Cognitive and Clinical Predictors of Prefrontal Cortical Thickness Change Following First-Episode of Psychosis

Cognitive Inflexibility Predicts Negative Symptoms Severity in Patients with First-Episode Psychosis: A 1-Year Follow-Up Study

Negative symptoms and cognitive deficits play a major role in psychosis and significantly influence the functional outcomes of patients, particularly those with a first episode of psychosis (FEP). However, limited research has explored the predictive capacity of cognitive deficits during FEP for subsequent negative symptomatology. Drawing from the Athens FEP research study, we conducted a retrospective longitudinal study in 80 individuals with FEP. All patients were drug naive at admission. Cognitive tests were administered at 1-month and 1-year post-admission, while negative symptomatology was assessed at the same time points using PANSS by trained raters. We considered confounding factors such as age, gender, duration of untreated psychosis (DUP), treatment received, premorbid social adjustment, and premorbid IQ. Univariate regression analysis identified cognitive domains that correlated with negative symptomatology. These, along with the confounders, were incorporated into a multiple regression, with the 1-year PANSS negative scale serving as the dependent variable. Employing the backward elimination technique, we found a statistically significant inverse relationship between the categories completed in the Wisconsin card sorting test (WCST) and the 1-year PANNS negative scale (p = 0.01), beyond the associations with DUP and the 1-month PANSS negative scale. Our results suggest that cognitive flexibility, a key component of executive functions, predicts negative symptom severity one year after FEP.

The relationship between negative symptoms and MATRICS neurocognitive domains: A meta-analysis and systematic review

BACKGROUND

Negative symptoms (NS) are a core symptom domain in schizophrenia spectrum disorders and are associated with poorer social and vocational functioning, and with increased likelihood and durations of hospital admission. NS are not well understood, limiting available interventions. However, numerous studies have reported associations between neurocognitive domains and NS severity. Thus, one promising area in understanding NS is in relation to neurocognition. Currently, the specificity of the relationship between NS and neurocognition is unknown, meaning that there is no consensus regarding which neurocognitive domain is most strongly associated with NS. There is a need to systematically examine the relationship between NS and various neurocognitive domains within study samples.

METHODS

A systematic search of Ovid PsycINFO, Ovid MEDLINE and Web of Science was performed for articles published since 2004 (year of MATRICS Consensus publication). Inclusion criteria were: 1) individuals with schizophrenia spectrum disorders, first episode psychosis or clinical high risk 2) assessed all six MATRICS neurocognitive domains (processing speed, attention, working memory, verbal learning & memory, visual learning & memory, reasoning & problem solving), 3) reported correlations between all six MATRICS neurocognitive domains and global NS. A three-level random effects hierarchical meta-analysis was performed to assess the relationship between NS (global, expressive, and experiential dimensions) and the six MATRICS neurocognitive domains.

RESULTS

21 studies were included in the review (n = 3619). All MATRICS neurocognitive domains had small significant correlations with global NS (r = -0.16 to -0.20, p < 0.0001). This relationship was significantly moderated by diagnosis and the moderating effect of sex/ gender trended on significance. Analysis of a subset of the studies revealed that MATRICS neurocognitive domains also had small significant correlations with the two NS dimensions, expressive and experiential. Correlations were stronger with the expressive NS dimension.

CONCLUSIONS

This review is novel in assessing the relationship between multiple neurocognitive domains and NS within the same sample, by synthesizing close to two decades of research. Our results suggest that there is a non-specific relationship between neurocognition and NS, and that expressive NS may have a stronger relationship with neurocognitive functioning-based on the MATRICS classification of neurocognition and the neurocognitive assessments used in the included studies. This has implications on our understanding of NS and neurocognition, as well as their treatments. As we gain better understanding of the directionality of the NS-cognition relationship, it could suggest that NS, particularly in the expressive domain, could be improved by targeting cognition globally or that neurocognitive treatments could be more effective if NS are addressed first. Further implications of these results are discussed.

Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

Cognitive Dysfunction as a Risk Factor for Psychosis

The current chapter summarizes recent evidence for cognition as a risk factor for the development of psychosis, including the range of cognitive impairments that exist across the spectrum of psychosis risk symptoms. The chapter examines several possible theories linking cognitive deficits with the development of psychotic symptoms, including evidence that cognitive deficits may be an intermediate risk factor linking genetic and/or neural metrics to psychosis spectrum symptoms. Although there is not strong evidence for unique cognitive markers associated specifically with psychosis compared to other forms of psychopathology, psychotic disorders are generally associated with the greatest severity of cognitive deficits. Cognitive deficits precede the development of psychotic symptoms and may be detectable as early as childhood. Across the psychosis spectrum, both the presence and severity of psychotic symptoms are associated with mild to moderate impairments across cognitive domains, perhaps most consistently for language, cognitive control, and working memory domains. Research generally indicates the size of these cognitive impairments worsens as psychosis symptom severity increases. The chapter points out areas of unclarity and unanswered questions in each of these areas, including regarding the mechanisms contributing to the association between cognition and psychosis, the timing of deficits, and whether any cognitive systems can be identified that function as specific predictors of psychosis risk symptoms.

Structural brain abnormalities associated with cognitive impairments in bipolar disorder

OBJECTIVE

Cognitive impairment has been highlighted as a core feature of bipolar disorder (BD) that often persists during remission. The specific brain correlates of cognitive impairment in BD remain unclear which impedes efficient therapeutic approaches. In a large sample of remitted BD patients, we investigated whether morphological brain abnormalities within dorsal prefrontal cortex (PFC) and hippocampus were related to cognitive deficits.

METHODS

Remitted BD patients (n = 153) and healthy controls (n = 52) underwent neuropsychological assessment and structural MRI. Based on hierarchical cluster analysis of neuropsychological test performance, patients were classified as either cognitively impaired (n = 91) or cognitively normal (n = 62). The neurocognitive subgroups were compared amongst each other and with healthy controls in terms of dorsal PFC cortical thickness and volume, hippocampus shape and volume, and total cerebral grey and white matter volumes.

RESULTS

Cognitively impaired patients displayed greater left dorsomedial prefrontal thickness compared to cognitively normal patients and healthy controls. Hippocampal grey matter volume and shape were similar across patient subgroups and healthy controls. At a whole-brain level, cognitively impaired patients had lower cerebral white matter volume compared to the other groups. Across all participants, lower white matter volume correlated with more impaired neuropsychological test performance.

CONCLUSIONS

Our findings associate cognitive impairment in bipolar disorder with cerebral white matter deficits, factors which may relate to the observed morphological changes in dorsomedial PFC possibly due to increased neurocognitive effort to maintain symptom stability in these remitted patients.

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study

BACKGROUND

Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learning and structural magnetic resonance imaging (MRI) to study the impact of psychotic illness and obesity on brain ageing/neuroprogression shortly after illness onset.

METHODS

We acquired 2 prospective MRI scans on average 1.61 years apart in 183 FEP and 155 control individuals. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants and calculated BrainAGE by subtracting chronological from the estimated brain age.

RESULTS

Individuals with FEP had a higher initial BrainAGE than controls (3.39 ± 6.36 vs 1.72 ± 5.56 years; β = 1.68, t(336) = 2.59, P = .01), but similar annual rates of brain ageing over time (1.28 ± 2.40 vs 1.07±1.74 estimated years/actual year; t(333) = 0.93, P = .18). Across both cohorts, greater baseline body mass index (BMI) predicted faster brain ageing (β = 0.08, t(333) = 2.59, P = .01). For each additional BMI point, the brain aged by an additional month per year. Worsening of functioning over time (Global Assessment of Functioning; β = -0.04, t(164) = -2.48, P = .01) and increases especially in negative symptoms on the Positive and Negative Syndrome Scale (β = 0.11, t(175) = 3.11, P = .002) were associated with faster brain ageing in FEP.

CONCLUSIONS

Brain alterations in psychosis are manifest already during the first episode and over time get worse in those with worsening clinical outcomes or higher baseline BMI. As baseline BMI predicted faster brain ageing, obesity may represent a modifiable risk factor in FEP that is linked with psychiatric outcomes via effects on brain structure.

White matter microstructure and structural networks in treatment-resistant schizophrenia patients after commencing clozapine treatment: A longitudinal diffusion imaging study

This study investigates changes on white matter microstructure and neural networks after 6 months of switching to clozapine in schizophrenia patients compared to controls, and whether any changes are related to clinical variables. T1 and diffusion-weighted MRI images were acquired at baseline before commencing clozapine and after 6 months of treatment for 22 patients with treatment-resistant schizophrenia and 23 controls. The Tract-based spatial statistics approach was used to compare changes over time between groups in fractional anisotropy (FA). Changes in structural network organisation weighted by FA and number of streamlines were assessed using graph theory. Patients displayed a significant reduction of FA over time (p<0.05) compared to controls in the genu and body of the corpus callosum and bilaterally in the anterior and superior corona radiata. There was no correlation between FA change in patients and changes in clinical variables or serum level of clozapine. There was no changes in structural network organisation between groups (F(7,280)=2.80;p = 0.187). This longitudinal study demonstrated progressive focal FA abnormalities in key anterior tracts, but preserved brain structural network organisation in patients. The FA reduction was independent of any clinical measures and may reflect progression of the underlying pathophysiology of this malignant form of schizophrenia illness.

Working memory training effects across the lifespan: Evidence from human and experimental animal studies

Working memory refers to a cognitive function that provides temporary storage and manipulation of the information necessary for complex cognitive tasks. Due to its central role in general cognition, several studies have investigated the possibility that training on working memory tasks could improve not only working memory function but also increase other cognitive abilities or modulate other behaviors. This possibility is still highly controversial, with prior studies providing contradictory findings. The lack of systematic approaches and methodological shortcomings complicates this debate even more. This review highlights the impact of working memory training at different ages on humans. Finally, it demonstrates several findings about the neural substrate of training in both humans and experimental animals, including non-human primates and rodents.