark transcription factors (i.e. ZNF683 and IKZF2), and polyclonal TCR repertoire with autoreactive features, exhibiting a bias towards early TCR alpha chain rearrangements. Single-cell RNA sequencing confirmed a common developmental trajectory between the thymic and peripheral uIELs, and clearly delineated this unconventional lineage in peripheral blood. This population is phenotypically defined as CD3+ TCRαβ+ CD4- CCR7- CD26-. It contains CD10+ recent thymic emigrants, Helios+ KIR+ CD8+ Tregs and CD8αα+ T cells. Thus, the uIEL lineage represents a well-defined but heterogeneous, unconventional TCRαβ+ lineage mostly confined in human within the CD8 single positive T cells.SummaryBilliet et al. identify the postthymic progeny of the intraepithelial lymphocyte precursors in human based on shared characteristics of the T cell receptor repertoire and the transcriptome. This lineage represents a well-defined but heterogeneous, unconventional TCRαβ+ lineage mostly confined within the CD8 single positive T cells.

Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as “translational machine learning”, joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.

The mechanisms underlying operational tolerance after hematopoietic stem cell transplantation in humans are poorly understood. We studied two independent cohorts of patients who underwent allogeneic hematopoietic stem cell transplantation from human leukocyte antigen–identical siblings. Primary tolerance was associated with long-lasting reshaping of the recipients’ immune system compared to their healthy donors with an increased proportion of regulatory T cell subsets and decreased T cell activation, proliferation, and migration. Transcriptomics profiles also identified a role for nicotinamide adenine dinucleotide biosynthesis in the regulation of immune cell functions. We then compared individuals with operational tolerance and nontolerant recipients at the phenotypic, transcriptomic, and metabolomic level. We observed alterations centered on CD38+-activated T and B cells in nontolerant patients. In tolerant patients, cell subsets with regulatory functions were prominent. RNA sequencing analyses highlighted modifications in the tolerant patients’ transcriptomic profiles, particularly with overexpression of the ectoenzymeNT5E(encoding CD73), which could counterbalance CD38 enzymatic functions by producing adenosine. Further, metabolomic analyses suggested a central role of androgens in establishing operational tolerance. These data were confirmed using an integrative approach to evaluating the immune landscape associated with operational tolerance. Thus, balance between a CD38-activated immune state and CD73-related production of adenosine may be a key regulator of operational tolerance.

Granulocyte-macrophage colony-stimulating factor (GM-CSF) instructs monocytes to differentiate into alveolar macrophages (AM) that preserve lung homeostasis. By comparing AM development in mouse and human, we discovered that COVID-19 patients showed marked defects in GM-CSF-dependent AM instruction. The multi-center, open-label, randomized, controlled SARPAC-trial evaluated the efficacy and safety of 5 days of inhalation of rhu-GM-CSF (sargramostim, Leukine®) in 81 non-ventilated patients with COVID-19 and hypoxemic respiratory failure identified by PaO2/FiO2 ratio < 350mmHg. At day 6, more patients in the sargramostim group experienced at least 25% improvement in oxygenation compared with the standard of care group. Higher numbers of circulating class-switched B cells and effector virus-specific CD8 lymphocytes were found in the sargramostim group. Treatment adverse events, including signs of cytokine storm, were not different between active and control group. This proof-of-concept study demonstrates the feasibility and safety of inhaled GM-CSF in restoring alveolar gas exchange, while simultaneously boosting anti-COVID-19 immunity. ClinicalTrials.gov (NCT04326920).

Multiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.

This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructed regulons (i.e., transcription factors and their target genes) assess the activity of these discovered regulons in individual cells and use these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon’s target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in

The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results.

