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In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents, and planning communication strategy with all study team members. Section 2 focuses on what to do once data collection has already begun. Topics covered include: (1) how to effectively monitor and maintain EEG data quality, (2) how to ensure consistent implementation of experimental protocols, and (3) how to develop rigorous preprocessing procedures that are feasible for use in a large-scale study. Links to resources are also provided, including sample protocols, sample equipment and software tracking forms, sample code, and tutorial videos (to access resources, please visit: https://osf.io/wdrj3/).

Cognitive control deficits are associated with impaired executive functioning in schizophrenia. The Dual Mechanisms of Control framework suggests that proactive control requires sustained dorsolateral prefrontal activity, whereas reactive control marshals a larger network. However, primate studies suggest these processes are maintained by dual-encoding regions. To distinguish between these theories, we compared the distinctiveness of proactive and reactive control functional neuroanatomy. In a reanalysis of data from a previous study, 47 adults with schizophrenia and 56 controls completed the Dot Pattern Expectancy task during an fMRI scan examining proactive and reactive control in frontoparietal and medial temporal regions. Areas suggesting specialized control or between-group differences were tested for association with symptoms and task performance. Elastic net models additionally explored these areas' predictive abilities regarding performance. Most regions were active in both reactive and proactive control. However, evidence of specialized proactive control was found in the left middle and superior frontal gyri. Control participants showed greater proactive control in the left middle and right inferior frontal gyri. Elastic net models moderately predicted task performance and implicated various frontal gyri regions in control participants, with additional involvement of anterior cingulate and posterior parietal regions for reactive control. Elastic nets for patient participants implicated the inferior and superior frontal gyri, and posterior parietal lobe. Specialized cognitive control was unassociated with either performance or schizophrenia symptomatology. Future work is needed to clarify the distinctiveness of proactive and reactive control, and its role in executive deficits in severe psychopathology.

BackgroundPsychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error-driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex.MethodsWe used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72).ResultsUsing accuracy, there was a main effect of group (F3,279 = 7.87, p < .001), such that all patient groups were less accurate than control subjects. Using computationally derived parameters, both medicated and unmediated individuals with schizophrenia, but not patients with bipolar disorder, demonstrated a reduced mixing parameter (F3,295 = 13.91, p < .001), indicating less dependence on learning explicit value representations as well as greater learning decay between training and test (F1,289 = 12.81, p < .001). Unmedicated patients with schizophrenia also showed greater decision noise (F3,295 = 2.67, p = .04).ConclusionsBoth medicated and unmedicated patients showed overreliance on prediction error-driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.

BackgroundSchizophrenia is a disorder characterized by pervasive deficits in cognitive functioning. However, few well-powered studies have examined the degree to which cognitive performance is impaired even among individuals with schizophrenia not currently on antipsychotic medications using a wide range of cognitive and reinforcement learning measures derived from cognitive neuroscience. Such research is particularly needed in the domain of reinforcement learning, given the central role of dopamine in reinforcement learning, and the potential impact of antipsychotic medications on dopamine function.MethodsThe present study sought to fill this gap by examining healthy controls (N = 75), unmedicated (N = 48) and medicated (N = 148) individuals with schizophrenia. Participants were recruited across five sites as part of the CNTRaCS Consortium to complete tasks assessing processing speed, cognitive control, working memory, verbal learning, relational encoding and retrieval, visual integration and reinforcement learning.ResultsIndividuals with schizophrenia who were not taking antipsychotic medications, as well as those taking antipsychotic medications, showed pervasive deficits across cognitive domains including reinforcement learning, processing speed, cognitive control, working memory, verbal learning and relational encoding and retrieval. Further, we found that chlorpromazine equivalency rates were significantly related to processing speed and working memory, while there were no significant relationships between anticholinergic load and performance on other tasks.ConclusionsThese findings add to a body of literature suggesting that cognitive deficits are an enduring aspect of schizophrenia, present in those off antipsychotic medications as well as those taking antipsychotic medications.

Background Research suggests that effort-cost decision-making (ECDM), the estimation of work required to obtain reward, may be a relevant framework for understanding motivational impairment in psychotic and mood pathology. Specifically, research has suggested that people with psychotic and mood pathology experience effort as more costly than controls, and thus pursue effortful goals less frequently. This study examined ECDM across psychotic and mood pathology. Hypothesis We hypothesized that patient groups would show reduced willingness to expend effort compared to controls. Study Design People with schizophrenia (N  = 33), schizoaffective disorder (N  = 28), bipolar disorder (N  = 39), major depressive disorder (N  = 40), and controls (N  = 70) completed a physical ECDM task. Participants decided between completing a low-effort or high-effort option for small or larger rewards, respectively. Reward magnitude, reward probability, and effort magnitude varied trial-by-trial. Data were analyzed using standard and hierarchical logistic regression analyses to assess the subject-specific contribution of various factors to choice. Negative symptoms were measured with a clinician-rated interview. Study Results There was a significant effect of group, driven by reduced choice of high-effort options in schizophrenia. Hierarchical logistic regression revealed that reduced choice of high-effort options in schizophrenia was driven by weaker contributions of probability information. Use of reward information was inversely associated with motivational impairment in schizophrenia. Surprisingly, individuals with major depressive disorder and bipolar disorder did not differ from controls. Conclusions Our results provide support for ECDM deficits in schizophrenia. Additionally, differences between groups in ECDM suggest a seemingly similar behavioral phenotype, reduced motivation, could arise from disparate mechanisms. [ABSTRACT FROM AUTHOR]

