Cerebral blood flow in schizophrenia: A systematic review and meta-analysis of MRI-based studies.

INTRODUCTION: Schizophrenia-spectrum disorders (SSD) represent one of the leading causes of disability worldwide and are usually underpinned by neurodevelopmental brain abnormalities observed on a structural and functional level. Nuclear medicine imaging studies of cerebral blood flow (CBF) have already provided insights into the pathophysiology of these disorders. Recent developments in non-invasive MRI techniques such as arterial spin labeling (ASL) have allowed broader examination of CBF across SSD prompting us to conduct an updated literature review of MRI-based perfusion studies. In addition, we conducted a focused meta-analysis of whole brain studies to provide a complete picture of the literature on the topic. METHODS: A systematic OVID search was performed in Embase, MEDLINEOvid, and PsycINFO. Studies eligible for inclusion in the review involved: 1) individuals with SSD, first-episode psychosis or clinical-high risk for psychosis, or; 2) had healthy controls for comparison; 3) involved MRI-based perfusion imaging methods; and 4) reported CBF findings. No time span was specified for the database queries (last search: 08/2022). Information related to participants, MRI techniques, CBF analyses, and results were systematically extracted. Whole-brain studies were then selected for the meta-analysis procedure. The methodological quality of each included studies was assessed. RESULTS: For the systematic review, the initial Ovid search yielded 648 publications of which 42 articles were included, representing 3480 SSD patients and controls. The most consistent finding was that negative symptoms were linked to cortical fronto-limbic hypoperfusion while positive symptoms seemed to be associated with hyperperfusion, notably in subcortical structures. The meta-analysis integrated results from 13 whole-brain studies, across 426 patients and 401 controls, and confirmed the robustness of the hypoperfusion in the left superior and middle frontal gyri and right middle occipital gyrus while hyperperfusion was found in the left putamen. CONCLUSION: This updated review of the literature supports the implication of hemodynamic correlates in the pathophysiology of psychosis symptoms and disorders. A more systematic exploration of brain perfusion could complete the search of a multimodal biomarker of SSD.

Subtyping negative symptoms in first-episode psychosis: Contrasting persistent negative symptoms with a data-driven approach.

Persistent negative symptoms (PNS) are linked to poor functional outcomes and may be primary or caused by secondary factors. Although several studies have examined PNS in first-episode psychosis (FEP), a comparison with a data-driven approach is lacking. Here, we compared clinically defined PNS subgroups with class trajectories identified through latent growth modeling (LGM). Patients admitted to an early intervention service (N = 392) were classified as PNS (n = 105), secondary PNS (sPNS; n = 74), or non-PNS (n = 213) based on longitudinal data collected six to twelve months after admission. LGM was used to stratify patients based on similar negative symptom course over the same time period. Using multiple linear regression, we assessed the utility of both approaches in predicting Social and Occupational Functioning Assessment Scale (SOFAS) scores at two-year follow-up. Three negative symptom trajectories were identified: low and remitting (LR; n = 158), moderate and improving (MI; n = 163) and delayed partial response (DR; n = 71). Most non-PNS patients followed the LR trajectory, while patients with PNS or sPNS were generally divided between MI and DR. Both PNS classification and trajectory membership were significant predictors of two-year functional outcomes; the DR and MI trajectories predicted greater increases in SOFAS scores (DR: b = -19.14; MI: b = -11.54) than either sPNS (b = -9.19) or PNS (b = -6.46). These findings demonstrate that combining PNS and symptom-based stratification can predict functional outcomes more accurately than either taxonomy alone. Such a combined approach could yield significant advances in developing more targeted interventions for patients at risk for poor functional outcomes.