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Recent findings raised evidence that in early-onset left temporal lobe epilepsy, women show greater functional plasticity for verbal memory than men. In particular, women with lesion- or epilepsy-driven atypical language dominance show an advantage over men. The question asked in this study was whether there is evidence of sex- and language dominance-dependent late, i.e. adult age, plasticity for verbal memory when epilepsy surgery is performed in these patients. Pre- and 1-year postoperative memory performance was evaluated in 169 patients (94 males and 75 females) who underwent left temporal lobe surgery and who had WADA testing of hemispheric language dominance prior to surgery. Verbal memory and figural memory were assessed by list-learning paradigms. According to the Bonn intracarotid amobarbital test (IAT) protocol, patients were categorized into left dominant or atypically dominant (right, incomplete left or right, and bilateral dominant) groups. Results were controlled for the hypothesized sex differences. Thirty-four percent of men and 47% of women displayed patterns of atypical language dominance. Atypical dominance was related to an early onset of epilepsy. Men showed a larger time window for development of atypical dominance but, differently from women, the pattern of atypical dominance was more strictly determined by the age at onset of epilepsy. Atypically dominant women showed better verbal memory than typically dominant women or men. After surgery, right dominant patients had better verbal memory outcome than patients with bilateral or left language dominance who showed significant memory loss. No effect of sex on verbal memory change was found. Figural memory deteriorated in men and improved in women, when they were not left dominant. Seizure outcome had no effect on performance changes. It was concluded that better preserved verbal memory in atypically dominant women before surgery indicates greater benefit from atypical dominance in women than men with regard to the initial damage associated with left hemisphere epilepsy. Later in life, when epilepsy surgery causes additional damage, no such sex difference is observed, indicating that the women?s advantage over men is fixed to an early time window in life. Postoperative changes in figural memory suggest dynamics in crowding and suppression patterns. Whether this reflects late plasticity and compensation needs further demonstration. For clinical practice, it is important to note that incomplete right hemisphere and bilateral language dominance do not protect against verbal memory loss after left sided temporal lobe surgery.

Climate change is currently one of humanity's greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries., (© 2024. The Author(s).)

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

Decades after the scientific debate about the anthropogenic causes of climate change was settled, climate disinformation still challenges the scientific evidence in public discourse. Here we present a comprehensive theoretical framework of (anti)science belief formation and updating to account for the psychological factors that influence the acceptance or rejection of scientific messages. We experimentally investigated, across 12 countries (N = 6,816), the effectiveness of six inoculation strategies targeting these factors-scientific consensus, trust in scientists, transparent communication, moralization of climate action, accuracy and positive emotions-to fight real-world disinformation about climate science and mitigation actions. While exposure to disinformation had strong detrimental effects on participants' climate change beliefs (δ = -0.16), affect towards climate mitigation action (δ = -0.33), ability to detect disinformation (δ = -0.14) and pro-environmental behaviour (δ = -0.24), we found almost no evidence for protective effects of the inoculations (all δ < 0.20). We discuss the implications of these findings and propose ways forward to fight climate disinformation., (© 2023. The Author(s).)

Introduction: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish., Methods: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements., Results: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P -value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml)., Discussion: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Tayebi Arasteh, Romanowicz, Pace, Golland, Powell, Maier, Truhn, Brosch, Weese, Lotfinia, van der Geest and Moghari.)

