BACKGROUND: Facet joint violation by pedicle screws may lead to adjacent-segment disease and postoperative pain. Previous studies have reported the incidence of rostral facet joint violation using various pedicle screw insertion techniques. However, the incidence of facet joint violations with robotic guidance has not been determined.
OBJECTIVE: To investigate and compare the incidence of rostral facet joint violation by pedicle screws under robotic guidance and computerized tomography (CT) navigation guidance.
METHODS: Retrospective Matched Cohort PATIENT SAMPLE: All patients who underwent robotic-assisted lumbar fusion at a major spine center up until 2023 were retrospectively identified and matched 1:3 to patients undergoing CT navigation guidance based by on age, sex, rostral vertebral level, and length of construct. Inclusion criteria consisted of age greater than 18 years, bilateral pedicle screw fixation, and presence of a postoperative CT scan of the lumbar spine or abdomen/pelvis at any point in the post-operative period.
METHODS: Rostral facet joint violations METHODS: Descriptive statistics were used to compare cohorts: frequencies, chi-squared analysis for categorical variables, and t-test for continuous variables.
RESULTS: A total of 408 rostral pedicle screws were implanted in 204 patients (Robot: 102; Navigation: 306). Overall, 13 (12.3%) rostral facet joint violations were observed in the robot cohort and 75 (24.5%) in the navigation cohort (p=0.01). Specifically, fewer robotic violations were observed at the L2 (3.5% vs 32.1%, p=0.003) and L3 levels (3.9% vs. 18.1%, p=0.08) compared to navigation. No difference was observed at L4 and L5. Bilateral violations are significantly reduced with robotic approaches (5.3% vs. 14.4%, p=0.03). Lastly, more facet joint violations were observed during open approaches (robot: 18.8%, navigation: 27.3%) than percutaneous approaches (robot: 11.6%, navigation: 7.1%) in both groups (p<0.001). The rate of L4 facet violations was 18.8% in the robotic cohort and 27.3% in the navigation cohort. The rate of L5 facet violations was 31.3% in the robotic cohort and 29.2% in the navigation cohort.
CONCLUSIONS: Use of robotic assistance in lumbar pedicle screws significantly reduced the rate of rostral facet joint violations compared to navigation guidance at L2 and L3 levels, but not at L4 and L5, with facet violations approaching nearly one-third of the patients at L5 screws. Rostral facet violations can play a significant role in adjacent segment degeneration and disease. Technical factors and trajectory issues likely play a role and addressing these components should minimize unintended facet violation and proximal adding on.

Navigational abilities decline with age, but the cognitive underpinnings of this cognitive decline remain partially understood. Navigation is guided by landmarks and self-motion cues, that we address when estimating our location. These sources of spatial information are often associated with noise and uncertainty, thus posing a challenge during navigation. To overcome this challenge, humans and other species rely on navigational cues according to their reliability: reliable cues are highly weighted and therefore strongly influence our spatial behavior, compared to less reliable ones. We hypothesize that older adults do not efficiently weigh spatial cues, and accordingly, the reliability levels of navigational cues may not modulate their spatial behavior, as with younger adults. To test this, younger and older adults performed a virtual navigational task, subject to modified reliability of landmarks and self-motion cues. The findings revealed that while increased reliability of spatial cues improved navigational performance across both age groups, older adults exhibited diminished sensitivity to changes in landmark reliability. The findings demonstrate a cognitive mechanism that could lead to impaired navigation abilities in older adults.

