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Formal thought disorders (FTD) are a hallmark diagnostic feature of schizophrenia (SZ) and (bipolar) mania (MA). FTD can be separated into positive (pFTD) and negative dimensions. It is unclear whether there are differences in pFTD on a single symptom level between acutely ill patients with SZ and MA, which cannot be attributed to cognitive impairment. We compared single pFTD symptoms in two groups of acutely ill patients with ICD-10 bipolar mania and schizophrenia, closely matched for age, sex, pFTD TALD score, verbal IQ and neuropsychological test performance (executive function, verbal fluency, attention, and working memory). SZ patients had higher severity of the TALD symptoms “perseverations” and “poverty of content of speech” than those with MA (Mann–Whitney U, significant, Bonferroni corrected). Speech in acute SZ patients differs from MA in that it conveys little information and adheres to previously mentioned ideas and topics. Matching for confounding variables, such as IQ and cognition, is important when comparing patients with different diagnoses.

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.

Self‐initiated movements are accompanied by an efference copy, a motor command sent from motor regions to the sensory cortices, containing a prediction of the movement's sensory outcome. Previous studies have proposed pre‐motor event‐related potentials (ERPs), including the readiness potential (RP) and its lateralized sub‐component (LRP), as potential neural markers of action feedback prediction. However, it is not known how specific these neural markers are for voluntary (active) movements as compared to involuntary (passive) movements, which produce much of the same sensory feedback (tactile, proprioceptive) but are not accompanied by an efference copy. The goal of the current study was to investigate how active and passive movements are distinguishable from premotor electroencephalography (EEG), and to examine if this change of neural activity differs when participants engage in tasks that differ in their expectation of sensory outcomes. Participants made active (self‐initiated) or passive (finger moved by device) finger movements that led to either visual or auditory stimuli (100 ms delay), or to no immediate contingency effects (control). We investigated the time window before the movement onset by measuring pre‐movement ERPs time‐locked to the button press. For RP, we observed an interaction between task and movement. This was driven by movement differences in the visual and auditory but not the control conditions. LRP conversely only showed a main effect of movement. We then used multivariate pattern analysis to decode movements (active vs. passive). The results revealed ramping decoding for all tasks from around −800 ms onwards up to an accuracy of approximately 85% at the movement. Importantly, similar to RP, we observed lower decoding accuracies for the control condition than the visual and auditory conditions, but only shortly (from −200 ms) before the button press. We also decoded visual vs. auditory conditions. Here, task is decodable for both active and passive conditions, but the active condition showed increased decoding shortly before the button press. Taken together, our results provide robust evidence that pre‐movement EEG activity may represent action‐feedback prediction in which information about the subsequent sensory outcome is encoded.

Gestures are an integral part of in-person conversations and complement the meaning of the speech they accompany. The neural processing of co-speech gestures is supported by a mostly left-lateralized network of fronto-temporal regions. However, in contrast to iconic gestures, metaphoric as well as unrelated gestures have been found to more strongly engage the left and right inferior frontal gyrus (IFG), respectively. With this study, we conducted the first systematic comparison of all three types of gestures and resulting potential laterality effects. During collection of functional imaging data, 74 subjects were presented with 5 s videos of abstract speech with related metaphoric gestures, concrete speech with related iconic gestures and concrete speech with unrelated gestures. They were asked to judge whether the content of the speech and gesture matched or not. Differential contrasts revealed that both abstract related and concrete unrelated compared to concrete related stimuli elicited stronger activation of the bilateral IFG. Analyses of lateralization indices for IFG activation further showed a left hemispheric dominance for metaphoric gestures and a right hemispheric dominance for unrelated gestures. Our results give support to the hypothesis that the bilateral IFG is activated specifically when processing load for speech-gesture combinations is high. In addition, laterality effects indicate a stronger involvement of the right IFG in mismatch detection and conflict processing, whereas the left IFG performs the actual integration of information from speech and gesture.