Emotions and values are fundamentally connected. They both are psychological markers of subjective relevance and are thought to be deeply functionally intertwined: According to appraisal theories of emotion, emotions arise when value concerns are at stake; according to theories of value, a value that is threatened or supported gets infused with feelings. Surprisingly, while these assumptions are considered well established by researchers in the respective domains, up to now, empirical research has not provided much evidence supporting a link between values and emotions. To fill this gap, here we report results from three experiments demonstrating that values are indeed antecedents of emotions when emotional experiences arise in response to value-relevant stimuli. Individual differences in biospheric values predicted the intensity of emotional responses toward positive and negative information concerning nature and climate change, both when measured via psychophysiology (Experiment 1) and via self-report (Experiments 1-3). Primary appraisal was identified as the key process connecting values and emotions (Experiments 2-3), supporting the notion of appraisal theories that specific mechanisms of relevance detection underlie the elicitation of emotion. These findings may lead to new developments in value and emotion theories, potentially resulting in a stronger integration of the two constructs in a shared theoretical framework. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: Implicit prejudice can lead to disparities in treatment. The effects of specialty and experience on implicit obesity and mental illness prejudice had not been explored. The main objective was to examine how specializing in psychiatry/general medicine and years of experience moderated implicit obesity and mental illness prejudice among Swiss physicians. Secondary outcomes included examining the malleability of implicit bias via two video interventions and a condition of cognitive load, correlations of implicit bias with responses to a clinical vignette, and correlations with explicit prejudice., Methods: In stage 1, participants completed an online questionnaire including a clinical vignette. In stage 2, implicit prejudice pre- and post- intervention was tested using a 4 × 4 between-subject design including a control group. In stage 3, explicit prejudice was tested with feeling thermometers and participants were debriefed. Participants were 133 psychiatrists and internists working in Geneva, hospital-based and private practice. Implicit prejudice was assessed using a Weight IAT (Implicit Association Test) and a Mental Illness IAT. Explicit feelings towards the obese and the mentally ill were measured using Feeling Thermometers. A clinical vignette assessed the level of concern felt for a fictional patient under four conditions: control, obese, depression, obese and depression. Linear regression was conducted to test for association of gender, experience, and specialty with responses to vignettes, pre-intervention IATs and explicit attitudes, and to test for association of interventions (or control) with post-intervention IATs and explicit attitudes. Reported effect sizes were computed using Cohen's d. Two-tailed p < 0.05 was selected as the significance threshold., Results: Compared to internists, psychiatrists showed significantly less implicit bias against mentally vs. physically ill people than internists and warmer explicit feelings towards the mentally ill. More experienced physicians displayed warmer explicit feelings towards the mentally ill and a greater level of concern for the fictional patients in the vignette than the less experienced, except when the patient was described as obese., Conclusions: Specialty moderates both implicit and explicit mental illness prejudice. Experience moderates explicit mental illness bias and concern for patients. The effect of specialty on implicit prejudice seems to be based principally on self-selection., (© 2022. The Author(s).)

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Over the past decade, choice architecture interventions or so-called nudges have received widespread attention from both researchers and policy makers. Built on insights from the behavioral sciences, this class of behavioral interventions focuses on the design of choice environments that facilitate personally and socially desirable decisions without restricting people in their freedom of choice. Drawing on more than 200 studies reporting over 450 effect sizes ( n = 2,149,683), we present a comprehensive analysis of the effectiveness of choice architecture interventions across techniques, behavioral domains, and contextual study characteristics. Our results show that choice architecture interventions overall promote behavior change with a small to medium effect size of Cohen's d = 0.45 (95% CI [0.39, 0.52]). In addition, we find that the effectiveness of choice architecture interventions varies significantly as a function of technique and domain. Across behavioral domains, interventions that target the organization and structure of choice alternatives (decision structure) consistently outperform interventions that focus on the description of alternatives (decision information) or the reinforcement of behavioral intentions (decision assistance). Food choices are particularly responsive to choice architecture interventions, with effect sizes up to 2.5 times larger than those in other behavioral domains. Overall, choice architecture interventions affect behavior relatively independently of contextual study characteristics such as the geographical location or the target population of the intervention. Our analysis further reveals a moderate publication bias toward positive results in the literature. We end with a discussion of the implications of our findings for theory and behaviorally informed policy making., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Emotions are powerful drivers of human behavior that may make people aware of the urgency to act to mitigate climate change and provide a motivational basis to engage in sustainable action. However, attempts to leverage emotions via climate communications have yielded unsatisfactory results, with many interventions failing to produce the desired behaviors. It is important to understand the underlying affective mechanisms when designing communications, rather than treating emotions as simple behavioral levers that directly impact behavior. Across two field experiments, we show that individual predispositions to experience positive emotions in an environmental context (trait affect) predict pro-environmental actions and corresponding shifts in affective states (towards personal as well as witnessed pro-environmental actions). Moreover, trait affect predicts the individual behavioral impact of positively valenced emotion-based intervention strategies from environmental messages. These findings have important implications for the targeted design of affect-based interventions aiming to promote sustainable behavior and may be of interest within other domains that utilize similar intervention strategies (e.g., within the health domain)., (© 2021. The Author(s).)

