Using our well- characterized cohort of Ugandan CM survivors who were then en­rolled into our later CCRT follow- up studies, Leonenko et al. (2023) explored the use of machine learning algorithms to model their neurodevelopmental outcomes using a new strictly stationary time series model with marginal generalized Gaussian distribu­tions. e EEG data previously prepared for their prior automated stochastic analyses (Veretennikova et al., 2018) also were used in these exploratory analyses. eir gen­eralized Gaussian time series modeling for EEG increments, however, was expanded to include clinical biomarkers during acute illness (Blantyre coma score, hemoglobin level, white blood cell count), CSF proinammatory biomarkers, and other immune­pathogenic regulators (interleukin (IL)- 10, IL- 1α, IL- 1α receptor levels (plasma), IL- 6, and vWF). eir analyses also included quality of the Home Environment Measurement Evaluation (HOME) assessments done at the child/ caregiver’s home. is measure was used to evaluate the child’s developmental milieu to control for the quality of the home environment as developmental and neuropsychological outcomes at 6- and 12- month follow- up aer hospital discharge. We could then also better gauge the extent to which CCRT neuropsychological benets were modied by CM acute illness severity based on acute illness biomarker levels in any subsequent CCRT in­tervention studies. Veretennikova and colleagues concluded that the addition of sto­chastic EEG modeling improved the prediction of children’s brain function 6 months following coma and that it can be used prognostically during the acute phase of illness for longer- term neurocognitive outcomes for CM survivors (Leonenko et al., 2023).