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

Conclusions on motor control depend on the type of model used to represent the periphery

Within the field of motor control, there is no consensus on which kinematic and kinetic aspects of movements are planned or controlled. Perturbing goal-directed movements is a frequently used tool to answer this question. To be able to draw conclusions about motor control from kinematic responses to perturbations, a model of the periphery (i.e., the skeleton, muscle-tendon complexes, and spinal reflex circuitry) is required. The purpose of the present study was to determine to what extent such conclusions depend on the level of simplification with which the dynamical properties of the periphery are modeled. For this purpose, we simulated fast goal-directed single-joint movement with four existing types of models. We tested how three types of perturbations affected movement trajectory if motor commands remained unchanged. We found that the four types of models of the periphery showed different robustness to the perturbations, leading to different predictions on how accurate motor commands need to be, i.e., how accurate the knowledge of external conditions needs to be. This means that when interpreting kinematic responses obtained in perturbation experiments the level of error correction attributed to adaptation of motor commands depends on the type of model used to describe the periphery

Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)

Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

International audienceVoluntary movement is hypothesized to rely on a limited number of muscle synergies, the recruitment of which translates task goals into effective muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of muscle synergies in task performance. To this end, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching movements

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Evaluation of Brain Iron Content Based on Magnetic Resonance Imaging (MRI): Comparison among Phase Value, R2* and Magnitude Signal Intensity

Background and Purpose: Several magnetic resonance imaging (MRI) techniques are being exploited to measure brain iron levels increasingly as iron deposition has been implicated in some neurodegenerative diseases. However, there remains no unified evaluation of these methods as postmortem measurement isn’t commonly available as the reference standard. The purpose of this study was to make a comparison among these methods and try to find a new index of brain iron. Methods: We measured both phase values and R2 * in twenty-four adults, and performed correlation analysis among the two methods and the previously published iron concentrations. We also proposed a new method using magnitude signal intensity and compared it with R2 * and brain iron. Results: We found phase value correlated with R2 * in substantia nigra (r = 20.723, p,0.001) and putamen (r = 20.514, p = 0.010), while no correlations in red nucleus (r = 20.236, p = 0.268) and globus pallidus (r = 20.111, p = 0.605). And the new magnitude method had significant correlations in red nucleus (r = 20.593, p = 0.002), substantia nigra (r = 20.521, p = 0.009), globus pallidus (r = 20.750, p,0.001) and putamen (r = 20.547, p = 0.006) with R2*. A strong inverse correlation was also found between the new magnitude method and previously published iron concentrations in seven brain regions (r = 20.982, P,0.001). Conclusions: Our study indicates that phase value may not be used for assessing the iron content in some brain region

Force-Field Compensation in a Manual Tracking Task

This study addresses force/movement control in a dynamic “hybrid” task: the master sub-task is continuous manual tracking of a target moving along an eight-shaped Lissajous figure, with the tracking error as the primary performance index; the slave sub-task is compensation of a disturbing curl viscous field, compatibly with the primary performance index. The two sub-tasks are correlated because the lateral force the subject must exert on the eight-shape must be proportional to the longitudinal movement speed in order to perform a good tracking. The results confirm that visuo-manual tracking is characterized by an intermittent control mechanism, in agreement with previous work; the novel finding is that the overall control patterns are not altered by the presence of a large deviating force field, if compared with the undisturbed condition. It is also found that the control of interaction-forces is achieved by a combination of arm stiffness properties and direct force control, as suggested by the systematic lateral deviation of the trajectories from the nominal path and the comparison between perturbed trials and catch trials. The coordination of the two sub-tasks is quickly learnt after the activation of the deviating force field and is achieved by a combination of force and the stiffness components (about 80% vs. 20%), which is a function of the implicit accuracy of the tracking task

Gene Expression Changes in the Motor Cortex Mediating Motor Skill Learning

The primary motor cortex (M1) supports motor skill learning, yet little is known about the genes that contribute to motor cortical plasticity. Such knowledge could identify candidate molecules whose targeting might enable a new understanding of motor cortical functions, and provide new drug targets for the treatment of diseases which impair motor function, such as ischemic stroke. Here, we assess changes in the motor-cortical transcriptome across different stages of motor skill acquisition. Adult rats were trained on a gradually acquired appetitive reach and grasp task that required different strategies for successful pellet retrieval, or a sham version of the task in which the rats received pellet reward without needing to develop the reach and grasp skill. Tissue was harvested from the forelimb motor-cortical area either before training commenced, prior to the initial rise in task performance, or at peak performance. Differential classes of gene expression were observed at the time point immediately preceding motor task improvement. Functional clustering revealed that gene expression changes were related to the synapse, development, intracellular signaling, and the fibroblast growth factor (FGF) family, with many modulated genes known to regulate synaptic plasticity, synaptogenesis, and cytoskeletal dynamics. The modulated expression of synaptic genes likely reflects ongoing network reorganization from commencement of training till the point of task improvement, suggesting that motor performance improves only after sufficient modifications in the cortical circuitry have accumulated. The regulated FGF-related genes may together contribute to M1 remodeling through their roles in synaptic growth and maturation.McGovern Institute for Brain Research at MITNational Institutes of Health (U.S.) ((NIH grant 1-RC1-NS068103-01)National Institutes of Health (U.S.) (NIH grant R01-MH084966)Roberto Rocca Education Program (Fellowship)Massachusetts Institute of Technology. Undergraduate Research Opportunities Program (Fellowship)Italy. Ministero dell'istruzione, dell'università e della ricerca (MIUR grant RBIN04H5AS)Italy. Ministero dell'istruzione, dell'università e della ricerca (MIUR grant RBLA03FLJC)Italy. Ministero dell'istruzione, dell'università e della ricerca (FIRB n. RBAP10L8TY