Metacognition in Early Phase Psychosis: Toward Understanding Neural Substrates

Individuals in the early phases of psychotic illness have disturbed metacognitive capacity, which has been linked to a number of poor outcomes. Little is known, however, about the neural systems associated with metacognition in this population. The purpose of this study was to elucidate the neuroanatomical correlates of metacognition. We anticipated that higher levels of metacognition may be dependent upon gray matter density (GMD) of regions within the prefrontal cortex. Examining whole-brain structure in 25 individuals with early phase psychosis, we found positive correlations between increased medial prefrontal cortex and ventral striatum GMD and higher metacognition. These findings represent an important step in understanding the path through which the biological correlates of psychotic illness may culminate into poor metacognition and, ultimately, disrupted functioning. Such a path will serve to validate and promote metacognition as a viable treatment target in early phase psychosis

Toward early estimation and treatment of addiction vulnerability: radial arm maze and N-acetyl cysteine before cocaine sensitization or nicotine self-administration in neonatal ventral hippocampal lesion rats

RATIONAL: Prefrontal cortical (PFC)-hippocampal-striatal circuits, interconnected via glutamatergic signaling, are dysfunctional in mental illnesses that involve addiction vulnerability. OBJECTIVES: In healthy and neurodevelopmentally altered rats, we examined how Radial Arm Maze (RAM) performance estimates addiction vulnerability, and how starting a glutamatergic modulating agent, N-acetyl cysteine (NAC) in adolescence alters adult mental illness and/or addiction phenotypes. METHODS: Rats with neonatal ventral hippocampal lesions (NVHL) vs. SHAM-operated controls were randomized to NAC vs. saline in adolescence followed by cognitive testing (RAM) in early adulthood and then cocaine behavioral sensitization (experiment 1; n = 80) or nicotine self-administration (experiment 2; n = 12). RESULTS: In experiment 1, NVHL rats showed over-consumption of food (Froot-Loops (FL)) baiting the RAM with poor working memory (low-arm entries to repeat (ETR)), producing an elevated FL to ETR ratio ("FLETR"; p < 0.001). FLETR was the best linear estimator (compared to FL or ETR) of magnitude of long-term cocaine sensitization (R (2) = 0.14, p < 0.001). NAC treatment did not alter FL, ETR, FLETR, or cocaine sensitization. In experiment 2, FLETR also significantly and uniquely correlated with subsequent drug seeking during nicotine-induced reinstatement after extinction of nicotine self-administration (R (2) = 0.47, p < 0.01). NAC did not alter RAM performance, but significantly reversed NVHL-induced increases in nicotine seeking during extinction and reinstatement. CONCLUSIONS: These findings demonstrate the utility of animal models of mental illness with addiction vulnerability for developing novel diagnostic measures of PFC-hippocampal-striatal circuit dysfunction that may reflect addiction risk. Such tests may direct pharmacological treatments prior to adulthood and addictive drug exposure, to prevent or treat adult addictions

Muscarinic Cholinergic Receptor Agonist and Peripheral Antagonist for Schizophrenia

Background: The muscarinic receptor agonist xanomeline has antipsychotic properties and is devoid of dopamine receptor-blocking activity but causes cholinergic adverse events. Trospium is a peripherally restricted muscarinic receptor antagonist that reduces peripheral cholinergic effects of xanomeline. The efficacy and safety of combined xanomeline and trospium in patients with schizophrenia are unknown. Methods: In this double-blind, phase 2 trial, we randomly assigned patients with schizophrenia in a 1:1 ratio to receive twice-daily xanomeline-trospium (increased to a maximum of 125 mg of xanomeline and 30 mg of trospium per dose) or placebo for 5 weeks. The primary end point was the change from baseline to week 5 in the total score on the Positive and Negative Syndrome Scale (PANSS; range, 30 to 210, with higher scores indicating more severe symptoms of schizophrenia). Secondary end points were the change in the PANSS positive symptom subscore, the score on the Clinical Global Impression-Severity (CGI-S) scale (range, 1 to 7, with higher scores indicating greater severity of illness), the change in the PANSS negative symptom subscore, the change in the PANSS Marder negative symptom subscore, and the percentage of patients with a response according to a CGI-S score of 1 or 2. Results: A total of 182 patients were enrolled, with 90 assigned to receive xanomeline-trospium and 92 to receive placebo. The PANSS total score at baseline was 97.7 in the xanomeline-trospium group and 96.6 in the placebo group. The change from baseline to week 5 was -17.4 points with xanomeline-trospium and -5.9 points with placebo (least-squares mean difference, -11.6 points; 95% confidence interval, -16.1 to -7.1; P<0.001). The results for the secondary end points were significantly better in the xanomeline-trospium group than in the placebo group, with the exception of the percentage of patients with a CGI-S response. The most common adverse events in the xanomeline-trospium group were constipation, nausea, dry mouth, dyspepsia, and vomiting. The incidences of somnolence, weight gain, restlessness, and extrapyramidal symptoms were similar in the two groups. Conclusions: In a 5-week trial, xanomeline-trospium resulted in a greater decrease in the PANSS total score than placebo but was associated with cholinergic and anticholinergic adverse events. Larger and longer trials are required to determine the efficacy and safety of xanomeline-trospium in patients with schizophrenia

