The change detection task is a common method for assessing the storage capacity of working memory, but estimates of memory capacity from this task can be distorted by lapses of attention. When combined with appropriate mathematical models, some versions of the change detection task make it possible to separately estimate working memory and the probability of attentional lapses. In principle, these models should allow researchers to isolate the effects of experimental manipulations, group differences, and individual differences on working memory capacity and on the rate of attentional lapses. However, the present research found that two variants of a widely accepted model of the change detection task are not mathematically identified.

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.

Objectives

Visual perceptual organization impairments in schizophrenia (SCZ) are well established, but their neurobiological bases are not. The current study used the previously validated Jittered Orientation Visual Integration (JOVI) task, along with fMRI, to examine the neural basis of contour integration (CI), and its impairment in SCZ. CI is an aspect of perceptual organization in which multiple distinct oriented elements are grouped into a single continuous boundary or shape.

Methods

On the JOVI, five levels of orientational jitter were added to non-contiguous closed contour elements embedded in background noise to progressively increase the difficulty in perceiving contour elements as left- or right-pointing ovals. Multi-site fMRI data were analyzed for 56 healthy control subjects and 47 people with SCZ.

Results

SCZ patients demonstrated poorer CI, and this was associated with increased activation in regions involved in global shape processing and visual attention, namely the lateral occipital complex and superior parietal lobules. There were no brain regions where controls demonstrated more activation than patients.

Conclusions

CI impairment in this sample of outpatients with SCZ was related to excessive activation in regions associated with object processing and allocation of visual-spatial attention. There was no evidence for basic impairments in contour element linking in the fMRI data. The latter may be limited to poor outcome patients, where more extensive structural and functional changes in the occipital lobe have been observed.

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.

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.

Background

Working 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.

Methods

A 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.

Results

Healthy 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.

Conclusions

The 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.

Background

Psychiatric 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.

Methods

We 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).

Results

Using accuracy, there was a main effect of group (F_3,279 = 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 (F_3,295 = 13.91, p < .001), indicating less dependence on learning explicit value representations as well as greater learning decay between training and test (F_1,289 = 12.81, p < .001). Unmedicated patients with schizophrenia also showed greater decision noise (F_3,295 = 2.67, p = .04).

Conclusions

Both 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.

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.

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.

Background

Motivational 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.

Methods

We 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).

Results

Internal 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.

Conclusions

Our 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.