Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Background: Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e.

Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by better and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modelling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models can predict clinical outcomes. We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression (TRD), across multiple coils and protocols.

FoxI1e activates ectoderm formation and controls cell position in the Xenopus blastula.

The segregation of the vertebrate embryo into three primary germ layers is one of the earliest developmental decisions. In Xenopus, where the process is best understood, the endoderm is specified by a vegetally localized transcription factor, VegT, which releases nodal signals that specify the adjacent marginal zone of the blastula to become mesoderm. However, little is known about how the ectoderm becomes specified.