Jared Van Snellenberg

This meta-analytic review examines the efficacy of antipsychotic medications in ameliorating schizophrenia-related long-term memory (LTM) impairments. Twenty-three studies were reviewed that compared schizophrenia spectrum patients treated (a) with atypical versus typical antipsychotic medications, or (b) with various atypical treatments. In 17 atypical versus typical trials aggregating 939 participants, superior overall (verbal and nonverbal) LTM was detected in patients assigned to atypical trials. However, this difference was small (effect size estimate (ES) 0.17; 95% Confidence Interval (CI) 0.04 to 0.31) and specific to certain atypical treatments. Relative to typical antipsychotic trials, LTM superiority was marginally significant for risperidone trials (ES 0.20; 95% CI -0.03 to 0.44) and significant for olanzapine trials (ES 0.29; 95% CI 0.08 to 0.49). In contrast, clozapine trials did not produce a LTM advantage over typical trials (ES -0.06; 95% CI -0.35 to 0.23). Due to the lack of available studies, the effect of quetiapine was indeterminate. Direct comparison between atypical trials revealed a similar effect pattern. A marginally significant superiority in overall LTM was detected for risperidone and olanzapine compared to clozapine (ES 0.28; 95% CI -0.04 to 0.59), which reached significance for verbal LTM (ES 0.36; 95% CI 0.04 to 0.67). Finally, the beneficial impact of antipsychotic medications emerged as a function of differences in the anticholinergic properties of the treatment arms being compared.

Recent findings demonstrate that patients with schizophrenia are worse at learning to predict rewards than losses, suggesting that motivational context modulates learning in this disease. However, these findings derive from studies in patients treated with antipsychotic medications, D2 receptor antagonists that may interfere with the neural systems that underlie motivation and learning. Thus, it remains unknown how motivational context affects learning in schizophrenia, separate from the effects of medication. To examine the impact of motivational context on learning in schizophrenia, we tested 16 unmedicated patients with schizophrenia and 23 matched controls on a probabilistic learning task while they underwent functional magnetic resonance imaging (fMRI) under 2 conditions: one in which they pursued rewards, and one in which they avoided losses. Computational models were used to derive trial-by-trial prediction error responses to feedback. Patients performed worse than controls on the learning task overall, but there were no behavioral effects of condition. FMRI revealed an attenuated prediction error response in patients in the medial prefrontal cortex, striatum, and medial temporal lobe when learning to predict rewards, but not when learning to avoid losses. Patients with schizophrenia showed differences in learning-related brain activity when learning to predict rewards, but not when learning to avoid losses. Together with prior work, these results suggest that motivational deficits related to learning in schizophrenia are characteristic of the disease and not solely a result of antipsychotic treatment.

Despite the well-established role of striatal dopamine in psychosis, current views generally agree that cortical dysfunction is likely necessary for the emergence of psychotic symptoms. The topographic organization of striatal-cortical connections is central to gating and integration of higher-order information, so a disruption of such topography via dysregulated dopamine could lead to cortical dysfunction in schizophrenia. However, this hypothesis remains to be tested using multivariate methods ascertaining the global pattern of striatal connectivity and without the confounding effects of antidopaminergic medication. To examine whether the pattern of brain connectivity across striatal subregions is abnormal in unmedicated patients with schizophrenia and whether this abnormality relates to psychotic symptoms and extrastriatal dopaminergic transmission. In this multimodal, case-control study, we obtained resting-state functional magnetic resonance imaging data from 18 unmedicated patients with schizophrenia and 24 matched healthy controls from the New York State Psychiatric Institute. A subset of these (12 and 17, respectively) underwent positron emission tomography with the dopamine D2 receptor radiotracer carbon 11-labeled FLB457 before and after amphetamine administration. Data were acquired between June 16, 2011, and February 25, 2014. Data analysis was performed from September 1, 2014, to January 11, 2016. Group differences in the striatal connectivity pattern (assessed via multivariable logistic regression) across striatal subregions, the association between the multivariate striatal connectivity pattern and extrastriatal baseline D2 receptor binding potential and its change after amphetamine administration, and the association between the multivariate connectivity pattern and the severity of positive symptoms evaluated with the Positive and Negative Syndrome Scale. Of the patients with schizophrenia (mean [SEM] age, 35.6 [11.8] years), 9 (50%) were male and 9 (50%) were female. Of the controls (mean [SEM] age, 33.7 [8.8] years), 10 (42%) were male and 14 (58%) were female. Patients had an abnormal pattern of striatal connectivity, which included abnormal caudate connections with a distributed set of associative cortex regions (χ229 = 53.55, P = .004). In patients, more deviation from the multivariate pattern of striatal connectivity found in controls correlated specifically with more severe positive symptoms (ρ = -0.77, P = .002). Striatal connectivity also correlated with baseline binding potential across cortical and extrastriatal subcortical regions (t25 = 3.01, P = .01, Bonferroni corrected) but not with its change after amphetamine administration. Using a multimodal, circuit-level interrogation of striatal-cortical connections, it was demonstrated that the functional topography of these connections is globally disrupted in unmedicated patients with schizophrenia. These findings suggest that striatal-cortical dysconnectivity may underlie the effects of dopamine dysregulation on the pathophysiologic mechanism of psychotic symptoms.

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. ...

The neural correlates of working memory (WM) impairment in schizophrenia remain a key puzzle in understanding the cognitive deficits and dysfunction of dorsolateral prefrontal cortex observed in this disorder. We sought to determine whether patients with schizophrenia exhibit an alteration in the inverted-U relationship between WM load and activation that we recently observed in healthy individuals and whether this could account for WM deficits in this population. Medicated (n = 30) and unmedicated (n = 21) patients with schizophrenia and healthy control subjects (n = 45) performed the self-ordered WM task during functional magnetic resonance imaging. We identified regions exhibiting an altered fit to an inverted-U relationship between WM load and activation that were also predictive of WM performance. A blunted inverted-U response was observed in left dorsolateral prefrontal cortex in patients and was associated with behavioral deficits in WM capacity. In addition, suppression of medial prefrontal cortex during WM was reduced in patients and was associated with poorer WM capacity in patients. Finally, activation of visual cortex in the cuneus was elevated in patients and associated with improved WM capacity. Together, these findings explained 55% of the interindividual variance in WM capacity when combined with diagnostic and medication status, which alone accounted for only 22% of the variance in WM capacity. These findings identify a novel biomarker and putative mechanism of WM deficits in patients with schizophrenia, a reduction or flattening of the inverted-U relationship between activation and WM load observed in healthy individuals in left dorsolateral prefrontal cortex.

The concept of capacity has become increasingly important in discussions of working memory (WM), in so far as most models of WM conceptualize it as a limited-capacity mechanism for maintaining information in an active state, and as capacity estimates from at least one type of WM task-complex span-are valid predictors of real-world cognitive performance. However, the term capacity is also often used in the context of a distinct set of WM tasks, change detection, and may or may not refer to the same cognitive capability. We here develop maximum-likelihood models of capacity from each of these tasks-as well as from a third WM task that places heavy demands on cognitive control, the self-ordered WM task (SOT)-and show that the capacity estimates from change detection and complex span tasks are not correlated with each other, although capacity estimates from change detection tasks do correlate with those from the SOT. Furthermore, exploratory factor analysis confirmed that performance on the SOT and change detection load on the same factor, with performance on our complex span task loading on its own factor. These findings suggest that at least two distinct cognitive capabilities underlie the concept of WM capacity as it applies to each of these three tasks.