Video games are an exciting part of new media. Although game play has been intensively studied, the underlying neurobiology is still poorly understood. Flow theory is a well-established model developed to describe subjective game experience. In 13 healthy male subjects, we acquired fMRI data during free play of a video game and analyzed brain activity based on the game content. In accordance with flow theory, we extracted the following factors from the game content: (i) balance between ability and challenge; (ii) concentration and focus; (iii) direct feedback of action results; (iv) clear goals; and (v) control over the situation/activity. We suggest that flow is characterized by specific neural activation patterns and that the latter can be assessed—at least partially—by content factors contributing to the emergence of flow. Each of the content factors was characterized by specific and distinguishable brain activation patterns, encompassing reward-related midbrain structures, as well as cognitive and sensorimotor networks. The activation of sensory and motor networks in the conjunction analyses underpinned the central role of simulation for flow experience. Flow factors can be validated with functional brain imaging which can improve the understanding of human emotions and motivational processes during media entertainment.

Large, longitudinal, multi-center MR neuroimaging studies require comprehensive quality assurance (QA) protocols for assessing the general quality of the compiled data, indicating potential malfunctions in the scanning equipment, and evaluating inter-site differences that need to be accounted for in subsequent analyses. We describe the implementation of a QA protocol for functional magnet resonance imaging (fMRI) data based on the regular measurement of an MRI phantom and an extensive variety of currently published QA statistics. The protocol is implemented in the MACS (Marburg-Münster Affective Disorders Cohort Study, http://for2107.de/), a two-center research consortium studying the neurobiological foundations of affective disorders. Between February 2015 and October 2016, 1214 phantom measurements have been acquired using a standard fMRI protocol. Using 444 healthy control subjects which have been measured between 2014 and 2016 in the cohort, we investigate the extent of between-site differences in contrast to the dependence on subject-specific covariates (age and sex) for structural MRI, fMRI, and diffusion tensor imaging (DTI) data. We show that most of the presented QA statistics differ severely not only between the two scanners used for the cohort but also between experimental settings (e.g. hardware and software changes), demonstrate that some of these statistics depend on external variables (e.g. time of day, temperature), highlight their strong dependence on proper handling of the MRI phantom, and show how the use of a phantom holder may balance this dependence. Site effects, however, do not only exist for the phantom data, but also for human MRI data. Using T1-weighted structural images, we show that total intracranial (TIV), grey matter (GMV), and white matter (WMV) volumes significantly differ between the MR scanners, showing large effect sizes. Voxel-based morphometry (VBM) analyses show that these structural differences observed between scanners are most pronounced in the bilateral basal ganglia, thalamus, and posterior regions. Using DTI data, we also show that fractional anisotropy (FA) differs between sites in almost all regions assessed. When pooling data from multiple centers, our data show that it is a necessity to account not only for inter-site differences but also for hardware and software changes of the scanning equipment. Also, the strong dependence of the QA statistics on the reliable placement of the MRI phantom shows that the use of a phantom holder is recommended to reduce the variance of the QA statistics and thus to increase the probability of detecting potential scanner malfunctions. •Quality assurance (QA) protocol for large, longitudinal, multi-center MR neuroimaging studies.•Dependence of QA statistics on MR-scanner type, hardware and software changes and external variables (e.g., time of day, temperature).•Consequences of phantom data variations for human MRI data.•Dependence of MR phantom placement on QA statistics.

Major depressive disorder is a serious psychiatric illness with a highly variable and heterogeneous clinical course. Due to the lack of consistent data from previous studies, the study of morphometric changes in major depressive disorder is still a major point of research requiring additional studies. The aim of the study presented here was to characterize and quantify regional gray matter abnormalities in a large sample of clinically well-characterized patients with major depressive disorder. For this study one-hundred thirty two patients with major depressive disorder and 132 age- and gender-matched healthy control participants were included, 35 with their first episode and 97 with recurrent depression. To analyse gray matter abnormalities, voxel-based morphometry (VBM8) was employed on T1 weighted MRI data. We performed whole-brain analyses as well as a region-of-interest approach on the hippocampal formation, anterior cingulate cortex and amygdala, correlating the number of depressive episodes. Compared to healthy control persons, patients showed a strong gray-matter reduction in the right anterior insula. In addition, region-of-interest analyses revealed significant gray-matter reductions in the hippocampal formation. The observed alterations were more severe in patients with recurrent depressive episodes than in patients with a first episode. The number of depressive episodes was negatively correlated with gray-matter volume in the right hippocampus and right amygdala. The anterior insula gray matter structure appears to be strongly affected in major depressive disorder and might play an important role in the neurobiology of depression. The hippocampal and amygdala volume loss cumulating with the number of episodes might be explained either by repeated neurotoxic stress or alternatively by higher relapse rates in patients showing hippocampal atrophy.

