Social reactivation of fear engrams enhances memory recall.

For group-living animals, the social environment provides salient experience that can weaken or strengthen aspects of cognition such as memory recall. Although the cellular substrates of individually acquired fear memories in the dentate gyrus (DG) and basolateral amygdala (BLA) have been well-studied and recent work has revealed circuit mechanisms underlying the encoding of social experience, the processes by which social experience interacts with an individual's memories to alter recall remain unknown. Here we show that stressful social experiences enhance the recall of previously acquired fear memories in male but not female mice, and that social buffering of conspecifics' distress blocks this enhancement. Activity-dependent tagging of cells in the DG during fear learning revealed that these ensembles were endogenously reactivated during the social experiences in males, even after extinction. These reactivated cells were shown to be functional components of engrams, as optogenetic stimulation of the cells active during the social experience in previously fear-conditioned and not naïve animals was sufficient to drive fear-related behaviors. Taken together, our findings suggest that social experiences can reactivate preexisting engrams to thereby strengthen discrete memories.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware.

OBJECTIVE: Recent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision. METHODS: Patient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors' institution over a 10-year period (2008-2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations. RESULTS: A total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance. CONCLUSIONS: A computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.

BACKGROUND: Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture. METHODS: We performed a retrospective review of patients with intracranial aneurysms detected on vascular imaging at our institution between 2002 and 2018. The dataset was used to train 3 ML models (random forest, linear support vector machine [SVM], and radial basis function kernel SVM). Relative contributions of individual predictors were derived from the linear SVM model. RESULTS: Complete data were available for 845 aneurysms in 615 patients. Ruptured aneurysms (n = 309, 37%) were larger (mean 6.51 mm vs. 5.73 mm; P = 0.02) and more likely to be in the posterior circulation (20% vs. 11%; P < 0.001) than unruptured aneurysms. Area under the receiver operating curve was 0.77 for the linear SVM, 0.78 for the radial basis function kernel SVM models, and 0.81 for the random forest model. Aneurysm location and size were the 2 features that contributed most significantly to the model. Posterior communicating artery, anterior communicating artery, and posterior inferior cerebellar artery locations were most highly associated with rupture, whereas paraclinoid and middle cerebral artery locations had the strongest association with unruptured status. CONCLUSIONS: ML models are capable of accurately distinguishing ruptured from unruptured aneurysms and identifying features associated with rupture. Consistent with prior studies, location and size show the strongest association with aneurysm rupture.