Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware.

PURPOSE: Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. METHODS: We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system's ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. RESULTS: Experimental validation of the system's navigated ultrasound component demonstrated accuracy of [Formula: see text] and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of [Formula: see text] and performed with a clinically acceptable level of accuracy. CONCLUSION: We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.

Sleep-wake regulation in preterm and term infants.

STUDY OBJECTIVES: In adults, wakefulness can be markedly prolonged at the expense of sleep, e.g. to stay vigilant in the presence of a stressor. These extra-long wake bouts result in a heavy-tailed distribution (highly right-skewed) of wake but not sleep durations. In infants, the relative importance of wakefulness and sleep are reversed, as sleep is necessary for brain maturation. Here, we tested whether these developmental pressures are associated with the unique regulation of sleep-wake states. METHODS: In 175 infants of 28-40 weeks postmenstrual age (PMA), we monitored sleep-wake states using electroencephalography and behavior. We constructed survival models of sleep-wake bout durations and the effect of PMA and other factors, including stress (salivary cortisol), and examined whether sleep is resilient to nociceptive perturbations (a clinically necessary heel lance). RESULTS: Wake durations followed a heavy-tailed distribution as in adults and lengthened with PMA and stress. However, differently from adults, active sleep durations also had a heavy-tailed distribution, and with PMA, these shortened and became vulnerable to nociception-associated awakenings. CONCLUSIONS: Sleep bouts are differently regulated in infants, with especially long active sleep durations that could consolidate this state's maturational functions. Curtailment of sleep by stress and nociception may be disadvantageous, especially for preterm infants given the limited value of wakefulness at this age. This could be addressed by environmental interventions in the future.

Unbiased Bayesian inference for population Markov jump processes via random truncations.

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state/parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work.