OBJECTIVE The supplementary motor area (SMA) syndrome is a well-studied lesional model of brain plasticity involving the sensorimotor network. Patients with diffuse low-grade gliomas in the SMA may exhibit this syndrome after resective surgery. They experience a temporary loss of motor function, which completely resolves within 3 months. The authors used functional MRI (fMRI) resting state analysis of the sensorimotor network to investigate large-scale brain plasticity between the immediate postoperative period and 3 months' follow-up. METHODS Resting state fMRI was performed preoperatively, during the immediate postoperative period, and 3 months postoperatively in 6 patients with diffuse low-grade gliomas who underwent partial surgical excision of the SMA. Correlation analysis within the sensorimotor network was carried out on those 3 time points to study modifications of its functional connectivity. RESULTS The results showed a large-scale reorganization of the sensorimotor network. Interhemispheric connectivity was decreased in the postoperative period, and increased again during the recovery process. Connectivity between the lesion side motor area and the contralateral SMA rose to higher values than in the preoperative period. Intrahemispheric connectivity was decreased during the immediate postoperative period and had returned to preoperative values at 3 months after surgery. CONCLUSIONS These results confirm the findings reported in the existing literature on the plasticity of the SMA, showing large-scale modifications of the sensorimotor network, at both inter- and intrahemispheric levels. They suggest that interhemispheric connectivity might be a correlate of SMA syndrome recovery.

Quantitative susceptibility mapping is a new technique and its processing pipeline has to be validated before clinical practice. We described an easy to build magnetic resonance (MR) susceptibility phantom based on iron sucrose.

Clock-dependent and system-driven oscillators interact in the suprachiasmatic nuclei to pace mammalian circadian rhythms

A methodology for non-invasive estimation of the pressure in internal carotid arteries is proposed. It uses data assimilation and Ensemble Kalman filters in order to identify unknown parameters in a mathematical description of the cerebral network. The approach uses patient specific blood flow rates extracted from Magnetic Resonance Angiography and Magnetic Resonance Imaging. This construction is necessary as the simulation of blood flows in complex arterial networks, such as the circle of Willis, is not straightforward because hemodynamic parameters are unknown as well as the boundary conditions necessary to close this complex system with many outlets. For instance, in clinical cases, the values of Windkessel model parameters or the Young's modulus and the thickness of the arteries are not available on per-patient cases. To make the approach computational efficient, a reduced order zero-dimensional compartment model is used for blood flow dynamics. Using this simplified model, the proof-of-concept study demonstrates how to use the EnKF as an optimization tool to find parameters and how to make the inverse hemodynamic problem tractable. The predicted blood flow rates in the internal carotid arteries and the predicted systolic and diastolic brachial blood pressures are found to be in good agreement with the clinical measurements.