Given the increasing amount of neuroimaging studies, there is a growing need to summarize published results. Coordinate-based meta-analyses use the locations of statistically significant local maxima with possibly the associated effect sizes to aggregate studies. In this paper, we investigate the influence of key characteristics of a coordinate-based meta-analysis on (1) the balance between false and true positives and (2) the activation reliability of the outcome from a coordinate-based meta-analysis. More particularly, we consider the influence of the chosen group level model at the study level [fixed effects, ordinary least squares (OLS), or mixed effects models], the type of coordinate-based meta-analysis [Activation Likelihood Estimation (ALE) that only uses peak locations, fixed effects, and random effects meta-analysis that take into account both peak location and height] and the amount of studies included in the analysis (from 10 to 35). To do this, we apply a resampling scheme on a large dataset (N = 1,400) to create a test condition and compare this with an independent evaluation condition. The test condition corresponds to subsampling participants into studies and combine these using meta-analyses. The evaluation condition corresponds to a high-powered group analysis. We observe the best performance when using mixed effects models in individual studies combined with a random effects meta-analysis. Moreover the performance increases with the number of studies included in the meta-analysis. When peak height is not taken into consideration, we show that the popular ALE procedure is a good alternative in terms of the balance between type I and II errors. However, it requires more studies compared to other procedures in terms of activation reliability. Finally, we discuss the differences, interpretations, and limitations of our results.

The importance of integrating research findings is incontrovertible and coordinate based meta-analyses have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. Similar to classical meta-analyses, coordinate based meta-analyses may be subject to different forms of publication bias which impacts results and possibly invalidates findings. We develop a tool that assesses the robustness to potential publication bias on cluster level. We investigate the possible influence of the file-drawer effect, where studies that do not report certain results fail to get published, by determining the number of noise studies that can be added to an existing fMRI meta-analysis before the results are no longer statistically significant. In this paper we illustrate this tool through an example and test the effect of several parameters through extensive simulations. We provide an algorithm for which code is freely available to generate noise studies and enables users to determine the robustness of meta-analytical results.

1 Introduction The reproducibility of fMRI studies suffers from small sample sizes, an overly stringent focus on minimizing type I errors (at the cost of power) and topological instability (Durnez et al., 2014; Roels et al., 2015). It is therefore increasingly recognized that further progress in understanding brain function will require integration of data across studies using meta-analyses (Wager et al., 2007). Methods for coordinate-based meta-analyses are designed to determine locations of activity based on studies that only report peak voxels whose evidence survive a selected threshold. In this contribution, we study the impact of analysis choices made at the individual study level on the coordinate-based meta-analysis methods. More specifically, we focus on pooling of subjects in which we consider (1) fixed effects pooling (no between-subjects variability), (2) ordinary least squares or OLS (homogeneous within-subjects variances) and (3) mixed effects pooling (estimating both). We use a large database to set up a simulation study and implement 3 coordinate- based meta-analysis techniques: activation likelihood estimation (ALE, Turkeltaub et al., 2002) and two weighted average meta-analyses. ALE calculates the convergence of activation at a specific voxel. The weighted average meta-analysis is either implemented as a fixed effects meta-analysis (assumes no between study variability) or a random effects meta-analysis (seed based d-mapping, SBdM, Radua et al. (2012)). 2 Method The design of the study is depicted in figure 1. We use data of 1400 subjects from the processed math > language contrast of the IMAGEN project (Schumann et al., 2010). In one iteration, 200 subjects are sampled that go into a ‘ground truth’ for which activation is assessed through mixed effects pooling with the False Discovery Rate (FDR) controlled at level 0.001. The other 200 subjects go into a test condition and are then sampled as 10 separate studies. Within each study, we apply the three different pooling methods and we obtain peak locations (FDR control at level 0.05). These study results are then combined in meta-analyses. For ALE, images are either uncorrected for multiple testing or corrected through FDR control (‘pID’ assumes independent or positive de- pendent test statistics among voxels, ‘pID’ makes no assumption on the joint distribution of test statistics). All meta-analyses are thresholded to control the type I error rate or FDR at level 0.05. The meta-analytic results are compared with the ground truth (200 different subjects) and a benchmark (group analysis as in ground truth but using the same 200 subjects as in the meta-analyses). We calculate the overlap-of-activation, power and false positive rate, FPR (Maitra, 2010). In total, 30 iterations are performed. 3 Results Results are given in figure 2. At study level, the OLS pooling method shows consistently lower levels of overlap, power and FPR while the mixed effects pooling method has the highest levels of overlap, power and FPR. For the meta-analysis methods, the fixed and random effects analyses show higher levels of overlap and power compared to ALE though with a slight increase in FPR. 4 Conclusions With this study, we show how analytical choices at the individual study level impact results on the meta-analytical level. More specifically, OLS leads to more conservative results while fixed/mixed effects pooling is more liberal. The different performance of the meta-analysis methods requires further research. One possible explanation is the implemented kernel sizes to model local maxima that differ between the methods.