In natural circumstances, sensory systems operate in a closed loop with motor output, whereby actions shape subsequent sensory experiences. A prime example of this is the sensorimotor processing required to align one\'s direction of travel, or heading, with one\'s goal, a behavior we refer to as steering. In steering, motor outputs work to eliminate errors between the direction of heading and the goal, modifying subsequent errors in the process. The closed-loop nature of the behavior makes it challenging to determine how deterministic and nondeterministic processes contribute to behavior. We overcome this by applying a nonparametric, linear kernel-based analysis to behavioral data of monkeys steering through a virtual environment in two experimental contexts. In a given context, the results were consistent with previous work that described the transformation as a second-order linear system. Classically, the parameters of such second-order models are associated with physical properties of the limb such as viscosity and stiffness that are commonly assumed to be approximately constant. By contrast, we found that the fit kernels differed strongly across tasks in these and other parameters, suggesting context-dependent changes in neural and biomechanical processes. We additionally fit residuals to a simple noise model and found that the form of the noise was highly conserved across both contexts and animals. Strikingly, the fitted noise also closely matched that found previously in a human steering task. Altogether, this work presents a kernel-based analysis that characterizes the context-dependence of deterministic and non-deterministic components of a closed-loop sensorimotor task.

The behavior of pedestrians in a non-constrained environment is difficult to predict. In wearable robotics, this poses a challenge, since devices like lower-limb exoskeletons and active orthoses need to support different walking activities, including level walking and climbing stairs. While a fixed movement trajectory can be easily supported, switches between these activities are difficult to predict. Moreover, the demand for these devices is expected to rise in the years ahead. In this work, we propose a cloud software system for use in wearable robotics, based on geographical mapping techniques and Human Activity Recognition (HAR). The system aims to give context to the surrounding pedestrians by providing hindsight information. The system was partially implemented and tested. The results indicate a viable concept with great extensibility prospects.

In 1985, Professor KWOH first introduced robots into neurosurgery. Since then, advancements of stereotactic frames, radiographic imaging and neuronavigation have led to the dominance of classic stereotactic robots. A comprehensive retrieval was performed using academic databases and search agents to acquire professional information, with a cutoff date of June, 2024. This reveals a multitude of emerging technologies are coming to the forefront, including tremor filtering, motion scaling, obstacle avoidance, force sensing, which have made significant contributions to the high efficiency, high precision, minimally invasive, and exact efficacy of robot-assisted neurosurgery. Those technologies have been applied in innovative magnetic resonance-compatible neurosurgical robots, such as Neuroarm and Neurobot, with real-time image-guided surgery. Despite these advancements, the major challenge is considered as magnetic resonance compatibility in terms of space, materials, driving, and imaging. Future research directions are anticipated to focus on (1) robotic precise perception; (2) artificial intelligence; and (3) the advancement of telesurgery.

METHODS: Retrospective Cohort Study.
OBJECTIVE: The aim of this study was to compare the efficacy of CT-based computer assisted navigation (CAN) to conventional pedicle screw placement for patients with Adolescent Idiopathic Scoliosis (AIS).
METHODS: This retrospective cohort study drew data from the National Readmissions Database, years 2016-2019. Patients undergoing posterior fusion for AIS, either via CAN or fluoroscopic-guided procedures, were identified via ICD-10 codes. Multivariate regression was performed to compare outcomes between operative techniques. Negative binomial regression was used to asses discharge disposition, while Gamma regression was performed to assess length of stay (LOS) and total charges. Patient demographics and comorbidities, measured via the Elixhauser comorbidity index, were both controlled for in our regression analysis.
RESULTS: 28,868 patients, 2095 (7.3%) undergoing a CAN procedure, were included in our analysis. Patients undergoing CAN procedures had increased surgical complications (Odds Ratio (OR) 2.23; P < 0.001), namely, blood transfusions (OR 2.47; P < 0.001). Discharge disposition and LOS were similar, as were reoperation and readmission rates; however, total charges were significantly greater in the CAN group (OR 1.37; P < 0.001). Mean charges were 191,489.42 (119,302.30) USD for conventional surgery vs 268 589.86 (105,636.78) USD for the CAN cohort.
CONCLUSIONS: CAN in posterior fusion for AIS does not appear to decrease postoperative complications and is associated with an increased need for blood transfusions. Given the much higher total cost of care that was also seen with CAN, this study calls into question whether the use of CAN is justified in this setting.

