During gaze shifts, the eyes and head collaborate to rapidly capture a target (saccade) and fixate it. Accordingly, models of gaze shift control should embed both saccadic and fixation modes and a mechanism for switching between them. We demonstrate a model in which the eye and head platforms are driven by a shared gaze error signal. To limit the number of free parameters, we implement a model reduction approach in which steady-state cerebellar effects at each of their projection sites are lumped with the parameter of that site. The model topology is consistent with anatomy and neurophysiology, and can replicate eye-head responses observed in multiple experimental contexts: 1) observed gaze characteristics across species and subjects can emerge from this structure with minor parametric changes; 2) gaze can move to a goal while in the fixation mode; 3) ocular compensation for head perturbations during saccades could rely on vestibular-only cells in the vestibular nuclei with postulated projections to burst neurons; 4) two nonlinearities suffice, i.e., the experimentally-determined mapping of tectoreticular cells onto brain stem targets and the increased recruitment of the head for larger target eccentricities; 5) the effects of initial conditions on eye/head trajectories are due to neural circuit dynamics, not planning; and 6) "compensatory" ocular slow phases exist even after semicircular canal plugging, because of interconnections linking eye-head circuits. Our model structure also simulates classical vestibulo-ocular reflex and pursuit nystagmus, and provides novel neural circuit and behavioral predictions, notably that both eye-head coordination and segmental limb coordination are possible without trajectory planning.

This work involved human subjects in its research. Approval of all ethical and experimental procedures and protocols was granted by the Mayo Clinic Institutional Review Board Department.

Introduction Neutropenic fever following high-dose chemotherapy and autologous stem cell transplantation (ASCT) is a common (incidence 63-100%) and potentially life-threatening complication. Recommended time to antibiotic (TTA) administration is within 1 hr of fever onset with delays associated with significant morbidity, prolonged hospitalization, and mortality. Standard of care guidelines emphasize patient self-monitoring for fever, with instructions to seek immediate medical attention if body temperature (temp) reaches 100.4°F or higher. In this study, we evaluated if a novel wearable, continuous temp monitor, tPatch, could reliably estimate core body temp and detect fever in an outpatient setting following ASCT. Additionally, we gathered preliminary data to explore early detection and prediction of clinically relevant temp rise in this clinical setting. Methods Patients (N = 86) with hematologic malignancies (62% multiple myeloma) who underwent high-dose chemotherapy followed by ASCT at Mayo Clinic, MN were prospectively enrolled between June 2018 and March 2019. Patients (82% male) wore an axilla-placed tPatch continuously for 7 days in an outpatient setting during the post ASCT period and were asked to record self-measured oral temp in 3-4 hr intervals daily using a standardized thermometer after appropriate training . Patients followed standard of care procedures with daily clinic assessment of temp, blood counts, and vital signs. An optional patient questionnaire was given at end-of-study. A model was trained using both patient- and clinic-assessed oral temp measures to estimate core temp from 2 sensors on the tPatch device. Core temp estimates and trends were then compared to patient- and clinic-assessed measurements. Fever was defined as a temperature ≥100.4°F for at least 1 hr. Results When compared to all oral temp reads, the tPatch estimated core temp within 0.03 ± 0.7℉. Among the 86 patients, clinic-assessed fever incidence was 29.4% while tPatch-assessed incidence was 58.8%. Using all clinic-recorded temp readings as "ground truth," the sensitivity and specificity of the tPatch algorithm in detecting fevers were 88% and 86%, respectively, while patient self fever detection sensitivity was 62% and specificity 93%. With "fever episode" defined as a temp ≥100.4°F for at least 1 hr, tPatch detected 9.6 times the number of fever episodes vs. clinic reads. The average lead time of tPatch detection of clinic-recorded fevers was 3.7 hours. In 25% of all intervals between clinic temp readings, either tPatch or patients detected at least 1 fever episode. The tPatch was well-tolerated, the only adverse events reported were grade 1 skin irritation and discomfort in 4 (5%) patients. Of 65 patients who completed the survey, 95% reported the tPatch as "quite" or "somewhat" comfortable and 94% stated no difficulty in using the tPatch. Exploration of tPatch temp trends over various time intervals for use in fever prediction is ongoing. Conclusions Patient self-monitoring of temp has low sensitivity and is not feasible for long intervals of time (e.g., overnight). Continuous temperature monitoring by a wearable device overcomes these challenges and has the potential to improve early detection and consequently shorten time to antibiotic initiation. A follow-up randomized study is planned to assess the clinical benefits of continuous temp monitoring through patient and clinician alerts triggering early clinical intervention for febrile neutropenia. Figure Disclosures Vera-Aguilera: Verily Life Sciences: Research Funding. Haji-Abolhassani:Verily Life Sciences: Employment. Kulig:Verily Life Sciences: Employment. Heitz:Verily Life Sciences: Employment. Paludo:Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ghoreyshi:Verily Life Sciences: Employment. Scheevel:Verily Life Sciences: Research Funding. Schimke:Verily Life Sciences: Research Funding. Markovic:Verily Life Sciences: Research Funding.

The gaze orientation system is a prime example of the CNS using multiple platforms to achieve its goal. To move the gaze in space, the eyes, head, and body cooperate to place the image of the target on the fovea. Understanding the underlying neural circuitry innervating this collaboration could also be a cue to understanding other movement related CNS tasks involving multiple platforms, i.e., posture and locomotion. Basically two major network topologies for modeling the gaze orientation system have been proposed: the independent controller model and the shared gaze feedback controller model. In the independent controller model, each platform (i.e., eyes, head or trunk) receives its own share of the retinal error (distance of the target from the current gaze position) independent from other platform(s) and its goal is to null its individual error, whereas, in the shared gaze feedback controller all platforms collaborate to null the shared global error, which is calculated on the fly using feedback from all platforms or reflexes. Each of the mentioned general topologies has its own supporters and the question is which does the CNS actually use. In this article, based on evidence from neurophysiology and behavior, complemented by simulation data, it will be shown why a shared feedback controller is the better candidate for this task. More specifically, simulations of an updated Prsa-Galiana model (the Shared Sensory-Motor Integration (SMI) model) will be discussed in more detail and, where applicable, compared with other popular models, including independent and shared controller models. It provides plausible explanations for observations on gaze shifts with various interventions.