BACKGROUND AND PURPOSE: Infants born preterm are commonly diagnosed with structural brain lesions known to affect long-term neurodevelopment negatively. Yet, the effects of preterm birth on brain development in the absence of intracranial lesions remain to be studied in detail. In this study, we aim to quantify long term consequences of preterm birth on brain development in this specific group. MATERIALS AND METHODS: Neonatal cranial sonography and follow-up T1-weighted MR imaging and DTI were performed to evaluate whether the anatomic characteristics of the cerebrum and cerebellum in a cohort of school-aged children (6 -12 years of age) were related to gestational age at birth in children free of brain lesions in the perinatal period. RESULTS: In the cohort consisting of 36 preterm (28-37 weeks' gestational age) and 66 term-born infants, T1-weighted MR imaging and DTI at 6-12 years revealed a reduction of cerebellar white matter volume (β=0.387, P=.001), altered fractional anisotropy of cerebellar white matter (β=-0.236, P=.02), and a reduction of cerebellar gray and white matter surface area (β=0.337, P=.001;β=0.375, P<.001, respectively) in relation to birth age. Such relations were not observed for the cerebral cortex or white matter volume, surface area, or diffusion quantities. CONCLUSIONS: The results of our study show that perinatal influences that are not primarily neurologic are still able to disturb long-term neurodevelopment, particularly of the developing cerebellum. Including the cerebellum in future neuroprotective strategies seems therefore essential.

Graphical user interfaces (GUIs) are commonly used to construct and postprocess numerical groundwater flow and transport models. Scripting model development with the programming language Python is presented here as an alternative approach. One advantage of Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulation, optimization, and data analysis. For MODFLOW-based models, the FloPy package was developed by the authors to construct model input files, run the model, and read and plot simulation results. Use of Python with the available scientific packages and FloPy facilitates data exploration, alternative model evaluations, and model analyses that can be difficult to perform with GUIs. Furthermore, Python scripts are a complete, transparent, and repeatable record of the modeling process. The approach is introduced with a simple FloPy example to create and postprocess a MODFLOW model. A more complicated capture-fraction analysis with a real-world model is presented to demonstrate the types of analyses that can be performed using Python and FloPy