Increased peripheral inflammation in schizophrenia is associated with worse cognitive performance and related cortical thickness reductions.

While the biological substrates of brain and behavioural changes in persons with schizophrenia remain unclear, increasing evidence implicates that inflammation is involved. In schizophrenia, including first-episode psychosis and anti-psychotic naïve patients, there are numerous reports of increased peripheral inflammation, cognitive deficits and neuropathologies such as cortical thinning. Research defining the relationship between inflammation and schizophrenia symptomatology and neuropathology is needed. Therefore, we analysed the level of C-reactive protein (CRP), a peripheral inflammation marker, and its relationship with cognitive functioning in a cohort of 644 controls and 499 schizophrenia patients. In a subset of individuals who underwent MRI scanning (99 controls and 194 schizophrenia cases), we tested if serum CRP was associated with cortical thickness. CRP was significantly increased in schizophrenia patients compared to controls, co-varying for age, sex, overweight/obesity and diabetes (p < 0.006E-10). In schizophrenia, increased CRP was mildly associated with worse performance in attention, controlling for age, sex and education (R =- 0.15, p = 0.001). Further, increased CRP was associated with reduced cortical thickness in three regions related to attention: the caudal middle frontal, the pars opercularis and the posterior cingulate cortices, which remained significant after controlling for multiple comparisons (all p < 0.05). Together, these findings indicate that increased peripheral inflammation is associated with deficits in cognitive function and brain structure in schizophrenia, especially reduced attention and reduced cortical thickness in associated brain regions. Using CRP as a biomarker of peripheral inflammation in persons with schizophrenia may help to identify vulnerable patients and those that may benefit from adjunctive anti-inflammatory treatments.

The maternal immune activation model uncovers a role for the Arx gene in GABAergic dysfunction in schizophrenia.

A hallmark feature of schizophrenia is altered high frequency neural oscillations, including reduced auditory-evoked gamma oscillatory power, which is underpinned by parvalbumin (PV) interneuron dysfunction. Maternal immune activation (MIA) in rodents models an environmental risk factor for schizophrenia and recapitulates these PV interneuron changes. This study sought to link reduced PV expression in the MIA model with alterations to auditory-evoked gamma oscillations and transcript expression. We further aligned transcriptional findings from the animal model with human genome sequencing data. We show that MIA, induced by the viral mimetic, poly-I:C in C57Bl/6 mice, caused in adult offspring reduced auditory-evoked gamma and theta oscillatory power paralleled by reduced PV protein levels. We then showed the Arx gene, critical to healthy neurodevelopment of PV interneurons, is reduced in the forebrain of MIA exposed mice. Finally, in a whole-genome sequenced patient cohort, we identified a novel missense mutation of ARX in a patient with schizophrenia and in the Psychiatric Genomics Consortium 2 cohort, a nominal association of proximal ARX SNPs with the disorder. This suggests MIA, as a risk factor for schizophrenia, may be influencing Arx expression to induce the GABAergic dysfunction seen in schizophrenia and that the ARX gene may play a role in the prenatal origins of schizophrenia pathophysiology.

The Impact of Childhood Adversity on Cognitive Development in Schizophrenia.

Childhood adversity, such as physical, sexual, and verbal abuse, as well as neglect and family conflict, is a risk factor for schizophrenia. Such adversity can lead to disruptions of cognitive function during development, undermining intellectual capabilities and academic achievement. Schizophrenia is a neurodevelopmental disorder that is associated with cognitive impairments that may become evident during childhood. The Australian Schizophrenia Research Bank database comprises a large community cohort (N = 1169) in which we previously identified 3 distinct cognitive groups among people with schizophrenia: (1) Compromised, current, and estimated premorbid cognitive impairment; (2) Deteriorated, substantial decline from estimated premorbid function; and (3) Preserved, performing in the normal cognitive range without decline. The compromised group displayed the worst functional and symptom outcomes. Here, we extend our previous work by assessing the relationship among these categories of cognitive abilities and reported childhood adversity in 836 patients and healthy controls. Exploratory factor analysis of the Childhood Adversity Questionnaire revealed 3 factors (lack of parental involvement; overt abuse; family breakdown and hardship). People with schizophrenia reported significantly more childhood adversity than healthy controls on all items and factors. People with schizophrenia in the compromised group reported significantly more lack of parental involvement and family breakdown and hardship and lower socioeconomic status than those in the deteriorated group. The cognitive groups were not related to family history of psychosis. These findings identify specific social and family factors that impact cognition, highlighting the important role of these factors in the development of cognitive and functional abilities in schizophrenia.

The impact of premorbid and current intellect in schizophrenia: cognitive, symptom, and functional outcomes.

BACKGROUND: Cognitive heterogeneity among people with schizophrenia has been defined on the basis of premorbid and current intelligence quotient (IQ) estimates. In a relatively large, community cohort, we aimed to independently replicate and extend cognitive subtyping work by determining the extent of symptom severity and functional deficits in each group. METHODS: A total of 635 healthy controls and 534 patients with a diagnosis of schizophrenia or schizoaffective disorder were recruited through the Australian Schizophrenia Research Bank. Patients were classified into cognitive subgroups on the basis of the Wechsler Test of Adult Reading (a premorbid IQ estimate) and current overall cognitive abilities into preserved, deteriorated, and compromised groups using both clinical and empirical (k-means clustering) methods. Additional cognitive, functional, and symptom outcomes were compared among the resulting groups. RESULTS: A total of 157 patients (29%) classified as 'preserved' performed within one s.d. of control means in all cognitive domains. Patients classified as 'deteriorated' (n=239, 44%) performed more than one s.d. below control means in all cognitive domains except estimated premorbid IQ and current visuospatial abilities. A separate 138 patients (26%), classified as 'compromised,' performed more than one s.d. below control means in all cognitive domains and displayed greater impairment than other groups on symptom and functional measures. CONCLUSIONS: In the present study, we independently replicated our previous cognitive classifications of people with schizophrenia. In addition, we extended previous work by demonstrating worse functional outcomes and symptom severity in the compromised group.

Predicting the risk of psychosis onset: advances and prospects.

AIM: To conduct a systematic review of the methods and performance characteristics of models developed for predicting the onset of psychosis. METHODS: We performed a comprehensive literature search restricted to English articles and identified using PubMed, Medline and PsychINFO, as well as the reference lists of published studies and reviews. Inclusion criteria included the selection of more than one variable to predict psychosis or schizophrenia onset, and selection of individuals at familial risk or clinical high risk. Eighteen studies met these criteria, and we compared these studies based on the subjects selected, predictor variables used and the choice of statistical or machine learning methods. RESULTS: Quality of life and life functioning as well as structural brain imaging emerged as the most promising predictors of psychosis onset, particularly when they were coupled with appropriate dimensionality reduction methods and predictive model algorithms like the support vector machine (SVM). Balanced accuracy ranged from 100% to 78% in four studies using the SVM, and 67% to 81% in 14 studies using general linear models. CONCLUSIONS: Performance of the predictive models improves with quality of life measures, life functioning measures, structural brain imaging data, as well as with the use of methods like SVM. Despite these advances, the overall performance of psychosis predictive models is still modest. In the future, performance can potentially be improved by including genetic variant and new functional imaging data in addition to the predictors that are used currently.