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.

Neuroscientists are increasingly aware of the limited reproducibility of functional Magnetic Resonance Imaging (fMRI) studies due to an inherent low signal to noise ratio, small sample sizes, questionable research practices and a strong focus on minimizing type I errors (Durnez et al., 2014). To overcome this, researchers now recognize the value of open science initiatives which leads to an increased amount of open access data and a higher inclusion of null results (Pernet and Poline, 2016). This in turn enables the integration of data across studies to increase the understanding of human brain function. In this contribution, we investigate two approaches of pooling data across studies. Traditionally, researchers can rely on methods for meta-analysis to aggregate effect sizes while accounting for within and between study variability. A second approach is to use a three-level linear model with an additional study level, an extension of the two-stage mixed effects model commonly used in fMRI to aggregate trials and subjects within individual studies. We compare both procedures in terms of the average standardized bias, length and coverage of confidence intervals using both simulations and resting state fMRI (under the null hypothesis). Results indicate that the three-level model does not outperform meta-analysis techniques.

fMRI localizer tasks are often used to define subject specific functional regions of interest (fROIs) that contain the relevant features for subsequent analyses. fROIs are typically small and show large interindividual differences in extent and effect size. As statistical testing procedures focus on con- trolling false positives, this may lead to an ad-hoc adjustment of thresholding in some individuals. The promising likelihood ratio (LR) testing approach for fMRI (Kang et al. 2015) provides simultaneous control of both false positives and negatives by contrasting evidence in favor of true activation against evidence in favor of the null hypothesis. The authors propose to estimate the expected alternative by a percentile (e.g. 95th) across the voxels of an effect size map. However, in the context of fROIs, pre-defined observed percentiles may induce inconsistent activation across subjects. In this study we show the potential of a maximized LR approach (Bickel, 2012) for this particular application. The maximum LR is calculated over the same interval of functionally relevant alternatives for all subjects, enabling consistent localization of the fROIs in both subjects with low levels and high levels of general activity.

HighlightsThe manuscript presents a method to calculate sample sizes for fMRI experimentsThe power analysis is based on the estimation of the mixture distribution of null and active peaksThe methodology is validated with simulated and real data.1AbstractMounting evidence over the last few years suggest that published neuroscience research suffer from low power, and especially for published fMRI experiments. Not only does low power decrease the chance of detecting a true effect, it also reduces the chance that a statistically significant result indicates a true effect (Ioannidis, 2005). Put another way, findings with the least power will be the least reproducible, and thus a (prospective) power analysis is a critical component of any paper. In this work we present a simple way to characterize the spatial signal in a fMRI study with just two parameters, and a direct way to estimate these two parameters based on an existing study. Specifically, using just (1) the proportion of the brain activated and (2) the average effect size in activated brain regions, we can produce closed form power calculations for given sample size, brain volume and smoothness. This procedure allows one to minimize the cost of an fMRI experiment, while preserving a predefined statistical power. The method is evaluated and illustrated using simulations and real neuroimaging data from the Human Connectome Project. The procedures presented in this paper are made publicly available in an online web-based toolbox available at www.neuropowertools.org.

