Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback

Whereas muscle spindles play a prominent role in current theories of human motor control, Golgi tendon organs (GTO) and their associated tendons are often neglected. This is surprising since there is ample evidence that both tendons and GTOs contribute importantly to neuromusculoskeletal dynamics. Using detailed musculoskeletal models, we provide evidence that simple feedback using muscle spindles alone results in very poor control of joint position and movement since muscle spindles cannot sense changes in tendon length that occur with changes in muscle force. We propose that a combination of spindle and GTO afferents can provide an estimate of muscle-tendon complex length, which can be effectively used for low-level feedback during both postural and movement tasks. The feasibility of the proposed scheme was tested using detailed musculoskeletal models of the human arm. Responses to transient and static perturbations were simulated using a 1-degree-of-freedom (DOF) model of the arm and showed that the combined feedback enabled the system to respond faster, reach steady state faster, and achieve smaller static position errors. Finally, we incorporated the proposed scheme in an optimally controlled 2-DOF model of the arm for fast point-to-point shoulder and elbow movements. Simulations showed that the proposed feedback could be easily incorporated in the optimal control framework without complicating the computation of the optimal control solution, yet greatly enhancing the system's response to perturbations. The theoretical analyses in this study might furthermore provide insight about the strong physiological couplings found between muscle spindle and GTO afferents in the human nervous system. © 2013 the American Physiological Society

Prediction of Isocitrate Dehydrogenase Genotype in Brain Gliomas with MRI : Single-Shell versus Multishell Diffusion Models

Purpose The primary aim of this prospective observational study was to assess whether diffusion MRI metrics correlate with isocitrate dehydrogenase (IDH) status in grade II and III gliomas. A secondary aim was to investigate whether multishell acquisitions with advanced models such as neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging offer greater diagnostic accuracy than diffusion-tensor imaging (DTI). Materials and Methods Diffusion MRI (b = 700 and 2000 sec/mm2) was performed preoperatively in 192 consecutive participants (113 male and 79 female participants; mean age, 46.18 years; age range, 14-77 years) with grade II (n = 62), grade III (n = 58), or grade IV (n = 72) gliomas. DTI, diffusion kurtosis imaging, and NODDI metrics were measured in regions with or without hyperintensity on diffusion MR images and compared among groups defined according to IDH genotype, 1p/19q codeletion status, and tumor grade by using Mann-Whitney tests. Results In grade II and III IDH wild-type gliomas, the maximum fractional anisotropy, kurtosis anisotropy, and restriction fraction were significantly higher and the minimum mean diffusivity was significantly lower than in IDH-mutant gliomas (P = .011, P = .002, P = .044, and P = .027, respectively); areas under the receiver operating characteristic curve ranged from 0.72 to 0.76. In IDH wild-type gliomas, no difference among grades II, III, and IV was found. In IDH-mutant gliomas, no difference between those with and those without 1p/19q loss was found. Conclusion Diffusion MRI metrics showed correlation with isocitrate dehydrogenase status in grade II and III gliomas. Advanced diffusion MRI models did not add diagnostic accuracy, supporting the inclusion of a single-shell diffusion-tensor imaging acquisition in brain tumor imaging protocols. \ua9 RSNA, 2018 Online supplemental material is available for this article

Multicriteria Optimization Model to Generate on-DEM Optimal Channel Networks

The theory of optimal channel networks (OCNs) explains the existence of self‐similarities in river networks by multiple optimality principles, namely, (i) the minimum energy expenditure in any link, (ii) the equal energy expenditure per unit area of channel anywhere, and (iii) the minimum total energy expenditure (TEE). These principles have been used to generate OCNs from 2‐D networks. The existing notion of OCN considers the concavity of river longitudinal profiles as a priori condition. Attempts to generate OCNs starting from a random 3‐D digital elevation model (DEM) and minimizing solely TEE have failed to reproduce concave profiles. Yet alternative approaches can be devised from the three optimality principles, for instance, focusing on the local energy expenditure (LEE). In this paper, we propose a Multiobjective modeling framework for Riverscape Exploration (MoRE) via simultaneous optimization of multiple OCN criteria. MoRE adopts a multiobjective evolutionary algorithm and radial basis functions to efficiently guide DEM elevation variations required to shape 3‐D OCNs. By minimizing both TEE and the variance in LEE, MoRE successfully reproduces realistic on‐DEM, OCN‐based riverscapes, for the first time. Simulated networks possess scaling laws of upstream area and length and river longitudinal profile resembling those of real river networks. The profile concavity of generated on‐DEM OCNs emerges as a consequence of the minimization of TEE constrained to the equalization of LEE. Minimizing TEE under this condition generates networks that possess specific patterns of LEE, where the scaling of slope with basin area resembles the patterns observed in real river networks

Prediction of Isocitrate Dehydrogenase Genotype in Brain Gliomas with MRI: Single-Shell versus Multishell Diffusion Models.

PURPOSE: The primary aim of this prospective observational study was to assess whether diffusion MRI metrics correlate with isocitrate dehydrogenase (IDH) status in grade II and III gliomas. A secondary aim was to investigate whether multishell acquisitions with advanced models such as neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging offer greater diagnostic accuracy than diffusion-tensor imaging (DTI). MATERIALS AND METHODS: Diffusion MRI (b = 700 and 2000 sec/mm2) was performed preoperatively in 192 consecutive participants (113 male and 79 female participants; mean age, 46.18 years; age range, 14-77 years) with grade II (n = 62), grade III (n = 58), or grade IV (n = 72) gliomas. DTI, diffusion kurtosis imaging, and NODDI metrics were measured in regions with or without hyperintensity on diffusion MR images and compared among groups defined according to IDH genotype, 1p/19q codeletion status, and tumor grade by using Mann-Whitney tests. RESULTS: In grade II and III IDH wild-type gliomas, the maximum fractional anisotropy, kurtosis anisotropy, and restriction fraction were significantly higher and the minimum mean diffusivity was significantly lower than in IDH-mutant gliomas (P = .011, P = .002, P = .044, and P = .027, respectively); areas under the receiver operating characteristic curve ranged from 0.72 to 0.76. In IDH wild-type gliomas, no difference among grades II, III, and IV was found. In IDH-mutant gliomas, no difference between those with and those without 1p/19q loss was found. CONCLUSION: Diffusion MRI metrics showed correlation with isocitrate dehydrogenase status in grade II and III gliomas. Advanced diffusion MRI models did not add diagnostic accuracy, supporting the inclusion of a single-shell diffusion-tensor imaging acquisition in brain tumor imaging protocols

A multi-scale hierarchical framework for developing understanding of river behaviour to support river management

The work leading to this paper was funded through the European Union’s FP7 programme under Grant Agreement No. 282656 (REFORM). The framework methodology was developed within the context of Deliverable D2.1 of the REFORM programme, and all partners who contributed to the development of the four parts of this deliverable are included in the author list of this paper. More details on the REFORM framework can be obtained from part 1 of Deliverable D2.1 (Gurnell et al. 2014), which is downloadable from http://​www.​reformrivers.​eu/​results/​deliverables