Objective: Although the clinical and biological importance of calcification is well recognized for the extracerebral vasculature, its role in cerebral vascular disease, particularly, intracranial aneurysms (IAs), remains poorly understood. Extracerebrally, 2 distinct mechanisms drive calcification, a nonatherosclerotic, rapid mineralization in the media and a slower, inflammation driven, atherosclerotic mechanism in the intima. This study aims to determine the prevalence, distribution, and type (atherosclerotic, nonatherosclerotic) of calcification in IAs and assess differences in occurrence between ruptured and unruptured IAs. Approach and Results: Sixty-five 65 IA specimens (48 unruptured, 17 ruptured) were resected perioperatively. Calcification and lipid pools were analyzed nondestructively in intact samples using high resolution (0.35 μm) microcomputed tomography. Calcification is highly prevalent (78%) appearing as micro (1 mm) calcifications. Calcification manifests in IAs as both nonatherosclerotic (calcification distinct from lipid pools) and atherosclerotic (calcification in the presence of lipid pools) with 3 wall types: Type I—only calcification, no lipid pools (20/51, 39%), Type II—calcification and lipid pools, not colocalized (19/51, 37%), Type III—calcification colocalized with lipid pools (12/51, 24%). Ruptured IAs either had no calcifications or had nonatherosclerotic micro- or meso-calcifications (Type I or II), without macro-calcifications. Conclusions: Calcification in IAs is substantially more prevalent than previously reported and presents as both nonatherosclerotic and atherosclerotic types. Notably, ruptured aneurysms had only nonatherosclerotic calcification, had significantly lower calcification fraction, and did not contain macrocalcifications. Improved understanding of the role of calcification in IA pathology should lead to new therapeutic targets.

Background Blebs are important secondary structures of intracranial aneurysms associated with increased rupture risk and can affect local wall stress and hemodynamics. Mechanisms of bleb development and evolution are not clearly understood. We investigate the relationship between blebs with different wall characteristics and local hemodynamics and rupture sites. Methods Blebs with different wall appearances in intra-operative videos were analyzed with image-based computational fluid dynamics. Thin red blebs were compared against thick atherosclerotic/hyperplastic white/yellow blebs. Rupture points were identified in videos of ruptured aneurysms harboring blebs. Results Thin blebs tended to be closer to the inflow than atherosclerotic blebs of the same aneurysm (P=0.0234). Blebs near the inflow had higher velocity (P=0.0213), vorticity (P=0.0057), shear strain rate (P=0.0084), wall shear stress (WSS) (P=0.0085), and WSS gradient (P=0.0151) than blebs far from the inflow. In a subset of 12 ruptured aneurysms harboring blebs, rupture points were associated with thin blebs in 42% of aneurysms, atherosclerotic blebs in 25%, and were away from blebs in the remaining 33%. Conclusions Not all blebs are equal; some have thin translucent walls while others have thick atherosclerotic walls. Thin blebs tend to be located closer to the inflow than atherosclerotic blebs. Blebs near the inflow are exposed to stronger flows with higher and spatially variable WSS than blebs far from the inflow which tend to have uniformly lower WSS. Aneurysms can rupture at thin blebs, atherosclerotic blebs, and even away from blebs. Further study of wall failure in aneurysms with different bleb types is needed.

Objective: Intraoperative neurophysiological monitoring (IONM) is the use of electrophysiological methods during certain high-risk surgeries to assess the functional integrity of nerves in real time and alert the surgeon to prevent damage. However, the efficiency of IONM in current practice is limited by latency of verbal communications, inter-rater variability, and the subjective manner in which electrophysiological signals are described. Methods: In an attempt to address these shortcomings, we investigate automated classification of free-running electromyogram (EMG) waveforms during IONM. We propose a hybrid model with a convolutional neural network (CNN) component and a long short-term memory (LSTM) component to better capture complicated EMG patterns under conditions of both electrical noise and movement artifacts. Moreover, a preprocessing pipeline based on data normalization is used to handle classification of data from multiple subjects. To investigate model robustness, we also analyze models under different methods for processing of artifacts. Results: Compared with several benchmark modeling methods, CNN-LSTM performs best in classification, achieving accuracy of 89.54% and sensitivity of 94.23% in cross-patient evaluation. Conclusion: The CNN-LSTM model shows promise for automated classification of continuous EMG in IONM. Significance: This technique has potential to improve surgical safety by reducing cognitive load and inter-rater variability.