The best established lateralized cerebral function is speech production, with the majority of the population having left hemisphere dominance. An important question is how to best assess the laterality of this function. Neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI) are increasingly used in clinical settings to replace the invasive Wada-test. We evaluated the usefulness of behavioral visual half field (VHF) tasks for screening a large sample of healthy left-handers. Laterality indices (LIs) calculated on the basis of the latencies in a word and picture naming VHF task were compared to the brain activity measured in a silent word generation task in fMRI (pars opercularis/BA44 and pars triangularis/BA45). Results confirmed the usefulness of the VHF-tasks as a screening device. None of the left-handed participants with clear right visual field (RVF) advantages in the picture and word naming task showed right hemisphere dominance in the scanner. In contrast, 16/20 participants with a left visual field (LVF) advantage in both word and picture naming turned out to have atypical right brain dominance. Results were less clear for participants who failed to show clear VHF asymmetries (below 20 ms RVF advantage and below 60 ms LVF advantage) or who had inconsistent asymmetries in picture and word naming. These results indicate that the behavioral tasks can mainly provide useful information about the direction of speech dominance when both VHF differences clearly point in the same direction.

Background: Hemi-neglect is a disorder characterized by a disregard of contralesional Stimuli. Some neglect patients also show an "exaggerated attention" for the ipsilesional field, reflected in perseverative responses, such as repetitive fixations in the ipsilesional field, ipsilesional revisitings on standard cancellation, or exaggerations in drawings oil the ipsilesional side. Objective and Method: It is still unclear whether neglect and perseveration are due to a single underlying mechanism. In the present study, we will examine the effect of 4-day-in-a-row prism adaptation on neglect and perseveration severity in a patient with severe perseverations. Additionally, we will examine whether the position of omissions and perseverations on the Star Cancellation will change during the intervention. Results: Our patient showed a decrease in neglect severity and ail increase in perseveration severity, suggesting that perseveration and neglect are dissociated phenomena. Interestingly, 4-day-in-a-row prism adaptation gradually moved the predominant position of perseverative responses from right to left as neglect decreased with treatment. Conclusions: Perseverative responses do not necessarily occur in the ipsilesional sector of space as is generally assumed. Instead, the position of the revisits may be determined by the severity of the neglect, and may shift when the focus of attention moves more contralesionally with recovery.

Objectives: We sought to evaluate the benefit of 3 T compared with 1.5 T during presurgical functional magnetic resonance imaging. Materials and Methods: Six participants performed a motor, a visual, and 2 language paradigms both at 1.5 and 3 T. The number of activated voxels, mean t-value, and assessment of language dominancy were compared between both field strengths. Group analysis was performed to evaluate the influence of field strength on the cortical language activation patterns. Results: The number of activated voxels and mean t-values were significantly higher at 3 T for all paradigms. Using the same statistical threshold, language activation was significantly less lateralized, and more activation zones were depicted at 3 T compared with 1.5 T. Conclusions: Sensitivity associated with visual, motor and language functional magnetic resonance imaging increased significantly at 3 T. Additional cortical areas were depicted during language processing at 3 T. For assessment of language dominancy, usage of more stringent statistical thresholds at 3 T is suggested.