Homing is often a critical aspect of an animal\'s behavioural and spatial ecology. Translocation is considered to be a wildlife management strategy that could reduce human-wildlife confrontation, but this strategy may not be effective if animals attempt to home to their original capture location. Translocation of animals from sites where possible human-wildlife interaction occurs is a widespread but controversial intervention to resolve conflicts. In India, snakes are often the subject of such translocations, but there is a paucity of information on the behaviour of translocated snakes compared to resident snakes. The Indian python (Python molurus), one of the largest carnivores in the Indian subcontinent, is classified as Near Threatened by the IUCN. We conducted a two-year radio-tracking study (December 2018 to December 2020) on the movements of 14 adult Indian pythons in the Moyar River Valley, within the Sathyamangalam and Mudumalai Tiger Reserves. Eleven of the 14 pythons were translocated 0.28-55.7 kms from their capture locations, while 3 pythons were not translocated: 6 were translocated short distances (<5 km from capture; range 0.28-4.67 kms), 2 were translocated to medium distances (9-11 kms from capture location), and 3 were translocated to long distances (21-55.7 kms from capture location). Four of the six snakes translocated short distances all returned to within 500 m of their original capture locations, and all 6 returned over 60 % of the translocated distance to the initial capture location. Of the two snakes translocated medium distances, both returned to within 1.1 km of the capture location (∼90 % of the distance home). None of the three snakes that were translocated long distances successfully returned to their capture locations. Translocated pythons exhibited greater net movement distances than resident snakes within the first 2 months of release. Based on these results, long-distance translocation may be an effective strategy to minimize human-python conflict, while short or medium distance translocation is unlikely to be successful. However, more research is needed about the long-term survival of translocated snakes as well as soft-release methodologies that could prevent aberrant movement behaviour directly following release.

This study explores visuomotor control in athletes for collision avoidance using virtual reality. Thirty-nine athletes navigated dynamic scenarios, pursuing a virtual target while avoiding two to five virtual defenders. Confirmatory Factor Analysis validated a model that captured two features of scanning behavior based on head movements recorded during activity: the overall amount and temporal pattern. Linear mixed models showed that these features significantly differentiated successful from unsuccessful defender avoidance (p < .05), suggesting that efficient environmental scanning is crucial for collision avoidance while highlighting the potential of visuomotor interventions to reduce collision-related sport injuries.

Wayfinding systems using inertial data recorded from a smartphone carried by the walker have great potential for increasing mobility independence of blind pedestrians. Pedestrian dead-reckoning (PDR) algorithms for localization require estimation of the step length of the walker. Prior work has shown that step length can be reliably predicted by processing the inertial data recorded by the smartphone with a simple machine learning algorithm. However, this prior work only considered sighted walkers, whose gait may be different from that of blind walkers using a long cane or a dog guide. In this work, we show that a step length estimation network trained on data from sighted walkers performs poorly when tested on blind walkers, and that retraining with data from blind walkers can dramatically increase the accuracy of step length prediction.

The question of What is learned when navigating to a place is reinforced has been the subject of considerable debate. Prevailing views emphasize cognitive structures (e.g., maps) or associative learning, which has shaped measurement in spatial navigation tasks (e.g., the Morris water task [MWT]) toward selection of coarse measures that do not capture precise behaviors of individual animals. We analyzed the navigation paths of 15 rats (60 trials each) in the MWT at high temporal resolution (30Hz) and utilized dynamic time warping to quantify the similarity of paths within and between animals. Paths were largely direct, yet suboptimal, and included changes in speed and trajectory that were established early in training and unique to each animal. Individual rats executed similar paths from the same release point from trial to trial, which were distinct from paths executed by other rats as well as paths performed by the same rat from other release points. These observations suggest that rats learn to execute similar path sequences from trial to trial for each release point in the MWT. Occasional spontaneous deviations from the established, unique behavioral sequence, resulted in profound disruption in navigation accuracy. We discuss the potential implications of sequence navigation behaviors for understanding relations between behavior and spatial neural signals such as place cells, grid cells, and head direction cells.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40614-024-00402-8.