mortality. The purpose of this paper is to review the biomechanical aspects, clinical features, radiologic criteria, and treatment strategies of AOD. Given that the diagnosis of AOD can be very challenging, a high degree of clinical suspicion is essential to ensure timely recognition and treatment, thus preventing neurological decline or death. Core tip: Atlanto-occipital dislocation (AOD) is being increasingly recognized as a potentially survivable injury as a result of improved prehospital management, increased awareness, and more aggressive management. However, despite overall improved outcomes, AOD is still associated with significant morbidity and mortality. Given that the diagnosis can be very challenging, a high degree of clinical suspicion is essential to ensure timely recognition and treatment, thus preventing neurological decline or death.Hall GC, Kinsman MJ, Nazar RG, Hruska RT, Mansfield KJ, Boakye M, Rahme R. Atlanto-occipital dislocation. INTRODUCTIONIn a recent study of 300 patients with cervical spine trauma, 30% of injuries were located between the occiput and C2. Among these, acute spondylolysis of C2 (hangman's fracture), C1 ring fractures, odontoid fractures, and atlanto-occipital dislocation (AOD) were the most common [1] . Graham AbstractAtlanto-occipital dislocation (AOD) is being increasingly recognized as a potentially survivable injury as a result of improved prehospital management of polytrauma patients and increased awareness of this entity, leading to earlier diagnosis and more aggressive treatment. However, despite overall improved outcomes, AOD is still associated with significant morbidity and MINIREVIEWS 236March 18, 2015|Volume 6|Issue 2|

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Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes (‘sources’) and the inhibited nodes themselves (‘sinks’). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians’ predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.