Perceptions of spiteful behavior are common, distinct from rational fear, and may undergird persecutory ideation. To test this hypothesis and investigate neural mechanisms of persecutory ideation, we employed a novel economic social decision‐making task, the Minnesota Trust Game (MTG), during neuroimaging in patients with schizophrenia (n = 30) and community monozygotic (MZ) twins (n = 38; 19 pairs). We examined distinct forms of mistrust, task‐related brain activation and connectivity, and investigated relationships with persecutory ideation. We tested whether co‐twin discordance on these measurements was correlated to reflect a common source of underlying variance. Across samples persecutory ideation was associated with reduced trust only during the suspiciousness condition, which assessed spite sensitivity given partners had no monetary incentive to betray. Task‐based activation contrasts for specific forms of mistrust were limited and unrelated to persecutory ideation. However, task‐based connectivity contrasts revealed a dorsal cingulate anterior insula network sensitive to suspicious mistrust, a left frontal–parietal (lF‐P) network sensitive to rational mistrust, and a ventral medial/orbital prefrontal (vmPFC/OFC) network that was sensitive to the difference between these forms of mistrust (all p <.005). Higher persecutory ideation was predicted only by reduced connectivity between the vmPFC/OFC and lF‐P networks (p =.005), which was only observed when the intentions of the other player were relevant. Moreover, co‐twin differences in persecutory ideation predicted co‐twin differences in both spite sensitivity and in vmPFC/OFC–lF‐P connectivity. This work found that interconnectivity may be particularly important to the complex neurobiology underlying persecutory ideation, and that unique environmental variance causally linked persecutory ideation, decision‐making, and brain connectivity. [ABSTRACT FROM AUTHOR]

Background: Psychotic-like experiences (PLEs) are considered the subclinical portion of the psychosis continuum. Research suggests that there are resting-state functional connectivity (rsFC) substrates of PLEs, yet it is unclear if the same substrates underlie more severe psychosis. Here, to our knowledge, we report the first study to build a cross-validated rsFC model of PLEs in a large community sample and directly test its ability to explain psychosis in an independent sample of patients with psychosis and their relatives., Methods: Resting-state FC of 855 healthy young adults from the WU-Minn Human Connectome Project (HCP) was used to predict PLEs with elastic net. An rsFC composite score based on the resulting model was correlated with psychotic traits and symptoms in 118 patients with psychosis, 71 nonpsychotic first-degree relatives, and 45 healthy control subjects from the psychosis HCP., Results: In the HCP, the cross-validated model explained 3.3% of variance in PLEs. Predictive connections spread primarily across the default, frontoparietal, cingulo-opercular, and dorsal attention networks. The model partially generalized to a younger, but not older, subsample in the psychosis HCP, explaining two measures of positive/disorganized psychotic traits (the Structured Interview for Schizotypy: β = 0.25, p one-tailed  = .027; the Schizotypy Personality Questionnaire positive factor: β = 0.14, p one-tailed  = .041). However, it did not differentiate patients from relatives and control subjects or explain psychotic symptoms in patients., Conclusions: Some rsFC substrates of PLEs are shared across the psychosis continuum. However, explanatory power was modest, and generalization was partial. It is equally important to understand shared versus distinct rsFC variances across the psychosis continuum., (© 2022 The Authors.)

Background: Generalization of conditioned-fear, a core feature of post-traumatic stress disorder (PTSD), has been the focus of several recent neuroimaging studies. A striking outcome of these studies is the frequency with which neural correlates of generalization fall within hubs of well-established functional networks including salience (SN), central executive (CEN), and default networks (DN). Neural substrates of generalization found to date may thus reflect traces of large-scale brain networks that form more expansive neural representations of generalization. The present study includes the first network-based analysis of generalization and PTSD-related abnormalities therein., Methods: fMRI responses in established intrinsic connectivity networks (ICNs) representing SN, CEN, and DN were assessed during a generalized conditioned-fear task in male combat veterans (N = 58) with wide-ranging PTSD symptom severity. The task included five rings of graded size. Extreme sizes served as conditioned danger-cues (CS+: paired with shock) and safety-cues (CS-), and the three intermediate sizes served as generalization stimuli (GSs) forming a continuum-of-size between CS+ and CS-. Generalization-gradients were assessed as behavioral and ICN response slopes from CS+, through GSs, to CS-. Increasing PTSD symptomatology was predicted to relate to less-steep slopes indicative of stronger generalization., Results: SN, CEN, and DN responses fell along generalization-gradients with levels of generalization within and between SN and CEN scaling with PTSD symptom severity., Conclusions: Neural substrates of generalized conditioned-fear include large-scale networks that adhere to the functional organization of the brain. Current findings implicate levels of generalization in SN and CEN as promising neural markers of PTSD.