Self-concept is deeply affected in schizophrenia. Positive symptoms in particular are related to disturbed self/other distinctions. The neural networks underlying self-evaluation in schizophrenia have barely been investigated. The study reported here involved 13 patients with schizophrenia and 13 matched controls. During functional MRI, participants decided in three conditions whether the presented positive and negative personality traits characterized themselves, an intimate person, or included a certain letter. Based on the responses, each experimental condition was designed using a flexible factorial model. Controls and patients showed a similar behavioral pattern during self-evaluation, with group comparison revealing decreased activation in patients in the left inferior temporal gyrus and both temporal poles during self-ascription of traits, and in the anterior medial prefrontal cortex during evaluation of an intimate person. In patients, positive symptoms correlated positively with brain activation in the left parahippocampus during trait self-ascription. Hence, while evaluating themselves, schizophrenia patients revealed decreased activation in areas related to self-awareness overlapping with networks involved in theory of mind, empathy and social knowledge. Moreover, patients’ brain activation during self-reflection was affected by the current positive symptomatology. The close interaction between self and other highlights the clinical and social relevance of self-processing deficits in schizophrenia.

Empathic behavior and related neural processing are strongly modified by group membership. Shared neural circuits for the production and perception of facial emotional expressions represent mirror neuron mechanisms which play a pivotal role for empathy. In this study, we investigate the influence of group membership on mirror neuron mechanisms for emotional facial expressions. In a functional magnetic resonance imaging task, 178 healthy subjects perceived emotional and neutral facial expressions of artificial ingroup and outgroup members, displayed as 5 s video clips, and produced these facial expressions themselves. Before scanning, artificial group membership was manipulated ad-hoc through a minimal group paradigm. Shared neural activity for emotional facial expression production and perception was revealed in a large network with right-hemispheric preponderance encompassing motor mirror neuron regions, i.e., inferior frontal gyrus, supplementary motor area and middle temporal gyrus, in addition to limbic regions, i.e., amygdala, hippocampus, para-hippocampus, and insula. Within this network there was greater neural activation for ingroup compared to outgroup members in temporal poles, amygdalae, the left insula, the left inferior frontal gyrus, and the inferior and middle temporal gyrus, the right hippocampus and parahippocampus. We validate and extend knowledge on brain regions with mirror neuron properties. Most crucially, we provide evidence for the influence of group membership on regions within the mirror neuron system, indicating more neural resonance (mirroring) for ingroup facial emotional expressions. •There is enhanced neural resonance (mirroring) for ingroup facial emotions.•Such ingroup bias affects amygdala, temporal pole, and hippocampus activation.•Ad-hoc created minimal group membership sufficiently induces this ingroup bias.•Knowledge about brain regions with mirror neuron properties is extended.

Fear conditioning and extinction are basic forms of associative learning that have gained considerable clinical relevance in enhancing our understanding of anxiety disorders and facilitating their treatment. Modern neuroimaging techniques have significantly aided the identification of anatomical structures and networks involved in fear conditioning. On closer inspection, there is considerable variation in methodology and results between studies. This systematic review provides an overview of the current neuroimaging literature on fear conditioning and extinction on healthy subjects, taking into account methodological issues such as the conditioning paradigm. A Pubmed search, as of December 2008, was performed and supplemented by manual searches of bibliographies of key articles. Two independent reviewers made the final study selection and data extraction. A total of 46 studies on cued fear conditioning and/or extinction on healthy volunteers using positron emission tomography or functional magnetic resonance imaging were reviewed. The influence of specific experimental factors, such as contingency and timing parameters, assessment of conditioned responses, and characteristics of conditioned and unconditioned stimuli, on cerebral activation patterns was examined. Results were summarized descriptively. A network consisting of fear-related brain areas, such as amygdala, insula, and anterior cingulate cortex, is activated independently of design parameters. However, some neuroimaging studies do not report these findings in the presence of methodological heterogeneities. Furthermore, other brain areas are differentially activated, depending on specific design parameters. These include stronger hippocampal activation in trace conditioning and tactile stimulation. Furthermore, tactile unconditioned stimuli enhance activation of pain related, motor, and somatosensory areas. Differences concerning experimental factors may partly explain the variance between neuroimaging investigations on human fear conditioning and extinction and should, therefore, be taken into serious consideration in the planning and the interpretation of research projects.