A framework for a brain-derived nosology of psychiatric disorders.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Neural and cortisol responses to acute psychosocial stress in work-related burnout: The Regensburg Burnout Project.

BACKGROUND: While several attempts have been made to elucidate the pathophysiology of burnout, neural stress responses have not yet been investigated. Therefore, the aim of this study was to examine salivary cortisol and - for the first time - neural responses to acute psychosocial stress within a strictly specified sample consisting of individuals suffering from burnout (BO group) and a healthy comparison group (HC group). METHODS: After a multi-stage recruitment procedure based on burnout symptomatology and pathogenesis, 55 individuals suffering from burnout (25 women) and 61 individuals serving as HC group (31 women) out of an initial sample of 1022 volunteers were exposed to acute psychosocial stress during functional magnetic resonance imaging (fMRI) applying ScanSTRESS. RESULTS: No differences were found between the BO and the HC group with respect to cortisol and mean neural stress responses. However, an exploratory comparison of neural stress responses of the first and second run of ScanSTRESS (exposure-time effect) revealed group-specific response patterns in one cluster peaking in the left dorsal anterior cingulate cortex (dACC). While the neural activation of the HC group was higher in the first compared to the second run of ScanSTRESS (i.e., decreasing activation), this pattern was reversed in the BO group (i.e., increasing activation). CONCLUSIONS: Our analysis mainly did not provide evidence for altered acute cortisol and mean neural stress responses in burnout. However, the BO group was characterized by a limited capacity to show decreasing activation over stress exposure-time and exhibited instead increasing activation. Importantly, this group difference manifested in the left dACC which is both involved in neural stress processing and affected in individuals suffering from burnout. Given the present results, it seems promising to further examining temporal dynamics of neural stress responses in (sub-) clinical conditions such as burnout.

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data.

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

Self-control and interoception: Linking the neural substrates of craving regulation and the prediction of aversive interoceptive states induced by inspiratory breathing restriction.

Following the interoceptive inference framework, we set out to replicate our previously reported association of self-control and interoceptive prediction and strived to investigate the neural underpinnings subserving the relationship between self-control and aversive interoceptive predictive models. To this end, we used fMRI and a within-subject design including an inspiratory breathing-load task to examine the prediction of aversive interoceptive perturbation and a craving-regulation for palatable foods task to measure self-control. In this current study, we could successfully replicate previous effects with an independent sample (n â€‹= â€‹39) and observed that individuals who 'over-estimated' their upcoming interoceptive state with respect to experienced dyspnea (i.e., anticipated versus experienced) were more effective in the down-regulation of craving using negative future-thinking strategies. These individuals, again, obtained higher scores on a measure of trait self-control, i.e. self-regulation to achieve long-term goals. On a neural level, we found evidence that the anterior insula (AI) and the presupplementary motor area (preSMA), which were recruited in both tasks, partly accounted for these effects. Specifically, levels of AI activation during the anticipation of the aversive interoceptive state (breathing restriction) were associated with self-controlled behavior in the craving task, whereas levels of interoceptive prediction during the breathing task were conversely associated with activation in preSMA during the down-regulation of craving, whose anticipatory activity was correlated with self-control success. Moreover, during the self-control task, levels of interoceptive prediction were associated with connectivity in a spatially distributed network including among other areas the insula and regions of cognitive control, while during the interoceptive prediction task, levels of self-control were associated with connectivity in a spatially distributed network including among other regions the insula and preSMA. In sum, these findings consolidate the notion that self-control is directly linked to interoceptive inference and highlight the contribution of AI and preSMA as candidate regions underlying this relationship possibly creating processing advantages in self-control situations referring to the prediction of future internal states.

Empathy in pedophilia and sexual offending against children: A multifaceted approach.

Empathy is regarded as dynamic risk factor of child sexual offending. However, empathy research in the context of child sexual abuse suffers from various problems. First, prior studies failed to differentiate between pedophilic and nonpedophilic sexual offenders. Second, there is no distinction made between cognitive and affective empathy. Third, cognitive and affective empathy toward emotional states of specific age groups (children and adults) has not been adequately addressed. The current study tackles these shortcomings investigating offending and nonoffending pedophiles and multiple aspects of empathy using self-reports and objective behavioral measures. Participants included 85 pedophilic men who committed hands-on child sexual offenses (P+CSO), 72 pedophilic men who never committed hands-on child sexual offenses (P-CSO), and 128 nonoffending teleiophilic male controls (TC). Several affective and cognitive aspects of empathy were assessed using the Multifaceted Empathy Test (MET) and the Interpersonal Reactivity Index (IRI). Whereas in self-reports (IRI) P+CSO scored lower than TC (P-CSO intermediate) in cognitive perspective-taking abilities, a performance-based measure (MET) revealed evidence for a better differentiation of emotional states in P-CSO as compared with P+CSO (TC intermediate). In addition, P+CSO and P-CSO showed significantly higher affective resonance while observing children (MET), which was paralleled by higher self-reported levels of personal distress in social situations (IRI). The results indicate evidence for higher general affective empathic resonance to children in pedophilic men but superior cognitive empathy abilities in nonoffending pedophiles only, which may act as a protective factor in the prevention of sexual offending. Together, these findings underline the importance of accounting for multiple facets of empathy when targeting pedophilia and child sexual offending. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol.

