Features of cortical morphology in aneurysmal subarachnoid hemorrhage

Abstract

Background and purpose: Structural brain damage was discovered in patients after aneurysmal subarachnoid hemorrhage (SAH). However, changes of cortical gray matter characteristics remain unknown. The aim of this study was to test the hypothesis that cortical morphometry features were disrupted after aneurysmal SAH. Methods: Structural MRI were acquired from 115 aneurysm patients and 32 healthy controls. The FreeSurfer pipeline was used to cortical construction for all subjects. Further, patients were separately divided into subgroups based on presence of SAH, clipping or coiling, and length of follow-up after treatment. Differences in cortical thickness, surface area and volume between groups were compared after controlling for covariates. Subsequently, correlation analyses were performed between abnormal cortical features and clinical data. Results: The vertex-wise analyses revealed that aneurysm patients present smaller cortical volume in the right superiorfrontal gyrus than healthy controls, but there was no significant difference for cortical thickness or surface area. As expected, both microsurgical clipping and postoperative follow-up time had no significant effect on cortical morphological measures following subgroups analyses. We only found patients who with ruptured aneurysm had decreased surface area in the left superiorparietal gyrus, left precuneus, and right superiorfrontal gyrus in comparison to the unruptured group. However, positive finding of decreased regional gray matter volume and surface area were disappeared after adding the total intracranial volume as additional covariate. Conclusion: These findings enhance our understanding for the mechanism of brain damage following aneurysmal SAH, and additional support the advantages of brain protection of keyhole surgery for aneurysm treatment.

Anomalous Thermal Transport across the Superionic Transition in Ice

“…Recent advances in machine-learning potential surface allow a full quantum-mechanical, ab initio treatment of the interatomic interactions efficiently. [16][17][18][19][20][21][22] The deep potential water model is reported to predict a phase diagram close to experiments, [23] and its following applications have demonstrated its success in estimating the transport properties of water. [24,25] Therefore, in this work, we adopt the deep-potential (DP) water model and conduct a series of deep potential molecular dynamics (DPMD) simulations to obtain the thermal conductivity of VII and VII ′′ , as well as diffusion coefficient, spectral energy density, and dynamic structure factor, to understand the heat transport and to unravel the impact of mobile protons across superionic phase transition.…”