The goal of this study was to investigate the neural dynamics of error processing and post-error adjustments in cognitive control and attention to a cognitive task in schizophrenia. We adopted a time-frequency approach in order to examine activity in the theta and alpha frequency bands as indices of cognitive control and attentional engagement. The results showed that error processing was characterized by increases in theta-band activity, accompanied by decreases in alpha-band activity, in both healthy control participants and participants with schizophrenia. However, both the theta and alpha effects were significantly reduced in participants with schizophrenia. Post-error increases in theta activity were associated with improved accuracy on subsequent trials in control participants but not in participants with schizophrenia. In addition, increases in alpha-band activity were found in the prestimulus period before partial attention lapses, but only for control participants and participants with schizophrenia with relatively low positive symptom severity. These results provide evidence for a deficit in cognitive control mechanisms mediated by midfrontal theta activity in schizophrenia, and suggest a particularly pronounced deficit in patients' ability to engage adaptive control mechanisms following errors. Our results also indicate that partial attention lapses can be indexed in both control participants and participants with schizophrenia by increases in alpha activity, but that in schizophrenia this varies as a function of positive symptom severity. We suggest that disrupted theta-band function represents a key deficit of schizophrenia, whereas disruptions in the alpha band may be the byproduct of atypically regulated attention.

Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic improvement after 1 year of coordinated specialty care with 66% accuracy. Here, we evaluated the ability of six machine learning (ML) algorithms and deep learning (DL) to predict "Improver" status (>20% improvement on Brief Psychiatric Rating Scale [BPRS] total score at 1-year follow-up vs. baseline) and continuous change in BPRS score using the same functional magnetic resonance imaging-based features (frontoparietal activations during the AX-continuous performance task) in the same sample (individuals with either schizophrenia (n = 65, 49M/16F, mean age 20.8 years) or Type I bipolar disorder (n = 17, 9M/8F, mean age 21.6 years)). 138 healthy controls were included as a reference group. "Shallow" ML methods included Naive Bayes, support vector machine, K Star, AdaBoost, J48 decision tree, and random forest. DL included an explainable artificial intelligence (XAI) procedure for understanding results. The best overall performances (70% accuracy for the binary outcome and root mean square error = 9.47 for the continuous outcome) were achieved using DL. XAI revealed left DLPFC activation was the strongest feature used to make binary classification decisions, with a classification activation threshold (adjusted beta = .017) intermediate to the healthy control mean (adjusted beta = .15, 95% CI = -0.02 to 0.31) and patient mean (adjusted beta = -.13, 95% CI = -0.37 to 0.11). Our results suggest DL is more powerful than shallow ML methods for predicting symptomatic improvement. The left DLPFC may be a functional target for future biomarker development as its activation was particularly important for predicting improvement.

Objective

The early course of illness in psychotic disorders is highly variable, and predictive biomarkers of treatment response have been lacking. Trial and error remains the basis for care in early psychosis, and poor outcomes are common. Early prediction of nonimprovement in response to treatment could help identify those who would benefit from alternative and/or supplemental interventions. The goal of this study was to evaluate the ability of functional MRI (fMRI) measures of cognitive control-related brain circuitry collected at baseline to predict symptomatic response in patients after 1 year.

Methods

Patients with recent-onset (<2 years) psychotic disorders (N=82) in early psychosis specialty care were classified as improvers (>20% improvement in total score on the Brief Psychiatric Rating Scale [BPRS] at 1-year follow-up compared with baseline) or as nonimprovers. Behavioral (d' context) and fMRI (proactive control-associated activation in a priori frontoparietal regions of interest) measures of cognitive control were then evaluated on their ability to predict BPRS improvement using linear and logistic regression.

Results

Cognitive control-associated measures significantly predicted BPRS improvement and improver status, with 70% positive predictive value, 60% negative predictive value, and 66% accuracy. Only the fMRI-based measure (and not the behavioral measure) significantly predicted status.

Conclusions

These results suggest that frontoparietal activation during cognitive control performance at baseline significantly predicts subsequent symptomatic improvement during early psychosis specialty care. Potential implications for fMRI-based personalized patient treatment are discussed.

