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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.

BackgroundMotivational deficits in people with psychosis may be a result of impairments in reinforcement learning (RL). Therefore, behavioral paradigms that can accurately measure these impairments and their change over time are essential.MethodsWe examined the reliability and replicability of 2 RL paradigms (1 implicit and 1 explicit, each with positive and negative reinforcement components) given at 2 time points to healthy controls (n = 75), and people with bipolar disorder (n = 62), schizoaffective disorder (n = 60), and schizophrenia (n = 68).ResultsInternal consistency was acceptable (mean α = 0.78 ± 0.15), but test-retest reliability was fair to low (mean intraclass correlation = 0.33 ± 0.25) for both implicit and explicit RL. There were no clear effects of practice for these tasks. Largely, performance on these tasks shows intact implicit and impaired explicit RL in psychosis. Symptom presentation did not relate to performance in any robust way.ConclusionsOur findings replicate previous literature showing spared implicit RL and impaired explicit reinforcement in psychosis. This suggests typical basal ganglia dopamine release, but atypical recruitment of the orbitofrontal and dorsolateral prefrontal cortices. However, we found that these tasks have only fair to low test-retest reliability and thus may not be useful for assessing change over time in clinical trials.

COVID-19 presents significant social, economic, and medical challenges. Because COVID-19 has already begun to precipitate huge increases in mental health problems, clinical psychological science must assert a leadership role in guiding a national response to this secondary crisis. In this article, COVID-19 is conceptualized as a unique, compounding, multidimensional stressor that will create a vast need for intervention and necessitate new paradigms for mental health service delivery and training. Urgent challenge areas across developmental periods are discussed, followed by a review of psychological symptoms that likely will increase in prevalence and require innovative solutions in both science and practice. Implications for new research directions, clinical approaches, and policy issues are discussed to highlight the opportunities for clinical psychological science to emerge as an updated, contemporary field capable of addressing the burden of mental illness and distress in the wake of COVID-19 and beyond. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Higher cognitive functioning is supported by adaptive reconfiguration of large-scale functional brain networks. Cognitive control (CC), which plays a vital role in flexibly guiding cognition and behavior in accordance with our goals, supports a range of executive functions via distributed brain networks. These networks process information dynamically and can be represented as functional connectivity changes between network elements. Using graph theory, we explored context-dependent network reorganization in 56 healthy adults performing fMRI tasks from two cognitive domains that varied in CC and episodic-memory demands. We examined whole-brain modular structure during the DPX task, which engages proactive CC in the frontal-parietal cognitive-control network (FPN), and the RiSE task, which manipulates CC demands at encoding and retrieval during episodic-memory processing, and engages FPN, the medial-temporal lobe and other memory-related networks in a context dependent manner. Analyses revealed different levels of network integration and segregation. Modularity analyses revealed greater brain-wide integration across tasks in high CC conditions compared to low CC conditions. Greater network reorganization occurred in the RiSE memory task, which is thought to require coordination across multiple brain networks, than in the DPX cognitive-control task. Finally, FPN, ventral attention, and visual systems showed within network connectivity effects of cognitive control; however, these cognitive systems displayed varying levels of network reorganization. These findings provide insight into how brain networks reorganize to support differing task contexts, suggesting that the FPN flexibly segregates during focused proactive control and integrates to support control in other domains such as episodic memory.

Although meta-analyses suggest that schizophrenia (SZ) is associated with a more severe neurocognitive phenotype than mood disorders such as bipolar disorder, considerable between-subject heterogeneity exists in the phenotypic presentation of these deficits across mental illnesses. Indeed, it is unclear whether the processes that underlie cognitive dysfunction in these disorders are unique to each disease or represent a common neurobiological process that varies in severity. Here we used latent profile analysis (LPA) across 3 distinct cognitive domains (cognitive control, episodic memory, and visual integration; using data from the CNTRACS consortium) to identify distinct profiles of patients across psychotic illnesses. LPA was performed on a sample of 223 psychosis patients (59 with Type I bipolar disorder, 88 with SZ, and 76 with schizoaffective disorder). Seventy-three healthy control participants were included for comparison but were not included in sample LPA. Three latent profiles ("Low," "Moderate," and "High" ability) were identified as the underlying covariance across the 3 domains. The 3-profile solution provided highly similar fit to a single continuous factor extracted by confirmatory factor analysis, supporting a unidimensional structure. Diagnostic ratios did not significantly differ between profiles, suggesting that these profiles cross diagnostic boundaries (an exception being the Low ability profile, which had only one bipolar patient). Profile membership predicted Brief Psychiatric Rating Scale and Young Mania Rating Scale symptom severity as well as everyday communication skills independent of diagnosis. Biological, clinical and methodological implications of these findings are discussed.

