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Current knowledge regarding how the focus of our attention during face processing influences neural responses largely comes from neuroimaging studies reporting on regional brain activations. The present study was designed to add novel insights to this research by studying how attention can differentially impact the way cortical regions interact during emotional face processing. High-density electroencephalogram was recorded in a sample of fifty-two healthy participants during an emotional face processing task. The task required participants to either attend to the expressions ( i.e . , overt processing) or attend to a perceptual distractor, which rendered the expressions task-irrelevant ( i.e. , covert processing). Functional connectivity in the alpha band was estimated in source space and modeled using graph theory to quantify whole-brain integration and segregation. Results revealed that overt processing of facial expressions is linked to reduced cortical segregation and increased cortical integration, this latter specifically for negative expressions of fear and sadness. Furthermore, we observed increased communication efficiency during overt processing of negative expressions between the core and the extended face processing systems. Overall, these findings reveal that attention makes the interaction among the nodes involved in face processing more efficient, also uncovering a connectivity signature of the prioritized processing mechanism of negative expressions, that is an increased cross-communication within the nodes of the face processing network.

Behavioral and electrophysiological correlates of the influence of task demands on the processing of happy, sad, and fearful expressions were investigated in a within-subjects study that compared a perceptual distraction condition with task-irrelevant faces (e.g., covert emotion task) to an emotion task-relevant categorization condition (e.g., overt emotion task). A state-of-the-art non-parametric mass univariate analysis method was used to address the limitations of previous studies. Behaviorally, participants responded faster to overtly categorized happy faces and were slower and less accurate to categorize sad and fearful faces; there were no behavioral differences in the covert task. Event-related potential (ERP) responses to the emotional expressions included the N170 (140–180 ms), which was enhanced by emotion irrespective of task, with happy and sad expressions eliciting greater amplitudes than neutral expressions. EPN (200–400 ms) amplitude was modulated by task, with greater voltages in the overt condition, and by emotion, however, there was no interaction of emotion and task. ERP activity was modulated by emotion as a function of task only at a late processing stage, which included the LPP (500–800 ms), with fearful and sad faces showing greater amplitude enhancements than happy faces. This study reveals that affective content does not necessarily require attention in the early stages of face processing, supporting recent evidence that the core and extended parts of the face processing system act in parallel, rather than serially. The role of voluntary attention starts at an intermediate stage, and fully modulates the response to emotional content in the final stage of processing.

Pediatric obsessive–compulsive disorder (OCD) has been associated with poorer planning in laboratory, school and home settings. It is unclear whether this impairment is a standalone cognitive issue or the result of OCD symptoms. No study has examined the influence of provoked distress on planning performance and neural correlates in pediatric OCD. Before and after a symptom provocation task, youth with OCD (n = 23; 9 boys; mean age ± standard deviation 15.1±2.6 years) and matched healthy controls (n = 23) completed the Tower of London task during functional MRI scanning. During planning, participants with OCD recruited the left superior frontal gyrus to a greater extent than healthy controls after symptom provocation (group × time point interaction; t44 = 5.22, p < 0.001). In a seeded, region of interest–constrained, functional connectivity analysis, we identified greater connectivity between the left superior frontal gyrus and the right middle frontal gyrus, left precuneus and left inferior parietal lobule in participants with OCD than healthy controls. We also identified greater connectivity between the right amygdala and right medial frontal gyrus in patients with OCD than healthy controls, but only before symptom provocation. The fixed-order design of the study and the number of participants taking medication (n = 20) should be noted. Participants with OCD demonstrated greater amygdalar–cortical connectivity before symptom provocation, while sustaining greater recruitment and connectivity of task-related planning areas throughout the task. These results suggest that brain activity and connectivity is altered after symptom provocation, in the absence of impaired planning performance.

Event-related potential (ERP) studies in the visual domain often report an emotion-evoked early posterior negativity (EPN). Studies in the auditory domain have recently shown a similar component. Little source localization has been done on the visual EPN, and no source localization has been done on the auditory EPN. The aim of the current study was to identify the neural generators of the auditory EPN using EEG–fMRI single-trial coupling. Data were recorded from 19 subjects who completed three auditory choice reaction tasks: (1) a control task using neutral tones; (2) a prosodic emotion task involving the categorization of syllables; and (3) a semantic emotion task involving the categorization of words. The waveforms of the emotion tasks diverged from the neutral task over parietal scalp during a very early time window (132–156ms) and later during a more traditional EPN time window (252–392ms). In the EEG–fMRI analyses, the variance of the voltage in the earlier time window was correlated with activity in the medial prefrontal cortex, but only in the word task. In the EEG–fMRI analyses of the traditional EPN time window both emotional tasks covaried with activity in the left superior parietal lobule. Our results support previous parietal cortex source localization findings for the visual EPN, and suggest enhanced selective attention to emotional stimuli during the EPN time window.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d : -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d : 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Background: Pediatric obsessive-compulsive disorder (OCD) is a common, debilitating illness. When childhood OCD symptom onset is described as acute and severe, diagnostic criteria for pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) and pediatric acute-onset neuropsychiatric syndrome (PANS) should be considered. However, the frequency and differentiating features of these putative syndromes within pediatric OCD remain poorly understood. Objectives: To determine the prevalence and characteristics of those meeting PANDAS and/or PANS criteria within pediatric OCD, as determined by parent report and clinician interview. Methods: Consecutive youth presenting to a subspecialty pediatric OCD clinic were rigorously assessed through the Anxiety Disorders Interview Schedule for DSM-IV, the Children's Yale–Brown Obsessive-Compulsive Scale, and through self- and parent-report measures, including a medical questionnaire. Strict diagnostic criteria for PANDAS and PANS were applied to determine prevalence rates, and comparative analyses were performed between subgroups. Results: Among 136 youth with a lifetime OCD diagnosis, 5% (n = 7; 95% adjusted Wald interval: 1%–10%) met proposed criteria for PANDAS and/or PANS, of whom two met PANDAS criteria, four met PANS criteria, and one met criteria for both. Those in the PANDAS/PANS subgroup were more likely to have autoimmune illness, less likely to report symmetry factor symptoms, and had greater OCD-related family impairment during their worst OCD episode. Conclusion: A small yet significant percentage of pediatric OCD outpatients met criteria for PANDAS and/or PANS, justifying routine screening and attention to related characteristics during assessment and management. Longitudinal studies of these putative subtypes are warranted.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T 1 -weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (<12 years) had larger lateral ( d  = 0.46), pulvinar ( d  = 0.33 ) , ventral ( d  = 0.35) and whole thalamus ( d  = 0.40) volumes at unadjusted p -values <0.05. Adolescent patients showed no volumetric differences. Adult OCD patients compared with controls had smaller volumes across all subregions (anterior, lateral, pulvinar, medial, and ventral) and smaller whole thalamic volume ( d  =  − 0.15 to −0.07) after multiple comparisons correction, mostly driven by medicated patients and associated with symptom severity. The anterior thalamus was also significantly smaller in patients after adjusting for thalamus size. Our results suggest that OCD-related thalamic volume differences are global and not driven by particular subregions and that the direction of effects are driven by both age and medication status.