Volume 5 Issue 4, April 2021

The dynamics of multiregional brain networks in response to temporally varying patterns of ongoing direct electrical stimulation can be predicted by modelling, with variabilities in prediction accuracy explained by at-rest functional connectivity.

The time course of tumour responses to immunotherapies can be mathematically predicted on the basis of tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing.

A mathematical model of the time course of tumour responses to immunotherapy predicts tumour burden and treatment sensitivity across cancer types and drug combinations.

Two clinically relevant subtypes of post-traumatic stress disorder and major depressive disorder have been identified via machine learning analyses of functional connectivity patterns in resting-state electroencephalography.

Input–output models can predict multiregional brain network dynamics in response to temporally varying patterns of ongoing direct electrical stimulation, with variabilities in prediction accuracy explained by at-rest functional connectivity.

A computational model of the spatiotemporal dynamics of the perivascular niche that incorporates relevant cellular and tissue-level phenomena can be used to optimize treatment schedules for glioblastoma.

An information-theoretic analysis of DNA methylation in samples from patients with paediatric acute lymphoblastic leukaemia reveals that a regulatory set of driver genes harbour the greatest differences in methylation stochasticity.