Over the past two decades, the awareness of the disabling and treatment-refractory effects of impaired cognition in schizophrenia has increased dramatically. In response to this still unmet need in the treatment of schizophrenia, the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative was developed. The goal of CNTRICS is to harness cognitive neuroscience to develop a brain-based set of tools for measuring cognition in schizophrenia and to test new treatments. CNTRICS meetings focused on development of tasks with cognitive construct validity for use in both human and animal model studies. This special issue presents papers discussing the cognitive testing paradigms selected by CNTRICS for animal model systems. These paradigms are designed to measure cognitive constructs within the domains of perception, attention, executive function, working memory, object/relational long-term memory, and social/affective processes.

Rationale

The default mode network (DMN) is a functional network which is implicated in a range of cognitive processes. This network is proposed to consist of hubs located in the ventromedial prefrontal cortex (vmPFC), posterior cingulate/retrosplenial cortex (PCC/rSpl), and inferior parietal lobule (IPL), with other midline cortical and temporal lobe nodes connected to these hubs. How this network is modulated by neurochemical systems during functional brain activity is not yet understood.

Objectives

In the present study, we used the norepinephrine/dopamine transporter inhibitor modafinil to test the hypothesis that this drug modulates the DMN.

Methods

Eighteen healthy right-handed adults participated in a double-blind, placebo-controlled study of single oral dose modafinil 200 mg. They performed a simple visual sensorimotor task during slow event-related fMRI. Drug effects were interrogated within the DMN defined by task-induced deactivation (TID) on placebo.

Results

There was a trend toward faster reaction time (RT) on modafinil (Cohen's d = 0.38). Brain regions within the DMN which exhibited significant modafinil-induced augmentation of TID included vmPFC, PCC/rSpl, and left IPL. Across subjects, the modafinil effect on TID in the vmPFC was significantly and specifically associated with drug effects on RT speeding.

Conclusions

Modafinil augments TID in the DMN to facilitate sensorimotor processing speed, an effect which may be particularly dependent on changes in vmPFC activity. This is consistent with the gain control function of catecholamine systems and may represent an important aspect of the pro-cognitive effects of modafinil.

Background

Magnetic resonance spectroscopy studies measuring brain glutamate separately from glutamine are helping elucidate schizophrenia pathophysiology. An expanded literature and improved methodologies motivate an updated meta-analysis examining effects of measurement quality and other moderating factors in characterizing abnormal glutamate levels in schizophrenia.

Methods

Searching previous meta-analyses and the MEDLINE database identified 83 proton magnetic resonance spectroscopy datasets published through March 25, 2020. Three quality metrics were extracted-Cramér-Rao lower bound (CRLB), line width, and coefficient of variation. Pooled effect sizes (Hedges' g) were calculated with random-effects, inverse variance-weighted models. Moderator analyses were conducted using quality metrics, field strength, echo time, medication, age, and stage of illness.

Results

Across 36 datasets (2086 participants), medial prefrontal cortex glutamate was significantly reduced in patients (g = -0.19, confidence interval [CI] = -0.07 to -0.32). CRLB and coefficient of variation quality subgroups significantly moderated this effect. Glutamate was significantly more reduced in studies with lower CRLB or coefficient of variation (g = -0.44, CI = -0.29 to -0.60, and g = -0.43, CI = -0.29 to -0.57, respectively). Studies using echo time ≤20 ms also showed significantly greater reduction in glutamate (g = -0.41, CI = -0.26 to -0.55). Across 11 hippocampal datasets, group differences and moderator effects were nonsignificant. Group effects in thalamus and dorsolateral prefrontal cortex were also nonsignificant.

Conclusions

High-quality measurements reveal consistently reduced medial prefrontal cortex glutamate in schizophrenia. Stricter CRLB criteria and reduced nuisance variance may increase the sensitivity of future studies examining additional regions and the pathophysiological significance of abnormal glutamate levels in schizophrenia.

Kraepelin, in his early descriptions of schizophrenia (SZ), characterized the illness as having "an orchestra without a conductor." Kraepelin further speculated that this "conductor" was situated in the frontal lobes. Findings from multiple studies over the following decades have clearly implicated pathology of the dorsolateral prefrontal cortex (DLPFC) as playing a central role in the pathophysiology of SZ, particularly with regard to key cognitive features such as deficits in working memory and cognitive control. Following an overview of the cognitive mechanisms associated with DLPFC function and how they are altered in SZ, we review evidence from an array of neuroscientific approaches addressing how these cognitive impairments may reflect the underlying pathophysiology of the illness. Specifically, we present evidence suggesting that alterations of the DLPFC in SZ are evident across a range of spatial and temporal resolutions: from its cellular and molecular architecture, to its gross structural and functional integrity, and from millisecond to longer timescales. We then present an integrative model based upon how microscale changes in neuronal signaling in the DLPFC can influence synchronized patterns of neural activity to produce macrocircuit-level alterations in DLPFC activation that ultimately influence cognition and behavior. We conclude with a discussion of initial efforts aimed at targeting DLPFC function in SZ, the clinical implications of those efforts, and potential avenues for future development.

