Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis.

Detecting patients at high relapse risk after the first episode of psychosis (HRR-FEP) could help the clinician adjust the preventive treatment. To develop a tool to detect patients at HRR using their baseline clinical and structural MRI, we followed 227 patients with FEP for 18-24 months and applied MRIPredict. We previously optimized the MRI-based machine-learning parameters (combining unmodulated and modulated gray and white matter and using voxel-based ensemble) in two independent datasets. Patients estimated to be at HRR-FEP showed a substantially increased risk of relapse (hazard ratio=4.58, P<0.05). Accuracy was poorer when we only used clinical or MRI data. We thus show the potential of combining clinical and MRI data to detect which individuals are more likely to relapse, who may benefit from increased frequency of visits, and which are unlikely, who may be currently receiving unnecessary prophylactic treatments. We also provide an updated version of the MRIPredict software.We would also like to thank the Instituto de Salud Carlos III, the Spanish Ministry of Science, Innovation, and Universities, the European Regional Development Fund (ERDF/FEDER), European Social Fund, “Investing in your future”, “A way of making Europe” (projects: PI08/0208, PI11/00325, PI14/00292, PI14/00612, PI14/01148, PI14/01151, PI17/00481, PI17/01997, PI18/01055, PI19/00394, PI19/00766, PI20/00721, PI20/01342, and PI21/00713; contracts: CP14/00041, JR19/00024, CPII19/00009, and CD20/00177); CERCA Program; Catalan Government, the Secretariat of Universities and Research of the Department of Enterprise and Knowledge (2017SGR01271, 2017SGR1355, and 2017SGR1365); Institut de Neurociències, Universitat de Barcelona; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2); the University of the Basque Country (2019 321218ELCY GIC18/107); the Basque Government (2017111104); European Union Structural Funds, European Union Seventh Framework Program, European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. The funding organizations played no role in the study design, data collection, analysis, or manuscript approval

Estimating time-varying networks

Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed $l_1$-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS308 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org