Functional network connectivity in early-stage schizophrenia

Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans. Gray matter regions were organized into nine distinct functional networks. Functional connectivity was calculated between 278 gray matter regions for each subject. Network connectivity properties were defined by the mean and variance of correlations of all regions. Whole-brain network measures of global efficiency (reflecting overall interconnectedness) and locations of hubs (key regions for communication) were also determined. The control sample had greater connectivity between the following network pairs: somatomotor-limbic, somatomotor-default mode, dorsal attention-default mode, ventral attention-limbic, and ventral attention-default mode. The patient sample had greater variance in interactions between ventral attention network and other functional networks. Illness duration was associated with overall increases in the variability of network connections. The control group had higher global efficiency and more hubs in the cerebellum network, while patient group hubs were more common in visual, frontoparietal, or subcortical networks. Thus, reduced functional connectivity in patients was largely present between distinct networks, rather than within-networks. The implications of these findings for the pathophysiology of schizophrenia are discussed

Relationship of Auditory Electrophysiological Responses to Magnetic Resonance Spectroscopy Metabolites in Early Phase Psychosis

Both auditory evoked responses and metabolites measured by magnetic resonance spectroscopy (MRS) are altered in schizophrenia and other psychotic disorders, but the relationship between electrophysiological and metabolic changes are not well characterized. We examined the relation of MRS metabolites to cognitive and electrophysiological measures in individuals during the early phase of psychosis (EPP) and in healthy control subjects. The mismatch negativity (MMN) of the auditory event-related potential to duration deviant tones and the auditory steady response (ASSR) to 40 Hz stimulation were assessed. MRS was used to quantify glutamate+glutamine (Glx), N-Acetylasparate (NAA), creatine (Cre), myo-inositol (Ins) and choline (Cho) at a voxel placed medially in the frontal cortex. MMN amplitude and ASSR power did not differ between groups. The MRS metabolites Glx, Cre and Cho were elevated in the psychosis group. Partial least squares analysis in the patient group indicated that elevated levels of MRS metabolites were associated with reduced MMN amplitude and increased 40 Hz ASSR power. There were no correlations between the neurobiological measures and clinical measures. These data suggest that elevated neurometabolites early in psychosis are accompanied by altered auditory neurotransmission, possibly indicative of a neuroinflammatory or excitotoxic disturbance which disrupts a wide range of metabolic processes in the cortex

The Transcriptional Regulator Rok Binds A+T-Rich DNA and Is Involved in Repression of a Mobile Genetic Element in Bacillus subtilis

The rok gene of Bacillus subtilis was identified as a negative regulator of competence development. It also controls expression of several genes not related to competence. We found that Rok binds to extended regions of the B. subtilis genome. These regions are characterized by a high A+T content and are known or believed to have been acquired by horizontal gene transfer. Some of the Rok binding regions are in known mobile genetic elements. A deletion of rok resulted in higher excision of one such element, ICEBs1, a conjugative transposon found integrated in the B. subtilis genome. When expressed in the Gram negative E. coli, Rok also associated with A+T-rich DNA and a conserved C-terminal region of Rok contributed to this association. Together with previous work, our findings indicate that Rok is a nucleoid associated protein that serves to help repress expression of A+T-rich genes, many of which appear to have been acquired by horizontal gene transfer. In these ways, Rok appears to be functionally analogous to H-NS, a nucleoid associated protein found in Gram negative bacteria and Lsr2 of high G+C Mycobacteria

Exon expression in lymphoblastoid cell lines from subjects with schizophrenia before and after glucose deprivation

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to examine the effects of glucose reduction stress on lymphoblastic cell line (LCL) gene expression in subjects with schizophrenia compared to non-psychotic relatives.</p> <p>Methods</p> <p>LCLs were grown under two glucose conditions to measure the effects of glucose reduction stress on exon expression in subjects with schizophrenia compared to unaffected family member controls. A second aim of this project was to identify cis-regulated transcripts associated with diagnosis.</p> <p>Results</p> <p>There were a total of 122 transcripts with significant diagnosis by probeset interaction effects and 328 transcripts with glucose deprivation by probeset interaction probeset effects after corrections for multiple comparisons. There were 8 transcripts with expression significantly affected by the interaction between diagnosis and glucose deprivation and probeset after correction for multiple comparisons. The overall validation rate by qPCR of 13 diagnosis effect genes identified through microarray was 62%, and all genes tested by qPCR showed concordant up- or down-regulation by qPCR and microarray. We assessed brain gene expression of five genes found to be altered by diagnosis and glucose deprivation in LCLs and found a significant decrease in expression of one gene, glutaminase, in the dorsolateral prefrontal cortex (DLPFC). One SNP with previously identified regulation by a 3' UTR SNP was found to influence IRF5 expression in both brain and lymphocytes. The relationship between the 3' UTR rs10954213 genotype and IRF5 expression was significant in LCLs (p = 0.0001), DLPFC (p = 0.007), and anterior cingulate cortex (p = 0.002).</p> <p>Conclusion</p> <p>Experimental manipulation of cells lines from subjects with schizophrenia may be a useful approach to explore stress related gene expression alterations in schizophrenia and to identify SNP variants associated with gene expression.</p

Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p

Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

Brain‐age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.<br/