Analysis of flexural isostasy of the northern Andes

The mode in which a lithosphere plate supports overlying topography is greatly driven by the strength of the plate. By analyzing the geophysical signature of lithosphere flexure, in the space and spectral domains, the strength of the plates that support the north Andean mountains and adjacent basins, and the topography of Kenya was investigated. In addition, the effect of windowing on elastic thickness estimates obtained via the coherence method was evaluated. ^ The coherence between the topography and Bouguer gravity spectra of northern South America suggests that the average elastic thickness of the lithosphere is 30 km. Although lateral variations were not resolved by the coherence implementation, these became apparent by modeling the foreland stratigraphy of the Llanos, Barinas and Maracaibo sub-Andean basins. Flexural models reveal a zone of lithosphere weakness beneath the eastern flank of the Eastern Cordillera and western flank of the Venezuelan Andes. The gravity anomaly calculated from these models is consistent with the observed Bouguer gravity anomaly. This zone of weakness appears to separate the strong, old Guyana shield lithosphere from the weaker and probably younger Andean lithosphere. The zone of weakness may correspond to a Paleozoic feature at the western margin of cratonic South America, or a Mesozoic rift arm that weakened the proto-Andean lithosphere. ^ Using synthetic data as well as the northern South America topography and gravity, this study demonstrates that lithosphere strength calculated from the coherence of mirrored data may overestimate the true lithosphere strength. As a result, many lithosphere plates may be weaker than currently thought. In light of this observation, gravity and topography data from Kenya were reevaluated using multitaper spectral techniques. The elastic thickness of this plate, currently undergoing rifting, was estimated at 7 to 8 km, a factor of 2 less than previously estimated. These estimates suggest that despite intense fracturing and sustained tensile stresses, continental lithosphere plates undergoing rifting are able to retain some strength. ^

A novel signal processing method for intraoperative neurophysiological monitoring in spinal surgeries

Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded. An algorithm was developed using Chebychev time windowing for preconditioning of SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials. In this study, the analysis was performed on the data recorded in 29 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm takes 4sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.