BackgroundWorking memory (WM) has been a central focus of cognitive neuroscience research because WM is a resource that is involved in many different cognitive operations. The goal of this study was to evaluate the clinical utility of WM paradigms developed in the basic cognitive neuroscience literature, including methods designed to estimate storage capacity without contamination by lapses of attention.MethodsA total of 61 people with schizophrenia, 49 with schizoaffective disorder, 47 with bipolar disorder with psychosis, and 59 healthy volunteers were recruited. Participants received multiple WM tasks, including two versions each of a multiple Change Detection paradigm, a visual Change Localization paradigm, and a Running Span task.ResultsHealthy volunteers performed better than the combined patient group on the visual Change Localization and running span measures. The multiple Change Detection tasks provided mixed evidence about WM capacity reduction in the patient groups, but a mathematical model of performance suggested that the patient groups differed from controls in their rate of attention lapsing. The 3 patient groups performed similarly on the WM tasks. Capacity estimates from the Change Detection and Localization tasks showed significant correlations with functional capacity and functional outcome.ConclusionsThe patient groups generally performed in a similarly impaired fashion across tasks, suggesting that WM impairment and attention lapsing are general features of psychotic disorders. Capacity estimates from the Change Localization and Detection tasks were related to functional capacity and outcome, suggesting that these methods may be useful in a clinical context.

BackgroundIn recent years, psychiatry research has increasingly focused on understanding mental illnesses from a cross-diagnostic, dimensional perspective in order to better align their neurocognitive features with underlying neurobiological mechanisms. In this multi-site study, we examined two measures of cognitive control (d-prime context and lapsing rate) during the Dot Probe Expectancy (DPX) version of the AX-Continuous Performance Task in patients with either schizophrenia (SZ), schizoaffective disorder (SZ-A), or Type I bipolar disorder (BD) as well as healthy control (HC) subjects. We hypothesized significantly lower d-prime context and higher lapsing rate in SZ and SZ-A patients and intermediate levels in BD patients relative to HC.Methods72 HC, 84 SZ, 77 SZ-A, and 58 BD patients (ages 18-56) were included in the final study sample.ResultsSignificant main effects of diagnosis were observed on d-prime context (F(3,279) = 9.59, p

The goal of the current study was to examine the relationships between insight and both cognitive function and depression in schizophrenia and schizoaffective disorder, and to determine if there were similar relationships across diagnostic categories. We examined discrepancies between self and informant reports of function on the Specific levels of function scale as a metric of insight for interpersonal, social acceptance, work and activities. We examined two samples of individuals with schizophrenia and/or schizoaffective disorder (Ns of 188 and 67 respectively). In Sample 1, cognition was measured using the Dot Probe Expectancy Task. In Sample 2, cognition was measured by averaging several subtests from the MATRICS consensus cognitive battery, as well as additional measures of working memory. In both samples, depression was measured using the Brief Psychiatric Rating Scale. In both samples, we found significant relationships between worse cognition and overestimations of work function, as well as between higher depression levels and underestimation of interpersonal function. These relationships were specific to interpersonal and work function, with significantly stronger correlations with interpersonal and work function compared to the other areas of function. Similar results were found across diagnostic categories. These results have important implications for treatment planning, as they suggest the need to take into account depression and cognitive function when evaluating the patient's self-report of function, and highlight the utility of informant reports in evaluating function and treatment planning. Further, they add to the literature on the similarity across schizophrenia and schizoaffective disorder in a variety of pathological mechanisms.

Motivational and hedonic impairments are core features of a variety of types of psychopathology. An important aspect of motivational function is reinforcement learning (RL), including implicit (i.e., outside of conscious awareness) and explicit (i.e., including explicit representations about potential reward associations) learning, as well as both positive reinforcement (learning about actions that lead to reward) and punishment (learning to avoid actions that lead to loss). Here we present data from paradigms designed to assess both positive and negative components of both implicit and explicit RL, examine performance on each of these tasks among individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis, and examine their relative relationships to specific symptom domains transdiagnostically. None of the diagnostic groups differed significantly from controls on the implicit RL tasks in either bias toward a rewarded response or bias away from a punished response. However, on the explicit RL task, both the individuals with schizophrenia and schizoaffective disorder performed significantly worse than controls, but the individuals with bipolar did not. Worse performance on the explicit RL task, but not the implicit RL task, was related to worse motivation and pleasure symptoms across all diagnostic categories. Performance on explicit RL, but not implicit RL, was related to working memory, which accounted for some of the diagnostic group differences. However, working memory did not account for the relationship of explicit RL to motivation and pleasure symptoms. These findings suggest transdiagnostic relationships across the spectrum of psychotic disorders between motivation and pleasure impairments and explicit RL. (PsycINFO Database Record