The goal of this study was to use transcranial direct current stimulation (tDCS) to examine the role of the prefrontal cortex (PFC) in neural oscillatory activity associated with proactive cognitive control in schizophrenia. To do so, we tested the impact of PFC-targeted tDCS on behavioral and electrophysiological markers of proactive cognitive control engagement in individuals with schizophrenia. Using a within-participants, double-blinded, sham-controlled crossover design, we recorded EEG while participants with schizophrenia completed a proactive cognitive control task (the Dot Pattern Expectancy (DPX) Task), after receiving 20 min of active prefrontal stimulation at 2 mA or sham stimulation. We hypothesized that active stimulation would enhance proactive cognitive control, leading to changes in behavioral performance on the DPX task and in activity in the gamma frequency band during key periods of the task designed to tax proactive cognitive control. The results showed significant changes in the pattern of error rates and increases in EEG gamma power as a function of tDCS condition (active or sham), that were indicative of enhanced proactive cognitive control. These findings, considered alongside our previous work in healthy adults, provides novel support for the role gamma oscillations in proactive cognitive control and they suggest that frontal tDCS may be a promising approach to enhance proactive cognitive control in schizophrenia.

Impairments in cognitive control are a defining feature of schizophrenia. Aspects of cognitive control include proactive control-the maintenance of task rules or goals to bias attention and maintain preparedness-and reactive control-the engagement of attention in reaction to changing cognitive demands. Proactive control is thought to be particularly impaired in schizophrenia. We sought to examine proactive and reactive control in schizophrenia, as measured by reaction time (RT) variability, and especially long RTs, which are thought to represent lapses in proactive control, during the Stroop paradigm. Furthermore, we sought to examine the neural underpinnings of lapses in proactive control and the subsequent engagement of reactive control in those with schizophrenia, as compared to healthy controls, using fMRI. We found that patients with schizophrenia displayed greater RT variability and more extremely long RTs than controls suggesting that proactive control was weaker in the schizophrenia than in the control group. All of the subjects engaged regions of the cognitive control network during long RTs, consistent with an engagement of reactive control following a failure in proactive control on these trials. The schizophrenia group, however, displayed significantly diminished activity in these regions relative to controls. Our results suggest increased failures in proactive control, but also impaired reactive control, in schizophrenia as compared to healthy subjects.

Background

Previous research in resting-state functional magnetic resonance imaging (rs-fMRI) has shown a mixed pattern of disrupted thalamocortical connectivity in psychosis. The clinical meaning of these findings and their stability over time remains unclear. We aimed to study thalamocortical connectivity longitudinally over a 1-year period in participants with recent-onset psychosis.

Methods

To this purpose, 129 individuals with recent-onset psychosis and 87 controls were clinically evaluated and scanned using rs-fMRI. Among them, 43 patients and 40 controls were re-scanned and re-evaluated 12 months later. Functional connectivity between the thalamus and the rest of the brain was calculated using a seed to voxel approach, and then compared between groups and correlated with clinical features cross-sectionally and longitudinally.

Results

At baseline, participants with recent-onset psychosis showed increased connectivity (compared to controls) between the thalamus and somatosensory and temporal regions (k = 653, T = 5.712), as well as decreased connectivity between the thalamus and left cerebellum and right prefrontal cortex (PFC; k = 201, T = -4.700). Longitudinal analyses revealed increased connectivity over time in recent-onset psychosis (relative to controls) in the right middle frontal gyrus.

Conclusions

Our results support the concept of abnormal thalamic connectivity as a core feature in psychosis. In agreement with a non-degenerative model of illness in which functional changes occur early in development and do not deteriorate over time, no evidence of progressive deterioration of connectivity during early psychosis was observed. Indeed, regionally increased connectivity between thalamus and PFC was observed.