Background: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches., Methods: The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma., Results: The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5-10 mm being 91.6% and 62.2% (p < 0.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%)., Conclusions: The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.

Background and Purpose: Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network., Materials and Methods: The network was trained to identify motion artifacts in axial T2-weighted spin-echo images of the brain. Using an extensive data augmentation scheme and a motion artifact simulation pipeline, we created a synthetic training dataset of 93,600 images based on only 16 artifact-free clinical MRI cases. A blinded reader study using a unique test dataset of 28 additional clinical MRI cases with real patient motion was conducted to evaluate the performance of the network., Results: Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases ( P  < .03)., Conclusions: Retrospective correction of motion artifacts using a multiscale fully convolutional network is promising and may mitigate the substantial motion-related problems in the clinical MRI workflow., (© 2020 by American Journal of Neuroradiology.)

Background: People with long-term unemployment and mental health problems often find it difficult to take active steps toward help-seeking and job search and to navigate the complex system of available services. Likewise, job center staff would welcome interventions to improve the reintegration of long-term unemployed individuals with mental health problems into the labor market., Aim: To examine the efficacy of a peer-led group program that supports unemployed people with mental health problems in terms of help-seeking, job search and recovery., Methods: Based on participatory research, a four-session group program was designed and evaluated in a pilot randomized controlled trial (RCT) with 42 participants, randomized to the program ( n = 23) or treatment as usual ( n = 19). Outcomes were assessed at baseline (T0), 3 weeks (T1), 6 weeks (T2) and 6 months later (T3)., Results: There were no significant intervention effects on primary outcomes (job search self-efficacy and help-seeking). But compared to the control group, intervention participants showed significant improvements in depressive symptoms ( p = .02) and recovery ( p = .04) at T2 with medium effect sizes. There were trend-level positive program effects on self-stigma, hopelessness and secrecy., Conclusion: This pilot RCT provides initial evidence for the efficacy of a peer-led group program to improve symptoms and recovery among unemployed participants with mental health problems.

Emotional situations are typically better remembered than neutral situations, but the psychological conditions and brain mechanisms underlying this effect remain debated. Stimulus valence and affective arousal have been suggested to explain the major role of emotional stimuli in memory facilitation. However, neither valence nor arousal are sufficient affective dimensions to explain the effect of memory facilitation. Several studies showed that negative and positive details are better remembered than neutral details. However, other studies showed that neutral information encoded and coupled with arousal did not result in a memory advantage compared with neutral information not coupled with arousal. Therefore, we suggest that the fundamental affective dimension responsible for memory facilitation is goal relevance. To test this hypothesis at behavioral and neural levels, we conducted a functional magnetic resonance imaging study and used neutral faces embedded in goal-relevant or goal-irrelevant daily life situations. At the behavioral level, we found that neutral faces encountered in goal-relevant situations were better remembered than those encountered in goal-irrelevant situations. To explain this effect, we studied neural activations involved in goal-relevant processing at encoding and in subsequent neutral face recognition. At encoding, activation of emotional brain regions (anterior cingulate, ventral striatum, ventral tegmental area, and substantia nigra) was greater for processing of goal-relevant situations than for processing of goal-irrelevant situations. At the recognition phase, despite the presentation of neutral faces, brain activation involved in social processing (superior temporal sulcus) to successfully remember identities was greater for previously encountered faces in goal-relevant than in goal-irrelevant situations.

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the intermediate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incomplete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Compared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.

Organ-at-risk (OAR) segmentation is a key step for radiotherapy treatment planning. Model-based segmentation (MBS) has been successfully used for the fully automatic segmentation of anatomical structures and it has proven to be robust to noise due to its incorporated shape prior knowledge. In this work, we investigate the advantages of combining neural networks with the prior anatomical shape knowledge of the model-based segmentation of organs-at-risk for brain radiotherapy (RT) on Magnetic Resonance Imaging (MRI). We train our boundary detectors using two different approaches: classic strong gradients as described in [4] and as a locally adaptive regression task, where for each triangle a convolutional neural network (CNN) was trained to estimate the distances between the mesh triangles and organ boundary, which were then combined into a single network, as described by [1]. We evaluate both methods using a 5-fold cross- validation on both T1w and T2w brain MRI data from sixteen primary and metastatic brain cancer patients (some post-surgical). Using CNN-based boundary detectors improved the results for all structures in both T1w and T2w data. The improvements were statistically significant ( p < 0.05) for all segmented structures in the T1w images and only for the auditory system in the T2w images.