Introduction While publications of fMRI studies have flourished, it is increasingly recognized that progress in understanding human brain function will require integration of data across studies using meta-analyses. In general, results that do not reach statistical significance are less likely to be published and included in a meta-analysis. Meta-analyses of fMRI studies are prone to this publication bias when studies are excluded because they fail to show activation in specific regions. Further, some studies only report a limited amount of peak voxels that survive a statistical threshold resulting in an enormous loss of data. Coordinate-based toolboxes have been specifically developed to combine the available information of such studies in a meta-analysis. Potential publication bias then stems from two sources: exclusion of studies and missing voxel information within studies. In this study, we focus on the assessment of the first source of bias in coordinate-based meta-analyses. A measure of publication bias indicates the degree to which the analysis might be distorted and helps to interpret results. We propose an adaptation of the Fail-Safe N (FSN; Rosenthal, 1979). The FSN reflects the number of null studies, i.e. studies without activation in a target region, that can be added to an existing meta-analysis without altering the result for the target region. A large FSN indicates robustness of the effect against publication bias. On the other hand, in this context, a FSN that is too large indicates that a small amount of studies might drive the entire analysis. Method We simulated 1000 simplistic meta-analyses, each consisting of 3 studies with real activation in a target area (quadrant 1 in Figure 1) and up to 100 null studies with activation in the remaining 3 quadrants. We calculated the FSN as the number of null studies (with a maximum of 100) that can be added to the original meta-analysis of 3 studies without altering the results for the target area. Meta-analyses were conducted with ALE (Eickhoff et al., 2009; 2012; Turkeltaub et al., 2012). We computed the FSN using an uncorrected threshold (α = 0.001) and 2 versions of a False Discovery Rate (FDR) threshold (q = 0.05), FDR pID (which assumes independence or positive dependence between test statistics) and FDR pN (which makes no assumptions and is more conservative). We varied the average sample size n of the individual studies, from small (n≈10), to medium (n≈20) and large (n≈30). Results Results are summarised in Figure 2 and visually presented in Figure 3. We find a large difference in average FSN between the different thresholding methods. In case of uncorrected thresholding, the target region remains labeled as active while only 3% of the studies in the meta-analysis report activation at that location. Further, if the sample size of the individual studies in the meta-analysis increases, the FSN decreases. Conclusions The FSN varies largely across thresholding methods and sample sizes. Uncorrected thresholding allows for the analysis to be driven by a small amount of studies and is therefore counter-indicated. While a decreasing FSN with increasing sample size might be counterintuitive in terms of robustness, it indicates that the analysis is less prone to be driven by a small number of studies. Publication bias assessment methods can be a valuable add-on to existing toolboxes for interpretation of meta-analytic results. In future work, we will extend our research to other methods for the assessment of publication bias, such as the Egger Test (Egger et al., 1997) and test for excess of success (Francis, 2014). References Egger, M., Davey Smith, G., Schneider, M., and Minder, C. (1997), ‘Bias in meta-analysis detected by a simple, graphical test’, British Medical Journal, vol. 315, pp. 629-634. Eickhoff, S.B., Laird, A.R., Grefkes, C., Wang, L.E., Zilles, K., and Fox, P.T. (2009), ‘Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty’, Human Brain Mapping, vol. 30, pp. 2907-2926. Eickhoff, S.B., Bzdok, D., Laird, A.R., Kurth, F., and Fox, P.T. (2012), ‘Activation likelihood estimation revisited’, Neuroimage, vol. 59, pp. 2349-2361. Francis, G. (2014), ‘The frequency of excess success for articles in Psychological Science’, Psychonomic Bulletin and Review, vol. 21, no. 5, pp. 1180-1187. Rosenthal, R. (1979), ‘The file drawer problem and tolerance for null results’, Psychological Bulletin, vol. 86, no. 3, pp. 638–641. Turkeltaub, P.E., Eickhoff, S.B., Laird, A.R., Fox, M., Wiener, M., and Fox, P. (2012), ‘Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses’, Human Brain Mapping, vol. 33, pp. 1-13.

There is increasing concern about statistical power in neuroscience research. Critically, an underpowered study has poor predictive power. That is, findings from a low-power study are unlikely to be reproducible, and thus a power analysis is a critical component of any paper. We present a simple way to characterize the spatial signal in an fMRI study, and a direct way to estimate power based on an existing pilot study, thus allowing to minimize the cost of an fMRI experiment, while attaining a given level of power. The procedure estimates the proportion of active peaks (π1) and the average effect peak height, which is a function of effect size δ and sample size n. Using an evaluation procedure based on simulations and data from the Human Connectome Project, we found that our method predicted well, for sufficiently high power, the effect size in a given study, which enables power and sample size calculations for new studies. We provide a user-friendly toolbox neuropower, which shows easy-to-interpret figures and results. This work enables researchers to get the best trade-off between cost and statistical power for any fMRI study.

