Secondary cranial reconstruction, or cranioplasty, can be challenging due to numerous reasons. These best practices, developed in collaboration with neuroplastic surgery and neurosurgery, appear to encompass the largest published experience to date. The authors find this approach to be both safe and reliable.

BACKGROUND Decompressive craniectomy is a lifesaving treatment for intractable intracranial hypertension. For patients who survive, a second surgery for cranial reconstruction (cranioplasty) is required. The effect of cranioplasty on intracranial pressure (ICP) is unknown. OBJECTIVE To integrate the recently Food and Drug Administration-approved, fully implantable, noninvasive ICP sensor within a customized cranial implant (CCI) for postoperative monitoring in patients at high risk for intracranial hypertension. METHODS A 16-yr-old female presented for cranioplasty 4-mo after decompressive hemicraniectomy for craniocerebral gunshot wound. Given the persistent transcranial herniation with concomitant subdural hygroma, there was concern for intracranial hypertension following cranioplasty. Thus, cranial reconstruction was performed utilizing a CCI with an integrated wireless ICP sensor, and noninvasive postoperative monitoring was performed. RESULTS Intermittent ICP measurements were obtained twice daily using a wireless, handheld monitor. The ICP ranged from 2 to 10 mmHg in the supine position and from −5 to 4 mmHg in the sitting position. Interestingly, an average of 7 mmHg difference was consistently noted between the sitting and supine measurements. CONCLUSION This first-in-human experience demonstrates several notable findings, including (1) newfound safety and efficacy of integrating a wireless ICP sensor within a CCI for perioperative neuromonitoring; (2) proven restoration of normal ICP postcranioplasty despite severe preoperative transcranial herniation; and (3) proven restoration of postural ICP adaptations following cranioplasty. To the best of our knowledge, this is the first case demonstrating these intriguing findings with the potential to fundamentally alter the paradigm of cranial reconstruction.

Epilepsy is a global epidemic and 30% of the 60 million patients do not respond to medication treatment. The only treatment options for patients with medically refractory epilepsy are surgical removal or electrical stimulation of the epileptogenic zone (EZ) i.e. the source of their seizures. Despite extensive evaluations with neuroimaging, visual EEG analysis and clinical testing, surgical success rates vary between 30-70%. Currently, no computational methods have been translated into the clinic to assist in localizing the EZ. Here, we applied a dynamical network model that quantifies the fragility of nodes within a patient's intracranial EEG (iEEG) brain network. Fragility is quantified as the minimal amount of perturbation that must to be applied to a node's influence on a "balanced" network to cause imbalance. Here, a balanced network is one in which the connectivity between excitatory and inhibitory nodes render a stable system, and an imbalanced network is unstable and hence can generate seizures. Using iEEG data from 91 patients treated across 5 epilepsy centers (44 successes, 47 failures), we demonstrated that nodal fragility is greater in electrodes within the EZ. In addition, we compared fragility of iEEG nodes to 7 frequency-based and 14 graph theoretic features of the EZ in both seizure (n=91) and non-seizure data (n=54). We calculated a confidence statistic, defined as the ratio of the value of a given feature averaged across electrodes in the clinically annotated seizure onset zone to its average across all other electrodes. Fragility has a significantly greater effect size difference between surgical outcomes when compared to other features. This novel feature, outperformed the most popular iEEG features when comparing across surgical outcomes, possibly defining a superior network-based EEG fingerprint for the EZ.1 Also reachable at adam2392 at gmail dot com. For more information, visit the NCSL website: https://sarmalab.icm.jhu.edu/.

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