Cognitive control is a cognitive and neural mechanism that contributes to managing the complex demands of day-to-day life. Studies have suggested that functional impairments in cognitive control associated brain circuitry contribute to a broad range of higher cognitive deficits in schizophrenia. To examine this issue, we assessed functional connectivity networks in healthy adults and individuals with schizophrenia performing tasks from two distinct cognitive domains that varied in demands for cognitive control, the RiSE episodic memory task and DPX goal maintenance task. We characterized general and cognitive control-specific effects of schizophrenia on functional connectivity within an expanded frontal parietal network (FPN) and quantified network topology properties using graph analysis. Using the network based statistic (NBS), we observed greater network functional connectivity in cognitive control demanding conditions during both tasks in both groups in the FPN, and demonstrated cognitive control FPN specificity against a task independent auditory network. NBS analyses also revealed widespread connectivity deficits in schizophrenia patients across all tasks. Furthermore, quantitative changes in network topology associated with diagnostic status and task demand were observed. The present findings, in an analysis that was limited to correct trials only, ensuring that subjects are on task, provide critical insights into network connections crucial for cognitive control and the manner in which brain networks reorganize to support such control. Impairments in this mechanism are present in schizophrenia and these results highlight how cognitive control deficits contribute to the pathophysiology of this illness.

Patients with schizophrenia (SZ) previously demonstrated specific deficits in an executive function known as goal maintenance, associated with reduced middle frontal gyrus (MFG) activity. This study aimed to validate a new tool-the Dot Pattern Expectancy (DPX) task-developed to facilitate multisite imaging studies of goal maintenance deficits in SZ or other disorders. Additionally, it sought to arrive at recommendations for scan length for future studies using the DPX. Forty-seven SZ and 56 healthy controls (HC) performed the DPX in 3-Tesla functional magnetic resonance imaging (fMRI) scanners at 5 sites. Group differences in DPX-related activity were examined with whole brain voxelwise analyses. SZs showed the hypothesized specific performance deficits with as little as 1 block of data. Reduced activity in SZ compared with HC was observed in bilateral frontal pole/MFG, as well as left posterior parietal lobe. Efficiency analyses found significant group differences in activity using 18 minutes of scan data but not 12 minutes. Several behavioral and imaging findings from the goal maintenance literature were robustly replicated despite the use of different scanners at different sites. We did not replicate a previous correlation with disorganization symptoms among patients. Results were consistent with an executive/attention network dysfunction in the higher levels of a cascading executive system responsible for goal maintenance. Finally, efficiency analyses found that 18 minutes of scanning during the DPX task is sufficient to detect group differences with a similar sample size.

Previous work suggests that individuals with schizophrenia display accelerated aging of white matter integrity, however, it is still unknown whether functional brain networks also decline at an elevated rate in schizophrenia. Given the known degradation of functional connectivity and the normal decline in cognitive functioning throughout healthy aging, we aimed to test the hypothesis that efficiency of large-scale functional brain networks supporting overall cognition, as well as integrity of hub nodes within those networks, show evidence of accelerated aging in schizophrenia. Using pseudo-resting state data in 54 healthy controls and 46 schizophrenia patients, in which task-dependent signal from 3 tasks was regressed out to approximate resting-state data, we observed a significant diagnosis by age interaction in the prediction of both global and local efficiency of the cingulo-opercular network, and of the local efficiency of the fronto-parietal network, but no interaction when predicting both default mode network and whole brain efficiency. We also observed a significant diagnosis by age interaction for the node degree of the right anterior insula, left dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex. All interactions were driven by stronger negative associations between age and network metrics in the schizophrenia group than the healthy controls. These data provide evidence that is consistent with accelerated aging of large-scale functional brain networks in schizophrenia that support higher-order cognitive ability.

Goal maintenance is an aspect of cognitive control that has been identified as critical for understanding psychopathology according to criteria of the NIMH-sponsored CNTRICS (Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia) and Research Domain Criteria (RDoC) initiatives. CNTRICS proposed the expectancy AX-CPT, and its visual-spatial parallel the dot probe expectancy (DPX), as valid measures of the cognitive and neural processes thought to be relevant for goal maintenance. The goal of this study was to specifically examine the functional neural correlates and connectivity patterns of both goal maintenance tasks in the same subset of subjects to further validate their neural construct validity and clarify our understanding of the nature and function of the neural circuitry engaged by the tasks. Twenty-six healthy control subjects performed both the letter (AX) and dot pattern (DPX) variants of the CPT during fMRI. Behavioral performance was similar between tasks. The 2 tasks engaged the same brain networks including dorsolateral prefrontal cortex (DLPFC) and dorsal parietal regions, supporting their validity as complementary measures of the goal maintenance construct. Interestingly there was greater engagement of the frontal opercular insula region during the expectancy AX-CPT (letter) and greater functional connectivity between the PFC and medial temporal lobe in the DPX (dot pattern). These differences are consistent with differential recruitment of phonological and visual-spatial processes by the two tasks and suggest that additional long-term memory systems may be engaged by the dot probe version.