Mental rotation tests traditionally show a male performance advantage. Some neuroimaging studies have reported sex-specific cortical activation patterns during mental rotation. However, these experiments used abstract stimuli and some studies did not systematically exclude performance as a confounding variable. The mental rotation of hands and hand-related objects, compared to abstract objects, is known to evoke an egocentric motor strategy. In this study, we used fMRI to explore potential gender-specific cortical activation patterns for the mental rotation of hands and tools in a sample with an adequate and equal performance for men and women. We found a common neural substrate for men and women comprising superior parietal lobe, dorsolateral premotor cortex, and extrastriate occipital areas, compatible with an egocentric motor strategy for the mental rotation of hands and tools. Sex differences were modest and limited to the mental rotation of hands. Women recruited more left ventral premotor cortex, which could imply that women rely more on imitation or use more perceptual comparisons. Men, on the other hand, drafted more the lingual gyrus, possibly referring to more extensive semantic or early visual processing. We conclude that men and women use a very similar motor strategy during egocentric mental rotation with a potential gender-specific accent.

When people are confronted with social dilemmas, their decision-making strategies tend to be associated with individual social preferences; prosocials have an intrinsic willingness to cooperate, while proselfs need extrinsic motivators signaling personal gain. In this study, the biological roots for the proselfs/prosocials concept are explored by investigating the neural correlates of cooperative versus defect decisions when participants engage in a series of one-shot, anonymous prisoner's dilemma situations. Our data are in line with previous studies showing that prosocials activate several social cognition regions of the brain more than proselfs (here: medial prefrontal cortex, temporo-parietal junction, and precuneus BA 7 (Brodmann area 7), and that dispositional trust positively affects prosocials' decisions to cooperate. At the neural level, however, dispositional trust appears to exert a greater marginal effect on brain activity of proselfs in three social cognition regions, which does not translate into an increase in cooperation. An event-related analysis shows that cooperating prosocials show significantly more activation in the precuneus (BA 7) than proselfs. Based on previous research, we interpret this result to be consistent with prosocials' enhanced tendency to infer the intentions of others in social dilemma games, and the importance of establishing norm congruence when they decide to cooperate.

Behavioral flexibility is an important aspect of self-regulation and involves effectively learning, unlearning, and relearning associations between actions and outcome. Using a probabilistic reversal learning paradigm (PRL), the neural correlates of flexibility have previously been associated with brain regions implicated in cognitive control, including the anterior cingulate cortex (ACC) and lateral prefrontal cortex, and with the nucleus accumbens (Nacc) implicated in reward. The current study on healthy young males (n = 40) extends this previously published work in 3 ways. First we corroborate the involvement of ACC, VLPFC, and DLPFC at the exact moment of behavioral switches. Second, we report increased activation of the dACC and caudate head with increasing number of perseverating errors preceding a behavioral switch. Third, better performance on the task is associated with increased activation of rACC and VLPFC during switching, suggesting that these regions contribute to individual differences in behavioral flexibility. These findings cannot be extended to individual differences in a self-reported measure of self-regulation.

Decision making under uncertainty is challenging for any autonomous agent. The challenge increases when the environment’s stochastic properties change over time, i.e., when the environment is volatile. In order to efficiently adapt to volatile environments, agents must primarily rely on recent outcomes to quickly change their decision strategies; in other words, they need to increase their knowledge plasticity. On the contrary, in stable environments, knowledge stability must be preferred to preserve useful information against noise. Here we propose that in mammalian brain, the locus coeruleus (LC) is one of the nuclei involved in volatility estimation and in the subsequent control of neural plasticity. During a reinforcement learning task, LC activation, measured by means of pupil diameter, coded both for environmental volatility and learning rate. We hypothesize that LC could be responsible, through norepinephrinic modulation, for adaptations to optimize decision making in volatile environments. We also suggest a computational model on the interaction between the anterior cingulate cortex (ACC) and LC for volatility estimation.

Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the R packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data.

In order to choose the best action for maximizing fitness, mammals can estimate the reward expectations (value) linked to available actions based on past environmental outcomes. Value updates are performed by comparing the current value with the actual environmental outcomes (prediction error). The anterior cingulate cortex (ACC) has been shown to be critically involved in the computation of value and its variability across time (volatility). Previously, we proposed a new neural model of the ACC based on single-unit ACC neurophysiology, the Reward Value and Prediction Model (RVPM). Here, using the RVPM in computer simulations and in a model-based fMRI study, we found that highly uncertain but non-volatile environments activate ACC more than volatile environments, demonstrating that value estimation by means of prediction error computation can account for the effect of volatility in ACC. These findings suggest that ACC response to volatility can be parsimoniously explained by basic ACC reward processing. (C) 2012 Elsevier Ltd. All rights reserved.

This study explores the fMRI correlates of observers making trait inferences about other people under conflicting social cues. Participants were presented with several behavioral descriptions involving an agent that implied a particular trait. The last behavior was either consistent or inconsistent with the previously implied trait. This was done under instructions that elicited either spontaneous trait inferences ('read carefully') or intentional trait inferences ('infer a trait'). The results revealed that when the behavioral descriptions violated earlier trait implications, regardless of instruction, the medial prefrontal cortex (mPFC) was more strongly recruited as well as the domain-general conflict network including the posterior medial frontal cortex (pmFC) and the right prefrontal cortex (rPFC). These latter two areas were more strongly activated under intentional than spontaneous instructions. These findings suggest that when trait-relevant behavioral information is inconsistent, not only is activity increased in the mentalizing network responsible for trait processing, but control is also passed to a higher level conflict monitoring network in order to detect and resolve the contradiction.

The role of the anterior cingulate cortex (ACC) in cognition has been extensively investigated with several techniques, including single-unit recordings in rodents and monkeys and EEG and fMRI in humans. This has generated a rich set of data and points of view. Important theoretical functions proposed for ACC are value estimation, error detection, error-likelihood estimation, conflict monitoring, and estimation of reward volatility. A unified view is lacking at this time, however. Here we propose that online value estimation could be the key function underlying these diverse data. This is instantiated in the reward value and prediction model (RVPM). The model contains units coding for the value of cues (stimuli or actions) and units coding for the differences between such values and the actual reward (prediction errors). We exposed the model to typical experimental paradigms from single-unit, EEG, and fMRI research to compare its overall behavior with the data from these studies. The model reproduced the ACC behavior of previous single-unit, EEG, and fMRI studies on reward processing, error processing, conflict monitoring, error-likelihood estimation, and volatility estimation, unifying the interpretations of the role performed by the ACC in some aspects of cognition.

This fMRI study analyzes inferences on other person's traits, whereby half of the participants were given spontaneous ("read") instructions while the other half were given intentional ("infer the person's trait") instructions. Several sentences described the behavior of a target person from which a strong trait could be inferred (trait diagnostic) or not (trait nondiagnostic). A direct contrast between spontaneous and intentional instructions revealed no significant differences, indicating that the same social mentalizing network was recruited. There was, however, a difference with respect to different brain areas that passed the significance threshold, suggesting that this common network was recruited to a different degree. Specifically, spontaneous inferences significantly recruited only core mentalizing areas, including the temporo-parietal junction and medial prefrontal cortex, whereas intentional inferences additionally recruited other brain areas, including the (pre)cuneus, superior temporal sulcus, temporal poles, and parts of the premotor and parietal cortex. These results suggest that intentional instructions invite observers to think more about the material they read, and consider it in many more different ways besides its social impact. Future research on the neurological underpinnings of trait inference might profit from the use of spontaneous instructions to get purer results that involve only the core brain areas in social judgment.