Use of proper high-permittivity material could improve radiofrequency (RF) coil efficiency and imaging signal-to-noise ratio (SNR) for human MRI applications. We designed, constructed, and evaluated RF transmit and receive head array coils integrated with a low-loss high permittivity ceramic (HPC) helmet with the relative permittivity (ϵr) of 200 and four-segment helmet structure for human brain MRI application at 7 Tesla (T). Both electromagnetic simulation and experimental MRI results consistently demonstrated that the novel HPC helmet largely increased the RF reception (B1[-]) and transmission (B1[+]) magnetic field strength by 82% and 19%, respectively, and net imaging SNR gain by 64% across the entire human brain compared to the baseline condition using the same RF coil configuration without the HPC helmet. The findings conclude that the new HPC helmet effectively enhances SNR primarily by boosting the RF coil detection sensitivity while introducing only a small noise increase (11%). The HPC helmet moderately reduced coil to-coil coupling of the receive array elements. Compared to the baseline condition, HPC helmet demonstrated an approximate 10 dB reduction in noise correlation and 2% to 7% increase in the 97[th] percentile of 1/g-factors with various acceleration factors, ultimately facilitating high-quality parallel imaging reconstruction. The four-segment HPC helmet showed a nearly 50% lower specific absorption rate (SAR) than the monolithic HPC helmet. This work demonstrates the advancement of the integrated HPC helmet and RF coil technology to largely improve the imaging quality at 7T for whole human brain MRI applications, and potentially for translation.

While conventional approaches characterize brain functional connectivity using static, pairwise interactions, higher-order interactions among multiple regions and their temporal organization are also essential for understanding complex brain dynamics. Here, we propose a temporal higher-order functional brain network framework (THFBN) that integrates simplicial complexes with temporal dynamical modeling to infer directed, time-varying higher-order interactions from stereoelectroencephalography (SEEG) recordings in epilepsy. We show that higher-order interactions across time reveals structured network reconfigurations that are not captured by static higher-order representations alone, giving rise to rapid yet structured network reconfigurations that are not captured by static or pairwise representations. Applying THFBN to epileptic seizures reveals a spatially heterogeneous redistribution of higher-order positive and negative predictive influences across seizure stages, with the strongest variability observed in temporal and insular regions, while positive-negative balance remains generally stable despite substantial changes in influence strength. Together, these findings demonstrate that integrating temporal dynamics with higher-order interactions reveals structured network reconfigurations during seizure evolution that are not captured by conventional connectivity models, and provide a principled framework for studying dynamic higher-order brain organization in epilepsy.