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Structural similarity networks predict clinical outcome in early-phase psychosis.

ABSTRACT: Despite recent advances, there is still a major need for prediction of treatment success in schizophrenia, a condition long considered a disorder of dysconnectivity in the brain. Graph theory provides a means to characterize the connectivity in both healthy and abnormal brains. We calculated structural similarity networks in each participant and hypothesized that the "hubness", i.e., the number of edges connecting a node to the rest of the network, would be associated with clinical outcome. This prospective controlled study took place at an academic research center and included 82 early-phase psychosis patients (23 females; mean age [SD]?=?21.6 [5.5] years) and 58 healthy controls. Medications were administered in a double-blind randomized manner, and patients were scanned at baseline prior to treatment with second-generation antipsychotics. Symptoms were assessed with the Brief Psychiatric Rating Scale at baseline and over the course of 12 weeks. Nodal degree of structural similarity networks was computed for each subject and entered as a predictor of individual treatment response into a partial least squares (PLS) regression. The model fit was significant in a permutation test with 1000 permutations (P?=?0.006), and the first two PLS regression components explained 29% (95% CI: 27; 30) of the variance in treatment response after cross-validation. Nodes loading strongly on the first PLS component were primarily located in the orbito- and prefrontal cortex, whereas nodes loading strongly on the second PLS component were primarily located in the superior temporal, precentral, and middle cingulate cortex. These data suggest a link between brain network morphology and clinical outcome in early-phase psychosis.

SUBMITTER: Homan P

PROVIDER: S-EPMC6461949 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Structural similarity networks predict clinical outcome in early-phase psychosis.

Homan Philipp P Argyelan Miklos M DeRosse Pamela P Szeszko Philip R PR Gallego Juan A JA Hanna Lauren L Robinson Delbert G DG Kane John M JM Lencz Todd T Malhotra Anil K AK

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20190124 5

Despite recent advances, there is still a major need for prediction of treatment success in schizophrenia, a condition long considered a disorder of dysconnectivity in the brain. Graph theory provides a means to characterize the connectivity in both healthy and abnormal brains. We calculated structural similarity networks in each participant and hypothesized that the "hubness", i.e., the number of edges connecting a node to the rest of the network, would be associated with clinical outcome. This ...[more]

PMID: 30679724

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Project description:Given the global burden of psychotic disorders, the identification of patients with early-onset psychosis (EOP; that is, onset before the age of 18) at higher risk of adverse outcome should be a priority. A systematic search of Pubmed, Embase, and PsycInfo (1980 through August 2014) was performed to identify longitudinal observational studies assessing correlates and/or predictors of clinical, functional, cognitive, and biological outcomes in EOP. Seventy-five studies were included in the review. Using multivariate models, the most replicated predictors of worse clinical, functional, cognitive, and biological outcomes in EOP were premorbid difficulties and symptom severity (especially of negative symptoms) at baseline. Longer duration of untreated psychosis (DUP) predicted worse clinical, functional, and cognitive outcomes. Higher risk of attempting suicide was predicted by greater severity of psychotic illness and of depressive symptoms at the first episode of psychosis. Age at onset and sex were not found to be relevant predictors of outcome in most multivariate models, whereas studies using bivariate analyses yielded inconsistent results. Lower intelligence quotient at baseline predicted lower insight at follow-up, worse functional outcomes, and a diagnostic outcome of schizophrenia. Biological predictors of outcome in EOP have been little studied and have not been replicated. Lower levels of antioxidants at baseline predicted greater brain volume changes and worse cognitive functioning at follow-up, whereas neuroimaging markers such as regional cortical thickness and gray matter volume at baseline predicted remission and better insight at follow-up, respectively. EOP patients with poorer premorbid adjustment and prominent negative symptoms at initial presentation are at risk of poor outcome. They should therefore be the target of careful monitoring and more intensive interventions to address whether the disease course can be modified in this especially severely affected group. Early intervention strategies to reduce DUP may also improve outcome in EOP.

Project description:BackgroundPeripheral blood parameters are correlated to immune-checkpoint inhibitor efficacy in solid tumors, such as melanoma and non-small cell lung cancer. Few data are currently available on the prognostic role of these immune-inflammatory biomarkers for other solid tumors and immunotherapy combinations.Material and methodsFrom August 2014 to May 2019, 153 patients with metastatic solid tumors were enrolled in phase I clinical trials testing immunotherapy both as single agents and as combinations. Primary endpoint was to evaluate the impact of baseline blood parameters on progression-free survival (PFS) and overall survival (OS).ResultsThe most common tumor types were gastrointestinal, breast, and gynecological cancers (22.9%, 22.2%, and 15.0%, respectively). Higher lactate dehydrogenase (LDH) and derived neutrophil-to-lymphocyte ratio (dNLR) were independently associated with reduced PFS (hazard ratio [HR], 1.97; 95% confidence interval [CI], 1.30-2.99; p = .001, and HR, 2.29; 95% CI, 1.39-3.77; p = .001, respectively) and reduced OS (HR, 2.04; 95% CI, 1.26-3.28; p = .004, and HR, 2.06; 95% CI, 1.12-3.79; p = .02, respectively). In the subgroup analysis, (single agent vs. combination), patients at "good" (dNLR <3 and LDH < upper limit of normal [ULN]) and "intermediate and poor" (dNLR >3 and/or LDH > ULN) risk had higher and lower PFS, respectively (p for interaction = .002). Conversely, patients receiving monotherapy presented statistically significant difference in OS according to the risk group, whereas this effect was not observed for those treated with combinations (p for interaction = .004).ConclusionElevated LDH and dNLR are associated with poorer survival outcomes in patients treated with immunotherapy in phase I clinical trials, regardless of tumor type. These parameters represent an easy tool that might be considered as stratification factors in immunotherapy-based clinical trials.Implications for practiceIn this retrospective cohort study of 153 patients with metastatic solid tumors treated with immunotherapy in the context of phase I clinical trials, elevated baseline lactate dehydrogenase and derived neutrophil-to-lymphocyte ratio were associated with reduced survival regardless of tumor subtype. If prospectively validated, these parameters might represent low-cost and easy biomarkers that could help patient selection for early phase immunotherapy trials and be applied as a stratification factor in randomized studies testing immunotherapy agents.

Dataset Information

Structural similarity networks predict clinical outcome in early-phase psychosis.

Publications

Structural similarity networks predict clinical outcome in early-phase psychosis.

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