Visual attention is an important condition for consumer decision-making. However, not much is known on individuals' determinants of this visual attention. Using eye tracking, this study investigated how psychological values (i.e., materialism) modulate visual attention to specific sources of information (i.e., product, brand and additional information) in the context of luxury consumption. Participants were asked to perform a forced-choice experiment, where products were randomly assigned with luxury and non-luxury brands (Experiment 1) and product information (Experiment 2). Experiment 1 revealed that materialism was related to relatively higher attention to luxury as opposed to non-luxury and higher choice proportion of products displayed with a luxury brand. Experiment 2 showed that when providing additional product information (e.g., regarding the material) in addition to the brand, all participants chose luxury products more often. Interestingly, choices seemed to be driven by enhanced attention to brand for participants with high levels of materialism when choosing luxury products. In contrast, choices were driven by text for participants with low levels of materialism for non-luxury products. This suggests that individuals with high levels of materialism may prefer luxury products for different reasons than individuals with low levels of materialism: while the first focus on the symbolic dimension conveyed by the brand (Experiment 1), the latter pay attention to the actual product characteristics (Experiment 2). Taken together, our results suggest that materialism as a psychological value has an impact on visual attention and information selection during decision-making in the context of luxury consumption.

Individuals with pronounced self-transcendence values have been shown to put greater weight on the long-term consequences of their actions when making decisions. Using functional magnetic resonance imaging, we investigated the neural mechanisms underlying the evaluation of events occurring several decades in the future as well as the role of core values in these processes. Thirty-six participants viewed a series of events, consisting of potential consequences of climate change, which could occur in the near future (around 2030), and thus would be experienced by the participants themselves, or in the far future (around 2080). We observed increased activation in anterior VMPFC (BA11), a region involved in encoding the personal significance of future events, when participants were envisioning far future events, demonstrating for the first time that the role of the VMPFC in future projection extends to the time scale of decades. Importantly, this activation increase was observed only in participants with pronounced self-transcendence values measured by self-report questionnaire, as shown by a statistically significant interaction of temporal distance and value structure. These findings suggest that future projection mechanisms are modulated by self-transcendence values to allow for a more extensive simulation of far future events. Consistent with this, these participants reported similar concern ratings for near and far future events, whereas participants with pronounced self-enhancement values were more concerned about near future events. Our findings provide a neural substrate for the tendency of individuals with pronounced self-transcendence values to consider the long-term consequences of their actions.

The present study investigated the extent to which luxury vs. non-luxury brand labels (i.e., extrinsic cues) randomly assigned to items and preferences for these items impact choice, and how this impact may be moderated by materialistic tendencies (i.e., individual characteristics). The main objective was to investigate the neural correlates of abovementioned effects using functional magnetic resonance imaging. Behavioural results showed that the more materialistic people are, the more they choose and like items labelled with luxury brands. Neuroimaging results revealed the implication of a neural network including the dorsolateral and ventromedial prefrontal cortex and the orbitofrontal cortex that was modulated by the brand label and also by the participants' preference. Most importantly, items with randomly assigned luxurious brand labels were preferentially chosen by participants and triggered enhanced signal in the caudate nucleus. This effect increased linearly with materialistic tendencies. Our results highlight the impact of brand-item association, although random in our study, and materialism on preference, relying on subparts of the brain valuation system for the integration of extrinsic cues, preferences and individual characteristics.