ImportanceIndividuals with schizophrenia can encode item-specific information to support familiarity-based recognition but are disproportionately impaired encoding interitem relationships (relational encoding) and recollecting information. The Relational and Item-Specific Encoding (RiSE) paradigm has been used to disentangle these encoding and retrieval processes, which may depend on specific medial temporal lobe (MTL) and prefrontal cortex (PFC) subregions. Functional magnetic resonance (fMRI) imaging during RiSE task performance could help to specify dysfunctional neural circuits in schizophrenia that can be targeted for interventions to improve memory and functioning in the illness.ObjectivesTo use fMRI to test the hypothesis that schizophrenia disproportionately affects MTL and PFC subregions during relational encoding and retrieval relative to item-specific memory processes, and to use fMRI results from healthy individuals serving as controls to establish neural construct validity for RiSE.Design, setting, and participantsThis multisite, case-control, cross-sectional fMRI study was conducted between November 1, 2010, and May 30, 2012, at 5 Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia sites. The final sample included 52 outpatients with clinically stable schizophrenia and 57 demographically matched healthy control participants. Data analysis was performed between February 1, 2013, and May 30, 2014.Main outcomes and measuresBehavioral performance speed and accuracy (d') on item recognition and associative recognition tasks. Voxelwise statistical parametric maps for a priori MTL and PFC regions of interest to test activation differences between relational and item-specific memory during encoding and retrieval.ResultsItem recognition was disproportionately impaired in patients with schizophrenia relative to healthy control participants following relational encoding (F1,107 = 4.7; P = .03). The differential deficit was accompanied by reduced dorsolateral PFC activation during relational encoding in patients with schizophrenia compared with healthy control participants (z > 2.3; P misses) was associated with hippocampal activation in healthy control participants during relational item recognition and associative recognition conditions, and hippocampal activation was specifically reduced in schizophrenia for recognition of relational but not item-specific information (z > 2.3; P

Growing evidence suggests that coordinated activity within specific functional brain networks supports cognitive ability, and that abnormalities in brain connectivity may underlie cognitive deficits observed in neuropsychiatric diseases, such as schizophrenia. Two functional networks, the fronto-parietal network (FPN) and cingulo-opercular network (CON), are hypothesized to support top-down control of executive functioning, and have therefore emerged as potential drivers of cognitive impairment in disease-states. Graph theoretic analyses of functional connectivity data can characterize network topology, allowing the relationships between cognitive ability and network integrity to be examined. In the current study we applied graph analysis to pseudo-resting state data in 54 healthy subjects and 46 schizophrenia patients, and measured overall cognitive ability as the shared variance in performance from tasks of episodic memory, verbal memory, processing speed, goal maintenance, and visual integration. We found that, across all participants, cognitive ability was significantly positively associated with the local and global efficiency of the whole brain, FPN, and CON, but not with the efficiency of a comparison network, the auditory network. Additionally, the participation coefficient of the right anterior insula, a major hub within the CON, significantly predicted cognition, and this relationship was independent of CON global efficiency. Surprisingly, we did not observe strong evidence for group differences in any of our network metrics. These data suggest that functionally efficient task control networks support better cognitive ability in both health and schizophrenia, and that the right anterior insula may be a particularly important hub for successful cognitive performance across both health and disease.

The goals of the present study were to assess the interrelationships among tasks from the MATRICS and CNTRACS batteries, to determine the degree to which tasks from each battery capture unique variance in cognitive dysfunction in schizophrenia, and to determine the ability of tasks from each battery to predict functional outcome. Subjects were 104 schizophrenia patients and 132 healthy control subjects recruited as part of the CNTRACS initiative. All subjects completed four CNTRACS tasks and two tasks from the MATRICS battery: Brief Assessment of Cognition in Schizophrenia Symbol Coding and the Hopkins Verbal Learning Test. Functional outcome was also assessed in the schizophrenia subjects. In both the patient and control groups, we found significant intercorrelations between all higher order cognitive tasks (episodic memory, goal maintenance, processing speed, verbal learning) but minimal relationships with the visual task. For almost all tasks, scores were significantly related to measures of functional outcome, with higher associations between CNTRACS tasks and performance-based measures of function and between one of the MATRICS tasks and self-reported functioning, relative to the other functioning measures. After regressing out variance shared by other tasks, we continued to observe group differences in performance among task residuals, particularly for measures of episodic memory from both batteries, although these residuals did not correlate as robustly with functional outcome as raw test scores. These findings suggest that there exists both shared and specific variance across cognitive tasks related to cognitive and functional impairments in schizophrenia and that measures derived from cognitive neuroscience can predict functional capacity and status in schizophrenia.