Nonlinear compensation with DBP aided by a memory polynomial

Using digital backpropagation (DBP) based on the split step Fourier method (SSFM) aided by a memory polynomial (MP) model, we demonstrate an improved DBP approach for fiber nonlinearity compensation. The proposed technique (DBP-SSFM&MP) is numerically validated and its performance and complexity are compared against the benchmark DBP-SSFM, considering a single-channel 336 Gb/s PM-64QAM transmission system. We demonstrate that the proposed technique allows to maintain the performance achieved by DBP-SSFM, while decreasing the required number of iterations, by over 60%. For a transmission length of 1600 km we obtain a complexity reduction gain of 50.7% in terms of real multiplications in comparison with the standard DBP-SSFM

Cortical resting motor threshold difference in asleep-awake craniotomy for motor eloquent gliomas:WHO grading influences motor pathway excitability

Developing neurophysiological tools to predict WHO tumor grade can empower the treating teams for a better surgical decision-making process. A total of 38 patients with supratentorial diffuse gliomas underwent an asleep-awake-sedated craniotomies for tumor removal with intraoperative neuromonitoring. The resting motor threshold was calculated for different train stimulation paradigms during awake and asleep phases. Receiver operating characteristic analysis and Bayesian regression models were performed to analyze the prediction of tumor grading based on the resting motor threshold differences. Significant positive spearman correlations were observed between resting motor threshold excitability difference and WHO tumor grade for train stimulation paradigms of 5 (R = 0.54, P = 0.00063), 4 (R = 0.49, P = 0.002), 3 (R = 0.51, P = 0.001), and 2 pulses (R = 0.54, P = 0.0007). Kruskal-Wallis analysis of the median revealed a positive significant difference between the median of excitability difference and WHO tumor grade in all paradigms. Receiver operating characteristic analysis showed 3 mA difference as the best predictor of high-grade glioma across different patterns of motor pathway stimulation. Bayesian regression found that an excitability difference above 3 mA would indicate a 75.8% probability of a glioma being high grade. Our results suggest that cortical motor excitability difference between the asleep and awake phases in glioma surgery could correlate with tumor grade.</p