In social interactions, we often need to quickly infer why other people do what they do. More often than not, we infer that behavior is a result of personality rather than circumstances. It is unclear how the tendency itself may contribute to psychopathology and interpersonal dysfunction. Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction. Here, we investigated if this dysfunction is related to the tendency to over-attribute behaviors to personality traits. Healthy controls and patients with BPD judged positive and negative behaviors presented within a situational constraint during functional magnetic resonance imaging. Before the experiment, we measured trait levels of empathy, paranoia, and need for cognition. Behaviorally, we found that empathy levels predicted the tendency to attribute behavior to traits in healthy controls, whereas in patients with BPD this relationship was significantly weakened. Whole brain analysis of group-by-empathy interaction revealed that when participants judged the behavior during the attribution phase, several brain regions implicated in mentalizing distinguished patients from controls: In healthy controls, neural activity scaled negatively with empathy, but this relationship was reversed in BPD patients. Due to the cross-sectional study design we cannot establish a causal link between empathy and social attributions. These findings indicate that the self-reported tendency to feel for others is related to the tendency to integrate situational information beyond personality. In BPD patients, by contrast, the association between empathy and attribution was significantly weaker, rendering empathy less informative in predicting the overall attribution style., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t -test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Once associating another person with an unpleasant smell, how do we perceive and judge this person from that moment on? Here, we used aversive olfactory conditioning followed by a social attribution task during functional magnetic resonance imaging to address this question. After conditioning, where one of two faces was repeatedly paired with an aversive smell, the participants reported negative affect when viewing the smell-conditioned but not the neutral face. When subsequently confronted with the smell-conditioned face (without any smell), the participants tended to judge both positive and negative behaviors as indicative of personality traits rather than related to the situation. This effect was predicted by the degree of the preceding olfactory evaluative conditioning. Whole brain analysis of stimulus by stage interaction indicated differential activation of the ventromedial prefrontal cortex and right angular gyrus to the conditioned versus the neutral person during the attribution phase only. These results suggest that negative smell associations do not simply induce a negative perception of the target person but rather bias the attribution style towards trait attributions. The fact that this bias was evident regardless of behavior valence suggests it may reflect enhanced psychological distance. Thus, the known observation of social rejection triggered by aversive smell may be driven by a shift in social attribution style., (Copyright © 2017 Elsevier Inc. All rights reserved.)

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks ( Eedn ) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.

Background: Previous research suggests that antisocial individuals present impairment in social cognitive processing, more specifically in emotion recognition (ER) and perspective taking (PT). The first aim of the present study was to investigate the recognition of a wide range of emotional expressions and visual PT capacities in a group of incarcerated male adolescents in comparison to a matched group of community adolescents. Secondly, we sought to explore the relationship between these two mechanisms in relation to psychopathic traits., Methods: Forty-five male adolescents (22 incarcerated adolescents (Mage = 16.52, SD = 0.96) and 23 community adolescents (Mage = 16.43, SD = 1.41)) participated in the study. ER abilities were measured using a dynamic and multimodal task that requires the participants to watch short videos in which trained actors express 14 emotions. PT capacities were examined using a task recognized and proven to be sensitive to adolescent development, where participants had to follow the directions of another person whilst taking into consideration his perspective., Results: We found a main effect of group on emotion recognition scores. In comparison to the community adolescents, the incarcerated adolescents presented lower recognition of three emotions: interest, anxiety and amusement. Analyses also revealed significant impairments in PT capacities in incarcerated adolescents. In addition, incarcerated adolescents' PT scores were uniquely correlated to their scores on recognition of interest., Conclusions: The results corroborate previously reported impairments in ER and PT capacities, in the incarcerated adolescents. The study also indicates an association between impairments in the recognition of interest and impairments in PT., Competing Interests: The authors have declared that no competing interests exist.

Using a dot-probe discrimination task and a between-subjects design, we examined the time course of attentional biases (facilitated attention, delayed disengagement, and avoidance) toward thin versus fat bodies and explored the influence of body dissatisfaction (BD) on attention allocation among a sample of 163 women from the general population. Three stimulus presentation times were used: 100ms, 500ms, and 1500ms. We also used neutral body-shape-related stimuli as neutral stimuli related to the concept of interest to overcome the limitations of previous studies. At 500ms, the results highlighted delayed disengagement from very thin and negatively assessed bodies among women with high BD. This mechanism, which leads to attentional focalization on bodies that are difficult to achieve, might be considered dysfunctional because it may maintain or reinforce BD. Results at 100ms and 1500ms, as well as results for fat bodies, were not conclusive., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden deviations from the monotonicity of its evolution trigger a reset after which another expert subnetwork can be activated-if it has already been established before-or new categories can be recruited and associated with novel behavioral patterns.