Disgust is described as a relevant emotion in OCD, particularly in contamination-type OCD, and may be involved in emotional processing in this OCD-subtype. The present study aimed to investigate the ...neural correlates of distress processing in contamination-type compared to non-contamination-type OCD, and the relation to disgust sensitivity.
Forty-three OCD patients (n = 19 contamination-type OCD) were exposed to OCD-related, fear-related and neutral pictures. Subjective distress per stimulus was assessed by a visual analogue scale (VAS) and disgust sensitivity by the DS-R. BOLD brain activation was compared between stimuli that provoked high versus low distress at individual level.
In contamination- and non-contamination-type OCD, the dorsomedial prefrontal cortex, operculum, visual association cortex and caudate nucleus were activated during high versus low distress. Only in contamination-type OCD, disgust sensitivity correlated positively with the VAS scores and was associated with neural activation in the dorsomedial and visual association cortex, but not with the operculum.
Brain activation during distress processing in OCD is similar across the OCD subtypes and related to effortful emotion regulation, processing of aversive internal states and attention. In contamination-type OCD, the distress response is related to disgust sensitivity, which correlates with brain regions associated with attention and emotion regulation.
•Neural distress processing is similar across the OCD subtypes.•Disgust sensitivity is higher in contamination-type OCD than in other OCD-subtypes.•Disgust sensitivity is related to distress response only in contamination-type OCD.
Background: Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, ...structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. Methods: We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. Results: Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. Limitations: This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. Conclusion: Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders.
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on ...simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.
•We propose a new approach to calibration recommendations for process-based models.•Approach is based on analyzing calibration in multi-model simulation exercises.•Resulting recommendations are holistic and anchored in actual practice.•We derive calibration recommendations for crop models used to simulate phenology.•Recommendations concern: objective function, parameters to estimate, software used.
•A large multi-model study predicting wheat phenology in Australia was performed.•Calibration and evaluation datasets were independently drawn from the same population.•Mean absolute prediction error ...ranged from 6 to 20 days (median 9 days).•Two thirds of modeling groups predicted better than a simple temperature sum.•Variability between groups using the same model structure was substantial.
Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
•27 modeling groups were evaluated for wheat phenology predictions for France.•Calibration and evaluation data were sampled from the same target population.•The calibration and evaluation data have ...neither year nor site in common.•The best groups had a mean absolute error comparable to the measurement error.•Model structure alone does not determine prediction accuracy.
Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.
Objectives: Impaired response inhibition is related to neurodevelopmental disorders, such as Tourette's disorder (TD) and obsessive-compulsive disorder (OCD). Unlike OCD, in which neural correlates ...of response inhibition have been extensively studied, TD literature is limited. By using a Stop-Signal task, we investigated the neural mechanisms underlying response inhibition deficits in TD compared to OCD and healthy controls (HCs).
Methods: Twenty-three TD patients, 20 OCD patients and 22 HCs were scanned (3T MRI). Region-of-interest analyses were performed between TD, OCD and HCs.
Results: Performance was similar across all subject groups. During inhibition TD compared with HCs showed higher right inferior parietal cortex (IPC) activation. During error processing TD compared with HCs showed hyperactivity in the left cerebellum, right mesencephalon, and right insula. Three-group comparison showed an effect of group for error-related activation in the supplementary motor area (SMA). Post-hoc analyses showed higher error-related SMA activity in TD compared with OCD and HCs. Error-related left cerebellar activity correlated positively with tic severity.
Conclusions: Hyperactivation of IPC during inhibition and a widespread hyperactivated network during error processing in TD suggest compensatory inhibition- and error-related circuit recruitment to boost task performance. The lack of overlap with activation pattern in OCD suggests such compensatory mechanism is TD-specific.
Abstract In this paper, we tentatively bring together the psychiatric, neurological and addiction perspectives on the impulsive–compulsive spectrum of neuropsychiatric disorders, in order to ...understand the pathophysiology of impulse control disorders (ICDs) in Parkinson's disease. In an attempt to try to pool the various levels of information we will therefore focus on three disorders within the impulse-compulsive spectrum, i.e., obsessive–compulsive disorder (OCD), ICDs in Parkinson's disease, and cocaine seeking behaviour. Whereas there are large differences between these three domains, each with their own nomenclature, hypotheses and study results, they share the focus on an imbalance within and between the frontal–striatal circuits as underlying substrate for the behaviours. For each disorder, we summarize the results from recent studies in order to describe in which way alterations in the frontal–striatal circuits contribute to the phenotype. The phenomenological overlap between ICDs in Parkinson's disease, addiction and OCD needs further investigation, since better understanding of the overlapping and differentiating characteristics will contribute to our understanding of the pathophysiology of the disturbances and treatment alternatives.
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