Neural correlates of positive and negative symptoms through the illness course: an fMRI study in early psychosis and chronic schizophrenia

Abstract

Psychotic illness is associated with cognitive control deficits and abnormal recruitment of neural circuits subserving cognitive control. It is unclear to what extent this dysfunction underlies the development and/or maintenance of positive and negative symptoms typically observed in schizophrenia. In this study we compared fMRI activation on a standard Stroop task and its relationship with positive and negative symptoms in early psychosis (EP, N = 88) and chronic schizophrenia (CHR-SZ, N = 38) patients. CHR-SZ patients showed reduced frontal, striatal, and parietal activation across incongruent and congruent trials compared to EP patients. Higher positive symptom severity was associated with reduced activation across both trial types in supplementary motor area (SMA), middle temporal gyrus and cerebellum in EP, but not CHR-SZ patients. Higher negative symptom severity was associated with reduced cerebellar activation in EP, but not in CHR-SZ patients. A negative correlation between negative symptoms and activation in SMA and precentral gyrus was observed in EP patients and in CHR-SZ patients. The results suggest that the neural substrate of positive symptoms changes with illness chronicity, and that cognitive control related neural circuits may be most relevant in the initial development phase of positive symptoms. These findings also highlight a changing role for the cerebellum in the development and later maintenance of both positive and negative symptoms.

Evidence of an interaction between FXR1 and GSK3β polymorphisms on levels of Negative Symptoms of Schizophrenia and their response to antipsychotics

“…Rather importantly, our results indicate that the impact of genetic variation potentially regulating FXR1-GSK3β signaling on psychopathology of SCZ is quite specific for NSs, whereas it is not significant for positive symptoms. One possible explanation of such a finding is that, since there is evidence that SCZ negative and positive symptom domains are supported by different neurobiological and brain circuitry systems [70,71], it is possible that genetic variation impacting on one of the two domains has less pronounced effects on the other one. This seems particularly plausible in light of existing evidence that genomic variation associated with SCZ at genome-wide level of significance clusters to different biological ontologies that distinctly support either positive or NSs [46].…”

Transdiagnostic neuroimaging markers of psychiatric risk: A narrative review

“…Alternatively, the finding of an association between cerebellar morphology and p-factor in younger ( Romer et al, 2018 ) but not older ( Romer et al, 2019 ) adults might also reflect a dynamic role of the cerebellum throughout the lifespan, where contributions of cerebellar structure to psychopathology are greater at a younger age. ( Vanes et al, 2019 )Finally, a correlation between reduced visual association cortex volume and the p -factor might reflect less efficient integration of bottom-up sensory signals with top-down executive control processes associated with a greater burden of psychopathology.…”

Multi-dimensional predictions of psychotic symptoms via machine learning

“…The limited prior work on predicting schizophrenia symptoms via machine learning has thus far only been performed on the basis of composite symptoms (10,11), general functioning (12,13) and polygenic risk scores for schizophrenia (14). Other neuroimaging studies have also reported univariate correlates (15,16), or lack thereof (17,18), with symptom severity on the basis of composite summary scores rather than those of the underlying symptoms, an approach which significantly comprises aetiological specificity (19). For example, if we were to compare two patients, one with disorganised thought processes which render them unable to bathe themselves to another with severe alogia who is unable to communicate, these are vastly different symptoms which in turn are likely to be caused by different sources of dysfunction in different neural networks.…”