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Regulating Craving by Anticipating Positive and Negative Outcomes: A Multivariate Pattern Analysis and Network Connectivity Approach.

During self-control, we may resist short-term temptations in order to reach a favorable future (e.g., resisting cake to stay healthy). The neural basis of self-control is typically attributed to "cold," unemotional cognitive control mechanisms which inhibit affect-related regions via the prefrontal cortex (PFC). Here, we investigate the neural underpinnings of regulating cravings by mentally evoking the positive consequences of resisting a temptation (e.g., being healthy) as opposed to evoking the negative consequences of giving in to a temptation (e.g., becoming overweight). It is conceivable that when using these types of strategies, regions associated with emotional processing [e.g., striatum, ventromedial prefrontal cortex (vmPFC)] are involved in addition to control-related prefrontal and parietal regions. Thirty-one participants saw pictures of unhealthy snacks in the fMRI scanner and, depending on the trial, regulated their craving by thinking of the positive consequences of resisting, or the negative consequences of not resisting. In a control condition, they anticipated the pleasure of eating and thus, allowed the craving to occur (now-condition). In line with previous studies, we found activation of a cognitive control network during self-regulation. In the negative future thinking condition, the insula was more active than in the positive condition, while there were no activations that were stronger in the positive (> negative) future thinking condition. However, additionally, multivariate pattern analysis showed that during craving regulation, information about the valence of anticipated emotions was present in the vmPFC, the posterior cingulate cortex (PCC) and the insula. Moreover, a network including vmPFC and PCC showed higher connectivity during the positive (> negative) future thinking condition. Since these regions are often associated with affective processing, these findings suggest that "hot," affective processes may, at least in certain circumstances, play a role in self-control.

Anticipating the good and the bad: A study on the neural correlates of bivalent emotion anticipation and their malleability via attentional deployment.

In everyday life, we often deliberate about affective outcomes of decisions which can be described as ambivalent; i.e. positive and negative at the same time. For example, when looking forward to meet a dear friend at her/his favorite concert although one dislikes the music that is being performed. Thus, anticipation of bivalent emotions and their volitional regulation is an important ingredient of everyday choices. However, previous studies investigating neural substrates involved in anticipating emotional events mostly focused on anticipating either negative emotions (punishment) or positive emotions (reward) in isolation, thus inducing either of them separately. Furthermore, these studies rather focused on the effortful down-regulation of affect (i.e. reducing negative or positive affect), whereas such conflict situations may also require us to deploy attention on and thereby upregulate anticipated emotions in order to resolve a decision conflict (e.g., by focusing on positive consequences while orienting away from negative consequences of that same situation). To address this gap, we performed a series of three fMRI-experiments using simple visual and auditory stimuli in order to (i) determine the neural correlates involved when anticipating a bivalent affective outcome that is both positive and negative at the same time - related to a conflict situation and (ii) investigate their malleability during anticipation via voluntary emotion regulation using attentional focusing. In these studies, we (i) demonstrate that brain areas involved in anticipating positive (ventral striatum) and negative (anterior insula) emotional events are co-activated when anticipating the occurrence of both punishment and reward at the same time and (ii) provide evidence that attention on either the positive or the negative correlates with a shift in activations of these co-activated neural networks and associated anticipated emotions towards either the positive (increased activity in ventral striatum, ventromedial prefrontal cortex, posterior cingulate cortex) or the negative (increased activity in insula) aspect of the upcoming bivalent outcome. In summary, we provide self-report and neural evidence for the assumption that affective brain systems associated with the processing of bivalent anticipated emotions can be voluntarily controlled by cognitive emotion regulation strategies.

Evaluating the replicability, specificity, and generalizability of connectome fingerprints.

Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested. In this replication study, we show both for the original and an extended HCP data set (n = 900 subjects), as well as for an additional data set of more commonly acquired imaging quality (n = 84) that (i) although the high accuracy can be replicated for the larger HCP 900 data set, accuracy is (ii) lower for standard neuroimaging data, and, that (iii) connectome fingerprinting may not be specific enough to distinguish between individuals. In addition, both accuracy and specificity are projected to drop considerably as the size of a data set increases. Although the moderate-to-high accuracies do suggest there is a portion of unique variance, our results suggest that connectomes may actually be quite similar across individuals. This outcome may be relevant to how precision psychiatry could benefit from inferences based on functional connectomes.

Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (PFWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (PFWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc.